Cognitive Speed–Accuracy Dissociation in Multiple Myeloma: A Cross‑Sectional Study

Cognitive Speed–Accuracy Dissociation in Multiple Myeloma: A Cross‑Sectional Study | 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 Research Article Cognitive Speed–Accuracy Dissociation in Multiple Myeloma: A Cross‑Sectional Study Sumayyah Patel, Christopher Parrish, Frances Seymour, Melanie Burke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8987235/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Purpose This study examined whether adults with Multiple Myeloma (MM) show measurable cognitive differences compared with neurologically healthy peers. The primary research question focused on identifying domain-specific cognitive deficits and determining whether standard screening tools adequately capture cognitive abilities. Methods A cross-sectional design compared 45 adults with MM to 40 age-matched controls. Participants completed 40–50 minutes of cognitive and psychological assessments, including the Montreal Cognitive Assessment (MoCA), validated measures of mood and daily functioning, and a digitised cognitive battery assessing key cognitive domains. Group differences in reaction time (RTs) and accuracy were analysed using ANCOVAs adjusting for age and education, Bayesian and EZ-drift diffusion modelling (EZ-DDM) to characterise domain specific deficits and latent decision-making processes. Results Group-level analyses revealed slower RTs in MM following adjustment for age and education, with accuracy largely preserved. Bayesian modelling identified multi-domain RT-related deficits in ~ 22% of MM patients, particularly in cognitive flexibility and semantic processing. EZ-DDM indicated that group differences were driven by reduced drift rates and prolonged non-decision times in MM, suggesting slower evidence accumulation with slower sensory encoding and/or motor execution. Conclusion Cognitive slowing in MM is selective and heterogeneous, with processing speed emerging as the most sensitive marker. Conventional screening may underestimated subtle deficits, underscoring the need for longitudinal and neuroimaging studies to disentangle disease and treatment related effects. Preserving processing speed, critical for everyday functioning and social interactions, should be a priority in cancer and chronic disease research. Blood Cancer Cognition Processing speed Drift Diffusion Modelling Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Cancer-Related Cognitive Impairment (CRCI) is an emerging, clinically neglected problem in non-CNS cancers, particularly in older adults. CRCI has been reported in multiple cancer populations, but its prevalence, mechanisms, and impact are not well documented. Multiple myeloma (MM) is an incurable plasma cell cancer accounting for 10% of haematological malignancies. Treatment advances have markedly improved survival rates; however, MM and the side-effects of therapy coupled with frequent comorbidities, polypharmacy and frailty, can exacerbate functional impairments [ 1 ]. The cognitive consequences of MM and its treatment on brain function remain largely underexplored and may not be well-managed in routine clinical practice. Given that MM disproportionately affects older adults, many of whom are already at risk of cognitive decline due to age or comorbidities, this gap poses direct challenges to patient-centred care, shared decision-making, and long-term survivorship planning. Prior work in long-term survivors of haematologic malignancies including leukaemia, lymphoma, and MM has shown lasting cognitive impairments in executive functioning following bone marrow transplantation [ 2 ]. Executive dysfunction has been associated with poorer overall survival and worse outcomes among patients receiving intensive treatment [ 3 ]. This underscores the prognostic value of cognitive screening and the need to integrate cognitive assessments into haematology care. CRCI appears multifactorial, driven by neuroinflammation from cytokine release and immune activation, oxidative stress from proteasome inhibitors and other MM therapies. Direct neurotoxic effects are evidenced by treatment-related structural and connectivity changes in the brain [ 4 – 6 ]. High-dose corticosteroids amplify these processes through hippocampal atrophy and impaired neurogenesis [ 7 ]. The chronic treatment-intensive nature of MM, combined with its prevalence in older adults, provides a useful model for examining the interplay of biological and therapeutic mechanisms underlying CRCI in non-CNS cancers. A recent systematic review and meta-analysis by Patel et al [ 8 ] found a significant decline in cognitive function, particularly within the first six months of MM treatment, compared with baseline. Unfortunately, contributing studies were largely based on limited self-reported data, highlighting the need for objective, controlled research. This underscores the need for systematically designed studies employing validated, computerised measures and well-characterised control groups to classify the cognitive profile of MM within the broader CRCI framework. The current study addresses this evidence gap through a comprehensive, computerised cognitive testing battery assessing memory, attention, inhibition, information processing, and flexibility. Bayesian modelling was used to estimate the likelihood and pattern of domain-specific deficits, based on the hypothesis that MM patients would show slower and less accurate performance within executive domains reflecting combined effects of ageing, disease, and treatment. As an exploratory post-hoc analysis, a summary-level EZ-Drift Diffusion Modelling (EZ-DDM) [ 9 ] approach examined latent decision parameters underlying observed response times and accuracy. 2. METHODS a. Design This cross-sectional study was used to determine cognition in MM patients relative to healthy, age- and education- matched controls across the adult age span. The experiment was hosted in-person, using the online platform, Gorilla Experiment Builder (www.gorila.sc) [10]. Study conception was inspired by patient and carer self-reports to clinicians. While patients were not formally involved in the design, recruitment, or conduct of this study, their priorities and experiences informed research questions and outcome measures. b. Recruitment and Ethical Considerations Patients were approached for recruitment using opportunity sampling during outpatient clinics at St James' Hospital. Controls were recruited via caregiver roles and from the Older Adults Research Panel at the University of Leeds. Ethical approval was granted by the NHS Health Research Authority (REC Reference: 23/PR/0461, IRAS 325678; Granted: 16/06/23) and the University of Leeds Research Ethics Committee (PSYC- PSCETHS-782; Granted: 30/11/2023) (Online Resource 1). The study followed British Psychological Society [11] and Declaration of Helsinki guidelines [12]. Exclusion criteria were age <18 years, usage of antipsychotic, antidepressant, antianxiety and antimanic medication, and known neurocognitive disorders (Alzheimer’s, Parkinson’s). Patient eligibility was confirmed via medical record review by healthcare professionals. c. Measures Testing took ~40-50 minutes in quiet controlled environments under research supervision on a standardised laptop (Online Resource 2). Demographic and clinical data were collected using self-report and NHS medical records, including age, gender, nationality, social status, education, language proficiency, sleep duration, time since diagnosis, and treatment profile (steroid use and transplant status). Self-reported measures included the Patient Health Questionnaire-9 (PHQ-9) [13], a 9-item diagnostic screening tool for the presence and severity of depression, Generalised Anxiety Disorder assessment-7 (GAD-7) [14], and a measure of Instrumental Activities of Daily Living (IADL), comprised of two subscales including Lawton-Brody’s IADL [15] and Katz Index of Independence [16]. All participants completed a pen-and-paper Montreal Cognitive Assessment (MoCA) [17], a standardised global cognitive tool sensitive for detecting Mild Cognitive Impairment (MCI) and early dementia. Domain-specific cognitive measures were collected through digitalized tasks: (i) Corsi block-tapping test, performed backwards to assess working memory [18]; (ii) task-switching to assess cognitive flexibility [19]; (iii) levels of processing task to assess surface and deep semantic function [20]; (iv) Posner cueing task to assess attention [21]; and (v) stop-signal delay task assessing inhibitory processing [22]. d. Data Analysis Statistical analysis was conducted using R Studio 2025.05.0. Corrupted datasets ( N = 3) were excluded prior to analysis. Missing values in age (2 cases) and MoCA scores (7 cases) arising from data entry errors were addressed using group-wise mean imputation; education was not imputed. Sensitivity analyses comparing imputated and complete-case results showed negligible differences. Structural, phonemic, and semantic processing were analysed both individually and as a composite Levels of Processing (LoP) score, to evaluate both global and process-specific effects. Subdomain scores were included in all analyses. Group differences in MoCA scores, psychological wellbeing and daily functioning were assessed using Wilcoxon