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Abstract
Graph theory enables a direct quantification of topological properties of any arbitrary network. Its application in neuroscience has unveiled topological changes of brain networks associated with various neurodegenerative diseases. This study used the graph theory to understand speech deficits in patients with Parkinson’s disease (PD). In particular, this study investigated the effect of subthalamic nucleus deep brain stimulation (STN-DBS) on the topology of speech graphs. Sixty patients with PD completed a standard semantic fluency test with DBS switched ON and OFF. A control group of sixty matched nonsurgical PD patients completed the test once. All verbal responses were recorded, transcripted, and transformed into directed speech graphs. Volumes of tissue activated (VTA) were estimated for three STN subregions, including sensorimotor, associative, and limbic parts. First, the patients with DBS OFF produced smaller and denser speech graphs than nonsurgical patients, showing fewer nodes, higher density, shorter diameter, and shorter average shortest path. Second, DBS partially reversed the effect of surgery, leading to larger and sparser speech graphs with more nodes, lower density, longer diameter, and longer average shortest path (ON versus OFF). Third, however, the left associative VTA negatively correlated with the DBS-induced diameter and average shortest path changes (ON versus OFF), suggesting that the patients with greater left associative STN stimulation tended to produce smaller and denser speech graphs. This study demonstrates that STN-DBS can partially restore the topological structure of speech graphs in PD patients. However, stimulating the left associative STN appears to disrupt speech graphs.
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1 Introduction
Networks consist of nodes linked by edges, mathematically represented as graphs, which have attracted much attention in recent years from physics to biology (Sporns et al. 2004). The framework of graph theory, which directly identifies network connectivity patterns, can provide an investigation of network structure and dynamics for understanding brain and cognitive function. Calissano et al. (2024) showed that all the brain regions across subjects are mostly permuted with first and second order neighbors by graph matching strategy with nodes referring to brain regions and edges to their structural connections. The graph theoretical analysis has also been applied to reveal topological changes in brain networks in various neurodegenerative diseases. For example, Wu et al. (2024) revealed differences between patients with Alzheimer’s disease (AD) and frontotemporal dementia (FTD) in the functional network topology. AD patients demonstrated an increase in theta frequency band node degree and a decrease in alpha frequency band clustering coefficient and local efficiency in the occipital cortex, but FTD patients did not exhibit this pattern.
The well-established semantic fluency test is used to measure the ability to generate as many different words as possible from a given semantic category (e.g., animals, fruits) within 1 min (Ardila et al. 2006). There are multiple indices of performance on the semantic fluency test. The most common index is the number of correct words. Patients with Parkinson’s disease (PD) always generate fewer correct words than healthy adults (Azuma et al. 1997). Clustering and switching analyses employed two key parameters: the mean cluster size, which refers to the average number of generated words within the same semantic subcategory (e.g., mammal), and total number of switches between clusters (Troyer et al. 1997). The PD patients showed smaller semantic cluster sizes and fewer switches than healthy adults in verbal fluency tests (Tröster et al. 1998). However, this method depends too heavily on the subjective judgement of semantic relatedness and cluster division. The computational data-driven evaluations were inconsistent with subjective experimenter-based assessments in determining clusters (Farzanfar et al. 2018).
Our recent study used the graph theory-based approach to analyze the semantic fluency data and found that the PD patients produced relatively smaller and denser speech graphs than healthy adults but larger and sparser speech graphs than AD patients (Zhang 2022). Bertola et al. (2014) found that the graph parameters of semantic fluency data sensitively sorted the healthy adults, patients with mild cognitive impairment, and patients with AD. To be specific, the speech graphs become smaller and denser with the cognitive decline. This analytical method can extract more information from the semantic fluency test.
