Predicting cognition after subthalamic Deep Brain Stimulation in Parkinson’s Disease

Predicting cognition after subthalamic Deep Brain Stimulation in Parkinson’s Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predicting cognition after subthalamic Deep Brain Stimulation in Parkinson’s Disease Dorothee Kübler-Weller, Heiner Stuke, Melanie Astalosch, Luísa Martins Ribeiro, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6973225/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Aug, 2025 Read the published version in npj Parkinson's Disease → Version 1 posted 9 You are reading this latest preprint version Abstract Background Cognitive deficits have a high impact on quality of life of patients with Parkinson’s disease (PD). Age > 70 years and dementia are considered risk factors for cognitive decline after deep brain stimulation (DBS) but evidence on the highly relevant individual cognitive prognosis is scarce. This study takes into account the multifaceted etiology of cognition in order to refine estimation of the cognitive outcome after DBS surgery in PD. Methods Clinical, neuropsychological, perioperative, neuroimaging- and laboratory-based risk factors for cognitive dysfunction were prospectively assessed prior to DBS surgery in 57 patients with PD (21 female; age 60.2 ± 8.2; disease duration 10.5 ± 5.9 years). In addition to univariable correlations, elastic net regularized regression and leave-one-out cross-validation were used to fit a multivariable model with the Montréal Cognitive Assessment (MoCA) one year after surgery as primary outcome. Results Of all assessed possible predictors, the backward span of the SSP had the most robust association with the cognitive outcome (rho = 0.499, p < 0.001**; c = 0.302). Other factors significantly associated with cognition after DBS surgery were CSF dementia markers, serum C-reactive protein, severity of motor fluctuations, the number of impaired cognitive domains, forward spatial span length, multitasking performance and the duration of postoperative delirium. Based on our multivariable model results, we propose a post-hoc prediction model for cognition based on the baseline MoCA and backward span (R² = 0.50). Conclusions Our findings highlight the multifaceted influencing factors on the cognitive outcome after DBS. After clinical validation, our short and easily applicable prediction model could improve informed therapeutic decision making. Biological sciences/Neuroscience Biological sciences/Psychology Health sciences/Medical research Health sciences/Neurology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Amongst the multitude of non-motor symptoms in Parkinson’s disease (PD), cognitive deficits have the highest impact on quality of life 1 . Impairments range from no or minimal cognitive decline up to severe dementia (PD-D) 2 and estimation of cognitive trajectories is challenging 3 . Nevertheless, early and reliable prediction is essential for therapeutic decision making, especially when deep brain stimulation (DBS) surgery is evaluated as an escalation of therapy. PD patients that underwent DBS surgery can suffer from deficits in long-term memory, verbal fluency and executive functions 4 , 5 . The long-term incidence of PD-D in patients with DBS, however, is comparable to medical treatment and considered a part of PD rather than an effect of DBS 6 , 7 . Nevertheless, considerable and sustained cognitive deterioration after DBS surgery has been described in a small subset of patients 8 . Preexisting cognitive deficits and higher age are the main risk factors for cognitive decline following DBS surgery in PD 9 . For an estimate of the cognitive trajectory of individual PD patients after DBS surgery, however, many aspects like the phenotype and course of PD, but also surgery- and stimulation-related aspects must be considered 10 . General factors influencing cognition include education, nutritional status, comorbidities, inflammation, depression and apathy 11 . Decreased CSF Amyloid β and increased TAU levels have been shown to be predictive of more rapid and severe cognitive deterioration 12 . Serum neurofilament light chain (NfL) has good predictive value for cognitive decline in PD as well 13 . PD-specific factors potentially influencing cognition are disease duration, symptom severity, dopaminergic dose and impulsivity. Other than that, PD patients with heterozygote gene mutations encoding glucocerebrosidase (GBA) display a more aggressive phenotype with a higher prevalence and a faster progression to PD-D 14 . Amongst anatomical characteristics, the volume of the cholinergic Ncl. basalis of Meynert (NBM) has been shown to be predictive of cognition in PD: Patients with lower-than-expected NBM volumes at baseline have a 3.5-fold greater risk of becoming cognitively impaired after five years 15 . This finding was extended by a retrospective voxel-based morphometry (VBM) study, which demonstrated that preoperative NBM volume also predicted cognitive decline one year after DBS 16 . Perioperative factors also increase the risk for complications such as postoperative delirium (POD), which in turn increases the risk of long-term cognitive decline 17 . Also, the length and depth of surgery and sedation have negative influences on postoperative cognitive outcomes 18 . To account for the multifaceted etiology of cognitive change, this prospective study comprehensively assessed PD patients prior to DBS surgery by detailed phenotyping regarding non-motor symptoms, imaging, dementia markers and genetics. These markers were used to predict cognitive outcomes after DBS aiming to improve clinical decision making and perioperative management for optimal long-term benefit. Methods The study was registered at clinicaltrials.gov (NCT03982953) and approved by the Charité ethics committee (EA2/040/19). TRIPOD guidelines were used for prediction model development and validation. Written informed consent was obtained from all patients. Study participation was proposed to all patients with the diagnosis of PD and the indication for bilateral DBS in the subthalamic nucleus (STN) after careful examination of eligibility for this treatment. Evaluation for the indication of DBS was conducted according to clinic-intern standard operation procedures that are in accordance with the guidelines for invasive therapies in PD 19 . The only exclusion criterium for study participation that did not apply with respect to a decision against DBS was a relevant language barrier. Figure 1 contains a study overview. Data from clinical routine Within the clinical routine work-up during the evaluation process for DBS, the following clinical scales and questionnaires were applied and tested for their predictive value: Need of support in activities of daily living (ADL), symptom severity in PD motor and non-motor domains (MDS-UPDRS I-IV) including motor part MDS-UPDRS III with (MedON) and after wash-out of dopaminergic medication (MedOFF), quality of life (PDQ39), depression (BDI-II), apathy (SAS) and impulsivity (QUIP-RS). Motor predominance type, PD duration and levodopa equivalent daily doses (LEDD) were recorded. The genetic background of PD was assessed by testing for monogenetic causes of PD as well as susceptibility loci and genes 20 . General predictors of cognition In addition to these clinical assessments, the following possible predictors of cognitive dysfunction after STN-DBS in PD were examined in the context of this prospective study: Years of education, nutritional status (BMI and MNA-SF). The burden of comorbidities (CKI) and the blood-based inflammation marker C-reactive protein (CRP) from the day before DBS surgery were assessed. In patients who agreed to undergo lumbar puncture (n = 23), total and phospho TAU as well as Amyloid β 1–40 and 1–42 was measured in the CSF. NfL, a specific marker for neuronal cell damage, was quantified from blood serum and Z-scores were calculated according to age and BMI 21 . Besides the global cognitive screening Montréal Cognitive Assessment 22 (MoCA), an extensive tablet-based cognitive test battery (CANTAB Connect™) was applied prior to DBS surgery and at follow-up one year after surgery (1yFU). A description of the neuropsychological domains tested by different tests and their respective outcome measures can be found in Table 1 and in the supplementary material. Theory of Mind (ToM), the capacity to draw conclusions on other persons thoughts or emotions was investigated with the Yoni paradigm 23 which includes an affective, cognitive and control condition. Outcomes measures are the percentages of correctly answered stimuli for each condition. Additionally, the number of domains rated as impaired in the standard neuropsychological test session during clinical evaluation for DBS surgery was assessed. Imaging As imaging biomarkers, volumes of the cholinergic basal forebrain, i.e. its main nucleus, the NBM, and surrounding nuclei (Ch1-3) were assessed using VBM as previously described 24 . Basis were the preoperative structural MRIs with 3 Tesla acquired with Siemens Magnetom Vida or Skyra scanners. The T1-weighted gradient echo sequence was analyzed with MATLAB (version R2022a) by means of the CAT12 toolbox, an extension to the software SPM12. As partial brain volumes depend on total intracranial volume (TIV), the ratios NBM/TIV and Ch1-3/TIV were used for further calculations. Perioperative procedures Perioperative procedures with a potential influence on cognitive outcomes were recorded. These include the duration of the perioperative pause of dopaminergic medication during electrode implantation (surgery 1) and doses of sedatives used during both surgeries (surgery 2 being the implantation of the impulse generator). Intraoperative sedation depth was measured with four EEG channels on the forehead (SedLine®). The power peak (averaged normalized power spectral density) and spectral