rank-sum tests. Cognitive performance was analysed using two separate two-way ANCOVAs: (i) mean RT (ms), and (ii) mean accuracy scores (%), with age and education as covariates. Although some assumptions (normality and homogeneity of variance) were violated, relationships with covariates were approximately linear and given the moderate sample size ( N = 85) and robustness of ANCOVA, results are interpreted cautiously. Adjusted means and effect sizes are reported. Bayesian statistical framework was conducted in parallel to provide probabilistic estimates of cognitive impairment. Weakly informative normal priors centred at zero were specified, with model diagnostics confirming convergence. Results are reported as posterior means with 95% credible intervals (CrI) and Bayes factors (Online Resource 3). Individual-level impairment was defined as performance ≥1.5 standard deviations below the control mean [22], with prevalence rates varying across cognitive domains. EZ-DDM was applied to summary-level RT and accuracy data to explore latent decision-making parameters: drift rate (v), boundary separation (a), and non-decision time (Ter), indexing evidence accumulation, response caution, and sensory–motor processes, respectively. Parameters were estimated at the participant-level for each domain and summarised descriptively at the group level. EZ-DDM was used to support interpretation rather than to derive precise mechanistic estimates. 3. RESULTS Data from 50 patients with MM and 43 neurologically healthy age-matched controls with normal or corrected-to-normal vision were collected. Three participants withdrew during testing due to task frustration, computer discomfort, or clinical distress. The final sample included 45 MM participants and 40 controls. Participant demographics are summarised in Table 1. Table 1. Summary Statistics of Participant Characteristics Variables MM Controls Demographics N ( % ) 45 (52.94) 40 (47.06) Age, Mean (Range) 65.75 (56.5) 69.49 (50.41) > 80y N ( % ) 4 (9) 2 (5) Sex, N(%) Male 34 (75.6) 13 (33.3) Female 11 (24.4) 26 (66.6) Nationality, N White, non-Hispanic 44 40 Black 1 0 Education, Mean yrs (SD) 13.2 (1.69) 15 (2.49) Prefer not to say, N ( % ) 5 (11.1) 7 (17.5) Questionnaire(s), M ( SD ) MoCA 25.4 (2.67) 26.33 (2.54) PHQ-9 7.7 (5.04) 10.8 (8.34) GAD-7 4.6 (4.11) 6.6 (6.16) Activities of Daily Living (ADL) Lawton-Brody Instrumental ADL 6.9 (1.60) 7.88 (0.82) Katz Index 5.6 (0.80) 5.96 (0.16) Treatment On steroids N (%) 31 (69) NA Unknown a 4 (9) NA HSCT N ( % ) 25 (56) NA Transplant in-eligible 13 (29) NA Note. MM = Multiple Myeloma; MoCA = Montreal Cognitive Assessment; PHQ-9 = Patient Health Questionnaire 9; GAD-7 = Generalised Anxiety Disorder 7; ADL = Activities of Daily Living; HSCT = Haematopoietic Stem Cell Transplantation; NA = Not Applicable . a Patients with unknown steroid treatment status were auto-referrals to the hospital; treatment data were unavailable for these cases. a. Cognitive and Psychosocial Screening Montreal Cognitive Assessment (MoCA) Group difference did not reach statistical significance ( W =1105, p =.070), though MM participants scored lower ( M =25.4, SD =2.67) than controls ( M =26.3, SD =2.54). Using the established clinical cutoff, over half the MM group (51.1%) met the criteria for potential MCI, compared with 27.5% of controls. Depression and Anxiety (PHQ-9 and GAD-7) Moderate depressive symptoms (PHQ-9) were reported by 13% of MM participants (6/45) and 28% of controls (15/40); more severe depression was rare in both groups. Moderate anxiety symptoms (GAD-7) were reported by 13% MM participants (6/45) versus 20% of controls (8/40), with severe anxiety observed only in controls (10%, 4/40). No group differences were statistically significant in depression (PHQ-9: W = 1086, p = 0.101) or anxiety (GAD-7: W = 978, p = 0.490). Daily functioning (ADLs) MM participants showed significantly lower functioning than controls on both instrumental (Lawton W = 1302, p < 0.001:) and basic ADLs (Katz: W = 1079.5, p = 0.0068). b. Mean Reaction Time (RT) Factorial ANCOVA revealed no significant effect of group on RT ( F (1, 566) = 1.87, p = .172). There was a significant effect of cognitive domain ( F (7, 566) = 13.61, p < .001), and among covariates of age ( F (1, 566) = 8.74, p = .003) and years of education ( F (1, 566) = 10.97, p = .001) on RT. There was no significant group x cognitive domain interaction ( F (7, 566) = 0.17, p = .99). Estimated marginal means (EMMs), adjusted for age and years of education, indicated comparable RTs between groups and across domains. Although MM participants exhibited slower RTs than controls, differences were small and did not reach significance, with Hedges’ g values ranging from −0.218 to 0.047 (all p > .35). EMM RTs are presented in Fig 1, and linear trends by age and education within each group and cognitive domain are illustrated in Online Resource 4. Fig 1 Estimated marginal mean reaction times (ms) across cognitive domains in myeloma patients and controls, adjusted for age and years of education. Error bars indicate 95% confidence intervals. Note LoP = Levels of Processing - with structural, semantic, and phonemic being subsets of this overall score Bayesian impairment analysis revealed the highest RT impairment in cognitive flexibility (17.8%), followed by semantic processing (13.3%), LoP (11.1%), working memory (11.1%), and inhibition (11.1%). Attention (6.67%) and structural processing (4.44%) showed lower impairment, and phonemic processing showed no impairment. Further analysis indicated 22.2% of MM patients had deficiencies in ≥2 domains, but only 11.1% in ≥3 cognitive domains. Global RT impairments were strongly driven by semantic processing and cognitive flexibility, followed by LoP, inhibition, and working memory. c. Mean accuracy Accuracy revealed no significant group effect ( F (1, 566) = 0.006, p = .939). There were significant effects of cognitive domain ( F (7, 566) = 32.52, p < .001), age ( F (1, 566) = 10.71, p = .001) and years of education ( F (1, 566) = 25.95, p < .001). The group x cognitive domain interaction was not significant ( F (7, 566) = 0.59, p = .767). EMMs revealed accuracy was generally high across all domains for both groups, with mean percentages ranging 63–99% depending on the task (Fig. 2) and Hedges’ g values ranging from −0.258 to 0.235 (all p > .27). Fig 2 Estimated marginal mean accuracy (%) across cognitive domains in myeloma patients and controls, adjusted for age and years of education. Error bars indicate 95% confidence intervals. Note: LoP = Levels of Processing - with structural, semantic, and phonemic being subsets of this overall score Bayesian modelling revealed accuracy-based performance was largely preserved with cognitive flexibility being the only domain showing measurable impairment (15.6%). No participants displayed deficits in ≥2 domains, indicating an absence of multi-domain impairment. d. EZ-Drift-Diffusion Modelling (EZ-DDM) EZ-DDM estimated latent decision-making parameters for each cognitive domain. Group means (±SD) for boundary separation (a), drift rate (v), and non-decision time (Ter) are visualised in Figure 3. EZ-DDM parameters were estimated only for participant–domain combinations with non-degenerate accuracy; exclusions were applied at the domain level only. Boundary separation was broadly comparable between groups, indicating similar response caution (Control: a_mean range = 0.006–0.019; MM: a_mean range = 0.006–0.016). Drift rate was generally lower in MM participants than controls across most domains (e.g., phonemic: 3.79 vs. 1.89; semantic: 2.68 vs. 1.83) except for attention, where rates were higher in MM than controls. However, attention accuracy was near ceiling in the full sample, resulting in a disproportionate exclusion of control participants when applying the non-degenerate accuracy criterion (0 < PC < 1). Consequently, the attention drift-rate comparison is based on a smaller and potentially unrepresentative subset (Control: N = 12; MM: N = 17) and should be interpreted cautiously. Non-decision time (Ter), reflecting sensory encoding and motor execution, was typically longer in MM participants (e.g., phonemic: 1.51 vs. 2.32; semantic: 1.98 vs. 2.59), suggesting modest delays in pre- and post-decision processing. Fig 3. EZ-DDM parameter estimates (boundary separation, drift rate, non-decision time) across cognitive domains. Solid lines indicate control participant responses and dashed lines individuals with myeloma. Error bars indication standard error of the mean. Note: Attn = Attention; Flex = Flexibility; Inhib = Inhibition; WM = Working Memory 4. DISCUSSION Our research assessed cognition in adults with and without MM. Over half of the patients with MM met the criteria for MCI on the MoCA [ 16 ], consistent with previous findings on objective cognitive deficits in haematological malignancies [ 2 , 3 ]. We also found 28% of controls met the criteria for MCI, higher than the expected ~ 15% of MCI in community-dwelling adults [ 23 ]. This likely reflects our sample, as over one-third (37.5%) were relatives or carers attending outpatient appointments. Carers also reported high rates of depressive and anxiety symptoms (93% and 33% respectively) compared to MM participants (13% for both). Depression is known to impair cognitive performance [ 