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Theta activity in the brain is related to different cognitive and functional states, including arousal, attention, voluntary movement, exploratory behavior, attention behavior, learning and memory, somatosensory integration, and rapid eye movement (REM) sleep1,2. Principally, theta activity as a rhythmic entity can be generated in various cerebral regions and is highly organized and synchronized as theta oscillations. Below, we will focus on the analysis and quantification of theta activity/oscillations that are generated within the septohippocampal system3,4. Within the septum, GABAergic, glutamatergic, and cholinergic neurons project to the hippocampus and contribute to the initiation and maintenance of theta oscillatory behavior. There is an ongoing discussion on whether hippocampal theta oscillations are initiated in the septum, i.e., the septal pacemaker-hippocampal follower model, (extrahippocampal theory) or intrinsically within the hippocampus (intrahippocampal theory)5,6,7.
Regardless of their origin, hippocampal theta oscillations have been in the focus of interest for years, particularly in transgenic mouse models. These models allow for the implantation of deep EEG electrodes and for the recording of hippocampal theta oscillations under specific cognitive and behavioral tasks8. Hippocampal theta oscillations are heterogenous in nature. Based on the so-called dualistic theory of theta oscillations, one can differentiate between atropine-sensitive type II theta and atropine-insensitive type I theta9,10,11. The latter can typically be induced by muscarinic M1/M3 receptor agonists, e.g., arecoline, pilocarpine, and urethane. However, urethane is a multi-target drug that, besides muscarinic activation, also exerts complex effects on other ion channel entities. For type II theta, the muscarinic pathway includes the activation of M1/M3 and a subsequent Gq/11 (Gα)-mediated activation of phospholipase C β1/4 (PLCβ1/4), inositol trisphosphate (InsP3), diacylglycerole (DAG), Ca2+, and protein kinase C (PKC). The role of PLCβ1 and PLCβ4 in thetagenesis has been validated in knock-out studies using PLCβ1-/- and PLCβ4-/- mice exhibiting a complete loss or significant attenuation of theta oscillation12,13,14. Additional M1, M3, and M5 downstream targets (channels/currents) of the muscarinic signaling cascade include various conductances, such as M-type K+ channel (KM) via voltage-dependent K+ channel (Kv7); slow after hyperpolarization K+ channel (KsAHP); leak K+ channel (Kleak), probably via TWIK-related acid-sensitive K+ channel (TASK1/3); cation current (ICAT), probably via Na+ leak channel (NALCN); and Ih via hyperpolarization and cyclic nucleotide gated channels (HCN). In addition, M2/M4 acetylcholine receptors (AChRs) were reported to interfere with inward rectifier K+ channel 3.1 (Kir3.1) and inward rectifier K+ channel 3.2 (Kir3.2)15.
Currently, commercially-available analytical software allows for fast FFT-based frequency analysis, e.g., analysis of power (P, mV2) or power spectrum density (PSD, mV2/Hz). Power or power spectrum density (PSD) analysis of the theta frequency range only gives a global overview of its activity. However, in order to get a detailed insight into cognitive and behavior-related theta activity, the analysis of highly-organized theta oscillations is mandatory. The assessment of highly-organized theta oscillations is of central importance in the field of neurodegenerative and neuropsychiatric diseases. Most experimental disease studies are carried out in transgenic mouse models using highly-sophisticated neurosurgical approaches to record epidural surface and deep intracerebral EEGs. These techniques include both tether systems16 and radiotelemetric setups17,18. Theta oscillations can be recorded as spontaneous and behavior-related theta oscillations under long-term recording conditions. Additionally, theta oscillations can be recorded following pharmacological induction but also following the exposure of animals to behavioral or cognitive tasks or to sensory stimuli, such as tail pinching.
Early approaches to characterize theta oscillations were described by Csicsvari et al.19. The authors designed a semi-automated tool for short-term theta analysis (15 - 50 min) that is not suitable for long-time EEG recordings. Our method, described here, allows for the analysis of long-term EEG recording > 48 h20. Csicsvari et al.10 also referred to the theta-delta ratio, but no threshold for the determination of highly-organized theta oscillations is provided. The delta and theta range definitions match our frequency range definitions. As it is not explicitly mentioned, we presume that an FFT-based method is used by Csicsvari et al. to calculate the power of the theta-delta frequency bands. This again clearly differs from our method, since we calculate wavelet-based amplitudes on a large number of frequency scales (frequency steps Δ(f) = 0.05 Hz), resulting in much higher precision. The duration of the individually-analyzed EEG epoch is similar to our definition.
Klausberger et al.21 also make use of theta-delta ratios for the analysis of long-term EEG recordings. However, there are three major differences compared to our approach: i) the EEG epoch duration is much longer, i.e., at least 6 s; ii) the theta-delta ratio is set to 4, which is much higher than our threshold, and is related to different frequency range definitions; and iii) the power definition is likely to be based on an FFT approach, which lacks high precision, particularly for very short time windows (2 s, i.e., 5 cycles for oscillations with a frequency of 2.5 Hz). In such cases, a wavelet-based procedure is more recommendable. A study by Caplan et al.22 solely calculated theta power while ignoring the theta-delta power ratio. Thus, the Caplan approach22 cannot differentiate between cognitive theta-rich processes accompanied by a high or low delta.
In the following protocol, we will present our analytical wavelet-based approach to reliably analyze highly-organized theta oscillations in hippocampal EEG recordings from mice. Since this procedure works automatically, it can be applied to large data sets and long-term EEG measurements.