{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T23:42:37Z","timestamp":1794440557937,"version":"4.9.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Petlier Foundation"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Neurodyn"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Cognitive impairment is one of the most functionally debilitating non-motor symptoms in Parkinson's disease (PD). Yet, current diagnostic and clinical practices rely heavily on subjective assessments and burdensome behavioral testing, which lack the temporal resolution to detect subtle deficits. While EEG and pupillometry offer promising non-invasive insights into cognitive processing, more scalable and objective methods are needed to detect subtle and often elusive dysfunction in early PD. Machine learning (ML) leverages patterns in neurophysiological signals recorded during cognitive tasks to identify subtle impairments that may not be evident in standard evaluations. We recorded EEG and pupillometry data from 68 participants (35 PD, 33 healthy controls (HC)) during a visual Change Detection working memory task. Using our custom, standardized feature-extraction toolbox, we extracted 108 features and incorporated the resulting feature matrices into an ML pipeline. We applied SMOTE to balance the classes, then used principal component analysis (PCA) to reduce dimensionality. An automated elbow method identified optimal cutoffs for principal components (PCs) and original features, which guided subsequent recursive elimination (RE) for computational efficiency. We trained a support vector machine with a radial basis function kernel (SVM-RBF) to classify PD and evaluated the final model's performance on a hold-out dataset. The automated elbow method identified optimal cutoffs at 20 PCs and 22 original features. The RE revealed that a model using 14 PCs and the top 7 PCA-weighted features achieved the highest performance, with 71% accuracy and an F1 score of 0.701. The classifier's performance declined slightly when tested on held-out data, with an accuracy of 63% and an F1 score of 0.626. This study demonstrates the potential of a task-aligned ML pipeline for uncovering early cognitive markers of PD. The framework supports post-hoc feature contribution mapping, linking retained physiological features and task stages back to classification-relevant patterns, supporting mechanistic investigation of cognitive dysfunction in PD.<\/jats:p>","DOI":"10.1007\/s11571-026-10526-z","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T02:40:53Z","timestamp":1786588853000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A task-aligned multimodal machine learning framework for studying working memory dysfunction in Parkinson\u2019s disease"],"prefix":"10.1007","volume":"20","author":[{"given":"Mercedes","family":"Terry","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel A.","family":"Birkholz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffery S.","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jau-Shin","family":"Lou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asenath X.","family":"Heuther","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"Keller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enrique Alvarez","family":"Vazquez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"issue":"1","key":"10526_CR1","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-021-00280-3","volume":"7","author":"D Aarsland","year":"2021","unstructured":"Aarsland D et al (2021) Parkinson disease-associated cognitive impairment. Nat Rev Dis Primers 7(1):47. https:\/\/doi.org\/10.1038\/s41572-021-00280-3","journal-title":"Nat Rev Dis Primers"},{"issue":"5","key":"10526_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12051033","volume":"12","author":"M Aljalal","year":"2022","unstructured":"Aljalal M, Aldosari SA, AlSharabi K, Abdurraqeeb AM, Alturki FA (2022) Parkinson\u2019s disease detection from resting-state EEG signals using common spatial pattern, entropy, and machine learning techniques. Diagnostics (Basel) 12(5):1033. https:\/\/doi.org\/10.3390\/diagnostics12051033","journal-title":"Diagnostics (Basel)"},{"issue":"5","key":"10526_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0303644","volume":"19","author":"C Altham","year":"2024","unstructured":"Altham C, Zhang H, Pereira E (2024) Machine learning for the detection and diagnosis of cognitive impairment in Parkinson\u2019s disease: a systematic review. PLoS ONE 19(5):e0303644. https:\/\/doi.org\/10.1371\/journal.pone.0303644","journal-title":"PLoS ONE"},{"key":"10526_CR4","doi-asserted-by":"publisher","DOI":"10.1038\/s41531-023-00602-0","author":"MF Anjum","year":"2024","unstructured":"Anjum MF et al (2024) Resting-state EEG measures cognitive impairment in Parkinson\u2019s disease. Npj Parkinsons Dis. https:\/\/doi.org\/10.1038\/s41531-023-00602-0","journal-title":"Npj Parkinsons Dis"},{"issue":"10","key":"10526_CR5","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1007\/s00702-017-1758-z","volume":"124","author":"T Annanmaki","year":"2017","unstructured":"Annanmaki T, Palmu K, Murros K, Partanen J (2017) Altered N100-potential associates with working memory impairment in Parkinson\u2019s disease. J Neural Transm (Vienna) 124(10):1197\u20131203. https:\/\/doi.org\/10.1007\/s00702-017-1758-z","journal-title":"J Neural Transm (Vienna)"},{"issue":"6","key":"10526_CR6","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1001\/jama.2019.22360","volume":"323","author":"MJ Armstrong","year":"2020","unstructured":"Armstrong MJ, Okun MS (2020) Diagnosis and treatment of Parkinson disease: a review. JAMA 323(6):548\u2013560. https:\/\/doi.org\/10.1001\/jama.2019.22360","journal-title":"JAMA"},{"issue":"5044","key":"10526_CR7","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1126\/science.1736359","volume":"255","author":"A Baddeley","year":"1992","unstructured":"Baddeley A (1992) Working memory. Science 255(5044):556\u2013559. https:\/\/doi.org\/10.1126\/science.1736359","journal-title":"Science"},{"key":"10526_CR8","doi-asserted-by":"publisher","unstructured":"Barnett, A. J. et al. (2024)\u201cImproving clinician performance in classifying EEG Patterns on the Ictal-Interictal Injury Continuum Using Interpretable Machine Learning,\u201d NEJM AI, https:\/\/doi.org\/10.1056\/aioa2300331","DOI":"10.1056\/aioa2300331"},{"key":"10526_CR9","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2017.00297","author":"PA Beach","year":"2017","unstructured":"Beach PA, Huck JT, Zhu DCD, Bozoki AC (2017) Altered behavioral and autonomic pain responses in Alzheimer\u2019s disease are associated with dysfunctional affective, self-reflective and salience network resting-state connectivity. Front Aging Neurosci. https:\/\/doi.org\/10.3389\/fnagi.2017.00297","journal-title":"Front Aging Neurosci"},{"issue":"6","key":"10526_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15060773","volume":"15","author":"S Bera","year":"2025","unstructured":"Bera S, Geem ZW, Cho Y-I, Singh PK (2025) A comparative study of machine learning and deep learning models for automatic Parkinson\u2019s disease detection from electroencephalogram signals. Diagnostics (Basel) 15(6):773. https:\/\/doi.org\/10.3390\/diagnostics15060773","journal-title":"Diagnostics (Basel)"},{"key":"10526_CR11","unstructured":"S. Birkholz, \u201cAttention and Memory Function in Healthy Adults and Adults With Parkinson\u2019s Disease - ProQuest,\u201d North Dakota State University, 2024. Accessed: Jul. 11, 2025. [Online]. Available: https:\/\/www.proquest.com\/openview\/3c6fa3478827442006c780ce2064f7d2\/1?pq-origsite=gscholar&cbl=18750&diss=y"},{"key":"10526_CR12","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-14-106","volume":"14","author":"R Blagus","year":"2013","unstructured":"Blagus R, Lusa L (2013) SMOTE for high-dimensional class-imbalanced data. BMC Bioinformatics 14:106. https:\/\/doi.org\/10.1186\/1471-2105-14-106","journal-title":"BMC Bioinformatics"},{"issue":"6","key":"10526_CR13","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1016\/j.neuropsychologia.2004.08.010","volume":"43","author":"H