Pallidal beta power is associated with depression in Parkinson’s disease

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Johnson, Patricia B. Coutinho, Lauren E. Kenney, Joshua K. Wong, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5952073/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jan, 2026 Read the published version in npj Parkinson's Disease → Version 1 posted 12 You are reading this latest preprint version Abstract Depression is increasingly recognized as a prevalent source of disability in individuals with Parkinson’s disease (PD), but its pathophysiology is not well understood. Neural activity in the basal ganglia, particularly the subthalamic nucleus, has been linked to depression in PD, but the role of the pallidum remains unclear. This retrospective study aimed to correlate preoperative depression symptoms with intraoperative resting-state neural activity recorded from the pallidum in N = 50 patients who underwent deep brain stimulation (DBS) implantation surgery. Patients with clinically elevated depression symptoms exhibited elevated beta (13–30 Hz) power compared to patients without depression. Beta power, particularly high beta (20–30 Hz) power, was also associated with depression symptom severity, even when controlling for other demographic, clinical, pharmacological, and neurophysiological variables. These results establish pallidal beta power as a potential biomarker of depression in PD and set the stage for tailoring DBS therapy to improve psychiatric symptoms in PD. Biological sciences/Physiology/Neurophysiology Health sciences/Biomarkers/Diagnostic markers Health sciences/Diseases/Psychiatric disorders/Depression Health sciences/Diseases/Neurological disorders/Movement disorders/Parkinsons disease Parkinson’s disease depression globus pallidus deep brain stimulation local field potential Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Parkinson's disease (PD) is a progressive neurodegenerative disorder mainly characterized by motor symptoms, such as bradykinesia, tremor, rigidity, and postural instability (Tanner and Ostrem, 2024 ). Although PD is primarily a movement disorder, depression and other psychiatric symptoms are increasingly recognized as prevalent sources of disability. Depression often presents before motor symptoms and affects an estimated 38% of patients with PD (Cong et al., 2022 ), a higher proportion than other chronic diseases (Nilsson et al., 2002 ) and the general population (Bromet et al., 2011 ). Depression in PD is also associated with worsened quality of life and increased caregiver burden (Starkstein et al., 1992 ; Schrag, 2006 ; Dissanayaka et al., 2011 ). Despite its high prevalence and immense burden, depression in PD is underrecognized, its pathophysiology is not well understood, and current treatments are often inadequate and non-specific to PD (Pontone and Mills, 2021 ). Deep brain stimulation (DBS) targeted to the basal ganglia is an established treatment for select PD patients experiencing refractory motor symptoms. DBS involves neurosurgical implantation of electrodes into the basal ganglia in either the subthalamic nucleus (STN) or the pallidum (specifically the globus pallidus internus (GPi)) to deliver electrical stimulation to modulate pathological neural activity. Improvement in motor symptoms with DBS is extensively documented in the literature (Lachenmayer et al., 2021 ), but some patients may also experience benefits in nonmotor symptoms. Recent meta-analyses have shown that, at a group level, DBS induces a small to moderate improvement in depression, mainly in the short term (Couto et al., 2014 ; Cartmill et al., 2021 ), with potentially greater improvements with pallidal DBS than STN DBS (Mansouri et al., 2018 ). However, these meta-analyses note substantial heterogeneity across studies, indicating that depression outcomes vary widely across individuals from significant improvement to worsening. This variability may be due to differences in how depression symptoms are assessed (e.g., scales, diagnostic interview) and the lack of data-driven methods to tailor DBS therapy for depression in PD. Identifying biomarkers of depression in PD, such as neural activity within the basal ganglia, could potentially enable clinicians to harness DBS to treat depression in PD while maintaining effective treatment of motor symptoms. DBS offers a unique opportunity to study neural activity in the basal ganglia involved in specific symptoms and behavior by recording local field potentials (LFP) in the intra-, peri-, and postoperative environments. Numerous studies have investigated neural activity associated with motor symptoms in PD (Yin et al., 2021 ), with the majority pointing to elevated beta power (13–30 Hz) in the STN and the pallidum associated with bradykinesia and rigidity (Brown et al., 2001 ; Eisinger et al., 2020 ) and with reduction in beta power in response to DBS and levodopa therapy associated with improvement in motor symptoms (Kühn et al., 2008 ; Wang et al., 2018 ; Cagle et al., 2021 ). In contrast, relatively few studies have investigated neural activity underlying nonmotor symptoms in PD, including depression (Eisinger et al., 2018 ). Previous studies focused on neural activity contributing to depression in PD have mainly focused on task paradigms with intra- or perioperative recordings exclusively from the STN. Their results generally indicate that low-frequency oscillations, particularly in theta (4–7 Hz) and alpha (8–12 Hz) bands, may be implicated in emotional responses during an affective picture-viewing task (Kühn et al., 2005 ; Brücke et al., 2006 ; Huebl et al., 2011 , 2014 ; Mandali et al., 2020 ). Only two studies have focused on resting-state neural activity, showing that PD patients with depression may exhibit lower theta power and higher alpha power in the left ventral STN compared to non-depressed individuals (Sun et al., 2021 ), and theta/alpha power may be localized to the ventral STN (Rappel et al., 2019 ). No prior studies have investigated neural activity in the pallidum associated with depression, either in resting-state or task paradigms. Additionally, previous research has not fully explored correlations across other frequency bands or controlled for potential confounds that may affect depression and neurophysiological activity, such as medication, motor severity, and demographics. Without controlling for such variables, the specificity of these candidate biomarkers to depression cannot be determined. The objective of this study was to identify neurophysiological activity in the pallidum associated with depression symptoms in patients undergoing DBS implantation surgery for the treatment of PD while controlling for potential confounding variables. Identifying neurophysiological activity linked to depression in PD could potentially elucidate the role of the pallidum in the pathophysiology of depression in PD and serve as the basis for further research to identify objective biomarkers of psychiatric symptoms in PD. Such biomarkers could then be used to guide DBS therapy to alleviate these nonmotor symptoms that represent an important determinant of quality of life in individuals with PD. RESULTS Cohort Characteristics The study cohort included N = 50 patients at the University of Florida who underwent DBS electrode implantation surgery targeted to the pallidum for the treatment of PD motor symptoms. The cohort characteristics are reported in Table 1 . A subset of N = 13 (26.0%) patients were classified as having PD with clinically elevated depression symptoms (preoperative BDI-II score ≥ 14, based on established criteria using BDI-II to detect and assess depression severity (Visser et al., 2006 )). Depression symptoms were evaluated on average approximately four months before DBS implantation surgery as part of the standard preoperative neuropsychological assessment to verify candidacy for DBS therapy. The depressed group had significantly more severe BDI-II scores (unpaired, two-tailed t-test; t = 10.45, p < 0.001), Apathy Scale (AS) scores (t = 2.62, p = 0.012), and State-Trait Anxiety Inventory Trait (STAI-Trait) scores (t = 5.48, p < 0.001) than the not depressed group. No statistically significant differences were detected across groups in sex, age, disease duration, recording hemisphere, evaluation time point, medication use (antidepressants, benzodiazepines, non-benzodiazepine hypnotics), Unified Parkinson’s Disease Rating Scale (UPDRS) Part III (UPDRS-III) scores (on and off levodopa medication), or levodopa equivalent daily dose (LEDD). Table 1 Cohort Characteristics Total Depressed (BDI-II ≥ 14) Not Depressed (BDI-II < 14) Statistical Analysis (Depressed vs. Not Depressed) Number of patients 50 13 (26.0%) 37 (74.0%) - Sex Male: 33 (66.0%) Male: 11 (84.6%) Male: 22 (59.5%) p* = 0.173 Age (years) 66.32 (8.34, 50–81) 63.46 (9.28, 50–80) 67.32 (7.88, 50–81) t = -1.45 p = 0.153 Disease Duration (years) 10.50 (5.06, 2–23) 12.62 (5.19, 6–23) 9.76 (4.87, 2–21) t = 1.79 p = 0.080 Recording Hemisphere Left: 28 (56.0%) Left: 7 (53.9%) Left: 21 (56.8%) p* = 1.0 Evaluation Time Point (months before surgery) 4.1 (1.40, 1–6) 4.39 (1.39, 1–6) 4.00 (1.41, 1–6) t = 0.85 p = 0.40 BDI-II 11.06 (8.14, 2–41) 22.38 (7.23, 14–41) 7.08 (3.17, 2–13) t = 10.45 p < 0.001 AS N = 48 11.56 (6.00, 0–24) N = 13 15.07 (5.74, 4–24) N = 35 10.26 (5.63, 0–22) t = 2.62 p = 0.012 STAI-Trait N = 47 36.83 (10.43, 20–67) N = 13 47.38 (11.40, 20–67) N = 34 32.79 (6.61, 21–49) t = 5.48 p < 0.001 Antidepressant Use 25 (50.0%) 8 (61.5%) 17 (45.6%) p* = 0.520 Benzodiazepine Use 20 (40.0%) 6 (42.9%) 14 (37.8%) p* = 0.744 Non-Benzodiazepine Hypnotic Use 22 (44.0%) 8 (61.5%) 14 (37.8%) p* = 0.197 UPDRS-III (Off Levodopa) N = 49 38.53 (13.66, 10–72) N = 13 34.62 (13.33, 10–56) N = 36 39.94 (13.68, 19–72) t = -1.21 p = 0.232 UPDRS-III (On Levodopa) N = 49 23.45 (12.09, 4–62) N = 13 21.77 (11.5, 4–44) N = 36 24.06 (12.39, 8–62) t = -0.58 p = 0.565 Levodopa Equivalent Daily Dose (mg/day) 1110.66 (555.59, 60-2275) 1159.77 (591.04, 60-2275) 1093.41 (550.03, 75-2000) t = 0.37 p = 0.72 Depressed = PD with clinically elevated depression symptoms (BDI-II ≥ 14). Not Depressed = PD without clinically elevated depression symptoms (BDI-II < 14). BDI-II = Beck Depression Inventory (2nd Edition) (score range: 0–63). AS = Apathy Scale (score range: 0–42). STAI-Trait = State-Trait Anxiety Inventory (Trait score) (score range: 20–80). UPDRS = Unified Parkinson’s Disease Rating Scale (score range: 0-108). t-values and p-values obtained from unpaired, two-tailed t-tests. p*-values obtained from Fisher’s exact tests. Data presented as mean (SD, range) or number of patients (%). Pallidal Activity Associated with Depression Imaging analysis confirmed that the DBS leads were well placed spanning the posterior GPi/GPe interface across the cohort (Fig. 1 A-B). Intraoperative LFP recordings were acquired during DBS lead implantation from all contacts on the DBS lead in monopolar configuration with the patient off dopaminergic medication for at least 6 hours and at rest (Fig. 1 C). The average normalized power spectral density (PSD) across all contacts for each patient was then computed to analyze neural activity in defined frequency bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (13–30 Hz) [separated into low beta (13–20 Hz) and high beta (20–30 Hz)], gamma (30–100 Hz) [separated into low gamma (30–50 Hz) and high gamma (50–100 Hz)], and high-frequency oscillations (HFO) (200–400 Hz). The average power in each frequency band was compared between PD patients with clinically elevated depression symptoms and PD patients without depression. PD patients with depression symptoms exhibited significantly higher pallidal beta power (13–30 Hz) than PD patients without depression, as shown by the average PSD curves in Fig. 1 D and the distribution comparison in Fig. 1 E (unpaired, two-tailed t-test; t = 2.68, p = 0.010, p FDR =0.049). Power in the low beta (13–20 Hz) (t = 2.61, p = 0.012, p FDR =0.049) and high beta (20–30 Hz) (t = 2.53, p = 0.015, p FDR =0.049) were both increased in depressed patients compared to non-depressed. A trend for reduced delta power (1–4 Hz) was found in depressed patients compared to non-depressed patients (t=-1.96, p = 0.056, p FDR =0.14). All other frequency bands showed no statistically significant differences between the two groups. The PSDs across the full frequency range analyzed (1-500 Hz) were averaged across patients and displayed in Supplementary Fig. 1 . Since beta power significantly differed between PD patients with versus without depression, we further investigated whether beta power was also associated with depression severity. Beta power was significantly positively correlated with depression severity (Pearson correlation; r = 0.31, p = 0.031, p FDR =0.047), including high beta power (r = 0.31, p = 0.028, p FDR =0.047) but not low beta power (r = 0.27, p = 0.059, p FDR =0.059). Controlling for Demographic, Clinical, and Neurophysiological Variables Depression is a complex symptom in PD that may be modulated by demographics, motor symptoms, medications, and other psychiatric symptoms. Therefore, a multivariate generalized linear model (GLM) was generated to evaluate the relationship between pallidal neural activity in the defined frequency bands and depression severity (BDI-II scores) while controlling for the following variables: demographics (age, sex, months between BDI-II evaluation and intraoperative LFP recordings), recording variables [hemisphere and normalization curve parameters (1/f curve exponent/slope and offset/intercept)], PD severity and treatment variables [disease duration, UPDRS motor scores both on and off levodopa medication, levodopa equivalent daily dose (LEDD)], severity of other psychiatric symptoms [apathy (AS scores) and anxiety (STAI-Trait scores)], and psychiatric medication use (antidepressants, benzodiazepines, and non-benzodiazepine hypnotics). A total of N = 45 patients were included in the model, excluding N = 5 patients due to missing UPDRS, Apathy Scale, and/or STAI-Trait scores. The GLM revealed a significant positive effect of beta power associated with BDI-II scores (coefficient β = 4.00, 95% CI=(0.23–7.78), p = 0.038), indicating that higher beta power was associated with higher depression severity (Fig. 2 , Supplementary Table 1 ). Other positive significant effects included STAI-Trait scores (trait anxiety symptom severity) (β = 6.37, 95% CI=(3.70–9.04), p < 0.001), which reflects a correlation between depression and anxiety symptoms, and the intercept of the GLM (β = 10.03, 95% CI=(4.50-15.56), p < 0.001), which indicates that BDI-II