edge frequency (SEF95) were calculated with EEGLAB 25 running in MATLAB (version R2022a) as described in 24 . Burst suppression patterns were manually extracted and set in relation to the overall duration of surgery. As postoperative delirium (POD) can lead to permanent cognitive deficits, screening for this complication was conducted with the CAM-ICU (Confusion Assessment Method for Intensive Care Unit) 26 or Nu-DESC (Nursing Delirium Screening Scale) 27 at non-ICU wards three times daily as previously described 24 . Delirium severity (mean score of days with positive POD screening) and duration were calculated. Statistical analysis Statistical analysis was carried out in IBM SPSS Statistics Version 28 and the sci-kit learn Version 1.3.0 in Python 3.9.1 28 . Descriptive statistics are reported as mean ± standard deviation (SD). Pre- and postoperative clinical scales and scores were compared by means of Wilcoxon signed-rank tests. The change in global cognition was calculated as the difference between MoCA points at 1yFU and baseline prior to surgery and will be referred to as “cognitive outcome” in the following. The relationships between the predictor variables and the cognitive outcome were first analyzed with unadjusted Spearman correlation coefficients rho. Second, a multivariable prediction model was estimated to test the predictive performance of the combined predictor variables and to obtain adjusted multivariable coefficients (see below for details of the modeling approach). The resulting univariable and multivariable coefficients provide readers with complementary information. The univariable correlation coefficients indicate how strongly cognition changes after surgery depending on a single predictor variable (a negative correlation with age, for example, would indicate that older patients who have undergone surgery experience a stronger decline in cognition). The multivariable coefficient, on the other hand, indicates how strongly cognition changes in relation to a predictor variable when the influence of the other variables included in the model has already been taken into account. Thus, if a negative coefficient for a neuropsychological test is found in an age-adjusted model, this means that there is a greater deterioration in patients who score high on this neuropsychological test for their age , i.e., even when taking into account that older patients might generally score higher on this test. Both coefficients therefore provide complementary information and, as in previous studies 29 , we report both to provide as comprehensive a picture of the relationships as possible. For the multivariable analysis, elastic net regularization 30 was used due to the high number and multicollinearity of assessed predictors. This machine learning analysis mitigates overfitting and increases the stability of coefficient estimates and thus increases the statistical power and generalization ability of the model. It is recommended as it offers several advantages compared to traditional regression analyses 31 . Predictors with missing data in > 50% of patients (CSF markers, NfL Z-scores, doses of Remifentanil and Fentanyl and burst suppression) were excluded from the multivariable analysis. To test the model’s predictive performance, leave-one-out cross-validation was conducted. In this approach, the model is repeatedly fitted to all but one patient (the training set) and is then applied to predict the cognitive outcome in the left out (test) patient. This procedure is repeated until all patients have been left out once. Hence, for each patient, there is a prediction of the cognitive outcome based on a model that was trained without using this patient’s data for training. The predictive performance of the model for test patients was assessed by calculating the mean absolute error as well as Spearman correlations between each patient’s predicted and true cognitive outcome at 1yFU. Each cross-validation fold comprised one-hot-encoding of categorical predictors, imputation of missing values using k-nearest-neighbors imputation as implemented in KNNImputer class of sklearn with default parameters 32 , z-standardization of numerical variables, and training of the regression model in the training set. The hyperparameters of the elastic net regression model (regularization strength and ratio between L1 and L2 penalty) were optimized in a nested 20-fold cross-validation loop within each training set. A final multivariable model was fit with all patients and the hyperparameter set optimal in most cross-validation folds and its coefficients are reported as multivariable coefficients c. Finally, based on these analyses, a concise post-hoc model to predict cognition one year after DBS surgery was developed and is proposed for clinical validation. To foster clinical applicability of this model, it only used the most predictive variable according to the multivariable analysis in addition to the preoperative MoCA baseline. The performance of this model was assessed using the coefficient of determination (R 2 ) and compared to a benchmark model using only preoperative MoCA baseline as a predictor in a leave-one-out cross-validation fashion. Results Study cohort 62 PD patients evaluated as eligible for DBS surgery were included in the study. Five decided against invasive treatment.57 patients with PD received bilateral STN-DBS at our center between June 2019 and September 2021, 21 of them women (36.8%). As shown in the study flow diagram (Fig. 2 ), three patients had to be explanted during the study, two due to an infection of the device within the first three months after implantation and one due to prolonged wound healing impairment. Another two patients dropped-out due to suicide and death of unknown cause. Two patients withdraw study consent, and four patients were lost to FU. Hence, the data of 46 of the 57 patients during the study period was available for 1yFU. Mean age of the cohort was 59.7 ± 8.2 and disease duration 10.6 ± 6.3 years. The results of all scales and scores assessed at preoperative baseline are shown in Table 2 . General outcome parameters As expected, patients improved significantly with DBS concerning motor symptoms (MDS-UPDRS III baseline MedOFF 53.9 ± 15.3, 1yFU StimON/MedOFF 27.1 ± 15.7, 1yFU StimOFF/MedOFF 49.9 ± 16.7; p < 0.001), fluctuations (MDS-UPDRS IV baseline 7.6 ± 4.7, 1yFU 2.2 ± 2.5; p < 0.001), LEDD reduction (baseline 1278.7 ± 341.0, 1yFU 514.3 ± 305.5; p < 0.001) and need of support in ADL (baseline 15.2 ± 12.4, 1yFU 12.5 ± 13.1; p = 0.020). UPDRS I (baseline 10.0 ± 5.9, 1yFU 9.4 ± 5.8; p = 0.694), UPDRS II (baseline 11.9 ± 6.5, 1yFU 11.5 ± 7.7; p = 0.285), nutritional status (MNA-SF baseline 12.2 ± 2.0, 1yFU 12.7 ± 1.7; p = 0.377), quality of life (PDQ-39 PDSI baseline 32.7 ± 10.7, 1yFU 27.2 ± 13.2; p = 0.081), symptoms of depression (BDI-II baseline 12.4 ± 8.0, 1yFU 13.1 ± 8.7; p = 0.938), apathy (SAS baseline 14.9 ± 6.3, 1yFU 17.0 ± 6.5; p = 0.409) and impulsivity (QUIP-RS baseline 8.6 ± 10.4, 1yFU 6.6 ± 9.3; p = 0.378) did not show significant differences between preoperative and 1yFU assessment. There was no significant MoCA change postoperatively in comparison to the preoperative baseline (baseline 25.6 ± 3.0, 1yFU 25.1 ± 3.6; p = 0.285) but the cognitive outcome varied between patients. Results from multi- and univariable analysis The multivariable analysis yielded a significant correlation between the true and predicted cognitive outcome (MoCA score difference at 1yFU in comparison to baseline, rho = 0.376, p = 0.010, mean absolute error = 2.200). All results from the multivariable (coefficients c) and univariable analyses (coefficients rho and respective p-values) can be found in Table 2 . Visuospatial memory and working memory as measured by the backward span of the SSP, was a strong predictor of the cognitive outcome and was the only predictor that was associated with cognitive change one year after surgery in both the univariable and the multivariable analysis (rho = 0.499, p < 0.001**; c = 0.302). Scatterplots of these correlations can be found in Fig. 3 . Higher ß Amyloid β 1–42 / 1–40 and Amyloid β 1–42 / total TAU ratios were associated with better cognitive outcomes in the univariable model (rho = 0.455, p = 0.044* and rho = 0.501, p = 0.048* respectively). The inflammation level measured by means of the preoperative CRP from blood serum was negatively associated with the cognitive outcome one year after surgery in the multivariable analysis (rho=-0.125, p = 0.408, c=-0.068). In terms of PD-specific predictors, the severity of fluctuations assessed by the MDS-UPDS IV was related to worse cognitive outcomes in the multivariable analysis (rho=-0,148, p = 0.344; c=-0.110). In addition to the highly predictive backward span, the number of deteriorated cognitive domains in the classic neuropsychology test set (rho=-0.163, p = 0.286; c=-0.196), the length of the forward spatial span of the SSP testing visuospatial memory (rho = 0.283, p = 0.059; c = 0.042) and lower multitasking time (rho=-0.190, p = 0.206; c=-0.144) as well as higher incongruency costs (rho = 0.102, p = 0.499; c = 0.012) for conflicting stimuli in the MTT trials were associated with better cognitive outcomes in the multivariable analysis. Finally, of the perioperatively assessed data, the duration of POD was associated with a negative cognitive outcome in the multivariable analysis (rho=-0.117, p = 0.439; c=-0.211). Post-hoc model The backward span of the SSP emerged as the strongest predictor of the cognitive outcome in the multivariable analysis and also showed a statistically significant univariable correlation with the cognitive outcome, suggesting that it is a strong predictor of cognitive outcome at 1yFU. To design a sparse and clinically applicable model for prospective evaluation, we performed a post-hoc analysis predicting the MoCA score at 1yFU