The thalamus is a critical node in the brain network and acts as a hub in information transmission (Tomasi and Volkow, 2011). This area is extensively connected to the cerebral cortex via thalamocortical radiations (Coenen et al., 2014). This massive connectivity means that the thalamus plays a vital role in many cognitive, sensory, and executive functions (Hwang et al., 2017). Manipulating the thalamus, such as through thalamotomy, can have a significant impact on behavior and the brain networks (Halpern et al., 2019; Krishna et al., 2020; Lin et al., 2022), making it an effective treatment for essential tremor (ET), Parkinson’s disease (PD) (Zaaroor et al., 2017), Holmes tremor (HT) (Kim et al., 2002), epilepsy (Sitnikov et al., 2016), neurogenic pain (Sarnthein et al., 2006), multiple sclerosis (MS) (Mathieu et al., 2007), and dyskinesia (Lee, 1997). Monitoring the effects of thalamotomy surgery on brain function is essential to evaluate the success of procedure, predict patient outcomes, and adjust treatment plans accordingly.

Thalamotomy is an effective method to decrease dominant disease symptoms, such as tremors, unwanted motor activity, poor quality of life, and seizures (Lipsman et al., 2013). The first thalamotomy surgery have done using the stereotactic approach was a pallido- thalamotomy, which Spiegel and Wycis carried out in 1946 on a patient with Huntington’s Disease (HD) to reduce emotional reactivity by targeting the medial nucleus of the thalamus (Ea and Ht, 1950; Sarnthein et al., 2006). Today, using new thalamotomy methods such as, Magnetic resonance guided focused ultrasound (MRgFUS) (Stanziano et al., 2022), and gamma knife thalamotomy (GKT) (Tuleasca et al., 2021) these surgeries have become prevalent. However, the system-level description of thalamotomy effectiveness remains rudimentary. The improvement resulting from thalamotomy surgery is thought to arise from changing the activity and the functional connectivity in the associated regions (Jang et al., 2016). To evaluate the functional connectivity and activity in the brain network before and after thalamotomy, it is necessary to use methods such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) (Tuleasca et al., 2018d), beside of other methods such as positron emission topography (PET), magnetoencephalography (MEG) and functional near-infrared spectroscopy (fNIRS). fMRI and EEG is currently the dominant methods due the superior spatial resolution of fMRI and the temporal resolution of EEG, we also evaluate several studies that used other methods during thalamotomy surgery.

Combining EEG and fMRI in preoperative and postoperative conditions will enable a comprehensive evaluation of the functional connectivity and activity during thalamotomy. Although simultaneous EEG-fMRI studies exist (Abreu et al., 2019; Boerwinkle et al., 2022), there is a lack of studies that compare pre and post-thalamotomy conditions. Therefore, the objective of this study is to summarize the application of EEG and fMRI in thalamotomy surgeries. Analysis of the EEG in pre-and postoperative conditions shows a decrease in EEG over-activities after surgery, while, fMRI studies demonstrate changes in functional connectivity, particularly in motor (Hesselmann et al., 2006; Jang et al., 2016; Park et al., 2017; Tuleasca et al., 2021), visuomotor (Tuleasca et al., 2018a,c, 2020; Xiong et al., 2022a), and default-mode networks (Wen et al., 2016; Tuleasca et al., 2018b,e).

We conducted a PubMed search up to March 2023, entering ‘thalamotomy’ ablation surgery’ in combination (AND) with the following search terms and their corresponding abbreviations: functional, magnetic resonance (fMRI), electroencephalogram (MRI, and EEG), positron emission tomography (PET), single-photon emission computed tomography (SPECT), Magnetoencephalography (MEG), Functional near-infrared spectroscopy (fNIRS), Electrocorticography (ECoG) and was restricted to articles published in English. We followed Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines for meta-analysis (Moher et al., 2009). The PRISMA of the included search results is presented in Figure 1. All data included in this study were related to human diseases that used thalamotomy surgery as a clinical approach. Both unilateral and bilateral thalamotomy were considered. The following inquiries were our focus: We only included articles that met the following criteria: (1) they were written in English, (2) they measured functional connectivity or activity, (3) they involved human subjects, and (4) they provided quantitative or semiquantitative functional activity and connectivity data analyses before and after thalamotomy surgery. We excluded case reports, method articles, case series, and research papers that primarily focused on therapeutic interventions, such as thalamotomy, MRI-guided focused ultrasound, or deep brain stimulation (DBS) and pre-surgical planning.