scores were significantly greater than zero. Similar to the univariate analyses, we also found that in separate GLM analyses, high beta power was significantly associated with depression severity (β = 4.49, 95% CI=(0.50–8.49), p = 0.027) but low beta power was not (β = 3.16, 95% CI=(-0.40-6.73), p = 0.082). Comparing the Akaike Information Criterion (AIC), which measures the relative predictive performance of statistical models, revealed that the GLM using high beta power was the best-fit model for predicting BDI with the lowest AIC (AIC = 300.29) compared to low beta power (AIC = 303.36) and overall beta power (AIC = 301.18). These results suggest that beta power, especially high beta power, was significantly associated with depression severity, even when controlling for other possible demographic, clinical, pharmacological, and neurophysiological confounds. DISCUSSION This retrospective study aimed to identify neurophysiological activity in the pallidum associated with depression symptoms in patients undergoing DBS therapy for the treatment of PD using intraoperative resting-state neural recordings and preoperative baseline neuropsychiatric assessments. The results revealed that (1) PD patients with elevated preoperative depression symptoms exhibited significantly increased pallidal beta power compared to patients without depression, and (2) pallidal beta power, especially high beta power, was significantly associated with depression severity, even when controlling for potential demographic, clinical, pharmacological, psychiatric, and neurophysiological confounds. To the best of our knowledge, this is the first report of neural activity in the pallidum associated with depression in PD. The involvement of pallidal beta power provides new insight into the role of basal ganglia networks in the pathophysiology of depression in PD and could potentially serve as an objective marker of depression symptoms with further validation. Role of the Basal Ganglia in Depression in PD The pathophysiology of depression in PD is not fully understood, but it is thought involve alterations in basal ganglia-thalamo-cortical networks as a result of PD-related neurodegeneration. Most evidence of the brain networks implicated in depression in PD has originated from human neuroimaging studies, which generally point to structural, functional, metabolic, and neurotransmitter (particularly dopamine and serotonin) abnormalities in widespread limbic networks involving the basal ganglia, mediodorsal thalamus, amygdala, prefrontal cortex (orbitofrontal, dorsolateral, and ventromedial cortices), and anterior cingulate cortex (Thobois et al., 2017 ; Sgambato-Faure and Tremblay, 2018 ; Ahmad et al., 2023 ). Pathophysiological changes in these networks point to abnormal top-down control of emotion-related limbic subcortical regions, causing impairments in motivational, reward, and response initiation processes commonly associated with depression symptoms. Notably, the networks implicated in PD-related depression and the affected cognitive and emotional processes share commonalities with those implicated in depression in non-PD populations (Mayberg, 1997 ; Phillips et al., 2003 ; Williams, 2016 ; Li et al., 2018 ). Our results contribute evidence that depression in PD is associated with altered neural activity in the pallidum. The pallidum plays a critical role in basal ganglia-thalamo-cortical networks by regulating information flow from the cortex via the striatum to the thalamus and ultimately back to the cortex. Although its role in regulating movement is most often discussed, the pallidum also regulates information flow in prefrontal/associative and limbic basal ganglia-thalamo-cortical networks, which contribute to cognitive and emotional processing, respectively (Saga et al., 2017 ). Therefore, pathological alterations in the basal ganglia, including the pallidum, have been increasingly linked to psychiatric disorders, including depression (Macpherson and Hikida, 2019 ). Alterations in pallidal structure and associated networks provide compelling evidence of its potential role in depression pathophysiology in non-PD and PD populations. Reduced pallidum volume has been consistently observed in non-PD individuals with depression across the lifespan (Baumann et al., 1999 ; Bielau et al., 2005 ; Kempton et al., 2011 ; Grieve et al., 2013 ; Wu et al., 2023 ) and even subthreshold depression (Li et al., 2017 ). Patients with depression and PD have been shown to exhibit reduced functional connectivity (Wei et al., 2017 ) and abnormal white matter network topology (Hu et al., 2020b ) within basal ganglia networks including the pallidum compared to non-depressed PD patients and healthy controls. Additionally, the pallidum has been increasingly recognized for its potential role in depression in PD due to its connections to the lateral habenula, which is known for its critical role in regulating negatively motivated behavior and for its implication in depression (Hu et al., 2020a ) and more specifically depression in PD (Borgonovo et al., 2017 ; Samanci et al., 2024 ). The pallidum and lateral habenula have been shown to contribute to reward processing in non-human primate studies (Arkadir et al., 2004 ; Hong and Hikosaka, 2008 , 2013 ), as well as studies using intracranial recordings during reward processing tasks in small cohorts of patients undergoing DBS (Howell et al., 2016 ; Münte et al., 2017 ). Altogether, these findings suggest that abnormalities in pallidal structural and functional connectivity and altered reward processing in the pallidal-limbic circuits may contribute to depression pathophysiology. Modulation of pallidal activity via lesions and DBS also provides clues into its role in depression. In non-PD populations, focal lesions (Lauterbach et al., 1997 ; Vataja et al., 2004 ; Miller et al., 2006 ) and hyperintensities (Iidaka et al., 2008 ) in the pallidum have been associated with developing depression. In contrast, improvements in depression symptoms have been observed following pallidotomy for the treatment of PD (Masterman et al., 1998 ; Laskowska et al., 2007 ), although other studies have found no change (Green et al., 2002 ). There is also evidence of pallidal DBS inducing a mild but clinically meaningful improvement in depression in PD in controlled trials and meta-analyses (Couto et al., 2014 ; Cartmill et al., 2021 ), as well as improving depression in other neurological disorders like Tourette syndrome (Liu et al., 2022 ) and dystonia (Eggink et al., 2018 ). However, depression responses to DBS are variable across individuals, and more controlled studies are needed to determine how and where to stimulate within the pallidum to improve and/or avoid worsening depression symptoms. Based on our results, posterior pallidal neural activity, particularly in the beta band, may show promise as a potential marker to guide pallidal DBS to more effectively and consistently alleviate depression in PD. Beta Power: A Potential Marker Beyond Motor Symptoms Elevated beta power in the basal ganglia has been classically associated with motor symptoms in PD, particularly bradykinesia and rigidity, in both the pallidum and the STN (Brown et al., 2001 ; Eisinger et al., 2020 ). Pathological beta power is generally reduced by both levodopa therapy (Kühn et al., 2006 ) and therapeutic DBS (Kühn et al., 2008 ). Although these findings make clear that beta power is linked to PD pathophysiology, this does not necessarily mean that beta power is only specific to motor symptoms in PD. We found that elevated beta power in the pallidum was associated with higher preoperative depression severity, even when controlling for other potentially confounding variables that could have impacted either beta power or depression symptom severity. The discovered relationship between beta power and depression severity was not explained by variability in sex or age, even though previous studies have found depression to be more common in females (Rojo et al., 2003 ; Riedel et al., 2010 ; Khedr et al., 2020 ) and older individuals (Antar et al., 2021 ) with PD. Other studies have also found both depression and beta power to be associated with longer PD disease duration (Khedr et al., 2020 ; Antar et al., 2021 ; Chendo et al., 2022 ) and more severe motor symptoms (Rojo et al., 2003 ; Riedel et al., 2010 ; Khedr et al., 2020 ), as well as fluctuations in depression tied to levodopa medication state (more severe depression off- than on-medication) (van der Velden et al., 2018 ). However, these potential confounds did not significantly impact our results. Finally, depression is a complex psychiatric disorder that is often comorbid with anxiety and apathy in PD (Cong et al., 2022 ; Weintraub et al., 2022 ) and is commonly treated with several different medications (Seppi et al., 2019 ; Pontone and Mills, 2021 ). We found that worse anxiety, but not apathy, was significantly associated with more severe depression, but this association did not negate the relationship between depression severity and elevated beta power. Additionally, the use of medications commonly used to treat psychiatric symptoms in PD, which could have potentially impacted either depression symptoms or neurophysiological activity, was not a relevant confound. These findings highlight that (1) depression symptoms were not associated with PD motor symptom severity or disease duration, suggesting that depression was likely not simply a reaction to more severe PD impairment, and (2) beta power was significantly associated with depression severity regardless of variables previously associated with depression. Although not in the pallidum, other investigations have also found that beta power in limbic networks may play a role in depression in PD and non-PD populations. A recent study in PD patients undergoing DBS who were also implanted with subdural electrodes over the prefrontal cortex found that beta power correlated with self-rated symptoms of depression and anxiety (de Hemptinne et al., 2021 ). Other studies using electroencephalography (EEG) have found that PD patients with depression (Espinoza et al., 2022 ) or anxiety (Betrouni et al., 2022 ; Yassine et al., 2024 ) exhibited higher beta power in prefrontal and central cortical regions. One study even found that prefrontal repetitive transcranial magnetic stimulation as an adjunctive therapy for depression in PD reduced prefrontal beta power measured with EEG (Tanaka et al., 2002 ). Furthermore, depression following basal ganglia stroke lesions, particularly left-sided, has been associated with increased high beta activity in the left frontal and central cortical regions (Wang et al., 2017 ). In patients undergoing DBS for treatment-resistant depression, studies have found beta power in LFP recordings from the subcallosal cingulate cortex is involved in emotional processing (Clark et al., 2016 ; Huebl et al., 2016 ; Merkl et al., 2016 ) and may be a marker of antidepressant response following DBS, specifically a decrease in beta power with acute stimulation (Smart et al., 2018 ; Sendi et al., 2021 ) and potentially an increase in beta power with chronic stimulation (Alagapan et al., 2023 ). Combined with our results, these previous findings suggest that elevated beta power in specific prefrontal cognitive/limbic networks involving the basal ganglia may play a role in depression pathophysiology. Other Neurophysiological Signals Relevant to Depression in PD While our study focused on resting-state neural activity in the pallidum, previous studies of basal ganglia neural activity associated with depression in PD have exclusively investigated the STN and mainly emotional processing tasks. Generally, these studies point to depression in PD potentially involving STN activity in the theta (4–7 Hz) and alpha (8–12 Hz) bands (Ricciardi et al., 2023 ). Event-related desynchronization (ERD) in the alpha band, or reduction in power in response to stimuli, has been demonstrated in the STN in response to emotional stimuli compared to neutral stimuli in PD patients (Brücke et al., 2006 ; Huebl et al., 2011 ; Mandali et al., 2020 ). This ERD was reduced for pleasant stimuli and increased for unpleasant stimuli in PD patients with mild to moderate depression compared to those without depression (Huebl et al., 2011 ). Dopaminergic medication may also modulate the observed ERD, with increased ERD in response to pleasant stimuli in the on-medication state and increased ERD in response to unpleasant stimuli in the off-medication state (Huebl et al., 2014 ). Alongside alpha activity, two studies also found ERD in the beta band (Eitan et al., 2013 ; Huebl et al., 2014 ), warranting further investigation into beta as another signal that may be implicated in emotional processing in PD. Only two studies have investigated resting-state neural activity in depression in PD, one of which found that depressed PD patients exhibited increased alpha power and decreased theta power in the left ventral STN (Sun et al., 2021 ); however, other frequency bands were not examined. Another study determined theta/alpha power may be frequently localized to ventromedial (limbic) STN, and depression severity was negatively correlated with broadband theta/alpha/low beta power (5–20 Hz) within the ventromedial STN region (Rappel et al., 2019 ). Their reported correlation including low beta activity piqued our interest; however, it is in the opposite direction of our present results. Given the variations in findings across studies, further resting-state neurophysiology research and correlations with clinical symptoms are essential to understanding how specific oscillatory patterns in the STN and pallidum relate to depression in PD. Additionally, studies with larger sample sizes and unbiased, comprehensive analyses across all frequency bands beyond only theta/alpha are needed. Limitations Our retrospective study used brief, intraoperative neural recordings acquired during DBS implantation surgery in the resting, awake, off-medication state as a measure of spontaneous neural activity. However, by definition, these recordings are not specific to emotional, cognitive, or reward processing that may be relevant to depression pathophysiology. Future studies, like previous ones in the STN, should explore the role of pallidal neural activity in task-based paradigms to complement our resting-state recordings. Additionally, our recordings were limited to sampling neural activity where the DBS electrodes were implanted clinically, mainly clustered around the middle and posterior regions of the pallidum, which is classically defined as the sensorimotor region (Alexander et al., 1986 ). However, functional subregions of the pallidum have not been well defined, and increasing evidence has revealed functional integration and topographical overlap across sensorimotor, associative, and limbic basal ganglia networks in nonhuman primate studies (Baunez and Lardeux, 2011 ; Weintraub and Zaghloul, 2013 ; Rossi et al., 2015 ), human behavioral and neuroimaging studies (Weintraub and Zaghloul, 2013 ; Voon et al., 2017 ; Greene et al., 2020 ), and computational models (Wiecki and Frank, 2013 ; Mandali et al., 2016 ). The current study focuses on neural activity across the pallidum, but future studies will investigate the localization of candidate biomarkers in larger cohorts. Our intraoperative LFP neural recordings and the preoperative clinical rating scale scores represent only a single snapshot in time. The BDI-II, although an established screening and evaluation scale for depression in PD (Visser et al., 2006 ), involves self-report, assesses symptoms only over the previous two weeks, and is not as comprehensive as a full diagnostic interview. However, our goal was to use data that is routinely and efficiently collected as part of the preoperative DBS assessments at our centers and others, which could be an asset for reproducibility and scalability. It is also important to note that there was a time delay between the preoperative depression assessment and the intraoperative neural recordings; however, we controlled for this potential confound in our GLM analysis and found it did not significantly affect our main findings. Similarly, we also controlled for anxiety and apathy in our GLM analysis, but it is often challenging to disentangle these comorbidities from depression (Weintraub et al., 2022 ). Our current sample size was too small to differentiate various subtypes and symptom combinations, but future research will address potential biomarkers related to anxiety, apathy, and depression in larger cohorts. Finally, the present cohort is predominantly limited to those with mild to moderate depression, as more severe and untreated psychiatric disorders are typical exclusion criteria from DBS candidacy; however, we still found that 26% of patients had clinically elevated depression symptoms despite the more limited range in depression severity. Future Directions DBS therapy is effective for reducing motor symptoms of PD, but effects on nonmotor symptoms like depression are not well understood and are often overlooked. Biomarkers specific to depression and other psychiatric symptoms could eventually enable more data-driven approaches to guide DBS therapy to reduce nonmotor symptoms while maintaining motor benefits. Our study used acute, intraoperative, resting-state neural recordings for initial discovery of putative pallidal biomarkers of depression in PD, which enabled recordings from a large cohort of patients with high spatiotemporal precision. However, the advent of new clinical DBS devices capable of chronic neural recordings could provide additional insight into longitudinal neural activity and depression symptoms in the naturalistic home environment, as demonstrated in a recent study focused on anxiety in PD (Swinnen et al., 2024 ). Longitudinal studies could potentially uncover new biomarkers of psychiatric symptoms not available in acute intraoperative settings, such as circadian patterns (shown in PD (Cagle et al., 2024 ) and obsessive-compulsive disorder (Provenza et al., 2024 )), medication-related fluctuations in neural activity and symptoms, and long-term response to DBS. Identifying robust biomarkers of psychiatric symptoms in PD could also potentially enable more targeted DBS paradigms to alleviate those symptoms. Promising research has emerged in this area, showing low-frequency (10 Hz – in the alpha band) stimulation in the STN may modulate emotional processing, with potentially a more pronounced effect in PD patients with depression (Mandali et al., 2020 ; Muhammad et al., 2023 ). Although these studies focused on the alpha band, stimulation frequencies and patterns could theoretically be designed to enhance or suppress oscillatory activity in specific frequencies (e.g., pallidal beta in the present study) (Duchet et al., 2023 ). However, improved knowledge of the anatomical specificity and the networks associated with pathophysiological neural activity and effects of DBS on behavior will be increasingly important for developing a fundamental understanding of depression in PD and moving toward clinical translation. Conclusions We present the first study investigating resting-state neural activity in the pallidum associated with depression in PD. The results indicate that beta power (particularly high beta power) may be a marker for differentiating between PD patients with versus without preoperative depression and a marker of depression severity, with higher beta power associated with worse depression symptoms. Notably, the relationship between beta power and depression symptoms was robust even after controlling for other demographic, clinical, pharmacological, and neurophysiological variables. These findings provide new insight into the potential role of the pallidum in depression in PD and broaden the view of beta power as a potential marker of psychiatric symptoms. METHODS Cohort and Clinical Assessment Patients in this study included individuals who underwent awake pallidal DBS implantation surgery for the treatment of PD and intraoperative neural recordings at the University of Florida Norman Fixel Institute for Neurological Diseases from January 2021 to December 2022. The study was carried out in accordance with the Declaration of Helsinki. All patients listed in our institutional database provided informed consent for research participation (Institutional Review Board 202202094). Exclusion criteria included: (1) absence of high-quality LFP recording; (2) missing preoperative neuropsychology evaluation and/or BDI-II score; (3) secondary movement disorder diagnosis (e.g., essential tremor); (4) neuropsychology evaluation closest to DBS surgery performed more than six months before surgery; (5) previous DBS surgery before neuropsychology assessment and/or LFP recordings (e.g., for patients who underwent bilateral staged DBS implantation, LFP recordings and preoperative assessment from only the first side surgery were included to exclude any effects of prior surgery or stimulation). As part of the standard pre-DBS neuropsychology evaluation at our center, preoperative baseline depressive symptoms were assessed using the Beck Depression Inventory, 2nd edition (BDI-II) (Beck et al., 1996). According to established thresholds for differentiating depression diagnosis using the BDI-II (Visser et al., 2006 ), patients were categorized as having PD with depression (BDI-II score ≥ 14) or PD without depression (BDI-II score < 14). The Apathy Scale (AS) and State-Trait Anxiety Index (STAI) Trait scores were also analyzed to control for other psychiatric symptoms. Preoperative baseline motor symptom severity was also assessed with the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III). A total of N = 95 patients underwent pallidal DBS surgery and intraoperative LFP recordings between January 2021 and December 2022. A subset of N = 17 patients had a previous DBS implantation and were excluded, and N = 14 patients were excluded due to the poor quality of recordings. Clinical evaluation data were absent for N = 9 patients or conducted more than six months before surgery for N = 5 patients. After enforcing exclusion criteria, a total of 50 patients were included in this study. Deep Brain Stimulation Surgery Following the conventional clinical practice of the UF surgical team, a quadripolar or directional DBS electrode was implanted while the patient was awake, targeted to the posterolateral GPi (lead model 3387 or B33015, Medtronic, USA). Each patient underwent a preoperative multisequence MRI scan that included FLAIR (fluid-attenuated inversion recovery), FGATIR (rapid grey matter acquisition T1 inversion recovery) (Sudhyadhom et al., 2009 ), and volumetric gadolinium-enhanced T1-weighted sequences. A digitized Schaltenbrand-Wahren atlas was overlaid on the imaging and warped to produce a patient-specific atlas match. A stereotactic computed tomography (CT) scan was acquired while the patient was wearing a Cosman-Roberts-Wells stereotactic frame. After image registration between the CT and MRI scan, the volumetric images and atlas were used to create an electrode placement trajectory, which aimed to optimize the lead location within the target structure while avoiding vasculature and the ventricles. Intraoperative neurophysiological testing, including microelectrode recordings and stimulation, was carried out to optimize DBS lead placement. After implantation, the DBS lead was connected to an external system (Neuro Omega, Alpha Omega, Israel) to acquire resting-state LFP recordings from each electrode on the DBS lead (further details in the following section). To limit the potential effects of stimulation on neural activity, the resting-state LFP recordings were acquired before performing any macrostimulation from the DBS leads. Pre- and postoperative imaging was then used to verify the final lead locations using methods reported previously (Johnson et al., 2023 ). In each patient, rigid registration was used to align the postoperative CT to the preoperative T1-weighted MRI. The lead was then localized using the artifact in the postoperative CT. To compare across patients in a common atlas space, the MNI PD25 atlas (Xiao et al., 2019 ) was nonlinearly registered using the SyN algorithm in ANTs ( http://stnava.github.io/ANTs/ ) software (Avants et al., 2008 ). The nonlinear transformations were then applied to the lead localizations to visualize the contact locations relative to anatomical nuclei. One patient was excluded from the contact location visualization during quality control procedures due to unsatisfactory registration to the atlas. Neural Recordings and Signal Processing Intraoperative LFP recordings (60 seconds) of spontaneous neural activity with the patient awake, at rest, and off-medication were acquired from all contacts on the DBS lead in a monopolar configuration at a high sampling rate (22 kHz; Neuro Omega system, Alpha) and referenced to a scalp electrode. The monopolar LFP recordings then underwent extensive preprocessing and quality control through a pipeline illustrated in Fig. 3 , showing all processing steps in an example patient for a recording from a single contact (Fig. 3 A-D) and the raw PSD (Fig. 3 E) and fully processed PSD (Fig. 3 F). In each patient’s recordings, the first 30-second period free of noise/artifacts across all contacts was selected for subsequent analysis. The LFPs from the selected period were then Butterworth filtered (5th order; band-pass 1-500 Hz) to remove low-frequency drift and high-frequency noise (Fig. 3 A). Then, the power spectral density (PSD) from 1-500 Hz was computed using Welch’s method (SciPy welch: 1-second Hann window, 0.5-second overlap) (Fig. 3 B). To be able to compare with 4-contact cylindrical leads, a “ring mode” PSD on the middle two contact levels (C1 and C2) of 8-contact directional leads was obtained by averaging the PSD across segments on each level. To identify any residual peaks in the PSD due to line noise (60 Hz and harmonics) or other intraoperative environment noise, a standard peak-finding algorithm (SciPy findpeaks: peaks occurring at ≥40 Hz with prominence = 0.5) was implemented (Fig. 3 C). Noise peaks that were detected in all contacts were extracted, measured for width (at 75% of relative height), and notch filtered for removal. All peaks marked for removal occurred at ≥60 Hz and were verified visually for quality control. No peaks required manual correction. Next, the PSD was normalized to account for variability in PSD curve offset and slope and enable direct comparisons across patients using established methods (Fig. 3 C). The Fitting Oscillations & One Over F (FOOOF) algorithm (Donoghue et al., 2020 ) was used to fit an exponential to the PSD from 1-100 Hz (the aperiodic 1/frequency portion of the PSD curve). A second-order exponential was fit to higher frequencies from 100–500 Hz (the periodic portion of the PSD curve). All exponential fits were visually inspected to verify accuracy (none required manual correction or removal), and the FOOOF R 2 values indicated accurate exponential fits to the PSD curves (mean, SD [range]: 0.97, 0.015 [0.93–0.99]). The FOOOF exponential and second-order exponentials were then subtracted from the original PSD to obtain a normalized PSD (Fig. 3 D). The raw and final processed PSDs are presented in Fig. 3 E-F, showing that all noise peaks were removed and the normalization successfully removed the PSD offset and slope and did not affect the rank order of PSD across contacts. The normalized PSD was then averaged over predefined frequency bands and across all contacts: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (13–30 Hz) [separated into low beta (13–20 Hz) and high beta (20–30 Hz)], gamma (30–100 Hz) [separated into low gamma (30–50 Hz) and high gamma (50–100 Hz)], and high-frequency oscillations (HFO) (200–400 Hz) (Fig. 3 F). For each patient, the slope and intercept of the FOOOF exponential were also extracted for each contact and then averaged across all contacts. Statistical Analysis Statistical analyses were conducted to compare clinical variables for PD patients with depression (BDI-II ≥ 14) versus those without depression (BDI-II < 14). Unpaired, two-tailed t-tests were used for continuous variables and Fischer’s exact tests were used for discrete variables to compare the distributions across groups. For all analyses, p < 0.05 was used as the threshold for statistical significance. Univariate analyses using unpaired, two-tailed t-tests were then performed to determine if neural activity in the defined frequency bands differed between PD patients with versus without depression. The frequency bands that significantly differed between PD patients with versus without depression were then further analyzed with Pearson correlations to determine if neural activity in those bands was also correlated with depression severity (BDI-II scores). For both series of t-tests and Pearson correlations, false discovery rate (FDR) via the Benjamini-Hochberg method was used to correct for multiple comparisons (family-wise error rate α = 0.05). Both raw and FDR-corrected p-values are reported. Univariate analyses to assess the relationship between depression in PD and neural activity did not account for other demographic and clinical variables that may play a role in depression or may be associated with neural activity. A multivariate generalized linear model (GLM) was generated to evaluate the relationship between neural power in the above-defined frequency bands and depression severity (BDI-II scores) while controlling for demographics (age, sex, months between BDI-II evaluation and intraoperative LFP recordings), recording variables [hemisphere of LFP recordings, normalization curve parameters (FOOOF exponent/slope and offset/intercept)], PD severity and treatment variables [disease duration, UPDRS motor scores both on and off levodopa medication, levodopa equivalent daily dose (LEDD)], severity of other psychiatric symptoms [apathy (AS scores) and anxiety (STAI-Trait scores)], and psychiatric medication use (antidepressants, benzodiazepines, and non-benzodiazepine hypnotics). The Akaike Information Criterion (AIC) (Akaike, 1973 ) was used to compare the relative predictive performance of GLMs. Declarations DATA AVAILABILITY The data generated in this study are provided in the Source Data file. The raw (identifiable) data are protected and are not available due to data privacy laws. Reasonable requests for additional information can be directed to, and will be fulfilled by, the corresponding author. Note that these data are part of ongoing research studies. CODE AVAILABILITY Code generated in this study for data processing and analysis are available at https://github.com/karajohnson/RestLFP-PD-Depression. ACKNOWLEDGMENTS The authors gratefully acknowledge support from the Norman Fixel Institute for Neurological Diseases at the University of Florida. The authors also thank thank Chuck Jacobson and his team for managing the INFORM database. AUTHOR CONTRIBUTIONS KAJ: Conceptualization, data collection and curation, formal analysis, data interpretation, visualization, writing manuscript. PBC: Data collection and curation, writing manuscript. LEK: Data collection, editing manuscript. JKW: Clinical feedback, statistics, editing manuscript. 