24 ], so the inflated MCI prevalence in controls may highlight caregiver burden. Despite preserved accuracy, functional independence was predictably reduced in participants with MM on the Katz and Lawton scales, indicating some loss of autonomy even in routine daily tasks. Slower processing speeds may compromise complex task performance even when accuracy appears intact. Many tasks such as decision-making, navigation, and cooking require quick comprehension and adaptation to potentially unexpected changes. This aligns with previous CRCI research linking slowed processing to functional decline in the absence of clinical dementia in haematological malignancies [ 25 ]. Digitalised cognitive testing revealed selective impairments in MM patients. Although ANCOVAs showed no significant group effects, Bayesian prevalence modelling identified clinically meaningful deficits in RT-based measures, particularly in language processing and cognitive flexibility tasks. This supports prior studies showing subtle domain-specific deficits like lexical fluency during chemotherapy, influenced by biological and demographic factors [ 26 ]. Mechanistically, processing speed deficits may relate to white matter changes and neurotransmitter dysregulation [ 27 ]. Nearly half (49%) our MM cohort underwent Heamatopoietic stem cell transplantation, a regimen involving high-dose chemotherapy and immunosuppressive agents, alongside corticosteroid exposure (64%), which may contribute to neurotoxicity and white matter integrity [ 28 – 29 ] (Online Resource 5). Our data also highlight commonly reported group accuracy averages in cognitive tests for patients may underestimate clinically meaningful deficits in MM and potentially other patients. Bayesian modelling showed cognitive flexibility was particularly affected, and over 20% of MM participants revealed processing speed deficits in two or more cognitive domains. RT measures were more sensitive than accuracy, demonstrating clinically meaningful difficulties exist even when group-level performance appears intact. Exploratory EZ-DDM analyses suggest mechanisms underlying slowed processing. While boundary separation (response caution) was broadly comparable between groups, drift rate (evidence accumulation) and non-decision time typically took longer in MM participants. This was indicative of slower information processing, reduced efficiency in decision-making. and modest delays in sensory encoding and motor response execution. This supports the view that processing slowing is driven by reduced evidence accumulation rather than changes in response caution [ 9 ]. Therapies managing cancer such as chemotherapy, immunotherapy and corticosteroids may contribute to poor processing speed in MM [ 30 – 32 ]. Impaired myelination and altered neurotransmitter signalling may additionally contribute to subtle neural connectivity changes, providing a potential avenue for future interventions [ 33 ]. Given expanding MM therapeutic options and their effect on quality of life, neurocognitive sequelae of MM therapies warrant closer scrutiny. Evidence increasingly indicates corticosteroids are a likely culprit, and high-dose prolonged use should be carefully considered in MM and other chronic diseases [ 34 – 35 ]. Strengths of this study include a comprehensive cognitive battery assessing multiple domains and the inclusion of age-matched controls, enabling objective domain-specific assessment that differentiates CRCI from normal ageing beyond global and/or subjective screening tools. Limitations include the cross-sectional design, which precludes conclusions about causality or longitudinal trajectory; potential self-selection bias from outpatient-only recruitment, whereby individuals receiving more intensive therapies and/or with more severe cognitive impairment may have been unable to participate; and heterogeneous treatment regimens limiting isolation of the independent therapeutic effects, or dissection of such effects from disease-driven changes, both of which warrant further investigation. Consequently, the magnitude of therapy-induced and CRCI may be underestimated. Additionally, several tasks reached ceiling accuracy (particularly attention, structural, and phonemic processing), suggesting some tests were insufficiently challenging although working memory, inhibition, and cognitive flexibility did not reach ceiling and still showed no significant group differences. MM serves as a useful exemplar for studying cancer-related cognitive impairment due to its chronic, treatment-intensive course. Similar cognitive effects are likely to occur across other haematological malignancies [ 36 ], highlighting broader clinical applications and potential challenges. Improved understanding of the neurocognitive mechanisms underlying CRCI could inform treatment selection and strategies to protect cognitive function. Future research should therefore investigate neurophysiological substrates of cognitive change in MM using techniques such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) [ 37 – 38 ], alongside longitudinal designs to track cognitive trajectories and disentangle age-, disease-, and treatment-related effects. Targeted interventions aimed at improving processing speed may help preserve cognitive function in vulnerable subgroups identified through prevalence modelling. 5. CONCLUSIONS While overall cognitive accuracy was relatively well-preserved in adults with MM, deficits in processing speed were identified as a major challenge for those living with MM compared to age-matched controls. The extent of CRCI in MM was heterogeneous, with deficits likely influenced by a combination of disease-, age-, and treatment-related factors. Reduced evidence accumulation and prolonged non-decision time suggest mechanistic targets for intervention, particularly within corticolimbic white matter and prefrontal networks. Our results underscore the importance of sensitive, domain-specific cognitive assessments (such as cognitive flexibility) in routine cancer care and suggest that testing of processing speed may highlight vulnerable subgroups and could guide personalized strategies to preserve cognitive function and maintain independence. Future research should investigate longitudinal trajectories, disentangle treatment-specific contributions, and evaluate interventions aimed at enhancing neuroplasticity and mitigating cognitive decline in this population. Declarations Disclaimers The views expressed in this article are those of the authors and do not necessarily represent the official position of the University of Leeds, Leeds Teaching Hospitals, or any affiliated organizations. Acknowledgements We thank the clinical nurse specialists, Rachel Backhouse, Lindsey Hankey, and Tessa Mason on the Haematology Unit at St James’ Hospital for their invaluable support with patient screening and facilitating recruitment. We also acknowledge the contributions of MSc student researchers Weiyang Wang and Emily Caton, and undergraduate volunteers, who assisted with cognitive assessments as part of their dissertation projects. Artificial intelligence was not involved in the writing of this manuscript and is not acknowledged as an author. However, ChatGPT (OpenAI) was used during the research process to support coding tasks in R Studio. Specifically, it assisted with adapting code and interpreting error messages during the refinement of statistical analyses, including ANCOVA and Bayesian modelling. All AI-generated outputs were reviewed and validated by the authors to ensure accuracy and appropriateness for the study. Funding This research was undertaken in partial fulfilment of the lead author's PhD, funded by the University of Leeds Doctoral Scholarship (2023–2027). The work was also supported by the UK Research and Innovation Impact Accelerator Award under Medical Research Council (MRC), grant reference: IAA4175/127410. The funders had no role in study design, data collection, analysis, interpretation, manuscript preparation, or the decision to submit for publication. Human Ethics and Consent to Participate declarations This study was performed in line with the principles of the Declaration of Helsinki and British Psychological Society Code of Ethics and Conduct. Approval was granted by the NHS Health Research Authority (REC Reference: 23/PR/0461, IRAS 325678; Granted: 16/06/23) and the University of Leeds Research Ethics Committee (PSYC- PSCETHS-782; Granted: 30/11/2023). Compliance with Ethical Standards All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Ethical approval was granted by the NHS Health Research Authority (REC reference: 23/PR/0461; IRAS ID: 325678; approval granted 16/06/2023) and the University of Leeds Research Ethics Committee (PSYC-PSCETHS-782; approval granted 30/11/2023). Informed consent was obtained from all individual participants included in the study. This manuscript does not contain any individual person’s data in any form that could lead to identification. Consent to publish anonymised data was obtained from all participants. Disclosure of Potential Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contributions Conceptualisation: Sumayyah Patel, Melanie Burke, Chris Parrish, Frances Seymour; Methodology: Sumayyah Patel, Melanie Burke, Chris Parrish, Frances Seymour; Formal analysis and investigation: Sumayyah Patel, Chris Parrish, Frances Seymour; Data collection: Sumayyah Patel; Recruitment: Chris Parrish, Frances Seymour; Visualization: Sumayyah Patel; Writing – original draft preparation: Sumayyah Patel; Writing – review and editing: Sumayyah Patel, Melanie Burke, Chris Parrish, Frances Seymour; Supervision: Melanie Burke, Chris Parrish, Frances Seymour. Data Availability De-identified participant data supporting the findings of this study are available from the corresponding author upon reasonable request. Requests should include a brief description of the intended use of the data. References Cook G, Larocca A, Facon T, Zweegman S, Engelhardt M. Defining the vulnerable patient with myeloma—a frailty position paper of the European Myeloma Network. 