using only the backward span and the preoperative MoCA score. The benchmark model including only the preoperative MoCA explained 27% of the variance in 1yFU-MoCA (R² = 0.27) in leave-one-out cross-validation. Adding the backward span of the SSP substantially improved model performance, increasing the explained variance to 50% (R² = 0.50) with a mean absolute error of 1.9 points. For clinical applicability, we rounded the multivariable coefficients to one decimal place yielding the following simplified linear model: MoCA ₁yFU = 0.7 × MoCA baseline + 1.7 × backward span baseline − 1 Figure 4 illustrates the clinical relevance of the model. Discussion In this prospective cohort study, we found that in PD, the most robust predictor for the cognitive outcome one year after DBS surgery was the backward span. This test assesses visuospatial memory in combination with working memory, a set of skills is mainly based on temporal and posterior cortical structures with predominantly cholinergic underpinning 33 . Consistent with our results, a retrospective study in PD patients found a trend towards less favorable functional outcomes after six to 12 months in patients with visuospatial impairment before DBS surgery 34 . Preoperative deficits in visuospatial skills, verbal memory and language processing were also linked to cognitive complaints after DBS surgery reported by patients and/or their caregivers 35 . However, we could not reproduce an influence of other classical cholinergic domains like learning and memory on postoperative cognition our study. In the case of the backward span, the memorized spatial sequence must additionally be manipulated in working memory recruiting fronto-striatal circuitry which suggests an additional involvement of dopaminergic structures. Also in a recent review, both executive dysfunction and memory deficits, present prior to surgery were associated with worse cognitive outcomes after STN-DBS in PD patients 36 . Mana and colleagues, on the other hand, have shown executive dysfunctions to be most predictive of cognitive decline after DBS-surgery in PD in their recently published retrospective study 37 . Higher Amyloid β 1–42 / 1–40 and Amyloid β 1–42 / total TAU ratios from the CSF were associated with better cognitive outcomes. This constellation is known to be predictive for the development of Alzheimer’s disease and has lately also been studied in PD. This is the first study investigating the cognitive prognosis in PD patients after DBS taking into consideration biomarker profiles from CSF. In patients willing to undergo lumbar puncture, these findings could help refine estimation of the cognitive trajectory. Other predictors also contributed significantly to the main multivariable model stressing the multifaceted influences on cognition in PD: The preoperative serum inflammation level, severity of motor fluctuations, deterioration in the classic neuropsychology test set and multitasking time were negatively associated whereas the forward spatial span and multitasking incongruency costs were positively associated with cognition one year after surgery. POD duration was also associated with a negative cognitive outcome one year after surgery in the multivariable analysis. Although, in a strict sense, POD is not a true predictor as it occurs only postoperatively, this finding strengthens the importance of POD for the long-term cognitive outcome. As both cognitive deterioration and POD share most of their risk factors, there is a logic connection but we are the first to show that in a prospective study. [24] Our cohort is representative for PD patients undergoing DBS surgery by age, disease duration, response to DBS etc. However, neither age nor the burden of comorbidities showed a significant impact on the cognitive outcome in our cohort. As more and more elderly PD patients with comorbidities ask for DBS, this finding stresses the importance of valid prediction models for the cognitive outcome. The missing effect of the genetic background on the cognitive outcome after DBS could be due to the small sample size (6 patients with GBA-mutations) and should be interpreted with caution. Post-hoc model To facilitate clinical application, we derived a practical and sparse post-hoc model using only the baseline MoCA score and the backward span from the SSP as predictors of the cognitive outcome one year after DBS. These two tests are quick to administer (approximately 10 and 4 minutes, respectively) and do not require medical staff, making them highly feasible for routine use. The resulting linear model showed good predictive performance and can be applied as follows: Predicted MoCA at 1yFU = 0.7 × MoCA + 1.7 × backward span − 1 at baseline. While this model needs to be validated in a prospective multicenter setting, it represents a promising first step to improve individualized risk stratification of PD patients undergoing DBS. Limitations This study must be discussed in the light of its limitations: Cognition is complex and multifaceted, more so in PD after STN-DBS [39]. It has been stressed that only a prospective assessment of combined predictors can substantially add to the existing literature 38 . Therefore, we assessed many influencing factors as we are convinced that a reliable impression on the predictors of cognitive change can only be achieved if all the available evidence is considered. To overcome the resulting shortcoming of the disproportion between patients and possible predictors, a sophisticated statistical design was applied to increase the reliability of the study results. However, some variables could not be obtained from all patients and were subsequently excluded from the multivariable analysis and therefore have a low statistical validity. Outlook Our results are expected to support evidence-based and personalized decision-making when advising PD patients considering STN-DBS. As a next step, our proposed slimmed down prediction model based on the MoCA and backward span of the SSP must be validated. Especially in combination with dementia markers from the CSF, the backward span has the potential as a fast and easily applicable predictor. In the future, we hope to contribute to the development of hypothesis-driven interventional trials, for example on preventive strategies like cognitive training, with the goal to achieve optimal outcomes for every individual patient. Data sharing Due to data privacy restrictions, patient datasets are not publicly available but can be provided by the corresponding author upon reasonable request. Author Roles 1) Conception and design of the study: DKW, HS, CS, FB, AAK 2) Acquisition and analysis of data: DKW, MA, LMR, EL, GHS, KF, PK, JR, SH, MM, BAF, CS, FB 3) Drafting the manuscript or figures: DKW, HS, JR, SH, BAF, AAK Abbreviations CKI Charlson Comorbidity Index MNA SF-Mini Nutritional Index-short form BDI II-Beck Depression Inventory-version 2 SAS Starkstein Apathy Scale CRP C-reactive protein MDS UPDRS-Unified Parkinson’s Disease Rating Scale by the Movement Disorder Society PDQ 39-Parkinson’s Disease Questionnaire PDSI Parkinson's Disease Summary Index QUIP RS-Questionnaire for Impulsive-Compulsive Disorders in Parkinson’s Disease-Rating Scale MOT Motor Screening Task RTI Reaction Time test PRM Pattern Recognition Memory test PAL Paired Associates Learning task SSP Spatial Span task VRM Verbal Recognition Memory test MTT Multitasking Test SWM Spatial Working Memory test ERT Emotion Recognition task Nu DESC-Nursing Delirium Screening Scale Declarations Author Contribution Author Roles 1) Conception and design of the study: DKW, HS, CS, FB, AAK2) Acquisition and analysis of data: DKW, MA, LMR, EL, GHS, KF, PK, JR, SH, MM, BAF, CS, FB3) Drafting the manuscript or figures: DKW, HS, JR, SH, BAF, AAK Data Availability Data sharing Due to data privacy restrictions, patient datasets are not publicly available but can be provided by the corresponding author upon reasonable request. 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Neuropsychological predictors of patient-reported cognitive decline after deep brain stimulation in Parkinson’s disease. J. Clin. Exp. Neuropsychol. 41, 219–228 (2019). Jahanshahi, M., Leimbach, F. & Rawji, V. Short and Long-Term Cognitive Effects of Subthalamic Deep Brain Stimulation in Parkinson’s Disease and Identification of Relevant Factors. J. Park.’s Dis. 12, 2191–2209 (2022). Mana, J. et al. Preoperative cognitive profile predictive of cognitive decline after subthalamic deep brain stimulation in Parkinson’s disease. Eur. J. Neurosci. 60, 5764–5784 (2024). Tröster, A. I. Developments in the prediction of cognitive changes following deep brain stimulation in persons with Parkinson’s disease. Expert Rev. Neurother. 24, 643–659 (2024). Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMethods.docx Table12.docx Cite Share Download PDF Status: Published Journal Publication published 28 Aug, 2025 Read the published version in npj Parkinson's Disease → Version 1 posted Editorial decision: Revision requested 13 Jul, 2025 Reviews received at journal 12 Jul, 2025 Reviews received at journal 07 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers invited by journal 29 Jun, 2025 Editor assigned by journal 29 Jun, 2025 Submission checks completed at journal 29 Jun, 2025 First submitted to journal 25 Jun, 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. 