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A prospective longitudinal study of depression, cognitive decline, and physical impairments in patients with Parkinson’s disease. Journal of Neurology, Neurosurgery & Psychiatry 55, 377–382. doi: 10.1136/jnnp.55.5.377 Sudhyadhom, A., Haq, I., Foote, K., Okun, M., and Bova, F. (2009). A high resolution and high contrast MRI for differentiation of subcortical structures for DBS targeting: the Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR). Neuroimage . Available at: http://www.sciencedirect.com/science/article/pii/S1053811909003759 (Accessed March 30, 2015). Sun, Y., Wang, Z., Hu, K., Mo, Y., Cao, P., Hou, X., et al. (2021). α and θ oscillations in the subthalamic nucleus are potential biomarkers for Parkinson’s disease with depressive symptoms. Parkinsonism & Related Disorders 90, 98–104. doi: 10.1016/j.parkreldis.2021.07.023 Swinnen, B. E. K. S., Hoy, C. W., Pegolo, E., Ishihara, B., Matzilevich, E. U., Sun, J., et al. (2024). 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Reviews in the Neurosciences 24, 125–138. doi: 10.1515/revneuro-2012-0075 Wiecki, T. V., and Frank, M. J. (2013). A computational model of inhibitory control in frontal cortex and basal ganglia. Psychological Review 120, 329–355. doi: 10.1037/a0031542 Williams, L. M. (2016). Precision psychiatry: a neural circuit taxonomy for depression and anxiety. The Lancet Psychiatry 3, 472–480. doi: 10.1016/S2215-0366(15)00579-9 Wu, L., Zhang, T., and Zhang, S. (2023). Comparative study of magnetic resonance imaging-based neuroimaging methods in older adults with depression. Psychiatry Research: Neuroimaging 331, 111637. doi: 10.1016/j.pscychresns.2023.111637 Xiao, Y., Lau, J. C., Anderson, T., DeKraker, J., Collins, D. L., Peters, T., et al. (2019). An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases. Scientific Data 6, 210. doi: 10.1038/s41597-019-0217-0 Yassine, S., Almarouk, S., Gschwandtner, U., Auffret, M., Fuhr, P., Verin, M., et al. (2024). Electrophysiological signatures of anxiety in Parkinson’s disease. Transl Psychiatry 14, 1–11. doi: 10.1038/s41398-024-02745-x Yin, Z., Zhu, G., Zhao, B., Bai, Y., Jiang, Y., Neumann, W.-J., et al. (2021). Local field potentials in Parkinson’s disease: A frequency-based review. Neurobiology of Disease 155, 105372. doi: 10.1016/j.nbd.2021.105372 Additional Declarations No competing interests reported. Supplementary Files Supplementary.pdf PDDepressionSourceData.xlsx Cite Share Download PDF Status: Published Journal Publication published 22 Jan, 2026 Read the published version in npj Parkinson's Disease → Version 1 posted Editorial decision: Revision requested 25 Aug, 2025 Reviews received at journal 16 Jul, 2025 Reviews received at journal 01 Jul, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviews received at journal 20 Mar, 2025 Reviewers agreed at journal 13 Mar, 2025 Reviewers agreed at journal 13 Mar, 2025 Reviewers invited by journal 07 Mar, 2025 Editor assigned by journal 05 Feb, 2025 Submission checks completed at journal 05 Feb, 2025 First submitted to journal 03 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5952073","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":431493122,"identity":"760a3d37-f558-44ca-893c-ebcf55fd515e","order_by":0,"name":"Kara A. 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Hilliard","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Justin","middleName":"D.","lastName":"Hilliard","suffix":""},{"id":431493132,"identity":"1af1d71d-1832-4604-8534-b93326b83b5a","order_by":5,"name":"Kelly D. Foote","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Kelly","middleName":"D.","lastName":"Foote","suffix":""},{"id":431493134,"identity":"7008d2f3-cff3-4346-90ae-7ba69582a993","order_by":6,"name":"Dawn Bowers","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Dawn","middleName":"","lastName":"Bowers","suffix":""},{"id":431493136,"identity":"5becdd6d-68ea-41ec-b9a0-851d2a19e14c","order_by":7,"name":"Gregory M. Pontone","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Gregory","middleName":"M.","lastName":"Pontone","suffix":""},{"id":431493137,"identity":"c106c41b-7f75-4cb1-b567-f8ce8dd62b9b","order_by":8,"name":"Coralie de Hemptinne","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Coralie","middleName":"","lastName":"de Hemptinne","suffix":""}],"badges":[],"createdAt":"2025-02-03 15:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5952073/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5952073/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41531-026-01264-4","type":"published","date":"2026-01-22T15:58:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79119154,"identity":"a81083ed-bfdd-49fe-9edc-103cd13b7059","added_by":"auto","created_at":"2025-03-24 15:47:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":278292,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeurophysiological activity in the pallidum associated with depression in PD. (A)\u003c/strong\u003e Example DBS lead implantations in the pallidum with anatomical segmentations of the globus pallidus internus (GPi) and externus (GPe) overlaid on the MNI PD25 atlas (Xiao et al., 2019). \u003cstrong\u003e(B)\u003c/strong\u003e DBS contact locations the study cohort. Each sphere depicts a DBS contact location and is colored by patient number. \u003cstrong\u003e(C)\u003c/strong\u003e Example LFP recording acquired from a DBS contact. \u003cstrong\u003e(D)\u003c/strong\u003e Normalized power averaged across contacts for each patient and compared across depressed PD patients (blue) and not depressed PD patients (gray). Shaded error limits depict standard error of the mean (SEM). \u003cstrong\u003e(E)\u003c/strong\u003e Depressed PD patients exhibited significantly higher average beta power (13-30 Hz) compared to not depressed PD patients (unpaired, two tailed t-test; t=2.68, p=0.010, p\u003csub\u003eFDR\u003c/sub\u003e=0.049). Box-and-whisker plots denote the following: median (center line), upper and lower quartiles (box limits), lowest/highest data point within 1.5x interquartile range (whiskers), and outliers (outside of the whisker limits). \u003cstrong\u003e(F)\u003c/strong\u003e Average beta power (13-30 Hz) was significantly correlated with depression symptom severity (BDI-II scores) (Pearson correlation; r=0.31, p=0.028, p\u003csub\u003eFDR\u003c/sub\u003e=0.047).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5952073/v1/bf5fe37b7d87b8941fe43eef.jpg"},{"id":79120082,"identity":"4ff8bbd3-b649-4fb7-b4a4-4fc7e789a219","added_by":"auto","created_at":"2025-03-24 15:55:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGLM to assess the role of other demographic, clinical, medication, and neurophysiological variables in depression severity in PD.\u003c/strong\u003eGLM coefficients and 95% confidence intervals for independent variables included in the model. Beta power remained associated with depression severity (BDI-II scores), even when controlling for these other potential confounds.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5952073/v1/d728ee685e7aba3660871714.jpg"},{"id":79119158,"identity":"bde96e5a-6809-403c-abb2-003497e92dd2","added_by":"auto","created_at":"2025-03-24 15:47:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":369584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocal field potential (LFP) processing pipeline in an example patient. \u003c/strong\u003ePanels A-D show the processing for contact 1 recordings as an example. Panels E-F show all four contacts.\u003cstrong\u003e (A)\u003c/strong\u003e Raw and Butterworth filtered LFP recordings (30 seconds). \u003cstrong\u003e(B) \u003c/strong\u003ePower spectral density (PSD) computed using Welch’s method. A peak detection algorithm was used to identify noise peaks for removal (marked in transparent bands). \u003cstrong\u003e(C)\u003c/strong\u003e The FOOOF algorithm (Donoghue et al., 2020) (blue) and a 2\u003csup\u003end\u003c/sup\u003e order exponential (pink) was fit to the peak-filtered PSD to reduce variability in offset and slope and enable direct comparisons across patients. \u003cstrong\u003e(D)\u003c/strong\u003e The normalization curves from (C) were subtracted from the PSD to obtain a normalized PSD. \u003cstrong\u003e(E)\u003c/strong\u003e Raw PSD curves for all contacts. \u003cstrong\u003e(F)\u003c/strong\u003e Final processed and normalized PSD curves for all contacts and the average PSD curve across all contacts (black).\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5952073/v1/354845677468bd579d742d98.jpg"},{"id":101151963,"identity":"35e7cbde-8e9d-4e02-b383-37c9a0a2b9f6","added_by":"auto","created_at":"2026-01-26 16:08:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1845531,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5952073/v1/2e018d41-b2e0-4cee-a191-6d5dd5e4ef69.pdf"},{"id":79119153,"identity":"82f312fb-7b7e-47e6-81aa-cc471c5d1b3c","added_by":"auto","created_at":"2025-03-24 15:47:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":207508,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5952073/v1/388be895b5d971e0ac33b3ad.pdf"},{"id":79119157,"identity":"a2d2a968-2d32-475f-b129-eef539bc9830","added_by":"auto","created_at":"2025-03-24 15:47:05","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":702438,"visible":true,"origin":"","legend":"","description":"","filename":"PDDepressionSourceData.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5952073/v1/959486f6093d88e15718afce.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pallidal beta power is associated with depression in Parkinson’s disease","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eParkinson's disease (PD) is a progressive neurodegenerative disorder mainly characterized by motor symptoms, such as bradykinesia, tremor, rigidity, and postural instability (Tanner and Ostrem, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although PD is primarily a movement disorder, depression and other psychiatric symptoms are increasingly recognized as prevalent sources of disability. Depression often presents before motor symptoms and affects an estimated 38% of patients with PD (Cong et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), a higher proportion than other chronic diseases (Nilsson et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and the general population (Bromet et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Depression in PD is also associated with worsened quality of life and increased caregiver burden (Starkstein et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Schrag, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Dissanayaka et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Despite its high prevalence and immense burden, depression in PD is underrecognized, its pathophysiology is not well understood, and current treatments are often inadequate and non-specific to PD (Pontone and Mills, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDeep brain stimulation (DBS) targeted to the basal ganglia is an established treatment for select PD patients experiencing refractory motor symptoms. DBS involves neurosurgical implantation of electrodes into the basal ganglia in either the subthalamic nucleus (STN) or the pallidum (specifically the globus pallidus internus (GPi)) to deliver electrical stimulation to modulate pathological neural activity. Improvement in motor symptoms with DBS is extensively documented in the literature (Lachenmayer et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but some patients may also experience benefits in nonmotor symptoms. Recent meta-analyses have shown that, at a group level, DBS induces a small to moderate improvement in depression, mainly in the short term (Couto et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cartmill et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with potentially greater improvements with pallidal DBS than STN DBS (Mansouri et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, these meta-analyses note substantial heterogeneity across studies, indicating that depression outcomes vary widely across individuals from significant improvement to worsening. This variability may be due to differences in how depression symptoms are assessed (e.g., scales, diagnostic interview) and the lack of data-driven methods to tailor DBS therapy for depression in PD. Identifying biomarkers of depression in PD, such as neural activity within the basal ganglia, could potentially enable clinicians to harness DBS to treat depression in PD while maintaining effective treatment of motor symptoms.