The subthalamic nucleus (STN) is a core basal ganglia structure, which is interconnected with widespread cortical and subcortical areas (Benarroch 2008). This connectivity allows for the regulation of motor, cognitive, and effective functions (Temel et al. 2005). Accordingly, the STN can be divided into three parts based on the topography of cortical projections, including sensorimotor, associative, and limbic subregions (van Wijk et al. 2020). Deep brain stimulation (DBS) of the STN is effective surgical treatment for the cardinal motor symptoms of PD (Krack et al. 1998; Zibetti et al. 2011). In terms of non-motor symptoms, however, the precise effect of STN-DBS on cognitive or effective functions remains obscure (Wu et al. 2014; Péron et al. 2010). Lee et al. (2021) pointed out that left dorsal theta frequency STN-DBS improves verbal fluency compared to no stimulation (OFF state) in PD patients. In contrast, Lam et al. (2021) reported that bilateral STN-DBS can not increase the performances of PD patients in semantic fluency test regardless of gamma or theta frequency. Wojtecki et al. (2006) depicted that the verbal fluency in PD patients showed a trend toward worsening at high-frequency stimulation compared with no stimulation.
In this study, we investigate the effect of STN-DBS on speech topology in patients with PD by psychophysiological models. Patients completed a standard semantic fluency test with DBS ON and OFF. Their verbal responses were represented as a directed speech graph, each correct word as a node, the temporal link between words as an edge (Fig. 1). First, we investigated group differences between DBS OFF and age- and education-matched nonsurgical PD patients in graph parameters, including nodes, density, diameter, and average shortest path. Second, we detected within-group differences between DBS ON and OFF in the graph parameters. Third, we explored the relationship between stimulated volumes within three subregions of STN and DBS-induced changes (ON versus OFF) of graph parameters.
2 Materials and methods
2.1 Patients with STN-DBS and clinical assessments
We included 60 idiopathic PD patients at the Fudan University Huashan Hospital from 2020 to 2022. Inclusion criteria were (1) diagnosed PD according to the Movement Disorder Society Clinical Diagnostic Criteria (Postuma et al. 2015); (2) receiving bilateral high-frequency STN-DBS for at least 6 months; (3) Hoehn and Yahr Stages 1 to 3 with DBS ON; (4) age 40 to 80 years; (5) education ≥ 5 years; and (6) Mandarin Chinese speaking. Exclusion criteria were (1) possible dementia (Montreal Cognitive Assessment, MoCA < 21/30) or intake of anti-dementia drugs; (2) alcohol or drug abuse; (3) a history of epilepsy, stroke, or brain injury; (4) unclear responses due to heavy accents or quietly speaking.
All patients were assessed on their regular anti-parkinsonian drugs, including levodopa (N = 55), amantadine (N = 18), pramipexole (N = 14), entacapone (N = 3), piribedil (N = 3), rasagiline (N = 3), selegiline (N = 3), and stalevo (N= 2). The levodopa equivalent daily dose was calculated using the equation of Tomlinson et al. (2010). The severity of motor symptoms was evaluated by the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III subscale with DBS ON and OFF. Other clinical assessments were administered with DBS ON. Table 1 shows demographic and clinical features and neuropsychological measures.
2.2 The control group
We recruited 60 age- and education-matched nonsurgical patients with PD as a control group. The inclusion and exclusion criteria of the control group were same as DBS group. All patients were assessed on their regular anti-parkinsonian drugs, including levodopa (N = 40), pramipexole (N = 22), selegiline (N = 11), piribedil (N = 10), amantadine (N = 7), entacapone (N = 4), and rasagiline (N = 1). They completed the same clinical and neuropsychological measures as the DBS group (Table 1).
2.3 Graph theoretical analysis
All participants performed the standard semantic fluency test. The DBS group completed the test twice with ON and OFF (animals or fruits) and the control group completed once. We transcripted and transformed their verbal responses into directed speech graphs with SpeechGraph software, in which each correct word was represented as a node and each temporal link between consecutive words was represented as an arc (Fig. 1) (Zhang 2022; Bertola et al. 2014). We computed four graph parameters: (1) nodes: the number of correct words; (2) density: the ratio of arcs to the maximum possible number of arcs; (3) diameter: length of the maximum shortest path between two nodes; and (4) average shortest path length: mean length of all the shortest path between two nodes, also known as the characteristic path length of the graph. More details were described in our previous study (Zhang 2022).