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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-6973225","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":478204179,"identity":"07466ae8-77e0-4270-994d-c9c5c1d7a45b","order_by":0,"name":"Dorothee Kübler-Weller","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIie2PMWvCQBTHnwi6vJo1QYhf4S8HKYL4WSIHmUINdLGTN6VLu+drdCkdEwSznLvdLIJzi1OpoJdCO0WxW4f7wTuO9+7H/x2RxfIfyU01lDmYqEkJkVPdCNUsPK0Uv4p56alvBX9QkFcKnVY65ePb5uNl6BPr/ibBcCLKZbGbJnuf2jKvUzxdChQ6EnT1IESGaPCsb6SnAUG8rY3BKmq5RTofK4eDLmOOYMXwFDBWbowzymFmlOsvxgEiY/FplJlyJ+9nlDw0iwVNRg64HFQpIblx7fc9vWhimcp+yovbbgYJV8fBQEGYzrZ2sU6ZNtZ36ajnsHzaJdMRnHstXtXe7zltua6N+aF1QcdisVgsl3IEF5Nb3X0vDV0AAAAASUVORK5CYII=","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":true,"prefix":"","firstName":"Dorothee","middleName":"","lastName":"Kübler-Weller","suffix":""},{"id":478204180,"identity":"b1f8d760-6f06-4ff7-8396-ea4a85791225","order_by":1,"name":"Heiner Stuke","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Heiner","middleName":"","lastName":"Stuke","suffix":""},{"id":478204181,"identity":"268025e9-b9d8-49ab-94e8-98680fed8500","order_by":2,"name":"Melanie Astalosch","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"Astalosch","suffix":""},{"id":478204182,"identity":"5197e99a-a2ad-4fd0-bb20-a4670a80c2b7","order_by":3,"name":"Luísa Martins Ribeiro","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Luísa","middleName":"Martins","lastName":"Ribeiro","suffix":""},{"id":478204183,"identity":"51a401a7-772d-4d4c-9f4c-100aba7debac","order_by":4,"name":"Elias Landfried","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Elias","middleName":"","lastName":"Landfried","suffix":""},{"id":478204184,"identity":"86789ca3-733e-4435-89e5-bc248abb7945","order_by":5,"name":"Gerd-Helge Schneider","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Gerd-Helge","middleName":"","lastName":"Schneider","suffix":""},{"id":478204193,"identity":"f1c2d8c1-c34c-4cd0-8ba3-95f4360ce7b5","order_by":6,"name":"Katharina Faust","email":"","orcid":"","institution":"University Hospital Düsseldorf","correspondingAuthor":false,"prefix":"","firstName":"Katharina","middleName":"","lastName":"Faust","suffix":""},{"id":478204194,"identity":"5d021b93-49d5-4580-b74c-6a2ee3dd25fb","order_by":7,"name":"Patricia Krause","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Krause","suffix":""},{"id":478204201,"identity":"fb96f74f-2cf9-4d17-93dc-b3f79454fc2f","order_by":8,"name":"Jan Roediger","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Roediger","suffix":""},{"id":478204204,"identity":"c80a1d5f-ef5d-4a37-aaab-ba331bf012bc","order_by":9,"name":"Stefan Haufe","email":"","orcid":"","institution":"Technische Universität Berlin","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Haufe","suffix":""},{"id":478204206,"identity":"65059d53-9450-4070-b90c-40169e1df855","order_by":10,"name":"Mahta Mousavi","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Mahta","middleName":"","lastName":"Mousavi","suffix":""},{"id":478204212,"identity":"c19b4dbe-2dcd-43af-a5ec-f559e24e460a","order_by":11,"name":"Bassam Al-Fatly","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Bassam","middleName":"","lastName":"Al-Fatly","suffix":""},{"id":478204213,"identity":"1dbcb85a-0a29-4378-b096-fa2a090d7e25","order_by":12,"name":"Claudia Spies","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Spies","suffix":""},{"id":478204221,"identity":"a6c6850a-66e6-4ddc-9b74-49e371307683","order_by":13,"name":"Friedrich Borchers","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Friedrich","middleName":"","lastName":"Borchers","suffix":""},{"id":478204226,"identity":"673e2915-202c-4f1d-ad69-4a32339e4dbf","order_by":14,"name":"Andrea A. Kühn","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"A.","lastName":"Kühn","suffix":""}],"badges":[],"createdAt":"2025-06-25 09:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6973225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6973225/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41531-025-01128-3","type":"published","date":"2025-08-28T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85924601,"identity":"f4256b39-302a-425f-8d41-7d331cfddd79","added_by":"auto","created_at":"2025-07-03 08:27:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":387295,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic study overview.\u003c/strong\u003e Abbreviations: DBS - deep brain stimulation, PD - Parkinson’s disease, STN - subthalamic nucleus, MoCA - Montréal Cognitive Assessment, NBM - Ncl. basalis of Meynert, NfL - Neurofilament light chain, CRP - C-reactive protein, CKI - Charlson Comorbidity Index, MNA-SF - Mini Nutritional Index - short form, BDI-II - Beck Depression Inventory, SAS - Starkstein Apathy Scale, MDS-UPDRS - Unified Parkinson’s Disease Rating Scale by the Movement Disorder Society, ADL - Need of support in activities of daily living, PDQ-39 - Parkinson’s Disease Questionnaire, QUIP-RS - Questionnaire for Impulsive-Compulsive Disorders in Parkinson’s Disease–Rating Scale, POD - Postoperative delirium\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6973225/v1/372e1593112522cf6ad572a6.png"},{"id":85922805,"identity":"2d37a011-3c6c-42c0-a0a4-ccea00a5995b","added_by":"auto","created_at":"2025-07-03 08:19:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":267804,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow diagram.\u003c/strong\u003e Abbreviations: PD - Parkinson’s disease, STN-DBS - deep brain stimulation in the subthalamic nucleus, 1yFU - Follow-up one year after DBS surgery\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6973225/v1/983a0abef02362a882ebc683.png"},{"id":85924604,"identity":"7c820da5-0cf3-4367-a7c3-33570d6accf7","added_by":"auto","created_at":"2025-07-03 08:27:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":253439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatterplots of statistically significant univariable correlations \u003c/strong\u003eaccording to Spearman between predictors (x-axis) and the difference in MoCA points between the preoperative baseline and 1yFU. Abbreviations: MoCA - Montréal Cognitive Assessment, SSP - Spatial Span task, rho - correlation coefficient according to Pearson with respective p-value, c - correlation coefficient from multivariable model\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6973225/v1/278a10998fa019ba2e19d854.png"},{"id":85922808,"identity":"0b96ad5b-75a4-48fd-8f0c-842411ee49ee","added_by":"auto","created_at":"2025-07-03 08:19:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":167999,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePost-hoc model for the prediction of cognition at 1yFU \u003c/strong\u003ewith MoCA +/- backward span from the SSP. Panel A shows the predicted probability distribution of the MoCA score at 1yFU for a representative patient (#13) using the baseline MoCA score (29) together with the actual backward span (4), based on the sparse post-hoc model. Panels B and C demonstrate how this distribution shifts toward better or worse cognitive outcomes when simulating higher (7) or lower (2) backward span performance, respectively. Applying a MoCA cutoff of 26 points to differentiate between normal and impaired cognitive functioning, these simulations correspond to probabilities of 51% (backward span \u0026nbsp;= 4), 2% (backward span = 7), and 90% (backward span \u0026nbsp;= 2) that this patient would be classified as cognitively impaired at 1yFU, highlighting the strong influence of backward span performance on the predicted risk of postoperative impairment\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6973225/v1/af40f5fcde531cb3e654a1f4.png"},{"id":90344829,"identity":"67aeb281-b59a-4255-aa88-8c79624ff2ed","added_by":"auto","created_at":"2025-09-01 16:04:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1818622,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6973225/v1/c6e64dc0-9917-4773-a8cc-aa8d386d0a74.pdf"},{"id":85925181,"identity":"1f264166-a3e6-4f5a-a5fe-f3a34f75e18b","added_by":"auto","created_at":"2025-07-03 08:35:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14646,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-6973225/v1/95149719655055e8ef70a4a5.docx"},{"id":85922801,"identity":"9eb87d48-b206-4321-8c5e-abfb18454b99","added_by":"auto","created_at":"2025-07-03 08:19:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":32536,"visible":true,"origin":"","legend":"","description":"","filename":"Table12.docx","url":"https://assets-eu.researchsquare.com/files/rs-6973225/v1/3f9442b92269237df3771549.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting cognition after subthalamic Deep Brain Stimulation in Parkinson’s Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAmongst the multitude of non-motor symptoms in Parkinson\u0026rsquo;s disease (PD), cognitive deficits have the highest impact on quality of life \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Impairments range from no or minimal cognitive decline up to severe dementia (PD-D) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and estimation of cognitive trajectories is challenging \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Nevertheless, early and reliable prediction is essential for therapeutic decision making, especially when deep brain stimulation (DBS) surgery is evaluated as an escalation of therapy. PD patients that underwent DBS surgery can suffer from deficits in long-term memory, verbal fluency and executive functions \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The long-term incidence of PD-D in patients with DBS, however, is comparable to medical treatment and considered a part of PD rather than an effect of DBS \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Nevertheless, considerable and sustained cognitive deterioration after DBS surgery has been described in a small subset of patients \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePreexisting cognitive deficits and higher age are the main risk factors for cognitive decline following DBS surgery in PD \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. For an estimate of the cognitive trajectory of individual PD patients after DBS surgery, however, many aspects like the phenotype and course of PD, but also surgery- and stimulation-related aspects must be considered \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGeneral factors influencing cognition include education, nutritional status, comorbidities, inflammation, depression and apathy \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Decreased CSF Amyloid β and increased TAU levels have been shown to be predictive of more rapid and severe cognitive deterioration \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Serum neurofilament light chain (NfL) has good predictive value for cognitive decline in PD as well \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePD-specific factors potentially influencing cognition are disease duration, symptom severity, dopaminergic dose and impulsivity. Other than that, PD patients with heterozygote gene mutations encoding glucocerebrosidase (GBA) display a more aggressive phenotype with a higher prevalence and a faster progression to PD-D \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmongst anatomical characteristics, the volume of the cholinergic Ncl. basalis of Meynert (NBM) has been shown to be predictive of cognition in PD: Patients with lower-than-expected NBM volumes at baseline have a 3.5-fold greater risk of becoming cognitively impaired after five years \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This finding was extended by a retrospective voxel-based morphometry (VBM) study, which demonstrated that preoperative NBM volume also predicted cognitive decline one year after DBS \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePerioperative factors also increase the risk for complications such as postoperative delirium (POD), which in turn increases the risk of long-term cognitive decline \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Also, the length and depth of surgery and sedation have negative influences on postoperative cognitive outcomes \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo account for the multifaceted etiology of cognitive change, this prospective study comprehensively assessed PD patients prior to DBS surgery by detailed phenotyping regarding non-motor symptoms, imaging, dementia markers and genetics. These markers were used to predict cognitive outcomes after DBS aiming to improve clinical decision making and perioperative management for optimal long-term benefit.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe study was registered at clinicaltrials.gov (NCT03982953) and approved by the Charit\u0026eacute; ethics committee (EA2/040/19). TRIPOD guidelines were used for prediction model development and validation. Written informed consent was obtained from all patients. Study participation was proposed to all patients with the diagnosis of PD and the indication for bilateral DBS in the subthalamic nucleus (STN) after careful examination of eligibility for this treatment. Evaluation for the indication of DBS was conducted according to clinic-intern standard operation procedures that are in accordance with the guidelines for invasive therapies in PD \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The only exclusion criterium for study participation that did not apply with respect to a decision against DBS was a relevant language barrier. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e contains a study overview.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData from clinical routine\u003c/h2\u003e\n \u003cp\u003eWithin the clinical routine work-up during the evaluation process for DBS, the following clinical scales and questionnaires were applied and tested for their predictive value: Need of support in activities of daily living (ADL), symptom severity in PD motor and non-motor domains (MDS-UPDRS I-IV) including motor part MDS-UPDRS III with (MedON) and after wash-out of dopaminergic medication (MedOFF), quality of life (PDQ39), depression (BDI-II), apathy (SAS) and impulsivity (QUIP-RS). Motor predominance type, PD duration and levodopa equivalent daily doses (LEDD) were recorded. The genetic background of PD was assessed by testing for monogenetic causes of PD as well as susceptibility loci and genes \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eGeneral predictors of cognition\u003c/h3\u003e\n\u003cp\u003eIn addition to these clinical assessments, the following possible predictors of cognitive dysfunction after STN-DBS in PD were examined in the context of this prospective study: Years of education, nutritional status (BMI and MNA-SF). The burden of comorbidities (CKI) and the blood-based inflammation marker C-reactive protein (CRP) from the day before DBS surgery were assessed. In patients who agreed to undergo lumbar puncture (n\u0026thinsp;=\u0026thinsp;23), total and phospho TAU as well as Amyloid \u0026beta; 1\u0026ndash;40 and 1\u0026ndash;42 was measured in the CSF. NfL, a specific marker for neuronal cell damage, was quantified from blood serum and Z-scores were calculated according to age and BMI \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBesides the global cognitive screening Montr\u0026eacute;al Cognitive Assessment \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e(MoCA), an extensive tablet-based cognitive test battery (CANTAB Connect\u0026trade;) was applied prior to DBS surgery and at follow-up one year after surgery (1yFU). A description of the neuropsychological domains tested by different tests and their respective outcome measures can be found in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and in the supplementary material. Theory of Mind (ToM), the capacity to draw conclusions on other persons thoughts or emotions was investigated with the Yoni paradigm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e which includes an affective, cognitive and control condition. Outcomes measures are the percentages of correctly answered stimuli for each condition. Additionally, the number of domains rated as impaired in the standard neuropsychological test session during clinical evaluation for DBS surgery was assessed.\u003c/p\u003e\n\u003ch3\u003eImaging\u003c/h3\u003e\n\u003cp\u003eAs imaging biomarkers, volumes of the cholinergic basal forebrain, i.e. its main nucleus, the NBM, and surrounding nuclei (Ch1-3) were assessed using VBM as previously described \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Basis were the preoperative structural MRIs with 3 Tesla acquired with Siemens Magnetom Vida or Skyra scanners. The T1-weighted gradient echo sequence was analyzed with MATLAB (version R2022a) by means of the CAT12 toolbox, an extension to the software SPM12. As partial brain volumes depend on total intracranial volume (TIV), the ratios NBM/TIV and Ch1-3/TIV were used for further calculations.\u003c/p\u003e\n\u003ch3\u003ePerioperative procedures\u003c/h3\u003e\n\u003cp\u003ePerioperative procedures with a potential influence on cognitive outcomes were recorded. These include the duration of the perioperative pause of dopaminergic medication during electrode implantation (surgery 1) and doses of sedatives used during both surgeries (surgery 2 being the implantation of the impulse generator). Intraoperative sedation depth was measured with four EEG channels on the forehead (SedLine\u0026reg;). The power peak (averaged normalized power spectral density) and spectral edge frequency (SEF95) were calculated with EEGLAB \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e running in MATLAB (version R2022a) as described in \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Burst suppression patterns were manually extracted and set in relation to the overall duration of surgery.\u003c/p\u003e\n\u003cp\u003eAs postoperative delirium (POD) can lead to permanent cognitive deficits, screening for this complication was conducted with the CAM-ICU (Confusion Assessment Method for Intensive Care Unit) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e or Nu-DESC (Nursing Delirium Screening Scale) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e at non-ICU wards three times daily as previously described\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Delirium severity (mean score of days with positive POD screening) and duration were calculated.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analysis was carried out in IBM SPSS Statistics Version 28 and the sci-kit learn Version 1.3.0 in Python 3.9.1 \u003csup\u003e28\u003c/sup\u003e. Descriptive statistics are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Pre- and postoperative clinical scales and scores were compared by means of Wilcoxon signed-rank tests. The change in global cognition was calculated as the difference between MoCA points at 1yFU and baseline prior to surgery and will be referred to as \u0026ldquo;cognitive outcome\u0026rdquo; in the following.\u003c/p\u003e\n \u003cp\u003eThe relationships between the predictor variables and the cognitive outcome were first analyzed with unadjusted Spearman correlation coefficients rho. Second, a multivariable prediction model was estimated to test the predictive performance of the combined predictor variables and to obtain adjusted multivariable coefficients (see below for details of the modeling approach). The resulting univariable and multivariable coefficients provide readers with complementary information. The univariable correlation coefficients indicate how strongly cognition changes after surgery depending on a single predictor variable (a negative correlation with age, for example, would indicate that older patients who have undergone surgery experience a stronger decline in cognition). The multivariable coefficient, on the other hand, indicates how strongly cognition changes in relation to a predictor variable when the influence of the other variables included in the model has already been taken into account. Thus, if a negative coefficient for a neuropsychological test is found in an age-adjusted model, this means that there is a greater deterioration in patients who score high on this neuropsychological test \u003cem\u003efor their age\u003c/em\u003e, i.e., even when taking into account that older patients might generally score higher on this test. Both coefficients therefore provide complementary information and, as in previous studies \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, we report both to provide as comprehensive a picture of the relationships as possible.