Our search turned up 607 results. Zotero (V6) reference manager imported the obtained references, and duplicates were removed. The validity of each title and abstract was verified separately by the authors. By searching through the references of the approved papers, one more paper was found that matched the criteria. In total, 33 publications met our inclusion criteria, with 20 of them being studies that used fMRI, 8 using EEG, 2 using fNIRS, and 3 using PET investigations.

A flowchart of the selection process is presented in Figure 1. The main result and analysis of the included EEG, fMRI, fNIRS, and PET studies are all summarized in Table 1, and the demographic data are summarized in Table 2. There more evidences for fMRI, EEG as non-invasive recordings, which are methods that have been used more than the others in the clinic.

Thalamotomy has proven to be effective in treating physical symptoms of various diseases such as parkinsonism, hyperkinetic movements, multiple sclerosis, and epilepsy (Spiegel et al., 1947; Ea and Ht, 1950; Elble, 2000; DeLong and Wichmann, 2007; Postuma et al., 2018). However, research examining the influence of thalamotomy on behavioral status is limited. Conducting an analysis on psychological and behavioral data before and after the procedure can help to classify and examine the neurological signs.

The types of surgery, brain recording methods, and brain network analysis influence the functional activity and connectivity results during thalamotomy surgery. Less invasive methods such as MRgFUS (Jang et al., 2016; Stanziano et al., 2022; Xiong et al., 2022a,b; Kato et al., 2022; Pae et al., 2023) and Gamma knife (Tuleasca et al., 2018a,b,c,d,e,f, 2020, 2021; Verger et al., 2018) techniques have emerged, which demonstrate differences in functional connectivity and activity outcomes in comparison with old surgery techniques due to the absence of unwanted tissue ablation in the electrode track.

While EEG was routinely employed to investigate the effects of thalamotomy surgery in the past, fMRI has become more widespread. Combining the both methods in preoperative and postoperative conditions can provide a more thorough examination of the effects of thalamotomy. Using more advanced equipment and greater numbers of electrodes in EEG studies can produce more consistent outcomes, allowing researchers to better understand the indirect impact of thalamotomy surgery on the resulting activity and overlapping functions of brain networks on the human cortex.

Table 1 demonstrates that despite the existence of various voxel-based functional connectivity techniques, such as fALFF (Xiong et al., 2022b), seed-based correlation analysis (SCA) (Jang et al., 2016), and ReHo (Wen et al., 2016), most present EEG and fMRI studies apply a data-driven ICA analysis. While new methods are continuously being developed, the brain is a dynamic and complex system that undergoes significant changes over time, making it a non-stationary network. To better model changes within the network, novel techniques such as Comprehensive Autonomic Predictor (CAP) (Tuleasca et al., 2020), Dynamic Causal Modeling (DCM) (Park et al., 2017), and windowed analysis may be more applicable. For future studies, more hypothesis-based approaches are recommended.