\u003c/p\u003e \u003cp\u003eDBS offers a unique opportunity to study neural activity in the basal ganglia involved in specific symptoms and behavior by recording local field potentials (LFP) in the intra-, peri-, and postoperative environments. Numerous studies have investigated neural activity associated with motor symptoms in PD (Yin et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with the majority pointing to elevated beta power (13\u0026ndash;30 Hz) in the STN and the pallidum associated with bradykinesia and rigidity (Brown et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Eisinger et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and with reduction in beta power in response to DBS and levodopa therapy associated with improvement in motor symptoms (K\u0026uuml;hn et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cagle et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, relatively few studies have investigated neural activity underlying nonmotor symptoms in PD, including depression (Eisinger et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies focused on neural activity contributing to depression in PD have mainly focused on task paradigms with intra- or perioperative recordings exclusively from the STN. Their results generally indicate that low-frequency oscillations, particularly in theta (4\u0026ndash;7 Hz) and alpha (8\u0026ndash;12 Hz) bands, may be implicated in emotional responses during an affective picture-viewing task (K\u0026uuml;hn et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Br\u0026uuml;cke et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Huebl et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mandali et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Only two studies have focused on resting-state neural activity, showing that PD patients with depression may exhibit lower theta power and higher alpha power in the left ventral STN compared to non-depressed individuals (Sun et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and theta/alpha power may be localized to the ventral STN (Rappel et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). No prior studies have investigated neural activity in the pallidum associated with depression, either in resting-state or task paradigms. Additionally, previous research has not fully explored correlations across other frequency bands or controlled for potential confounds that may affect depression and neurophysiological activity, such as medication, motor severity, and demographics. Without controlling for such variables, the specificity of these candidate biomarkers to depression cannot be determined.\u003c/p\u003e \u003cp\u003eThe objective of this study was to identify neurophysiological activity in the pallidum associated with depression symptoms in patients undergoing DBS implantation surgery for the treatment of PD while controlling for potential confounding variables. Identifying neurophysiological activity linked to depression in PD could potentially elucidate the role of the pallidum in the pathophysiology of depression in PD and serve as the basis for further research to identify objective biomarkers of psychiatric symptoms in PD. Such biomarkers could then be used to guide DBS therapy to alleviate these nonmotor symptoms that represent an important determinant of quality of life in individuals with PD.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCohort Characteristics\u003c/h2\u003e \u003cp\u003eThe study cohort included N\u0026thinsp;=\u0026thinsp;50 patients at the University of Florida who underwent DBS electrode implantation surgery targeted to the pallidum for the treatment of PD motor symptoms. The cohort characteristics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A subset of N\u0026thinsp;=\u0026thinsp;13 (26.0%) patients were classified as having PD with clinically elevated depression symptoms (preoperative BDI-II score \u0026ge; 14, based on established criteria using BDI-II to detect and assess depression severity (Visser et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)). Depression symptoms were evaluated on average approximately four months before DBS implantation surgery as part of the standard preoperative neuropsychological assessment to verify candidacy for DBS therapy. The depressed group had significantly more severe BDI-II scores (unpaired, two-tailed t-test; t\u0026thinsp;=\u0026thinsp;10.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Apathy Scale (AS) scores (t\u0026thinsp;=\u0026thinsp;2.62, p\u0026thinsp;=\u0026thinsp;0.012), and State-Trait Anxiety Inventory Trait (STAI-Trait) scores (t\u0026thinsp;=\u0026thinsp;5.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than the not depressed group. No statistically significant differences were detected across groups in sex, age, disease duration, recording hemisphere, evaluation time point, medication use (antidepressants, benzodiazepines, non-benzodiazepine hypnotics), Unified Parkinson\u0026rsquo;s Disease Rating Scale (UPDRS) Part III (UPDRS-III) scores (on and off levodopa medication), or levodopa equivalent daily dose (LEDD).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCohort Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepressed\u003c/p\u003e \u003cp\u003e(BDI-II \u0026ge; 14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot Depressed\u003c/p\u003e \u003cp\u003e(BDI-II\u0026thinsp;\u0026lt;\u0026thinsp;14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical Analysis (Depressed vs. Not Depressed)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (74.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale: 33 (66.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale: 11 (84.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale: 22 (59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep* = 0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.32 (8.34, 50\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.46 (9.28, 50\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.32 (7.88, 50\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et = -1.45\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease Duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.50 (5.06, 2\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.62 (5.19, 6\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.76 (4.87, 2\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;1.79\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecording Hemisphere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft: 28 (56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeft: 7 (53.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeft: 21 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep* = 1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluation Time Point (months before surgery)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1 (1.40, 1\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.39 (1.39, 1\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00 (1.41, 1\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.85\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.06 (8.14, 2\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.38 (7.23, 14\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.08 (3.17, 2\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;10.45\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;48\u003c/p\u003e \u003cp\u003e11.56 (6.00, 0\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;13\u003c/p\u003e \u003cp\u003e15.07 (5.74, 4\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;35\u003c/p\u003e \u003cp\u003e10.26 (5.63, 0\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;2.62\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTAI-Trait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;47\u003c/p\u003e \u003cp\u003e36.83 (10.43, 20\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;13\u003c/p\u003e \u003cp\u003e47.38 (11.40, 20\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;34\u003c/p\u003e \u003cp\u003e32.79 (6.61, 21\u0026ndash;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;5.48\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidepressant Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep* = 0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzodiazepine Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (37.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep* = 0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Benzodiazepine Hypnotic Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (37.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep* = 0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPDRS-III (Off Levodopa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;49\u003c/p\u003e \u003cp\u003e38.53 (13.66, 10\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;13\u003c/p\u003e \u003cp\u003e34.62 (13.33, 10\u0026ndash;56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003cp\u003e39.94 (13.68, 19\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et = -1.21\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPDRS-III (On Levodopa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;49\u003c/p\u003e \u003cp\u003e23.45 (12.09, 4\u0026ndash;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;13\u003c/p\u003e \u003cp\u003e21.77 (11.5, 4\u0026ndash;44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003cp\u003e24.06 (12.39, 8\u0026ndash;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et = -0.58\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevodopa Equivalent Daily Dose (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1110.66 (555.59, 60-2275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1159.77 (591.04, 60-2275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1093.41 (550.03, 75-2000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.37\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDepressed\u0026thinsp;=\u0026thinsp;PD with clinically elevated depression symptoms (BDI-II \u0026ge; 14).\u003c/p\u003e \u003cp\u003eNot Depressed\u0026thinsp;=\u0026thinsp;PD without clinically elevated depression symptoms (BDI-II\u0026thinsp;\u0026lt;\u0026thinsp;14).\u003c/p\u003e \u003cp\u003eBDI-II\u0026thinsp;=\u0026thinsp;Beck Depression Inventory (2nd Edition) (score range: 0\u0026ndash;63).\u003c/p\u003e \u003cp\u003eAS\u0026thinsp;=\u0026thinsp;Apathy Scale (score range: 0\u0026ndash;42).\u003c/p\u003e \u003cp\u003eSTAI-Trait\u0026thinsp;=\u0026thinsp;State-Trait Anxiety Inventory (Trait score) (score range: 20\u0026ndash;80).\u003c/p\u003e \u003cp\u003eUPDRS\u0026thinsp;=\u0026thinsp;Unified Parkinson\u0026rsquo;s Disease Rating Scale (score range: 0-108).\u003c/p\u003e \u003cp\u003et-values and p-values obtained from unpaired, two-tailed t-tests.\u003c/p\u003e \u003cp\u003ep*-values obtained from Fisher\u0026rsquo;s exact tests.\u003c/p\u003e \u003cp\u003eData presented as mean (SD, range) or number of patients (%).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePallidal Activity Associated with Depression\u003c/h3\u003e\n\u003cp\u003eImaging analysis confirmed that the DBS leads were well placed spanning the posterior GPi/GPe interface across the cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). Intraoperative LFP recordings were acquired during DBS lead implantation from all contacts on the DBS lead in monopolar configuration with the patient off dopaminergic medication for at least 6 hours and at rest (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The average normalized power spectral density (PSD) across all contacts for each patient was then computed to analyze neural activity in defined frequency bands: delta (1\u0026ndash;4 Hz), theta (4\u0026ndash;8 Hz), alpha (8\u0026ndash;12 Hz), beta (13\u0026ndash;30 Hz) [separated into low beta (13\u0026ndash;20 Hz) and high beta (20\u0026ndash;30 Hz)], gamma (30\u0026ndash;100 Hz) [separated into low gamma (30\u0026ndash;50 Hz) and high gamma (50\u0026ndash;100 Hz)], and high-frequency oscillations (HFO) (200\u0026ndash;400 Hz).\u003c/p\u003e \u003cp\u003eThe average power in each frequency band was compared between PD patients with clinically elevated depression symptoms and PD patients without depression. PD patients with depression symptoms exhibited significantly higher pallidal beta power (13\u0026ndash;30 Hz) than PD patients without depression, as shown by the average PSD curves in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD and the distribution comparison in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE (unpaired, two-tailed t-test; t\u0026thinsp;=\u0026thinsp;2.68, p\u0026thinsp;=\u0026thinsp;0.010, p\u003csub\u003eFDR\u003c/sub\u003e=0.049). Power in the low beta (13\u0026ndash;20 Hz) (t\u0026thinsp;=\u0026thinsp;2.61, p\u0026thinsp;=\u0026thinsp;0.012, p\u003csub\u003eFDR\u003c/sub\u003e=0.049) and high beta (20\u0026ndash;30 Hz) (t\u0026thinsp;=\u0026thinsp;2.53, p\u0026thinsp;=\u0026thinsp;0.015, p\u003csub\u003eFDR\u003c/sub\u003e=0.049) were both increased in depressed patients compared to non-depressed. A trend for reduced delta power (1\u0026ndash;4 Hz) was found in depressed patients compared to non-depressed patients (t=-1.96, p\u0026thinsp;=\u0026thinsp;0.056, p\u003csub\u003eFDR\u003c/sub\u003e=0.14). All other frequency bands showed no statistically significant differences between the two groups. The PSDs across the full frequency range analyzed (1-500 Hz) were averaged across patients and displayed in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eSince beta power significantly differed between PD patients with versus without depression, we further investigated whether beta power was also associated with depression severity. Beta power was significantly positively correlated with depression severity (Pearson correlation; r\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;=\u0026thinsp;0.031, p\u003csub\u003eFDR\u003c/sub\u003e=0.047), including high beta power (r\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;=\u0026thinsp;0.028, p\u003csub\u003eFDR\u003c/sub\u003e=0.047) but not low beta power (r\u0026thinsp;=\u0026thinsp;0.27, p\u0026thinsp;=\u0026thinsp;0.059, p\u003csub\u003eFDR\u003c/sub\u003e=0.059).