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Glucocorticoid regulation of cancer development and progression. Front Endocrinol. 2023;14:1161768. Rudolph LM, Cornil CA, Mittelman-Smith MA, Rainville JR, Remage-Healey L, Sinchak K, Micevych PE. Actions of steroids: new neurotransmitters. J Neurosci. 2016;36(45):11449–58. Oray M, Abu Samra K, Ebrahimiadib N, Meese H, Foster CS. Long-term side effects of glucocorticoids. Exp Opin Drug Saf. 2016;15(4):457–65. Ferrari MV, Conti L, Capetti B, Marzorati C, Grasso R, Pravettoni G. Patients' and clinicians' knowledge in cancer-related cognitive impairment and its implications: current perspective. Future Oncol. 2024;20(40):3569–78. Sousa H, Almeida S, Bessa J, Pereira MG. The developmental trajectory of cancer-related cognitive impairment in breast cancer patients: a systematic review of longitudinal neuroimaging studies. Neuropsychol Rev. 2020;30(3):287–309. Saita K, Tanabe K, Akabane S, Amano S, Okamura H. P28-3 Clinical application of portable fNIRS to measure cognitive impairment after chemotherapy in a colorectal cancer patient. Ann Oncol. 2024;35:S1374–5. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformationNeuroOncology.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers invited by journal 03 Mar, 2026 Editor invited by journal 03 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Submission checks completed at journal 02 Mar, 2026 First submitted to journal 27 Feb, 2026 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. 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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-8987235","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600204435,"identity":"30f5c587-7d4a-46c2-9e2d-34f4bec05239","order_by":0,"name":"Sumayyah Patel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYFACxsYHDAw2MkCWAUSAh6AW5mag0jQeUrSwt0kwMBwmQYs5/8E2yZ87zvPwszdv/sBQY8dgcOYAfi2WMxKbrXnP3OaR7DlWJsFwLJnB4GwDfi0GNxgbbzO23eYxuJFjxsDAdoDB4DwBhxmcP9gg+bPtHI/9/TfGHxj+EaPlQGKTBG/bAR4DCSBibDtA2GEgvxjznknmkTiTViaR2JfMI0nI++b8xx8+/LnDTo6//fDmDx++2cnxnUkg4DAQwQhzSgIxEYmqZRSMglEwCkYBNgAA+eJD87N/+6YAAAAASUVORK5CYII=","orcid":"","institution":"University of Leeds","correspondingAuthor":true,"prefix":"","firstName":"Sumayyah","middleName":"","lastName":"Patel","suffix":""},{"id":600204437,"identity":"fbed5df6-f6b5-4396-ad46-2a61108ef784","order_by":1,"name":"Christopher Parrish","email":"","orcid":"","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Parrish","suffix":""},{"id":600204438,"identity":"7039d59f-a644-410e-924a-d288bfd3f51d","order_by":2,"name":"Frances Seymour","email":"","orcid":"","institution":"Leeds Teaching Hospitals, St James’ University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Frances","middleName":"","lastName":"Seymour","suffix":""},{"id":600204439,"identity":"6ced354d-9c82-4590-89fb-ebad2769e7c5","order_by":3,"name":"Melanie Burke","email":"","orcid":"","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"Burke","suffix":""}],"badges":[],"createdAt":"2026-02-27 11:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8987235/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8987235/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104182220,"identity":"294b550f-de7e-42f8-b5f2-529f563ab5d6","added_by":"auto","created_at":"2026-03-08 17:36:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81874,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated marginal mean reaction times (ms) across cognitive domains in myeloma patients and controls, adjusted for age and years of education. Error bars indicate 95% confidence intervals. Note\u003cem\u003e LoP = Levels of Processing - with structural, semantic, and phonemic being subsets of this overall score\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8987235/v1/78af9383f78b1f188510ad35.png"},{"id":104182221,"identity":"8862e55d-fe6a-489a-80d7-aede46194ca1","added_by":"auto","created_at":"2026-03-08 17:36:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22903,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated marginal mean accuracy (%) across cognitive domains in myeloma patients and controls, adjusted for age and years of education. Error bars indicate 95% confidence intervals. \u003cem\u003eNote: LoP = Levels of Processing - with structural, semantic, and phonemic being subsets of this overall score\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8987235/v1/722c86d750e0338ad4edf93c.png"},{"id":104404398,"identity":"8b27010e-f489-4314-b309-1b739677a411","added_by":"auto","created_at":"2026-03-11 12:20:11","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":464226,"visible":true,"origin":"","legend":"\u003cp\u003eEZ-DDM parameter estimates (boundary separation, drift rate, non-decision time) across cognitive domains. Solid lines indicate control participant responses and dashed lines individuals with myeloma. Error bars indication standard error of the mean.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: Attn = Attention; Flex = Flexibility; Inhib = Inhibition; WM = Working Memory\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8987235/v1/5e1b1613cbcade1dae8b5c30.jpeg"},{"id":106414525,"identity":"1ca3df3e-4950-4f65-8e25-00bea017d85c","added_by":"auto","created_at":"2026-04-08 10:10:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1179862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8987235/v1/1dba5083-059b-4008-aed6-18f57bc11aae.pdf"},{"id":104182222,"identity":"d7d36dfd-738c-41ce-93a0-74e95904434d","added_by":"auto","created_at":"2026-03-08 17:36:48","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":472630,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationNeuroOncology.docx","url":"https://assets-eu.researchsquare.com/files/rs-8987235/v1/c13fa5948d29344042cefc1f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive Speed–Accuracy Dissociation in Multiple Myeloma: A Cross‑Sectional Study","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eCancer-Related Cognitive Impairment (CRCI) is an emerging, clinically neglected problem in non-CNS cancers, particularly in older adults. CRCI has been reported in multiple cancer populations, but its prevalence, mechanisms, and impact are not well documented. Multiple myeloma (MM) is an incurable plasma cell cancer accounting for 10% of haematological malignancies. Treatment advances have markedly improved survival rates; however, MM and the side-effects of therapy coupled with frequent comorbidities, polypharmacy and frailty, can exacerbate functional impairments [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The cognitive consequences of MM and its treatment on brain function remain largely underexplored and may not be well-managed in routine clinical practice. Given that MM disproportionately affects older adults, many of whom are already at risk of cognitive decline due to age or comorbidities, this gap poses direct challenges to patient-centred care, shared decision-making, and long-term survivorship planning.\u003c/p\u003e \u003cp\u003ePrior work in long-term survivors of haematologic malignancies including leukaemia, lymphoma, and MM has shown lasting cognitive impairments in executive functioning following bone marrow transplantation [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. Executive dysfunction has been associated with poorer overall survival and worse outcomes among patients receiving intensive treatment [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. This underscores the prognostic value of cognitive screening and the need to integrate cognitive assessments into haematology care. CRCI appears multifactorial, driven by neuroinflammation from cytokine release and immune activation, oxidative stress from proteasome inhibitors and other MM therapies. Direct neurotoxic effects are evidenced by treatment-related structural and connectivity changes in the brain [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. High-dose corticosteroids amplify these processes through hippocampal atrophy and impaired neurogenesis [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. The chronic treatment-intensive nature of MM, combined with its prevalence in older adults, provides a useful model for examining the interplay of biological and therapeutic mechanisms underlying CRCI in non-CNS cancers.