2.4 Estimation of volumes of tissue activated (VTA)
The volume of tissue activation (VTA) were estimated by preoperative structural T1- and T2-weighted magnetic resonance imaging (MRI) and postoperative computed tomography (CT) images. Eight patients were excluded from the VTA analysis because of the lack of MRI images. The VTA analysis were conducted via the Lead-DBS toolbox following a standard pipeline (Horn and Kühn 2015; Horn et al. 2019). The MRI and CT images were coregistered, normalized to the Montreal Neurological Institute space (Avants et al. 2008), and corrected for brain shift with a Coarse mask (Schönecker et al. 2009). The quality of coregistration and normalization was visually monitored. Locations of ring electrodes (Medtronic 3389, PINS L301, and SceneRay 1200) were reconstructed by PaCER and manually refined (Husch et al. 2017). Next, each patient’s bilateral VTAs were overlapped with the sensorimotor, associative, and limbic parts of the STN by the DISTAL Minimal Atlas using patient-specific stimulation parameters (Fig.2A) (Ewert et al. 2018). For each STN subregion, the size of the overlap cluster was normalized by taking its ratio to total VTA size. More details were described in our previous study (Xiao 2024).
2.5 Statistical analysis
Data were analyzed with IBM SPSS Statistics 20. First, we compared the sensorimotor, associative, and limbic VTAs for both hemispheres using repeated-measures ANOVA (two-tailed, p < 0.05). As a validity check, we examined whether the MDS-UPDRS Part III score was lower with DBS ON than OFF using a paired-sample t-test (one-tailed, p < 0.05).
Second, we investigated whether the surgery did harm to speech graphs by examining the difference between DBS OFF and control group in the four graph parameters using one-way ANOVAs (one-tailed, p < 0.05). The ANOVA had a factor Group (DBS OFF, Control), and covariates Hoehn and Yahr Scale, Disease duration, and Beck Depression Inventory-II due to significant group differences (see Table 1).
Third, we examined the difference between DBS ON and OFF in the four graph parameters using paired-sample t-tests (two-tailed, p < 0.05).
Finally, we examined whether the VTA within three subregions of STN correlated with the differences between ON and OFF (DBS-induced change) in four graph parameters using linear stepwise regression models (two-tailed, p < 0.05).
3 Results
3.1 Improved motor functions under DBS
Figure 2B shows the sensorimotor, associative, and limbic VTAs of each hemisphere. The ANOVA revealed a main effect of Subregion (F(2, 102) = 44.23, p < 0.001, ηp2 = 0.46) and a interaction between Subregion and Hemisphere (F(2, 102) = 5.38, p = 0.007, ηp2 = 0.10), but no main effect of Hemisphere (F < 1). In general, the sensorimotor and associative VTAs were larger than the limbic VTAs regardless of the hemisphere (ps < 0.001). In addition, left sensorimotor and right associative VTAs were larger than the right sensorimotor VTAs (ps ≤ 0.016).
The MDS-UPDRS Part III score was lower with DBS ON than OFF (Fig. 2C, t(58) = -12.77, p < 0.001), indicating that the DBS contributed to the improvement of motor symptoms in PD patients.
3.2 Different effects of surgery and DBS on speech graphs
Figure 2D shows the four graph parameters in each group. For the comparison between DBS OFF and control group, group differences were found in the nodes (F(1, 106) = 5.25, p = 0.012, ηp2 = 0.05), density (F(1, 106) = 6.18, p = 0.008, ηp2 = 0.06), diameter (F(1, 106) = 2.85, p = 0.047, ηp2 = 0.03), and average shortest path length (F(1, 106) = 3.39, p = 0.035, ηp2 = 0.03). The speech graphs of the patients with DBS OFF showed fewer nodes, higher density, shorter diameter, and shorter average shortest path than those of control group. Namely, the patients with DBS OFF produced smaller and denser speech graphs than the control group.
For the comparison between DBS ON and OFF, within-group differences were found in the nodes (t(59) = 2.54, p = 0.014), density (t(59)= -2.32, p = 0.024), diameter (t(59) = 2.20, p = 0.032), and average shortest path length (t(59) = 2.24, p = 0.029). The speech graphs of the patients with DBS ON showed more nodes, lower density, longer diameter, and longer average shortest path than those of patients with DBS OFF. Namely, the patients produced larger and sparser speech graphs with DBS ON than OFF.