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eControlling for Demographic, Clinical, and Neurophysiological Variables\u003c/h3\u003e\n\u003cp\u003eDepression is a complex symptom in PD that may be modulated by demographics, motor symptoms, medications, and other psychiatric symptoms. Therefore, a multivariate generalized linear model (GLM) was generated to evaluate the relationship between pallidal neural activity in the defined frequency bands and depression severity (BDI-II scores) while controlling for the following variables: demographics (age, sex, months between BDI-II evaluation and intraoperative LFP recordings), recording variables [hemisphere and normalization curve parameters (1/f curve exponent/slope and offset/intercept)], PD severity and treatment variables [disease duration, UPDRS motor scores both on and off levodopa medication, levodopa equivalent daily dose (LEDD)], severity of other psychiatric symptoms [apathy (AS scores) and anxiety (STAI-Trait scores)], and psychiatric medication use (antidepressants, benzodiazepines, and non-benzodiazepine hypnotics). A total of N\u0026thinsp;=\u0026thinsp;45 patients were included in the model, excluding N\u0026thinsp;=\u0026thinsp;5 patients due to missing UPDRS, Apathy Scale, and/or STAI-Trait scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe GLM revealed a significant positive effect of beta power associated with BDI-II scores (coefficient β\u0026thinsp;=\u0026thinsp;4.00, 95% CI=(0.23\u0026ndash;7.78), p\u0026thinsp;=\u0026thinsp;0.038), indicating that higher beta power was associated with higher depression severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Other positive significant effects included STAI-Trait scores (trait anxiety symptom severity) (β\u0026thinsp;=\u0026thinsp;6.37, 95% CI=(3.70\u0026ndash;9.04), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which reflects a correlation between depression and anxiety symptoms, and the intercept of the GLM (β\u0026thinsp;=\u0026thinsp;10.03, 95% CI=(4.50-15.56), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which indicates that BDI-II scores were significantly greater than zero. Similar to the univariate analyses, we also found that in separate GLM analyses, high beta power was significantly associated with depression severity (β\u0026thinsp;=\u0026thinsp;4.49, 95% CI=(0.50\u0026ndash;8.49), p\u0026thinsp;=\u0026thinsp;0.027) but low beta power was not (β\u0026thinsp;=\u0026thinsp;3.16, 95% CI=(-0.40-6.73), p\u0026thinsp;=\u0026thinsp;0.082). Comparing the Akaike Information Criterion (AIC), which measures the relative predictive performance of statistical models, revealed that the GLM using high beta power was the best-fit model for predicting BDI with the lowest AIC (AIC\u0026thinsp;=\u0026thinsp;300.29) compared to low beta power (AIC\u0026thinsp;=\u0026thinsp;303.36) and overall beta power (AIC\u0026thinsp;=\u0026thinsp;301.18). These results suggest that beta power, especially high beta power, was significantly associated with depression severity, even when controlling for other possible demographic, clinical, pharmacological, and neurophysiological confounds.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis retrospective study aimed to identify neurophysiological activity in the pallidum associated with depression symptoms in patients undergoing DBS therapy for the treatment of PD using intraoperative resting-state neural recordings and preoperative baseline neuropsychiatric assessments. The results revealed that (1) PD patients with elevated preoperative depression symptoms exhibited significantly increased pallidal beta power compared to patients without depression, and (2) pallidal beta power, especially high beta power, was significantly associated with depression severity, even when controlling for potential demographic, clinical, pharmacological, psychiatric, and neurophysiological confounds. To the best of our knowledge, this is the first report of neural activity in the pallidum associated with depression in PD. The involvement of pallidal beta power provides new insight into the role of basal ganglia networks in the pathophysiology of depression in PD and could potentially serve as an objective marker of depression symptoms with further validation.\u003c/p\u003e\n\u003ch3\u003eRole of the Basal Ganglia in Depression in PD\u003c/h3\u003e\n\u003cp\u003eThe pathophysiology of depression in PD is not fully understood, but it is thought involve alterations in basal ganglia-thalamo-cortical networks as a result of PD-related neurodegeneration. Most evidence of the brain networks implicated in depression in PD has originated from human neuroimaging studies, which generally point to structural, functional, metabolic, and neurotransmitter (particularly dopamine and serotonin) abnormalities in widespread limbic networks involving the basal ganglia, mediodorsal thalamus, amygdala, prefrontal cortex (orbitofrontal, dorsolateral, and ventromedial cortices), and anterior cingulate cortex (Thobois et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sgambato-Faure and Tremblay, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Pathophysiological changes in these networks point to abnormal top-down control of emotion-related limbic subcortical regions, causing impairments in motivational, reward, and response initiation processes commonly associated with depression symptoms. Notably, the networks implicated in PD-related depression and the affected cognitive and emotional processes share commonalities with those implicated in depression in non-PD populations (Mayberg, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Williams, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results contribute evidence that depression in PD is associated with altered neural activity in the pallidum. The pallidum plays a critical role in basal ganglia-thalamo-cortical networks by regulating information flow from the cortex via the striatum to the thalamus and ultimately back to the cortex. Although its role in regulating movement is most often discussed, the pallidum also regulates information flow in prefrontal/associative and limbic basal ganglia-thalamo-cortical networks, which contribute to cognitive and emotional processing, respectively (Saga et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, pathological alterations in the basal ganglia, including the pallidum, have been increasingly linked to psychiatric disorders, including depression (Macpherson and Hikida, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlterations in pallidal structure and associated networks provide compelling evidence of its potential role in depression pathophysiology in non-PD and PD populations. Reduced pallidum volume has been consistently observed in non-PD individuals with depression across the lifespan (Baumann et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Bielau et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kempton et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Grieve et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and even subthreshold depression (Li et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Patients with depression and PD have been shown to exhibit reduced functional connectivity (Wei et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and abnormal white matter network topology (Hu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e) within basal ganglia networks including the pallidum compared to non-depressed PD patients and healthy controls. Additionally, the pallidum has been increasingly recognized for its potential role in depression in PD due to its connections to the lateral habenula, which is known for its critical role in regulating negatively motivated behavior and for its implication in depression (Hu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e) and more specifically depression in PD (Borgonovo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Samanci et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The pallidum and lateral habenula have been shown to contribute to reward processing in non-human primate studies (Arkadir et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Hong and Hikosaka, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), as well as studies using intracranial recordings during reward processing tasks in small cohorts of patients undergoing DBS (Howell et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; M\u0026uuml;nte et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Altogether, these findings suggest that abnormalities in pallidal structural and functional connectivity and altered reward processing in the pallidal-limbic circuits may contribute to depression pathophysiology.\u003c/p\u003e \u003cp\u003eModulation of pallidal activity via lesions and DBS also provides clues into its role in depression. In non-PD populations, focal lesions (Lauterbach et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Vataja et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Miller et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and hyperintensities (Iidaka et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) in the pallidum have been associated with developing depression. In contrast, improvements in depression symptoms have been observed following pallidotomy for the treatment of PD (Masterman et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Laskowska et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), although other studies have found no change (Green et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). There is also evidence of pallidal DBS inducing a mild but clinically meaningful improvement in depression in PD in controlled trials and meta-analyses (Couto et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cartmill et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), as well as improving depression in other neurological disorders like Tourette syndrome (Liu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and dystonia (Eggink et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, depression responses to DBS are variable across individuals, and more controlled studies are needed to determine how and where to stimulate within the pallidum to improve and/or avoid worsening depression symptoms. Based on our results, posterior pallidal neural activity, particularly in the beta band, may show promise as a potential marker to guide pallidal DBS to more effectively and consistently alleviate depression in PD.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBeta Power: A Potential Marker Beyond Motor Symptoms\u003c/h2\u003e \u003cp\u003eElevated beta power in the basal ganglia has been classically associated with motor symptoms in PD, particularly bradykinesia and rigidity, in both the pallidum and the STN (Brown et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Eisinger et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Pathological beta power is generally reduced by both levodopa therapy (K\u0026uuml;hn et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and therapeutic DBS (K\u0026uuml;hn et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Although these findings make clear that beta power is linked to PD pathophysiology, this does not necessarily mean that beta power is only specific to motor symptoms in PD.\u003c/p\u003e \u003cp\u003eWe found that elevated beta power in the pallidum was associated with higher preoperative depression severity, even when controlling for other potentially confounding variables that could have impacted either beta power or depression symptom severity. The discovered relationship between beta power and depression severity was not explained by variability in sex or age, even though previous studies have found depression to be more common in females (Rojo et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Riedel et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Khedr et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and older individuals (Antar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) with PD. Other studies have also found both depression and beta power to be associated with longer PD disease duration (Khedr et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Antar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chendo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and more severe motor symptoms (Rojo et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Riedel et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Khedr et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as well as fluctuations in depression tied to levodopa medication state (more severe depression off- than on-medication) (van der Velden et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, these potential confounds did not significantly impact our results. Finally, depression is a complex psychiatric disorder that is often comorbid with anxiety and apathy in PD (Cong et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Weintraub et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and is commonly treated with several different medications (Seppi et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pontone and Mills, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We found that worse anxiety, but not apathy, was significantly associated with more severe depression, but this association did not negate the relationship between depression severity and elevated beta power. Additionally, the use of medications commonly used to treat psychiatric symptoms in PD, which could have potentially impacted either depression symptoms or neurophysiological activity, was not a relevant confound. These findings highlight that (1) depression symptoms were not associated with PD motor symptom severity or disease duration, suggesting that depression was likely not simply a reaction to more severe PD impairment, and (2) beta power was significantly associated with depression severity regardless of variables previously associated with depression.