\u003c/p\u003e\n \u003cp\u003eFor the multivariable analysis, elastic net regularization\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e was used due to the high number and multicollinearity of assessed predictors. This machine learning analysis mitigates overfitting and increases the stability of coefficient estimates and thus increases the statistical power and generalization ability of the model. It is recommended as it offers several advantages compared to traditional regression analyses \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Predictors with missing data in \u0026gt;\u0026thinsp;50% of patients (CSF markers, NfL Z-scores, doses of Remifentanil and Fentanyl and burst suppression) were excluded from the multivariable analysis. To test the model\u0026rsquo;s predictive performance, leave-one-out cross-validation was conducted. In this approach, the model is repeatedly fitted to all but one patient (the training set) and is then applied to predict the cognitive outcome in the left out (test) patient. This procedure is repeated until all patients have been left out once. Hence, for each patient, there is a prediction of the cognitive outcome based on a model that was trained without using this patient\u0026rsquo;s data for training. The predictive performance of the model for test patients was assessed by calculating the mean absolute error as well as Spearman correlations between each patient\u0026rsquo;s predicted and true cognitive outcome at 1yFU. Each cross-validation fold comprised one-hot-encoding of categorical predictors, imputation of missing values using k-nearest-neighbors imputation as implemented in KNNImputer class of sklearn with default parameters\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, z-standardization of numerical variables, and training of the regression model in the training set. The hyperparameters of the elastic net regression model (regularization strength and ratio between L1 and L2 penalty) were optimized in a nested 20-fold cross-validation loop within each training set. A final multivariable model was fit with all patients and the hyperparameter set optimal in most cross-validation folds and its coefficients are reported as multivariable coefficients c.\u003c/p\u003e\n \u003cp\u003eFinally, based on these analyses, a concise post-hoc model to predict cognition one year after DBS surgery was developed and is proposed for clinical validation. To foster clinical applicability of this model, it only used the most predictive variable according to the multivariable analysis in addition to the preoperative MoCA baseline. The performance of this model was assessed using the coefficient of determination (R\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) and compared to a benchmark model using only preoperative MoCA baseline as a predictor in a leave-one-out cross-validation fashion.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy cohort\u003c/h2\u003e\n \u003cp\u003e62 PD patients evaluated as eligible for DBS surgery were included in the study. Five decided against invasive treatment.57 patients with PD received bilateral STN-DBS at our center between June 2019 and September 2021, 21 of them women (36.8%). As shown in the study flow diagram (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), three patients had to be explanted during the study, two due to an infection of the device within the first three months after implantation and one due to prolonged wound healing impairment. Another two patients dropped-out due to suicide and death of unknown cause. Two patients withdraw study consent, and four patients were lost to FU. Hence, the data of 46 of the 57 patients during the study period was available for 1yFU.\u003c/p\u003e\n \u003cp\u003eMean age of the cohort was 59.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2 and disease duration 10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3 years. The results of all scales and scores assessed at preoperative baseline are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eGeneral outcome parameters\u003c/h3\u003e\n\u003cp\u003eAs expected, patients improved significantly with DBS concerning motor symptoms (MDS-UPDRS III baseline MedOFF 53.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3, 1yFU StimON/MedOFF 27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7, 1yFU StimOFF/MedOFF 49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fluctuations (MDS-UPDRS IV baseline 7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7, 1yFU 2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LEDD reduction (baseline 1278.7\u0026thinsp;\u0026plusmn;\u0026thinsp;341.0, 1yFU 514.3\u0026thinsp;\u0026plusmn;\u0026thinsp;305.5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and need of support in ADL (baseline 15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4, 1yFU 12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1; p\u0026thinsp;=\u0026thinsp;0.020). UPDRS I (baseline 10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9, 1yFU 9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8; p\u0026thinsp;=\u0026thinsp;0.694), UPDRS II (baseline 11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5, 1yFU 11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7; p\u0026thinsp;=\u0026thinsp;0.285), nutritional status (MNA-SF baseline 12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0, 1yFU 12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7; p\u0026thinsp;=\u0026thinsp;0.377), quality of life (PDQ-39 PDSI baseline 32.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7, 1yFU 27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2; p\u0026thinsp;=\u0026thinsp;0.081), symptoms of depression (BDI-II baseline 12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0, 1yFU 13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7; p\u0026thinsp;=\u0026thinsp;0.938), apathy (SAS baseline 14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3, 1yFU 17.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5; p\u0026thinsp;=\u0026thinsp;0.409) and impulsivity (QUIP-RS baseline 8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4, 1yFU 6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3; p\u0026thinsp;=\u0026thinsp;0.378) did not show significant differences between preoperative and 1yFU assessment. There was no significant MoCA change postoperatively in comparison to the preoperative baseline (baseline 25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0, 1yFU 25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6; p\u0026thinsp;=\u0026thinsp;0.285) but the cognitive outcome varied between patients.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eResults from multi- and univariable analysis\u003c/h2\u003e\n \u003cp\u003eThe multivariable analysis yielded a significant correlation between the true and predicted cognitive outcome (MoCA score difference at 1yFU in comparison to baseline, rho\u0026thinsp;=\u0026thinsp;0.376, p\u0026thinsp;=\u0026thinsp;0.010, mean absolute error\u0026thinsp;=\u0026thinsp;2.200). All results from the multivariable (coefficients c) and univariable analyses (coefficients rho and respective p-values) can be found in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eVisuospatial memory and working memory as measured by the backward span of the SSP, was a strong predictor of the cognitive outcome and was the only predictor that was associated with cognitive change one year after surgery in both the univariable and the multivariable analysis (rho\u0026thinsp;=\u0026thinsp;0.499, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001**; c\u0026thinsp;=\u0026thinsp;0.302). Scatterplots of these correlations can be found in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eHigher \u0026szlig; Amyloid \u0026beta; 1\u0026ndash;42 / 1\u0026ndash;40 and Amyloid \u0026beta; 1\u0026ndash;42 / total TAU ratios were associated with better cognitive outcomes in the univariable model (rho\u0026thinsp;=\u0026thinsp;0.455, p\u0026thinsp;=\u0026thinsp;0.044* and rho\u0026thinsp;=\u0026thinsp;0.501, p\u0026thinsp;=\u0026thinsp;0.048* respectively).\u003c/p\u003e\n \u003cp\u003eThe inflammation level measured by means of the preoperative CRP from blood serum was negatively associated with the cognitive outcome one year after surgery in the multivariable analysis (rho=-0.125, p\u0026thinsp;=\u0026thinsp;0.408, c=-0.068). In terms of PD-specific predictors, the severity of fluctuations assessed by the MDS-UPDS IV was related to worse cognitive outcomes in the multivariable analysis (rho=-0,148, p\u0026thinsp;=\u0026thinsp;0.344; c=-0.110). In addition to the highly predictive backward span, the number of deteriorated cognitive domains in the classic neuropsychology test set (rho=-0.163, p\u0026thinsp;=\u0026thinsp;0.286; c=-0.196), the length of the forward spatial span of the SSP testing visuospatial memory (rho\u0026thinsp;=\u0026thinsp;0.283, p\u0026thinsp;=\u0026thinsp;0.059; c\u0026thinsp;=\u0026thinsp;0.042) and lower multitasking time (rho=-0.190, p\u0026thinsp;=\u0026thinsp;0.206; c=-0.144) as well as higher incongruency costs (rho\u0026thinsp;=\u0026thinsp;0.102, p\u0026thinsp;=\u0026thinsp;0.499; c\u0026thinsp;=\u0026thinsp;0.012) for conflicting stimuli in the MTT trials were associated with better cognitive outcomes in the multivariable analysis. Finally, of the perioperatively assessed data, the duration of POD was associated with a negative cognitive outcome in the multivariable analysis (rho=-0.117, p\u0026thinsp;=\u0026thinsp;0.439; c=-0.211).