3.3 Correlations between VTAs of STN and the parameter changes
Figure 2E shows the correlations of VTAs within three subregions of STN and the DBS-induced changes of parameters. The stepwise regression model for the diameter change (F(1, 50) = 8.63, p = 0.005, R2 = 0.15) included the left associative VTA (beta = -11.88, t = -2.94, p = 0.005) but removed the left sensorimotor (beta = -0.14, t = -1.10, p = 0.276), right sensorimotor (beta = -0.18, t = -1.41, p = 0.165), right associative (|t|<1), left limbic (|t|<1), and right limbic VTAs (|t|<1).
The stepwise regression model for the average shortest path length change (F(1, 50) = 7.43, p = 0.009, R2 = 0.13) included the left associative VTA (beta = -3.59, t = -2.73, p = 0.009) but removed the left sensorimotor (beta = -0.16, t = -1.19, p = 0.240), right sensorimotor (beta = -0.19, t = -1.42, p = 0.162), right associative (|t|<1), left limbic (|t|<1), and right limbic VTAs (|t|<1).
It suggested that the patients with greater left associative STN stimulation tended to perform worse in the semantic fluency test, represented by smaller and denser speech graphs.
The VTA was not correlated with nodes change or density change.
4 Discussion
In this study, we used a graph theory-based approach to examine the effect of STN-DBS on the speech graphs in nondemented patients with PD. We found that the surgery made PD patients produce smaller and denser speech graphs. Comfortingly, DBS partially restored this situation to some extent. To be specific, the speech graphs of the patients with DBS OFF exhibited fewer nodes, higher density, shorter diameter, and shorter average shortest path than those of the nonsurgical patients. But the speech graphs of patients with DBS ON showed more nodes, lower density, longer diameter, and longer average shortest path than those of patients with DBS OFF. Moreover, the left associative VTA negatively correlated with DBS-induced diameter and average shortest path changes, indicating that the left associative STN stimulation interferes with patients’ speech topology.
4.1 The relationship between STN-DBS and speech function
The speech function bases on executive functions and working memory, in addition to semantic knowledge. Higginson et al. (2003) pointed out that the speech impairment in patients with PD may result from executive dysfunction and working memory deficits. It has been confirmed that STN-DBS could improve the executive functions and working memory in PD patients (Jahanshahi et al. 2000; Salehi et al. 2024). The prefrontal cortex is known to play a key role in mediating executive functions and working memory (Kimberg et al. 1997). It has been confirmed that the human STN is monosynaptically connected with the prefrontal cortex during cognitive processing (Kelley et al. 2018). Besides, Thames et al. (2012) suggested a double dissociation of basal ganglia involvement in verbal fluency tasks through their connectivities with cortical structures. For example, the caudate subserved word production and the putamen facilitated switching. The STN is anatomically a relay station within the basal ganglia-thalamocortical networks and is functionally a potent modulator of these circuits (Temel et al. 2005).
This study utilized a high-frequency STN stimulation and proposed a DBS-induced improvement of speech topology in PD patients. Previous studies with similar results applied a low-frequency STN stimulation (Lee et al. 2021), and suggested that the high-frequency STN stimulation had no effect or even adverse effect on speech function in PD patients (Wojtecki et al. 2006; Tremblay et al. 2015). Nevertheless, it can’t be denied that these two different frequencies may all contribute to speech function. Wojtecki et al. (2017) used the time-frequency analysis and revealed increased subthalamic low-frequency oscillations and enhanced coherence between the STN and frontal cortex during the verbal fluency task. Anzak et al. (2011) demonstrated the increased high-frequency local field potential power in STN during verbal fluency tests and the power changes correlated with the number of correct words.
4.2 The relationship between associative STN and speech function
The importance of precise stimulation position within the STN should be stressed to avoid adverse effects on speech function. The human STN is anatomically parcellated into three parts, including sensorimotor, associative, and limbic subregions occupying the dorsolateral, ventrolateral, and ventromedial portions of the STN respectively (Lambert et al. 2012). This makes the STN become a nexus that integrates the motor, cognitive, and emotional functions and might consequently be a target for the effective therapy of such behavioral disorders (Mallet et al. 2007). A previous study pointed out that the interindividual variability of non-motor symptoms after STN-DBS depends on the location of neurostimulation (Petry-Schmelzer et al. 2019). Thereinto, the cognitive change is associated with the stimulation of associative STN. PD patients with more VTA inside the ventral associative STN correlated with decreased verbal fluency performance (Mikos et al. 2011). Moreover, substantial anatomical and imaging evidence confirmed that the prefrontal areas project to the associative STN via hyperdirect pathways (Aron et al. 2016). This indicates that the associative STN is involved in speech function and also proves the functional segregation of the three STN subdivisions.