Bokura","year":"2005","unstructured":"Bokura H, Yamaguchi S, Kobayashi S (2005) Event-related potentials for response inhibition in Parkinson\u2019s disease. Neuropsychologia 43(6):967\u2013975. https:\/\/doi.org\/10.1016\/j.neuropsychologia.2004.08.010","journal-title":"Neuropsychologia"},{"key":"10526_CR14","doi-asserted-by":"publisher","unstructured":"M. Borys et al. (2017)\u201cAn analysis of eye-tracking and electroencephalography data for cognitive load measurement during arithmetic tasks,\u201d in 2017 10th International Symposium on Advanced Topics in Electrical Engineering (ATEE), , pp. 287\u2013292. https:\/\/doi.org\/10.1109\/ATEE.2017.7905130.","DOI":"10.1109\/ATEE.2017.7905130"},{"issue":"10","key":"10526_CR15","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1016\/j.neuron.2023.03.021","volume":"111","author":"M Carceles-Cordon","year":"2023","unstructured":"Carceles-Cordon M, Weintraub D, Chen-Plotkin AS (2023) Cognitive heterogeneity in Parkinson\u2019s disease: a mechanistic view. Neuron 111(10):1531\u20131546. https:\/\/doi.org\/10.1016\/j.neuron.2023.03.021","journal-title":"Neuron"},{"issue":"2","key":"10526_CR16","doi-asserted-by":"publisher","DOI":"10.2217\/fnl-2018-0048","volume":"14","author":"JA Carmona Arroyave","year":"2019","unstructured":"Carmona Arroyave JA et al (2019) Resting functional connectivity and mild cognitive impairment in Parkinson\u2019s disease. An electroencephalogram study. Future Neurol 14(2):FNL18. https:\/\/doi.org\/10.2217\/fnl-2018-0048","journal-title":"Future Neurol"},{"issue":"1","key":"10526_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-43132-8","volume":"13","author":"P Demirayak","year":"2023","unstructured":"Demirayak P, K\u0131y\u0131 \u0130, \u0130\u015fbitiren Y\u00d6, Yener G (2023) Cognitive load associates prolonged P300 latency during target stimulus processing in individuals with mild cognitive impairment. Sci Rep 13(1):15956. https:\/\/doi.org\/10.1038\/s41598-023-43132-8","journal-title":"Sci Rep"},{"issue":"12","key":"10526_CR18","doi-asserted-by":"publisher","first-page":"5727","DOI":"10.1093\/cercor\/bhx242","volume":"27","author":"SJ Fallon","year":"2017","unstructured":"Fallon SJ, Mattiesing RM, Muhammed K, Manohar S, Husain M (2017) Fractionating the neurocognitive mechanisms underlying working memory: independent effects of dopamine and Parkinson\u2019s disease. Cereb Cortex 27(12):5727\u20135738. https:\/\/doi.org\/10.1093\/cercor\/bhx242","journal-title":"Cereb Cortex"},{"key":"10526_CR19","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.gaitpost.2024.06.001","volume":"113","author":"JR Fang","year":"2024","unstructured":"Fang JR, Pahwa R, Lyons KE, Zanotto T, Sosnoff JJ (2024) Examining the validity of smart glasses in measuring spatiotemporal parameters of gait among people with Parkinson\u2019s disease. Gait Posture 113:139\u2013144. https:\/\/doi.org\/10.1016\/j.gaitpost.2024.06.001","journal-title":"Gait Posture"},{"issue":"1","key":"10526_CR20","doi-asserted-by":"publisher","DOI":"10.1136\/bmjno-2024-000953","volume":"7","author":"K Gerner","year":"2025","unstructured":"Gerner K et al (2025) Visual processing capacity and cognitive decline in Parkinson\u2019s disease. BMJ Neurol Open 7(1):e000953. https:\/\/doi.org\/10.1136\/bmjno-2024-000953","journal-title":"BMJ Neurol Open"},{"issue":"3","key":"10526_CR21","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1016\/j.ejor.2017.08.040","volume":"265","author":"B Ghaddar","year":"2018","unstructured":"Ghaddar B, Naoum-Sawaya J (2018) High dimensional data classification and feature selection using support vector machines. Eur J Oper Res 265(3):993\u20131004. https:\/\/doi.org\/10.1016\/j.ejor.2017.08.040","journal-title":"Eur J Oper Res"},{"key":"10526_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2017.00616","volume":"11","author":"S Grissmann","year":"2017","unstructured":"Grissmann S, Faller J, Scharinger C, Sp\u00fcler M, Gerjets P (2017) Electroencephalography based analysis of working memory load and