\u003c/p\u003e \u003cp\u003eA recent systematic review and meta-analysis by Patel et al [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] found a significant decline in cognitive function, particularly within the first six months of MM treatment, compared with baseline. Unfortunately, contributing studies were largely based on limited self-reported data, highlighting the need for objective, controlled research. This underscores the need for systematically designed studies employing validated, computerised measures and well-characterised control groups to classify the cognitive profile of MM within the broader CRCI framework. The current study addresses this evidence gap through a comprehensive, computerised cognitive testing battery assessing memory, attention, inhibition, information processing, and flexibility. Bayesian modelling was used to estimate the likelihood and pattern of domain-specific deficits, based on the hypothesis that MM patients would show slower and less accurate performance within executive domains reflecting combined effects of ageing, disease, and treatment. As an exploratory post-hoc analysis, a summary-level EZ-Drift Diffusion Modelling (EZ-DDM) [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] approach examined latent decision parameters underlying observed response times and accuracy.\u003c/p\u003e "},{"header":"2. METHODS","content":"\u003cp\u003e\u003cem\u003ea. \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cem\u003eDesign\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study was used to determine cognition in MM patients relative to healthy, age- and education- matched controls across the adult age span. The experiment was hosted in-person, using the online platform, Gorilla Experiment Builder (www.gorila.sc) [10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy conception was inspired by patient and carer self-reports to clinicians. While patients were not formally involved in the design, recruitment, or conduct of this study, their priorities and experiences informed research questions and outcome measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eb. \u0026nbsp; \u0026nbsp;Recruitment and Ethical Considerations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients were approached for recruitment using opportunity sampling during outpatient clinics at St James\u0026apos; Hospital. Controls were recruited via caregiver roles and from the Older Adults Research Panel at the University of Leeds. Ethical approval was granted by the NHS Health Research Authority (REC Reference: 23/PR/0461, IRAS 325678; Granted: 16/06/23) and the University of Leeds Research Ethics Committee (PSYC-\u0026nbsp;PSCETHS-782; Granted: 30/11/2023) (Online Resource 1). The study followed British Psychological Society [11] and Declaration of Helsinki guidelines [12]. Exclusion criteria were age \u0026lt;18 years, usage of antipsychotic, antidepressant, antianxiety and antimanic medication, and known neurocognitive disorders (Alzheimer\u0026rsquo;s, Parkinson\u0026rsquo;s). Patient eligibility was confirmed via medical record review by healthcare professionals.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ec. \u0026nbsp; \u0026nbsp;Measures\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTesting took ~40-50 minutes in quiet controlled environments under research supervision on a standardised laptop (Online Resource 2). Demographic and clinical data were collected using self-report and NHS medical records, including age, gender, nationality, social status, education, language proficiency, sleep duration, time since diagnosis, and treatment profile (steroid use and transplant status).\u003c/p\u003e\n\u003cp\u003eSelf-reported measures included the Patient Health Questionnaire-9 (PHQ-9) [13], a 9-item diagnostic screening tool for the presence and severity of depression, Generalised Anxiety Disorder assessment-7 (GAD-7) [14], and a measure of Instrumental Activities of Daily Living (IADL), comprised of two subscales including Lawton-Brody\u0026rsquo;s IADL [15] and Katz Index of Independence [16].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll participants completed a pen-and-paper Montreal Cognitive Assessment (MoCA) [17], a standardised global cognitive tool sensitive for detecting Mild Cognitive Impairment (MCI) and early dementia. Domain-specific cognitive measures were collected through digitalized tasks: (i) Corsi block-tapping test, performed backwards to assess working memory [18]; (ii) task-switching to assess cognitive flexibility [19]; (iii) levels of processing task to assess surface and deep semantic function [20]; (iv) Posner cueing task to assess attention [21]; and (v) stop-signal delay task assessing inhibitory processing [22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ed. \u0026nbsp; \u0026nbsp;Data Analysis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted using R Studio 2025.05.0. Corrupted datasets (\u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 3) were excluded prior to analysis. Missing values in age (2 cases) and MoCA scores (7 cases) arising from data entry errors were addressed using group-wise mean imputation; education was not imputed. Sensitivity analyses comparing imputated and complete-case results showed negligible differences. Structural, phonemic, and semantic processing were analysed both individually and as a composite Levels of Processing (LoP) score, to evaluate both global and process-specific effects. Subdomain scores were included in all analyses.\u003c/p\u003e\n\u003cp\u003eGroup differences in MoCA scores, psychological wellbeing and daily functioning were assessed using Wilcoxon rank-sum tests. Cognitive performance was analysed using two separate two-way ANCOVAs: (i) mean RT (ms), and (ii) mean accuracy scores (%), with age and education as covariates. Although some assumptions (normality and homogeneity of variance) were violated, relationships with covariates were approximately linear and given the moderate sample size (\u003cem\u003eN\u003c/em\u003e = 85) and robustness of ANCOVA, results are interpreted cautiously. Adjusted means and effect sizes are reported.\u003c/p\u003e\n\u003cp\u003eBayesian statistical framework was conducted in parallel to provide probabilistic estimates of cognitive impairment. Weakly informative normal priors centred at zero were specified, with model diagnostics confirming convergence. Results are reported as posterior means with 95% credible intervals (CrI) and Bayes factors (Online Resource 3). Individual-level impairment was defined as performance \u0026ge;1.5 standard deviations below the control mean [22], with prevalence rates varying across cognitive domains. EZ-DDM was applied to summary-level RT and accuracy data to explore latent decision-making parameters: drift rate (v), boundary separation (a), and non-decision time (Ter), indexing evidence accumulation, response caution, and sensory\u0026ndash;motor processes, respectively. Parameters were estimated at the participant-level for each domain and summarised descriptively at the group level. EZ-DDM was used to support interpretation rather than to derive precise mechanistic estimates.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eData from 50 patients with MM and 43 neurologically healthy age-matched controls with normal or corrected-to-normal vision were collected. Three participants withdrew during testing due to task frustration, computer discomfort, or clinical distress. The final sample included 45 MM participants and 40 controls.\u0026nbsp;Participant demographics are summarised in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Summary Statistics of Participant Characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"83%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMM\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e (\u003cem\u003e%\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e45 (52.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e40 (47.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eAge, Mean (Range)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e65.75 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e69.49 (50.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026gt; 80y \u003cem\u003eN\u003c/em\u003e(\u003cem\u003e%\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e4 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e2 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eSex, N(%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e34 (75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e13 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e11 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e26 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eNationality, N\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eWhite, non-Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eEducation,\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMean yrs (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e13.2 (1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e15 (2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePrefer not to say, \u003cem\u003eN\u0026nbsp;\u003c/em\u003e(\u003cem\u003e%\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e5 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e7 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuestionnaire(s),\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003cstrong\u003eM\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(\u003cem\u003eSD\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMoCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e25.4 (2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e26.33 (2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePHQ-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e7.7 (5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e10.8 (8.