\u003c/p\u003e \u003cp\u003eAlthough not in the pallidum, other investigations have also found that beta power in limbic networks may play a role in depression in PD and non-PD populations. A recent study in PD patients undergoing DBS who were also implanted with subdural electrodes over the prefrontal cortex found that beta power correlated with self-rated symptoms of depression and anxiety (de Hemptinne et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Other studies using electroencephalography (EEG) have found that PD patients with depression (Espinoza et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) or anxiety (Betrouni et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yassine et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) exhibited higher beta power in prefrontal and central cortical regions. One study even found that prefrontal repetitive transcranial magnetic stimulation as an adjunctive therapy for depression in PD reduced prefrontal beta power measured with EEG (Tanaka et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Furthermore, depression following basal ganglia stroke lesions, particularly left-sided, has been associated with increased high beta activity in the left frontal and central cortical regions (Wang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In patients undergoing DBS for treatment-resistant depression, studies have found beta power in LFP recordings from the subcallosal cingulate cortex is involved in emotional processing (Clark et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Huebl et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Merkl et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and may be a marker of antidepressant response following DBS, specifically a decrease in beta power with acute stimulation (Smart et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sendi et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and potentially an increase in beta power with chronic stimulation (Alagapan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Combined with our results, these previous findings suggest that elevated beta power in specific prefrontal cognitive/limbic networks involving the basal ganglia may play a role in depression pathophysiology.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOther Neurophysiological Signals Relevant to Depression in PD\u003c/h3\u003e\n\u003cp\u003eWhile our study focused on resting-state neural activity in the pallidum, previous studies of basal ganglia neural activity associated with depression in PD have exclusively investigated the STN and mainly emotional processing tasks. Generally, these studies point to depression in PD potentially involving STN activity in the theta (4\u0026ndash;7 Hz) and alpha (8\u0026ndash;12 Hz) bands (Ricciardi et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Event-related desynchronization (ERD) in the alpha band, or reduction in power in response to stimuli, has been demonstrated in the STN in response to emotional stimuli compared to neutral stimuli in PD patients (Br\u0026uuml;cke et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Huebl et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mandali et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This ERD was reduced for pleasant stimuli and increased for unpleasant stimuli in PD patients with mild to moderate depression compared to those without depression (Huebl et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Dopaminergic medication may also modulate the observed ERD, with increased ERD in response to pleasant stimuli in the on-medication state and increased ERD in response to unpleasant stimuli in the off-medication state (Huebl et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Alongside alpha activity, two studies also found ERD in the beta band (Eitan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Huebl et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), warranting further investigation into beta as another signal that may be implicated in emotional processing in PD.\u003c/p\u003e \u003cp\u003eOnly two studies have investigated resting-state neural activity in depression in PD, one of which found that depressed PD patients exhibited increased alpha power and decreased theta power in the left ventral STN (Sun et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); however, other frequency bands were not examined. Another study determined theta/alpha power may be frequently localized to ventromedial (limbic) STN, and depression severity was negatively correlated with broadband theta/alpha/low beta power (5\u0026ndash;20 Hz) within the ventromedial STN region (Rappel et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Their reported correlation including low beta activity piqued our interest; however, it is in the opposite direction of our present results. Given the variations in findings across studies, further resting-state neurophysiology research and correlations with clinical symptoms are essential to understanding how specific oscillatory patterns in the STN and pallidum relate to depression in PD. Additionally, studies with larger sample sizes and unbiased, comprehensive analyses across all frequency bands beyond only theta/alpha are needed.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eOur retrospective study used brief, intraoperative neural recordings acquired during DBS implantation surgery in the resting, awake, off-medication state as a measure of spontaneous neural activity. However, by definition, these recordings are not specific to emotional, cognitive, or reward processing that may be relevant to depression pathophysiology. Future studies, like previous ones in the STN, should explore the role of pallidal neural activity in task-based paradigms to complement our resting-state recordings. Additionally, our recordings were limited to sampling neural activity where the DBS electrodes were implanted clinically, mainly clustered around the middle and posterior regions of the pallidum, which is classically defined as the sensorimotor region (Alexander et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). However, functional subregions of the pallidum have not been well defined, and increasing evidence has revealed functional integration and topographical overlap across sensorimotor, associative, and limbic basal ganglia networks in nonhuman primate studies (Baunez and Lardeux, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Weintraub and Zaghloul, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rossi et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), human behavioral and neuroimaging studies (Weintraub and Zaghloul, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Voon et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Greene et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and computational models (Wiecki and Frank, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mandali et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The current study focuses on neural activity across the pallidum, but future studies will investigate the localization of candidate biomarkers in larger cohorts. Our intraoperative LFP neural recordings and the preoperative clinical rating scale scores represent only a single snapshot in time. The BDI-II, although an established screening and evaluation scale for depression in PD (Visser et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), involves self-report, assesses symptoms only over the previous two weeks, and is not as comprehensive as a full diagnostic interview. However, our goal was to use data that is routinely and efficiently collected as part of the preoperative DBS assessments at our centers and others, which could be an asset for reproducibility and scalability. It is also important to note that there was a time delay between the preoperative depression assessment and the intraoperative neural recordings; however, we controlled for this potential confound in our GLM analysis and found it did not significantly affect our main findings. Similarly, we also controlled for anxiety and apathy in our GLM analysis, but it is often challenging to disentangle these comorbidities from depression (Weintraub et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our current sample size was too small to differentiate various subtypes and symptom combinations, but future research will address potential biomarkers related to anxiety, apathy, and depression in larger cohorts. Finally, the present cohort is predominantly limited to those with mild to moderate depression, as more severe and untreated psychiatric disorders are typical exclusion criteria from DBS candidacy; however, we still found that 26% of patients had clinically elevated depression symptoms despite the more limited range in depression severity.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFuture Directions\u003c/h2\u003e \u003cp\u003eDBS therapy is effective for reducing motor symptoms of PD, but effects on nonmotor symptoms like depression are not well understood and are often overlooked. Biomarkers specific to depression and other psychiatric symptoms could eventually enable more data-driven approaches to guide DBS therapy to reduce nonmotor symptoms while maintaining motor benefits. Our study used acute, intraoperative, resting-state neural recordings for initial discovery of putative pallidal biomarkers of depression in PD, which enabled recordings from a large cohort of patients with high spatiotemporal precision. However, the advent of new clinical DBS devices capable of chronic neural recordings could provide additional insight into longitudinal neural activity and depression symptoms in the naturalistic home environment, as demonstrated in a recent study focused on anxiety in PD (Swinnen et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Longitudinal studies could potentially uncover new biomarkers of psychiatric symptoms not available in acute intraoperative settings, such as circadian patterns (shown in PD (Cagle et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and obsessive-compulsive disorder (Provenza et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)), medication-related fluctuations in neural activity and symptoms, and long-term response to DBS.\u003c/p\u003e \u003cp\u003eIdentifying robust biomarkers of psychiatric symptoms in PD could also potentially enable more targeted DBS paradigms to alleviate those symptoms. Promising research has emerged in this area, showing low-frequency (10 Hz \u0026ndash; in the alpha band) stimulation in the STN may modulate emotional processing, with potentially a more pronounced effect in PD patients with depression (Mandali et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Muhammad et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although these studies focused on the alpha band, stimulation frequencies and patterns could theoretically be designed to enhance or suppress oscillatory activity in specific frequencies (e.g., pallidal beta in the present study) (Duchet et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, improved knowledge of the anatomical specificity and the networks associated with pathophysiological neural activity and effects of DBS on behavior will be increasingly important for developing a fundamental understanding of depression in PD and moving toward clinical translation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe present the first study investigating resting-state neural activity in the pallidum associated with depression in PD. The results indicate that beta power (particularly high beta power) may be a marker for differentiating between PD patients with versus without preoperative depression and a marker of depression severity, with higher beta power associated with worse depression symptoms. Notably, the relationship between beta power and depression symptoms was robust even after controlling for other demographic, clinical, pharmacological, and neurophysiological variables. These findings provide new insight into the potential role of the pallidum in depression in PD and broaden the view of beta power as a potential marker of psychiatric symptoms.\u003c/p\u003e "},{"header":"METHODS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eCohort and Clinical Assessment\u003c/h2\u003e \u003cp\u003ePatients in this study included individuals who underwent awake pallidal DBS implantation surgery for the treatment of PD and intraoperative neural recordings at the University of Florida Norman Fixel Institute for Neurological Diseases from January 2021 to December 2022. The study was carried out in accordance with the Declaration of Helsinki. All patients listed in our institutional database provided informed consent for research participation (Institutional Review Board 202202094).\u003c/p\u003e \u003cp\u003eExclusion criteria included: (1) absence of high-quality LFP recording; (2) missing preoperative neuropsychology evaluation and/or BDI-II score; (3) secondary movement disorder diagnosis (e.g., essential tremor); (4) neuropsychology evaluation closest to DBS surgery performed more than six months before surgery; (5) previous DBS surgery before neuropsychology assessment and/or LFP recordings (e.g., for patients who underwent bilateral staged DBS implantation, LFP recordings and preoperative assessment from only the first side surgery were included to exclude any effects of prior surgery or stimulation).\u003c/p\u003e \u003cp\u003eAs part of the standard pre-DBS neuropsychology evaluation at our center, preoperative baseline depressive symptoms were assessed using the Beck Depression Inventory, 2nd edition (BDI-II) (Beck et al., 1996). According to established thresholds for differentiating depression diagnosis using the BDI-II (Visser et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), patients were categorized as having PD with depression (BDI-II score\u0026thinsp;\u0026ge;\u0026thinsp;14) or PD without depression (BDI-II score\u0026thinsp;\u0026lt;\u0026thinsp;14). The Apathy Scale (AS) and State-Trait Anxiety Index (STAI) Trait scores were also analyzed to control for other psychiatric symptoms. Preoperative baseline motor symptom severity was also assessed with the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III).\u003c/p\u003e \u003cp\u003eA total of N\u0026thinsp;=\u0026thinsp;95 patients underwent pallidal DBS surgery and intraoperative LFP recordings between January 2021 and December 2022. A subset of N\u0026thinsp;=\u0026thinsp;17 patients had a previous DBS implantation and were excluded, and N\u0026thinsp;=\u0026thinsp;14 patients were excluded due to the poor quality of recordings. Clinical evaluation data were absent for N\u0026thinsp;=\u0026thinsp;9 patients or conducted more than six months before surgery for N\u0026thinsp;=\u0026thinsp;5 patients. After enforcing exclusion criteria, a total of 50 patients were included in this study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDeep Brain Stimulation Surgery\u003c/h2\u003e \u003cp\u003eFollowing the conventional clinical practice of the UF surgical team, a quadripolar or directional DBS electrode was implanted while the patient was awake, targeted to the posterolateral GPi (lead model 3387 or B33015, Medtronic, USA). Each patient underwent a preoperative multisequence MRI scan that included FLAIR (fluid-attenuated inversion recovery), FGATIR (rapid grey matter acquisition T1 inversion recovery) (Sudhyadhom et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and volumetric gadolinium-enhanced T1-weighted sequences. A digitized Schaltenbrand-Wahren atlas was overlaid on the imaging and warped to produce a patient-specific atlas match. A stereotactic computed tomography (CT) scan was acquired while the patient was wearing a Cosman-Roberts-Wells stereotactic frame. After image registration between the CT and MRI scan, the volumetric images and atlas were used to create an electrode placement trajectory, which aimed to optimize the lead location within the target structure while avoiding vasculature and the ventricles.