affective valence in an N-back task with emotional stimuli. Front Hum Neurosci 11:616. https:\/\/doi.org\/10.3389\/fnhum.2017.00616","journal-title":"Front Hum Neurosci"},{"key":"10526_CR23","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3389\/fnagi.2018.00090","volume":"10","author":"M Kahya","year":"2018","unstructured":"Kahya M, Moon S, Lyons KE, Pahwa R, Akinwuntan AE, Devos H (2018) Pupillary response to cognitive demand in Parkinson\u2019s disease: a pilot study. Front Aging Neurosci 10:90. https:\/\/doi.org\/10.3389\/fnagi.2018.00090","journal-title":"Front Aging Neurosci"},{"issue":"12","key":"10526_CR24","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.tics.2012.10.007","volume":"16","author":"W Klimesch","year":"2012","unstructured":"Klimesch W (2012) Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci 16(12):606\u2013617. https:\/\/doi.org\/10.1016\/j.tics.2012.10.007","journal-title":"Trends Cogn Sci"},{"key":"10526_CR25","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.parkreldis.2016.04.009","volume":"27","author":"RA Lawson","year":"2016","unstructured":"Lawson RA et al (2016) Cognitive decline and quality of life in incident Parkinson\u2019s disease: the role of attention. Parkinsonism Relat Disord 27:47\u201353. https:\/\/doi.org\/10.1016\/j.parkreldis.2016.04.009","journal-title":"Parkinsonism Relat Disord"},{"issue":"9","key":"10526_CR26","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.1093\/brain\/awq197","volume":"133","author":"E-Y Lee","year":"2010","unstructured":"Lee E-Y, Cowan N, Vogel EK, Rolan T, Valle-Incl\u00e1n F, Hackley SA (2010) Visual working memory deficits in patients with Parkinson\u2019s disease are due to both reduced storage capacity and impaired ability to filter out irrelevant information. Brain 133(9):2677\u20132689. https:\/\/doi.org\/10.1093\/brain\/awq197","journal-title":"Brain"},{"issue":"10","key":"10526_CR27","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1145\/3233231","volume":"61","author":"ZC Lipton","year":"2018","unstructured":"Lipton ZC (2018) The mythos of model interpretability. Commun ACM 61(10):36\u201343. https:\/\/doi.org\/10.1145\/3233231","journal-title":"Commun ACM"},{"issue":"3","key":"10526_CR28","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1002\/mds.24893","volume":"27","author":"I Litvan","year":"2012","unstructured":"Litvan I et al (2012) Diagnostic criteria for mild cognitive impairment in Parkinson\u2019s disease: Movement disorder society task force guidelines. Mov Disord 27(3):349\u2013356. https:\/\/doi.org\/10.1002\/mds.24893","journal-title":"Mov Disord"},{"key":"10526_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2024.105795","volume":"164","author":"G Marsicano","year":"2024","unstructured":"Marsicano G, Bertini C, Ronconi L (2024) Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data. Neurosci Biobehav Rev 164:105795. https:\/\/doi.org\/10.1016\/j.neubiorev.2024.105795","journal-title":"Neurosci Biobehav Rev"},{"key":"10526_CR30","doi-asserted-by":"publisher","first-page":"10927","DOI":"10.1007\/s00521-018-3689-5","volume":"32","author":"SL Oh","year":"2018","unstructured":"Oh SL et al (2018) A deep learning approach for Parkinson\u2019s disease diagnosis from EEG signals. Neural Comput Appl 32:10927\u201310933. https:\/\/doi.org\/10.1007\/s00521-018-3689-5","journal-title":"Neural Comput Appl"},{"issue":"4","key":"10526_CR31","doi-asserted-by":"publisher","first-page":"369","DOI":"10.3758\/BF03210419","volume":"44","author":"H Pashler","year":"1988","unstructured":"Pashler H (1988) Familiarity and visual change detection. Percept Psychophys 44(4):369\u2013378. https:\/\/doi.org\/10.3758\/BF03210419","journal-title":"Percept Psychophys"},{"key":"10526_CR32","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2020.572037","author":"J Salmi","year":"2020","unstructured":"Salmi J, Ritakallio L, Fellman D, Ellfolk U, Rinne JO, Laine M (2020) Disentangling the role of working memory in Parkinson\u2019s disease. Front Aging Neurosci. https:\/\/doi.org\/10.3389\/fnagi.2020.572037","journal-title":"Front