4.3 The adverse effect of surgery on speech function
Our results showed that the patients with DBS OFF performed worse than nonsurgical patients, which may be due to a surgical/lesional effect. A prospective study demonstrated a deterioration of verbal fluency in PD patients at each of the STN-DBS state compared to pre-DBS, especially when DBS OFF (Okun et al. 2009). Coincidentally, the adverse effect of surgery on verbal fluency was larger than that of STN stimulation in PD patients (Morrison et al. 2004). In comparison with the pre-surgical state, PD patients had poorer performance in semantic fluency after STN-DBS surgery (Pillon et al. 2000; Gironell et al. 2003; Witt et al. 2013). These observations suggest that the decline in speech function following STN-DBS surgery may be more closely associated with the surgical intervention.
4.4 Limitations and future directions
This study has limitations. First, we did not create probabilistic stimulation maps of DBS for target selection and evaluation. Thus, we could not obtain a very effective target for speech topology in the associative STN (Dembek et al. 2016). In addition, we did not compute the functional connectivity between the stimulation site and other brain regions, so we could not evaluate the effect of the connectivity between STN and brain networks on speech graphs (Reich et al. 2022). Second, this study can not compare the effect of different stimulation frequencies on speech topology (Lam et al. 2021; Wojtecki et al. 2006). We focus more on the difference between high-frequency STN-DBS ON and OFF.
Lastly, it will be meaningful to track longitudinal network changes over the duration of PD and determine examine the effect of different DBS frequencies and electrode locations on speech function in PD patients from a topological perspective. Our results confirmed the different effects of surgery and DBS on speech graph in the early stage of nondemented PD patients, which provides an important baseline for understanding topology reflecting response to treatment. In turn, this should be beneficial to the development of a spatially and temporally structured pattern for clinical use.
5 Conclusion
This study used the graph theory-based approach and demonstrated that STN-DBS can partially restore the damage of speech topology from surgery in PD patients. Moreover, patients with larger left associative VTAs exhibit smaller DBS-induced changes of graph parameters, suggesting that stimulating the left associative STN may damage speech graphs of PD patients. Further research is required to find a mode of long-term stimulation to improve speech topology and explore the role of specific electrode location within the STN.
Data availability
The datasets and documents generated, analyzed or used during the current study are available from corresponding author upon reasonable request.
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Acknowledgements
We thank Feiyang Ye, Jue Zhao, Bin Wu, Bo Shen, and Juanjuan He for their assistance in data acquisition.
Funding
This work was supported by the STI2030-Major Project (2021ZD0203600 to Z. Ye), and the National Health Commission of China (Pro20211231084249000238 to J. Wu).
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Authors
Contributions
Conceptualisation: Z. Ye; Data curation: G. Zhang, H. Xiao, and Z. Ye; Methodology: G. Zhang and Z. Ye; Investigation: H. Xiao, J. Ma, and L. Lang; Visualization: G. Zhang; Formal analysis: G. Zhang, H. Xiao, and Z. Ye; Project administration: J. Wu and Z. Ye; Resources: J. Wu and Z. Ye; Supervision: J. Wu and Z. Ye; Funding acquisition: J. Wu and Z. Ye; Writing-original draft: G. Zhang; Writing-review & editing: H. Xiao, J. Ma, L. Lang, J. Wu, and Z. Ye.
Corresponding authors
Correspondence to Guanyu Zhang or Zheng Ye.
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This study was approved by the ethics committee of the Fudan University Huashan Hospital according to the Declaration of Helsinki. Each participant signed a written informed consent before participating in this study.
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Not applicable.
Competing interests
Author Zheng Ye is a member of the Editorial Board for Current Medicine. The paper was handled by the other Editor and has undergone rigorous peer review process. Author Zheng Ye was not involved in the journal’s review of, or decisions related to, this manuscript. The other authors declared no conflict of interest.
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