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003ePost-hoc model\u003c/h2\u003e\n \u003cp\u003eThe backward span of the SSP emerged as the strongest predictor of the cognitive outcome in the multivariable analysis and also showed a statistically significant univariable correlation with the cognitive outcome, suggesting that it is a strong predictor of cognitive outcome at 1yFU. To design a sparse and clinically applicable model for prospective evaluation, we performed a post-hoc analysis predicting the MoCA score at 1yFU using only the backward span and the preoperative MoCA score. The benchmark model including only the preoperative MoCA explained 27% of the variance in 1yFU-MoCA (R\u0026sup2; = 0.27) in leave-one-out cross-validation. Adding the backward span of the SSP substantially improved model performance, increasing the explained variance to 50% (R\u0026sup2; = 0.50) with a mean absolute error of 1.9 points. For clinical applicability, we rounded the multivariable coefficients to one decimal place yielding the following simplified linear model:\u003c/p\u003e\n \u003cp\u003eMoCA\u003csub\u003e₁yFU\u003c/sub\u003e = 0.7 \u0026times; MoCA\u003csub\u003ebaseline\u003c/sub\u003e + 1.7 \u0026times; backward span\u003csub\u003ebaseline\u003c/sub\u003e \u0026minus;\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the clinical relevance of the model.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective cohort study, we found that in PD, the most robust predictor for the cognitive outcome one year after DBS surgery was the backward span. This test assesses visuospatial memory in combination with working memory, a set of skills is mainly based on temporal and posterior cortical structures with predominantly cholinergic underpinning \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Consistent with our results, a retrospective study in PD patients found a trend towards less favorable functional outcomes after six to 12 months in patients with visuospatial impairment before DBS surgery \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Preoperative deficits in visuospatial skills, verbal memory and language processing were also linked to cognitive complaints after DBS surgery reported by patients and/or their caregivers \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. However, we could not reproduce an influence of other classical cholinergic domains like learning and memory on postoperative cognition our study. In the case of the backward span, the memorized spatial sequence must additionally be manipulated in working memory recruiting fronto-striatal circuitry which suggests an additional involvement of dopaminergic structures. Also in a recent review, both executive dysfunction and memory deficits, present prior to surgery were associated with worse cognitive outcomes after STN-DBS in PD patients \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Mana and colleagues, on the other hand, have shown executive dysfunctions to be most predictive of cognitive decline after DBS-surgery in PD in their recently published retrospective study \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHigher Amyloid β 1\u0026ndash;42 / 1\u0026ndash;40 and Amyloid β 1\u0026ndash;42 / total TAU ratios from the CSF were associated with better cognitive outcomes. This constellation is known to be predictive for the development of Alzheimer\u0026rsquo;s disease and has lately also been studied in PD. This is the first study investigating the cognitive prognosis in PD patients after DBS taking into consideration biomarker profiles from CSF. In patients willing to undergo lumbar puncture, these findings could help refine estimation of the cognitive trajectory.\u003c/p\u003e \u003cp\u003eOther predictors also contributed significantly to the main multivariable model stressing the multifaceted influences on cognition in PD: The preoperative serum inflammation level, severity of motor fluctuations, deterioration in the classic neuropsychology test set and multitasking time were negatively associated whereas the forward spatial span and multitasking incongruency costs were positively associated with cognition one year after surgery.\u003c/p\u003e \u003cp\u003ePOD duration was also associated with a negative cognitive outcome one year after surgery in the multivariable analysis. Although, in a strict sense, POD is not a true predictor as it occurs only postoperatively, this finding strengthens the importance of POD for the long-term cognitive outcome. As both cognitive deterioration and POD share most of their risk factors, there is a logic connection but we are the first to show that in a prospective study. [24]\u003c/p\u003e \u003cp\u003eOur cohort is representative for PD patients undergoing DBS surgery by age, disease duration, response to DBS etc. However, neither age nor the burden of comorbidities showed a significant impact on the cognitive outcome in our cohort. As more and more elderly PD patients with comorbidities ask for DBS, this finding stresses the importance of valid prediction models for the cognitive outcome. The missing effect of the genetic background on the cognitive outcome after DBS could be due to the small sample size (6 patients with GBA-mutations) and should be interpreted with caution.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePost-hoc model\u003c/h2\u003e \u003cp\u003eTo facilitate clinical application, we derived a practical and sparse post-hoc model using only the baseline MoCA score and the backward span from the SSP as predictors of the cognitive outcome one year after DBS. These two tests are quick to administer (approximately 10 and 4 minutes, respectively) and do not require medical staff, making them highly feasible for routine use. The resulting linear model showed good predictive performance and can be applied as follows:\u003c/p\u003e \u003cp\u003ePredicted MoCA at 1yFU\u0026thinsp;=\u0026thinsp;0.7 \u0026times; MoCA\u0026thinsp;+\u0026thinsp;1.7 \u0026times; backward span \u0026minus;\u0026thinsp;1 at baseline.\u003c/p\u003e \u003cp\u003eWhile this model needs to be validated in a prospective multicenter setting, it represents a promising first step to improve individualized risk stratification of PD patients undergoing DBS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study must be discussed in the light of its limitations: Cognition is complex and multifaceted, more so in PD after STN-DBS [39]. It has been stressed that only a prospective assessment of combined predictors can substantially add to the existing literature \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Therefore, we assessed many influencing factors as we are convinced that a reliable impression on the predictors of cognitive change can only be achieved if all the available evidence is considered. To overcome the resulting shortcoming of the disproportion between patients and possible predictors, a sophisticated statistical design was applied to increase the reliability of the study results. However, some variables could not be obtained from all patients and were subsequently excluded from the multivariable analysis and therefore have a low statistical validity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eOutlook\u003c/h2\u003e \u003cp\u003eOur results are expected to support evidence-based and personalized decision-making when advising PD patients considering STN-DBS. As a next step, our proposed slimmed down prediction model based on the MoCA and backward span of the SSP must be validated. Especially in combination with dementia markers from the CSF, the backward span has the potential as a fast and easily applicable predictor. In the future, we hope to contribute to the development of hypothesis-driven interventional trials, for example on preventive strategies like cognitive training, with the goal to achieve optimal outcomes for every individual patient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData sharing\u003c/h2\u003e \u003cp\u003eDue to data privacy restrictions, patient datasets are not publicly available but can be provided by the corresponding author upon reasonable request.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAuthor Roles\u003c/b\u003e \u003c/p\u003e \u003cp\u003e1) Conception and design of the study: DKW, HS, CS, FB, AAK\u003c/p\u003e \u003cp\u003e2) Acquisition and analysis of data: DKW, MA, LMR, EL, GHS, KF, PK, JR, SH, MM, BAF, CS, FB\u003c/p\u003e \u003cp\u003e3) Drafting the manuscript or figures: DKW, HS, JR, SH, BAF, AAK\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSF-Mini Nutritional Index-short form\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eII-Beck Depression Inventory-version 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStarkstein Apathy Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUPDRS-Unified Parkinson\u0026rsquo;s Disease Rating Scale by the Movement Disorder Society\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePDQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e39-Parkinson\u0026rsquo;s Disease Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePDSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParkinson's Disease Summary Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQUIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRS-Questionnaire for Impulsive-Compulsive Disorders in Parkinson\u0026rsquo;s Disease-Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMOT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMotor Screening Task\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRTI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReaction Time test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePattern Recognition Memory test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePaired Associates Learning task\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpatial Span task\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVRM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e Verbal Recognition Memory test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultitasking Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSWM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpatial Working Memory test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eERT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEmotion Recognition task\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNu\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDESC-Nursing Delirium Screening Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Roles 1) Conception and design of the study: DKW, HS, CS, FB, AAK2) Acquisition and analysis of data: DKW, MA, LMR, EL, GHS, KF, PK, JR, SH, MM, BAF, CS, FB3) Drafting the manuscript or figures: DKW, HS, JR, SH, BAF, AAK\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData sharing Due to data privacy restrictions, patient datasets are not publicly available but can be provided by the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLawson, R. 