Aging Neurosci"},{"issue":"1","key":"10526_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41531-025-01025-9","volume":"11","author":"S Shokrpour","year":"2025","unstructured":"Shokrpour S, MoghadamFarid A, Bazzaz Abkenar S, Haghi Kashani M, Akbari M, Sarvizadeh M (2025) Machine learning for Parkinson\u2019s disease: a comprehensive review of datasets, algorithms, and challenges. Npj Parkinsons Dis 11(1):187. https:\/\/doi.org\/10.1038\/s41531-025-01025-9","journal-title":"Npj Parkinsons Dis"},{"key":"10526_CR34","doi-asserted-by":"publisher","first-page":"13813","DOI":"10.1038\/s41598-024-64004-9","volume":"14","author":"S Srinivasan","year":"2024","unstructured":"Srinivasan S, Ramadass P, Mathivanan SK, Panneer Selvam K, Shivahare BD, Shah MA (2024) Detection of Parkinson disease using multiclass machine learning approach. Sci Rep 14:13813. https:\/\/doi.org\/10.1038\/s41598-024-64004-9","journal-title":"Sci Rep"},{"key":"10526_CR35","doi-asserted-by":"crossref","unstructured":"Terry, M., Birkholz, S., Lou, J.-S., Huether, A., and Keller, J., (2026) \u201cA toolbox for task-based EEG and pupillometry feature extraction for cognitive biomarker discovery in Parkinson\u2019s disease,\u201d (In Review) .","DOI":"10.2139\/ssrn.6086736"},{"issue":"4","key":"10526_CR36","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.3758\/s13423-016-1220-5","volume":"24","author":"N Unsworth","year":"2017","unstructured":"Unsworth N, Robison MK (2017) A locus coeruleus-norepinephrine account of individual differences in working memory capacity and attention control. Psychon Bull Rev 24(4):1282\u20131311. https:\/\/doi.org\/10.3758\/s13423-016-1220-5","journal-title":"Psychon Bull Rev"},{"issue":"3","key":"10526_CR37","doi-asserted-by":"publisher","DOI":"10.1093\/braincomms\/fcac096","volume":"4","author":"Z Ye","year":"2022","unstructured":"Ye Z, Heldmann M, Herrmann L, Br\u00fcggemann N, M\u00fcnte TF (2022) Altered alpha and theta oscillations correlate with sequential working memory in Parkinson\u2019s disease. Brain Commun 4(3):fcac096. https:\/\/doi.org\/10.1093\/braincomms\/fcac096","journal-title":"Brain Commun"},{"key":"10526_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2019.101922","volume":"23","author":"M Zhu","year":"2019","unstructured":"Zhu M, Hajihosseini A, Baumeister T, Garg S, Appel-Cresswell S, McKeown M (2019) Altered EEG alpha and theta oscillations characterize apathy in Parkinson\u2019s disease during incentivized movement. Neuroimage Clin 23:101922. https:\/\/doi.org\/10.1016\/j.nicl.2019.101922","journal-title":"Neuroimage Clin"}],"container-title":["Cognitive Neurodynamics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11571-026-10526-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11571-026-10526-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11571-026-10526-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T02:40:56Z","timestamp":1786588856000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11571-026-10526-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,13]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["10526"],"URL":"https:\/\/doi.org\/10.1007\/s11571-026-10526-z","relation":{},"ISSN":["1871-4080","1871-4099"],"issn-type":[{"value":"1871-4080","type":"print"},{"value":"1871-4099","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,13]]},"assertion":[{"value":"6 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"All procedures were approved by the North Dakota State University Institutional Review Board (Protocol IRB0004611, approved February 9th, 2023). Written informed consent was obtained from all participants before participation, and their privacy rights were protected throughout the study. All research procedures were conducted in accordance with the principles embodied in the Declaration of Helsinki and in compliance with applicable institutional and national guidelines governing research involving human participants.","order":1,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"158"}}