However, in terms of maintaining the brain’s equilibrium and functionality, how do the tremor circuit and other networks collaborate? The tremor circuit is not a subset of a specific network, so it’s possible that other essential contributors are present alongside the precentral gyrus, thalamus, and dentate nucleus. As reported by Miyagishima et al. (2007) the fundus of the central sulcus (Brodmann area 3a) is an additional critical relay in the tremor circuit, receiving proprioceptive inputs from the VIM nucleus. Therefore, a decrease in activation within the sensorimotor cortex and SMA can occur following thalamotomy. Tremors have been found to be associated with functional connection abnormalities in higher-level cortical and visual areas. The network-level functional connectivity changes in cortical, basal ganglia, and cerebellar systems have also been linked to tremors (DeSimone et al., 2019). Thus, among all the studies that have focused on the pathophysiology of essential tremor (ET), the role of the cerebellum’s dentate nuclei has been strongly supported (Jang et al., 2016; Park et al., 2017; Tuleasca et al., 2021; Stanziano et al., 2022). Tuleasca et al. (2020) have proposed a hypothesis that considers the broader impact of the visual system on tremor production. They suggest that if more than two brain networks are activated, it may be difficult to determine which one affects the other. This hypothesis indicates that the visual network could impact the motor network or vice versa. In an attempt to address such questions, Park et al. (2017) and Tuleasca et al. (2020) utilized DCM and CAP, respectively.

Passive movement in the brain is demonstrated by hemodynamic changes through fMRI and PET, while direct synchrony in the brain is depicted by EEG. However, functional association between basal ganglia and motor cortex (Brooks, 1999), as well as the asymmetric effect in postoperative patients by cortical metabolic (Baron et al., 1992), has been confirmed by both fMRI and PET studies. In PET scans comparing thalamotomy and pallidotomy of basal ganglia in pre- and post-surgical cases, the (pre)frontal cortex, lateral prefrontal, and parietal cortex were identified (Henselmans et al., 2000), and these are potential locations for future fMRI studies to track DMN changes. Basal ganglia connect with the SMA, dorsal prefrontal cortex, and frontal association areas. Consequently, insufficient activity in basal ganglia triggers compensatory overactivity of the lateral premotor and parietal cortex, which is primarily responsible for facilitating motor responses to visual and auditory cues. The association of subcortical activity areas that primarily receive input from basal ganglia demonstrates dysfunction of motor and visuomotor network activity in the tremor circuit (Jones, 1985).

Research studies that solely focused on structural changes have revealed alterations in the temporal pole and occipital cortex, indicating the significance of targeting specific visuomotor networks (Wen et al., 2016). Allowing researchers to understand the indirect effect of thalamotomy surgery on the human motor cortex and its impact on the overlapping activity of brain networks. While the majority of studies have focused on network connectivity alterations in imaging results, this hypothesis can be verified by comparing the functional connectivity and activity observed in EEG and fMRI studies in the future. According to Table 2, a common pattern noticed in both EEG and fMRI studies was the decrease-increase pattern reconfiguration in EEG studies (Brooks, 1999; Park et al., 2017) and the pattern evaluated with Clinical Rating Scale for Tremor (CRST) in fMRI studies (Jang et al., 2016; Park et al., 2017; Xiong et al., 2022b).

Studies have demonstrated the impact of thalamotomy on visuomotor network changes (Tuleasca et al., 2018a,c, 2020; Xiong et al., 2022a). According to Table 2, the VAS is a standard clinical evaluation technique utilized in EEG studies (Sarnthein et al., 2006; Stern et al., 2006; Michels et al., 2011; Jeanmonod et al., 2012). This investigation suggests that the effect of thalamotomy on the visual network is based on an earlier pathological belief that thalamotomy alters the visual network. Studies on the thalamus from as early as 1,664 and 1,681 by Thomas Willis described the human thalamus as the “chambers of the Optic Nerves.” Luys (1865) reported that thalamic nuclei are correlated with sensory, motor, limbic, and intrinsic functions. Karl Friedrich Burdach, since 1,822, has followed the optic tract and the superior colliculum’s brachium to the lateral geniculate body (Jones, 1985).

Although studies have examined various networks, such as the visual network, default mode network (DMN), and motor networks, the role of the mediodorsal thalamic nucleus in olfaction remains an unanswered question, considering that all senses except olfaction pass through the thalamus.

M-HN, SE, ER, M-RD, A-HV, MSa, AE, MSh, and YB contributed to the study design. M-HN, SE, and ER performed the data collection and analysis. M-HN, SE, ER, M-RD, A-HV, and MSa contributed to manuscript drafting and approved the submitted version. All authors contributed to the article and approved the submitted version.