\u003c/p\u003e \u003cp\u003eIntraoperative neurophysiological testing, including microelectrode recordings and stimulation, was carried out to optimize DBS lead placement. After implantation, the DBS lead was connected to an external system (Neuro Omega, Alpha Omega, Israel) to acquire resting-state LFP recordings from each electrode on the DBS lead (further details in the following section). To limit the potential effects of stimulation on neural activity, the resting-state LFP recordings were acquired before performing any macrostimulation from the DBS leads.\u003c/p\u003e \u003cp\u003ePre- and postoperative imaging was then used to verify the final lead locations using methods reported previously (Johnson et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In each patient, rigid registration was used to align the postoperative CT to the preoperative T1-weighted MRI. The lead was then localized using the artifact in the postoperative CT. To compare across patients in a common atlas space, the MNI PD25 atlas (Xiao et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was nonlinearly registered using the SyN algorithm in ANTs (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://stnava.github.io/ANTs/\u003c/span\u003e\u003cspan address=\"http://stnava.github.io/ANTs/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) software (Avants et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The nonlinear transformations were then applied to the lead localizations to visualize the contact locations relative to anatomical nuclei. One patient was excluded from the contact location visualization during quality control procedures due to unsatisfactory registration to the atlas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNeural Recordings and Signal Processing\u003c/h2\u003e \u003cp\u003eIntraoperative LFP recordings (60 seconds) of spontaneous neural activity with the patient awake, at rest, and off-medication were acquired from all contacts on the DBS lead in a monopolar configuration at a high sampling rate (22 kHz; Neuro Omega system, Alpha) and referenced to a scalp electrode.\u003c/p\u003e \u003cp\u003eThe monopolar LFP recordings then underwent extensive preprocessing and quality control through a pipeline illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, showing all processing steps in an example patient for a recording from a single contact (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-D) and the raw PSD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) and fully processed PSD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). In each patient\u0026rsquo;s recordings, the first 30-second period free of noise/artifacts across all contacts was selected for subsequent analysis. The LFPs from the selected period were then Butterworth filtered (5th order; band-pass 1-500 Hz) to remove low-frequency drift and high-frequency noise (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Then, the power spectral density (PSD) from 1-500 Hz was computed using Welch\u0026rsquo;s method (SciPy welch: 1-second Hann window, 0.5-second overlap) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To be able to compare with 4-contact cylindrical leads, a \u0026ldquo;ring mode\u0026rdquo; PSD on the middle two contact levels (C1 and C2) of 8-contact directional leads was obtained by averaging the PSD across segments on each level. To identify any residual peaks in the PSD due to line noise (60 Hz and harmonics) or other intraoperative environment noise, a standard peak-finding algorithm (SciPy findpeaks: peaks occurring at \u0026ge;40 Hz with prominence\u0026thinsp;=\u0026thinsp;0.5) was implemented (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Noise peaks that were detected in all contacts were extracted, measured for width (at 75% of relative height), and notch filtered for removal. All peaks marked for removal occurred at \u0026ge;60 Hz and were verified visually for quality control. No peaks required manual correction.\u003c/p\u003e \u003cp\u003eNext, the PSD was normalized to account for variability in PSD curve offset and slope and enable direct comparisons across patients using established methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The Fitting Oscillations \u0026amp; One Over F (FOOOF) algorithm (Donoghue et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was used to fit an exponential to the PSD from 1-100 Hz (the aperiodic 1/frequency portion of the PSD curve). A second-order exponential was fit to higher frequencies from 100\u0026ndash;500 Hz (the periodic portion of the PSD curve). All exponential fits were visually inspected to verify accuracy (none required manual correction or removal), and the FOOOF R\u003csup\u003e2\u003c/sup\u003e values indicated accurate exponential fits to the PSD curves (mean, SD [range]: 0.97, 0.015 [0.93\u0026ndash;0.99]). The FOOOF exponential and second-order exponentials were then subtracted from the original PSD to obtain a normalized PSD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The raw and final processed PSDs are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F, showing that all noise peaks were removed and the normalization successfully removed the PSD offset and slope and did not affect the rank order of PSD across contacts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe normalized PSD was then averaged over predefined frequency bands and across all contacts: delta (1\u0026ndash;4 Hz), theta (4\u0026ndash;8 Hz), alpha (8\u0026ndash;12 Hz), beta (13\u0026ndash;30 Hz) [separated into low beta (13\u0026ndash;20 Hz) and high beta (20\u0026ndash;30 Hz)], gamma (30\u0026ndash;100 Hz) [separated into low gamma (30\u0026ndash;50 Hz) and high gamma (50\u0026ndash;100 Hz)], and high-frequency oscillations (HFO) (200\u0026ndash;400 Hz) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). For each patient, the slope and intercept of the FOOOF exponential were also extracted for each contact and then averaged across all contacts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted to compare clinical variables for PD patients with depression (BDI-II \u0026ge; 14) versus those without depression (BDI-II\u0026thinsp;\u0026lt;\u0026thinsp;14). Unpaired, two-tailed t-tests were used for continuous variables and Fischer\u0026rsquo;s exact tests were used for discrete variables to compare the distributions across groups. For all analyses, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used as the threshold for statistical significance.\u003c/p\u003e \u003cp\u003eUnivariate analyses using unpaired, two-tailed t-tests were then performed to determine if neural activity in the defined frequency bands differed between PD patients with versus without depression. The frequency bands that significantly differed between PD patients with versus without depression were then further analyzed with Pearson correlations to determine if neural activity in those bands was also correlated with depression severity (BDI-II scores). For both series of t-tests and Pearson correlations, false discovery rate (FDR) via the Benjamini-Hochberg method was used to correct for multiple comparisons (family-wise error rate α\u0026thinsp;=\u0026thinsp;0.05). Both raw and FDR-corrected p-values are reported.\u003c/p\u003e \u003cp\u003eUnivariate analyses to assess the relationship between depression in PD and neural activity did not account for other demographic and clinical variables that may play a role in depression or may be associated with neural activity. A multivariate generalized linear model (GLM) was generated to evaluate the relationship between neural power in the above-defined frequency bands and depression severity (BDI-II scores) while controlling for demographics (age, sex, months between BDI-II evaluation and intraoperative LFP recordings), recording variables [hemisphere of LFP recordings, normalization curve parameters (FOOOF exponent/slope and offset/intercept)], PD severity and treatment variables [disease duration, UPDRS motor scores both on and off levodopa medication, levodopa equivalent daily dose (LEDD)], severity of other psychiatric symptoms [apathy (AS scores) and anxiety (STAI-Trait scores)], and psychiatric medication use (antidepressants, benzodiazepines, and non-benzodiazepine hypnotics). The Akaike Information Criterion (AIC) (Akaike, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1973\u003c/span\u003e) was used to compare the relative predictive performance of GLMs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated in this study are provided in the Source Data file. The raw (identifiable) data are protected and are not available due to data privacy laws. Reasonable requests for additional information can be directed to, and will be fulfilled by, the corresponding author. Note that these data are part of ongoing research studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCODE AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCode generated in this study for data processing and analysis are available at https://github.com/karajohnson/RestLFP-PD-Depression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge support from the Norman Fixel Institute for Neurological Diseases at the University of Florida. The authors also thank thank Chuck Jacobson and his team for managing the INFORM database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKAJ: Conceptualization, data collection and curation, formal analysis, data interpretation, visualization, writing manuscript. PBC: Data collection and curation, writing manuscript. LEK: Data collection, editing manuscript. JKW: Clinical feedback, statistics, editing manuscript. JDH: Data collection, clinical feedback, editing manuscript. KDF: Data collection, clinical feedback, editing manuscript. DB: Data collection, clinical feedback, editing manuscript. GMP: Conceptualization, clinical feedback, editing manuscript. CDH: Supervision, conceptualization, data interpretation, editing manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad, M. H., Rizvi, M. A., Ali, M., and Mondal, A. C. (2023). Neurobiology of depression in Parkinson\u0026rsquo;s disease: Insights into epidemiology, molecular mechanisms and treatment strategies. 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Neurobiology of Disease 155, 105372. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.nbd.2021.105372\u003c/span\u003e\u003cspan address=\"10.1016/j.nbd.2021.105372\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, depression, globus pallidus, deep brain stimulation, local field potential","lastPublishedDoi":"10.21203/rs.3.rs-5952073/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5952073/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDepression is increasingly recognized as a prevalent source of disability in individuals with Parkinson\u0026rsquo;s disease (PD), but its pathophysiology is not well understood. Neural activity in the basal ganglia, particularly the subthalamic nucleus, has been linked to depression in PD, but the role of the pallidum remains unclear. This retrospective study aimed to correlate preoperative depression symptoms with intraoperative resting-state neural activity recorded from the pallidum in N\u0026thinsp;=\u0026thinsp;50 patients who underwent deep brain stimulation (DBS) implantation surgery. Patients with clinically elevated depression symptoms exhibited elevated beta (13\u0026ndash;30 Hz) power compared to patients without depression. Beta power, particularly high beta (20\u0026ndash;30 Hz) power, was also associated with depression symptom severity, even when controlling for other demographic, clinical, pharmacological, and neurophysiological variables. These results establish pallidal beta power as a potential biomarker of depression in PD and set the stage for tailoring DBS therapy to improve psychiatric symptoms in PD.\u003c/p\u003e","manuscriptTitle":"Pallidal beta power is associated with depression in Parkinson’s disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 15:47:00","doi":"10.21203/rs.3.rs-5952073/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-25T19:25:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T20:05:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T08:33:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70635379012700582483787592355157785769","date":"2025-06-24T11:17:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257268011692746460834696357671269738738","date":"2025-06-24T08:46:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-20T09:41:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299516113679267497692508026724644928388","date":"2025-03-14T02:29:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17483602542572963012743621384423083506","date":"2025-03-14T01:09:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-07T16:13:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-05T18:21:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-05T07:24:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Parkinson's Disease","date":"2025-02-03T15:03:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61d21407-14bc-46cb-88ea-ab2aae242b2d","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45957840,"name":"Biological sciences/Physiology/Neurophysiology"},{"id":45957841,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":45957842,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"},{"id":45957843,"name":"Health sciences/Diseases/Neurological disorders/Movement disorders/Parkinsons disease"}],"tags":[],"updatedAt":"2026-01-26T16:05:30+00:00","versionOfRecord":{"articleIdentity":"rs-5952073","link":"https://doi.org/10.1038/s41531-026-01264-4","journal":{"identity":"npj-parkinsons-disease","isVorOnly":false,"title":"npj Parkinson's Disease"},"publishedOn":"2026-01-22 15:58:41","publishedOnDateReadable":"January 22nd, 2026"},"versionCreatedAt":"2025-03-24 15:47:00","video":"","vorDoi":"10.1038/s41531-026-01264-4","vorDoiUrl":"https://doi.org/10.1038/s41531-026-01264-4","workflowStages":[]},"version":"v1","identity":"rs-5952073","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5952073","identity":"rs-5952073","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}