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Neurol.\u003c/em\u003e 29, 2580\u0026ndash;2595 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkrahina, V. \u003cem\u003eet al.\u003c/em\u003e The Rostock International Parkinson\u0026rsquo;s Disease (ROPAD) Study: Protocol and Initial Findings. \u003cem\u003eMov. Disord.\u003c/em\u003e 36, 1005\u0026ndash;1010 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenkert, P. \u003cem\u003eet al.\u003c/em\u003e Serum neurofilament light chain for individual prognostication of disease activity in people with multiple sclerosis: a retrospective modelling and validation study. \u003cem\u003eLancet Neurol.\u003c/em\u003e 21, 246\u0026ndash;257 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026uuml;lke, E. \u003cem\u003eet al.\u003c/em\u003e Comparison of Montreal cognitive assessment and Mattis dementia rating scale in the preoperative evaluation of subthalamic stimulation in Parkinson\u0026rsquo;s disease. \u003cem\u003ePLoS ONE\u003c/em\u003e 17, e0265314 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBodden, M. 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Notfallmed Schmerzther\u003c/em\u003e 43, 98\u0026ndash;102 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedregosa, F. \u003cem\u003eet al.\u003c/em\u003e Scikit-learn: Machine Learning in Python. \u003cem\u003eJournal of Machine Learning Research\u003c/em\u003e 12, 2825\u0026thinsp;\u0026ndash;\u0026thinsp;2830 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrandt, L. \u003cem\u003eet al.\u003c/em\u003e Predicting psychotic relapse following randomised discontinuation of paliperidone in individuals with schizophrenia or schizoaffective disorder: an individual participant data analysis. \u003cem\u003eLancet Psychiatry\u003c/em\u003e 10, 184\u0026ndash;196 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou, H. \u0026amp; Hastie, T. Regularization and variable selection via the elastic net. \u003cem\u003eJ. R. Stat. Soc.: Ser. B (Stat. Methodol.)\u003c/em\u003e 67, 301\u0026ndash;320 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabyak, M. A. What You See May Not Be What You Get: A Brief, Nontechnical Introduction to Overfitting in Regression-Type Models. \u003cem\u003ePsychosom. Med.\u003c/em\u003e 66, 411\u0026ndash;421 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTroyanskaya, O. \u003cem\u003eet al.\u003c/em\u003e Missing value estimation methods for DNA microarrays. \u003cem\u003eBioinformatics\u003c/em\u003e 17, 520\u0026ndash;525 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKehagia, A. A., Barker, R. A. \u0026amp; Robbins, T. W. Cognitive Impairment in Parkinson\u0026rsquo;s Disease: The Dual Syndrome Hypothesis. \u003cem\u003eNeurodegener. Dis.\u003c/em\u003e 11, 79\u0026ndash;92 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbboud, H. \u003cem\u003eet al.\u003c/em\u003e Impact of mild cognitive impairment on outcome following deep brain stimulation surgery for Parkinson\u0026rsquo;s disease. \u003cem\u003ePark. Relat. Disord.\u003c/em\u003e 21, 249\u0026ndash;253 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMills, K. A. \u003cem\u003eet al.\u003c/em\u003e Neuropsychological predictors of patient-reported cognitive decline after deep brain stimulation in Parkinson\u0026rsquo;s disease. \u003cem\u003eJ. Clin. Exp. Neuropsychol.\u003c/em\u003e 41, 219\u0026ndash;228 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJahanshahi, M., Leimbach, F. \u0026amp; Rawji, V. Short and Long-Term Cognitive Effects of Subthalamic Deep Brain Stimulation in Parkinson\u0026rsquo;s Disease and Identification of Relevant Factors. \u003cem\u003eJ. Park.\u0026rsquo;s Dis.\u003c/em\u003e 12, 2191\u0026ndash;2209 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMana, J. \u003cem\u003eet al.\u003c/em\u003e Preoperative cognitive profile predictive of cognitive decline after subthalamic deep brain stimulation in Parkinson\u0026rsquo;s disease. \u003cem\u003eEur. J. Neurosci.\u003c/em\u003e 60, 5764\u0026ndash;5784 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTr\u0026ouml;ster, A. I. Developments in the prediction of cognitive changes following deep brain stimulation in persons with Parkinson\u0026rsquo;s disease. \u003cem\u003eExpert Rev. Neurother.\u003c/em\u003e 24, 643\u0026ndash;659 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\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":"","lastPublishedDoi":"10.21203/rs.3.rs-6973225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6973225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCognitive deficits have a high impact on quality of life of patients with Parkinson\u0026rsquo;s disease (PD). Age\u0026thinsp;\u0026gt;\u0026thinsp;70 years and dementia are considered risk factors for cognitive decline after deep brain stimulation (DBS) but evidence on the highly relevant individual cognitive prognosis is scarce. This study takes into account the multifaceted etiology of cognition in order to refine estimation of the cognitive outcome after DBS surgery in PD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eClinical, neuropsychological, perioperative, neuroimaging- and laboratory-based risk factors for cognitive dysfunction were prospectively assessed prior to DBS surgery in 57 patients with PD (21 female; age 60.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2; disease duration 10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 years). In addition to univariable correlations, elastic net regularized regression and leave-one-out cross-validation were used to fit a multivariable model with the Montr\u0026eacute;al Cognitive Assessment (MoCA) one year after surgery as primary outcome.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf all assessed possible predictors, the backward span of the SSP had the most robust association with the cognitive outcome (rho\u0026thinsp;=\u0026thinsp;0.499, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001**; c\u0026thinsp;=\u0026thinsp;0.302). Other factors significantly associated with cognition after DBS surgery were CSF dementia markers, serum C-reactive protein, severity of motor fluctuations, the number of impaired cognitive domains, forward spatial span length, multitasking performance and the duration of postoperative delirium. Based on our multivariable model results, we propose a post-hoc prediction model for cognition based on the baseline MoCA and backward span (R\u0026sup2; = 0.50).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings highlight the multifaceted influencing factors on the cognitive outcome after DBS. After clinical validation, our short and easily applicable prediction model could improve informed therapeutic decision making.\u003c/p\u003e","manuscriptTitle":"Predicting cognition after subthalamic Deep Brain Stimulation in Parkinson’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 08:19:40","doi":"10.21203/rs.3.rs-6973225/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-13T07:02:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-12T08:44:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-07T22:09:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318362304044179279254228472897707590490","date":"2025-07-02T09:45:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211493773579209180479000203638634464009","date":"2025-07-02T09:12:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-29T18:48:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-29T16:37:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-29T11:44:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Parkinson's Disease","date":"2025-06-25T09:42:37+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":"296ac104-9d08-43d7-afa4-fa2189181215","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50767872,"name":"Biological sciences/Neuroscience"},{"id":50767873,"name":"Biological sciences/Psychology"},{"id":50767874,"name":"Health sciences/Medical research"},{"id":50767875,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2025-09-01T15:59:15+00:00","versionOfRecord":{"articleIdentity":"rs-6973225","link":"https://doi.org/10.1038/s41531-025-01128-3","journal":{"identity":"npj-parkinsons-disease","isVorOnly":false,"title":"npj Parkinson's Disease"},"publishedOn":"2025-08-28 15:57:02","publishedOnDateReadable":"August 28th, 2025"},"versionCreatedAt":"2025-07-03 08:19:40","video":"","vorDoi":"10.1038/s41531-025-01128-3","vorDoiUrl":"https://doi.org/10.1038/s41531-025-01128-3","workflowStages":[]},"version":"v1","identity":"rs-6973225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6973225","identity":"rs-6973225","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}