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eGAD-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e4.6 (4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e6.6 (6.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eActivities of Daily Living (ADL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Lawton-Brody Instrumental ADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e6.9 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e7.88 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Katz Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e5.6 (0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e5.96 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eOn steroids \u003cem\u003eN (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e31 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eUnknown\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e4 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eHSCT \u003cem\u003eN\u0026nbsp;\u003c/em\u003e(\u003cem\u003e%\u003c/em\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e25 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eTransplant in-eligible\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e13 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote. MM = Multiple Myeloma; MoCA = Montreal Cognitive Assessment; PHQ-9 = Patient Health Questionnaire 9; GAD-7 = Generalised Anxiety Disorder 7; ADL = Activities of Daily Living; HSCT = Haematopoietic Stem Cell Transplantation; NA = Not Applicable\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003csup\u003ea\u003c/sup\u003ePatients with unknown steroid treatment status were auto-referrals to the hospital; treatment data were unavailable for these cases.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ea.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003cem\u003eCognitive and Psychosocial Screening\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMontreal Cognitive Assessment (MoCA)\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGroup difference did not reach statistical significance (\u003cem\u003eW\u003c/em\u003e=1105,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e=.070), though MM participants scored lower (\u003cem\u003eM\u003c/em\u003e=25.4, \u003cem\u003eSD\u003c/em\u003e=2.67) than controls (\u003cem\u003eM\u003c/em\u003e=26.3, \u003cem\u003eSD\u003c/em\u003e=2.54). Using the established clinical cutoff, over half the MM group (51.1%) met the criteria for potential MCI, compared with 27.5% of controls. \u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDepression and Anxiety (PHQ-9 and GAD-7)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModerate depressive symptoms (PHQ-9) were reported by 13% of MM participants (6/45) and 28% of controls (15/40); more severe depression was rare in both groups. Moderate anxiety symptoms (GAD-7) were reported by 13% MM participants (6/45) versus 20% of controls (8/40), with severe anxiety observed only in controls (10%, 4/40). No group differences were statistically significant in depression (PHQ-9: \u003cem\u003eW\u0026nbsp;\u003c/em\u003e= 1086, \u003cem\u003ep\u003c/em\u003e = 0.101) or anxiety (GAD-7: \u003cem\u003eW\u003c/em\u003e = 978, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.490).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDaily functioning (ADLs)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMM participants showed significantly lower functioning than controls on both instrumental (Lawton\u003cem\u003e\u0026nbsp;W\u003c/em\u003e = 1302, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001:) and basic ADLs (Katz:\u003cem\u003e\u0026nbsp;W\u003c/em\u003e = 1079.5, \u003cem\u003ep\u003c/em\u003e = 0.0068).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eb. \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cem\u003eMean Reaction Time (RT)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFactorial ANCOVA revealed no significant effect of group on RT (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 566)\u003c/sub\u003e = 1.87, \u003cem\u003ep\u003c/em\u003e = .172). There was a significant effect of cognitive domain (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(7, 566)\u003c/sub\u003e = 13.61, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and among covariates of age (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 566)\u003c/sub\u003e = 8.74, \u003cem\u003ep\u003c/em\u003e = .003) and years of education (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 566)\u003c/sub\u003e = 10.97, \u003cem\u003ep\u003c/em\u003e = .001) on RT. There was no significant group x cognitive domain interaction (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(7, 566)\u003c/sub\u003e = 0.17, \u003cem\u003ep\u003c/em\u003e = .99). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEstimated marginal means (EMMs), adjusted for age and years of education, indicated comparable RTs between groups and across domains. Although MM participants exhibited slower RTs than controls, differences were small and did not reach significance, with Hedges\u0026rsquo; g values ranging from \u0026minus;0.218 to 0.047 (all p \u0026gt; .35). EMM RTs are presented in Fig 1, and linear trends by age and education within each group and cognitive domain are illustrated in Online Resource 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFig 1\u003c/em\u003e\u003c/strong\u003e Estimated marginal mean reaction times (ms) across cognitive domains in myeloma patients and controls, adjusted for age and years of education. Error bars indicate 95% confidence intervals. Note\u003cem\u003e\u0026nbsp;LoP = Levels of Processing - with structural, semantic, and phonemic being subsets of this overall score\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBayesian impairment analysis revealed the highest RT impairment in cognitive flexibility (17.8%), followed by semantic processing (13.3%), LoP (11.1%), working memory (11.1%), and inhibition (11.1%). Attention (6.67%) and structural processing (4.44%) showed lower impairment, and phonemic processing showed no impairment. Further analysis indicated 22.2% of MM patients had deficiencies in \u0026ge;2 domains, but only 11.1% in \u0026ge;3 cognitive domains.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eGlobal RT impairments were strongly driven by semantic processing and cognitive flexibility, followed by LoP, inhibition, and working memory.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003ec. \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cem\u003eMean accuracy\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAccuracy revealed no significant group effect (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 566)\u0026nbsp;\u003c/sub\u003e= 0.006, \u003cem\u003ep\u003c/em\u003e = .939). There were significant effects of cognitive domain (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(7, 566)\u0026nbsp;\u003c/sub\u003e= 32.52, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001), age (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 566)\u003c/sub\u003e = 10.71, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .001) and years of education (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 566)\u003c/sub\u003e = 25.95, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The group x cognitive domain interaction was not significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(7, 566)\u0026nbsp;\u003c/sub\u003e= 0.59, \u003cem\u003ep\u003c/em\u003e = .767). EMMs revealed accuracy was generally high across all domains for both groups, with mean percentages ranging 63\u0026ndash;99% depending on the task (Fig. 2) and Hedges\u0026rsquo; g values ranging from \u0026minus;0.258 to 0.235 (all p \u0026gt; .27).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig 2\u003c/strong\u003e Estimated marginal mean accuracy (%) across cognitive domains in myeloma patients and controls, adjusted for age and years of education. Error bars indicate 95% confidence intervals. \u003cem\u003eNote: LoP = Levels of Processing - with structural, semantic, and phonemic being subsets of this overall score\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBayesian modelling revealed accuracy-based performance was largely preserved with cognitive flexibility being the only domain showing measurable impairment (15.6%). No participants displayed deficits in \u0026ge;2 domains, indicating an absence of multi-domain impairment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ed. \u0026nbsp; \u0026nbsp;\u003c/em\u003eEZ-Drift-Diffusion Modelling (EZ-DDM)\u003c/p\u003e\n\u003cp\u003eEZ-DDM estimated latent decision-making parameters for each cognitive domain. Group means (\u0026plusmn;SD) for boundary separation (a), drift rate (v), and non-decision time (Ter) are visualised in Figure 3. EZ-DDM parameters were estimated only for participant\u0026ndash;domain combinations with non-degenerate accuracy; exclusions were applied at the domain level only. \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBoundary separation was broadly comparable between groups, indicating similar response caution (Control: a_mean range = 0.006\u0026ndash;0.019; MM: a_mean range = 0.006\u0026ndash;0.016). Drift rate was generally lower in MM participants than controls across most domains (e.g., phonemic: 3.79 vs. 1.89; semantic: 2.68 vs. 1.83) except for attention, where rates were higher in MM than controls. However, attention accuracy was near ceiling in the full sample, resulting in a disproportionate exclusion of control participants when applying the non-degenerate accuracy criterion (0 \u0026lt; PC \u0026lt; 1). Consequently, the attention drift-rate comparison is based on a smaller and potentially unrepresentative subset (Control: \u003cem\u003eN\u003c/em\u003e = 12; MM: \u003cem\u003eN\u003c/em\u003e = 17) and should be interpreted cautiously. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNon-decision time (Ter), reflecting sensory encoding and motor execution, was typically longer in MM participants (e.g., phonemic: 1.51 vs. 2.32; semantic: 1.98 vs. 2.59), suggesting modest delays in pre- and post-decision processing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig 3.\u003c/strong\u003e EZ-DDM parameter estimates (boundary separation, drift rate, non-decision time) across cognitive domains. Solid lines indicate control participant responses and dashed lines individuals with myeloma. Error bars indication standard error of the mean.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: Attn = Attention; Flex = Flexibility; Inhib = Inhibition; WM = Working Memory\u003c/em\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eOur research assessed cognition in adults with and without MM. Over half of the patients with MM met the criteria for MCI on the MoCA [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], consistent with previous findings on objective cognitive deficits in haematological malignancies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. We also found 28% of controls met the criteria for MCI, higher than the expected\u0026thinsp;~\u0026thinsp;15% of MCI in community-dwelling adults [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This likely reflects our sample, as over one-third (37.5%) were relatives or carers attending outpatient appointments. Carers also reported high rates of depressive and anxiety symptoms (93% and 33% respectively) compared to MM participants (13% for both). Depression is known to impair cognitive performance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], so the inflated MCI prevalence in controls may highlight caregiver burden.\u003c/p\u003e \u003cp\u003eDespite preserved accuracy, functional independence was predictably reduced in participants with MM on the Katz and Lawton scales, indicating some loss of autonomy even in routine daily tasks. Slower processing speeds may compromise complex task performance even when accuracy appears intact. Many tasks such as decision-making, navigation, and cooking require quick comprehension and adaptation to potentially unexpected changes. This aligns with previous CRCI research linking slowed processing to functional decline in the absence of clinical dementia in haematological malignancies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDigitalised cognitive testing revealed selective impairments in MM patients. Although ANCOVAs showed no significant group effects, Bayesian prevalence modelling identified clinically meaningful deficits in RT-based measures, particularly in language processing and cognitive flexibility tasks. This supports prior studies showing subtle domain-specific deficits like lexical fluency during chemotherapy, influenced by biological and demographic factors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Mechanistically, processing speed deficits may relate to white matter changes and neurotransmitter dysregulation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Nearly half (49%) our MM cohort underwent Heamatopoietic stem cell transplantation, a regimen involving high-dose chemotherapy and immunosuppressive agents, alongside corticosteroid exposure (64%), which may contribute to neurotoxicity and white matter integrity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] (Online Resource 5).\u003c/p\u003e \u003cp\u003eOur data also highlight commonly reported group accuracy averages in cognitive tests for patients may underestimate clinically meaningful deficits in MM and potentially other patients. Bayesian modelling showed cognitive flexibility was particularly affected, and over 20% of MM participants revealed processing speed deficits in two or more cognitive domains. RT measures were more sensitive than accuracy, demonstrating clinically meaningful difficulties exist even when group-level performance appears intact.\u003c/p\u003e \u003cp\u003eExploratory EZ-DDM analyses suggest mechanisms underlying slowed processing. While boundary separation (response caution) was broadly comparable between groups, drift rate (evidence accumulation) and non-decision time typically took longer in MM participants. This was indicative of slower information processing, reduced efficiency in decision-making. and modest delays in sensory encoding and motor response execution. This supports the view that processing slowing is driven by reduced evidence accumulation rather than changes in response caution [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherapies managing cancer such as chemotherapy, immunotherapy and corticosteroids may contribute to poor processing speed in MM [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Impaired myelination and altered neurotransmitter signalling may additionally contribute to subtle neural connectivity changes, providing a potential avenue for future interventions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Given expanding MM therapeutic options and their effect on quality of life, neurocognitive sequelae of MM therapies warrant closer scrutiny. Evidence increasingly indicates corticosteroids are a likely culprit, and high-dose prolonged use should be carefully considered in MM and other chronic diseases [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStrengths of this study include a comprehensive cognitive battery assessing multiple domains and the inclusion of age-matched controls, enabling objective domain-specific assessment that differentiates CRCI from normal ageing beyond global and/or subjective screening tools. Limitations include the cross-sectional design, which precludes conclusions about causality or longitudinal trajectory; potential self-selection bias from outpatient-only recruitment, whereby individuals receiving more intensive therapies and/or with more severe cognitive impairment may have been unable to participate; and heterogeneous treatment regimens limiting isolation of the independent therapeutic effects, or dissection of such effects from disease-driven changes, both of which warrant further investigation. Consequently, the magnitude of therapy-induced and CRCI may be underestimated. Additionally, several tasks reached ceiling accuracy (particularly attention, structural, and phonemic processing), suggesting some tests were insufficiently challenging although working memory, inhibition, and cognitive flexibility did not reach ceiling and still showed no significant group differences.\u003c/p\u003e \u003cp\u003eMM serves as a useful exemplar for studying cancer-related cognitive impairment due to its chronic, treatment-intensive course. Similar cognitive effects are likely to occur across other haematological malignancies [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], highlighting broader clinical applications and potential challenges. Improved understanding of the neurocognitive mechanisms underlying CRCI could inform treatment selection and strategies to protect cognitive function. Future research should therefore investigate neurophysiological substrates of cognitive change in MM using techniques such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], alongside longitudinal designs to track cognitive trajectories and disentangle age-, disease-, and treatment-related effects. Targeted interventions aimed at improving processing speed may help preserve cognitive function in vulnerable subgroups identified through prevalence modelling.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eWhile overall cognitive accuracy was relatively well-preserved in adults with MM, deficits in processing speed were identified as a major challenge for those living with MM compared to age-matched controls. The extent of CRCI in MM was heterogeneous, with deficits likely influenced by a combination of disease-, age-, and treatment-related factors. Reduced evidence accumulation and prolonged non-decision time suggest mechanistic targets for intervention, particularly within corticolimbic white matter and prefrontal networks. Our results underscore the importance of sensitive, domain-specific cognitive assessments (such as cognitive flexibility) in routine cancer care and suggest that testing of processing speed may highlight vulnerable subgroups and could guide personalized strategies to preserve cognitive function and maintain independence. Future research should investigate longitudinal trajectories, disentangle treatment-specific contributions, and evaluate interventions aimed at enhancing neuroplasticity and mitigating cognitive decline in this population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclaimers\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe views expressed in this article are those of the authors and do not necessarily represent the official position of the University of Leeds, Leeds Teaching Hospitals, or any affiliated organizations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eWe thank the clinical nurse specialists, Rachel Backhouse, Lindsey Hankey, and Tessa Mason on the Haematology Unit at St James\u0026rsquo; Hospital for their invaluable support with patient screening and facilitating recruitment. We also acknowledge the contributions of MSc student researchers Weiyang Wang and Emily Caton, and undergraduate volunteers, who assisted with cognitive assessments as part of their dissertation projects. Artificial intelligence was not involved in the writing of this manuscript and is not acknowledged as an author. However, ChatGPT (OpenAI) was used during the research process to support coding tasks in R Studio. Specifically, it assisted with adapting code and interpreting error messages during the refinement of statistical analyses, including ANCOVA and Bayesian modelling. All AI-generated outputs were reviewed and validated by the authors to ensure accuracy and appropriateness for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis research was undertaken in partial fulfilment of the lead author\u0026apos;s PhD, funded by the University of Leeds Doctoral Scholarship (2023\u0026ndash;2027). The work was also supported by the UK Research and Innovation Impact Accelerator Award under Medical Research Council (MRC), grant reference: IAA4175/127410. The funders had no role in study design, data collection, analysis, interpretation, manuscript preparation, or the decision to submit for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u0026nbsp;\u003c/strong\u003eThis study was performed in line with the principles of the Declaration of Helsinki and British Psychological Society Code of Ethics and Conduct. Approval was granted by the NHS Health Research Authority (REC Reference: 23/PR/0461, IRAS 325678; Granted: 16/06/23) and the University of Leeds Research Ethics Committee (PSYC- PSCETHS-782; Granted: 30/11/2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Ethical approval was granted by the NHS Health Research Authority (REC reference: 23/PR/0461; IRAS ID: 325678; approval granted 16/06/2023) and the University of Leeds Research Ethics Committee (PSYC-PSCETHS-782; approval granted 30/11/2023).\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study. This manuscript does not contain any individual person\u0026rsquo;s data in any form that could lead to identification. Consent to publish anonymised data was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of Potential Competing Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003cbr\u003e\u003c/strong\u003e\u003cem\u003eConceptualisation:\u0026nbsp;\u003c/em\u003eSumayyah Patel, Melanie Burke, Chris Parrish, Frances Seymour; \u003cem\u003eMethodology:\u003c/em\u003e Sumayyah Patel, Melanie Burke, Chris Parrish, Frances Seymour; \u003cem\u003eFormal analysis and investigation:\u003c/em\u003e Sumayyah Patel, Chris Parrish, Frances Seymour; \u003cem\u003eData collection:\u003c/em\u003e Sumayyah Patel; \u003cem\u003eRecruitment:\u0026nbsp;\u003c/em\u003eChris Parrish, Frances Seymour; \u003cem\u003eVisualization:\u003c/em\u003e Sumayyah Patel; \u003cem\u003eWriting \u0026ndash; original draft preparation:\u003c/em\u003e Sumayyah Patel; \u003cem\u003eWriting \u0026ndash; review and editing:\u003c/em\u003e Sumayyah Patel, Melanie Burke, Chris Parrish, Frances Seymour; \u003cem\u003eSupervision:\u003c/em\u003e Melanie Burke, Chris Parrish, Frances Seymour.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;De-identified participant data supporting the findings of this study are available from the corresponding author upon reasonable request. Requests should include a brief description of the intended use of the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCook G, Larocca A, Facon T, Zweegman S, Engelhardt M. Defining the vulnerable patient with myeloma\u0026mdash;a frailty position paper of the European Myeloma Network. Leukemia. 2020;34(9):2285\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarder H, Cornelissen JJ, Van Gool AR, Duivenvoorden HJ, Eijkenboom WM, van den Bent MJ. Cognitive functioning and quality of life in long-term adult survivors of bone marrow transplantation. Cancer. 2002;95(1):183\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHshieh TT, Jung WF, Grande LJ, Chen J, Stone RM, Soiffer RJ, Driver JA, Abel GA. Prevalence of cognitive impairment and association with survival among older patients with hematologic cancers. 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P28-3 Clinical application of portable fNIRS to measure cognitive impairment after chemotherapy in a colorectal cancer patient. Ann Oncol. 2024;35:S1374\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neuroscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nros","sideBox":"Learn more about [BMC Neuroscience](http://bmcneurosci.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nros/default.aspx","title":"BMC Neuroscience","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Blood Cancer, Cognition, Processing speed, Drift Diffusion Modelling","lastPublishedDoi":"10.21203/rs.3.rs-8987235/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8987235/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study examined whether adults with Multiple Myeloma (MM) show measurable cognitive differences compared with neurologically healthy peers. The primary research question focused on identifying domain-specific cognitive deficits and determining whether standard screening tools adequately capture cognitive abilities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA cross-sectional design compared 45 adults with MM to 40 age-matched controls. Participants completed 40\u0026ndash;50 minutes of cognitive and psychological assessments, including the Montreal Cognitive Assessment (MoCA), validated measures of mood and daily functioning, and a digitised cognitive battery assessing key cognitive domains. Group differences in reaction time (RTs) and accuracy were analysed using ANCOVAs adjusting for age and education, Bayesian and EZ-drift diffusion modelling (EZ-DDM) to characterise domain specific deficits and latent decision-making processes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGroup-level analyses revealed slower RTs in MM following adjustment for age and education, with accuracy largely preserved. Bayesian modelling identified multi-domain RT-related deficits in ~\u0026thinsp;22% of MM patients, particularly in cognitive flexibility and semantic processing. EZ-DDM indicated that group differences were driven by reduced drift rates and prolonged non-decision times in MM, suggesting slower evidence accumulation with slower sensory encoding and/or motor execution.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCognitive slowing in MM is selective and heterogeneous, with processing speed emerging as the most sensitive marker. Conventional screening may underestimated subtle deficits, underscoring the need for longitudinal and neuroimaging studies to disentangle disease and treatment related effects. Preserving processing speed, critical for everyday functioning and social interactions, should be a priority in cancer and chronic disease research.\u003c/p\u003e","manuscriptTitle":"Cognitive Speed–Accuracy Dissociation in Multiple Myeloma: A Cross‑Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 17:36:43","doi":"10.21203/rs.3.rs-8987235/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T17:25:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T15:50:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T12:23:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170350289711807340865753067659336100520","date":"2026-03-30T01:40:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167011016787753579459429739693229309181","date":"2026-03-24T14:01:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-03T08:05:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-03T06:53:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T13:10:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T13:08:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neuroscience","date":"2026-02-27T10:53:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neuroscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nros","sideBox":"Learn more about [BMC Neuroscience](http://bmcneurosci.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nros/default.aspx","title":"BMC Neuroscience","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cae5664f-efda-4e44-b6a6-2b00e93a7942","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T13:08:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 17:36:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8987235","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8987235","identity":"rs-8987235","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}