Cognitive impairment and brain atrophy in patients with newly diagnosed aggressive lymphoma undergoing standard chemotherapy: a normative analysis

Cognitive impairment and brain atrophy in patients with newly diagnosed aggressive lymphoma undergoing standard chemotherapy: a normative analysis | 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 impairment and brain atrophy in patients with newly diagnosed aggressive lymphoma undergoing standard chemotherapy: a normative analysis Charlotte Ditchfield, Priscilla Gates, Juan Dominguez Duque, Haryana M Dhillon, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5969242/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Cancer-related cognitive impairment (CRCI) can impact daily-life functioning of people with aggressive lymphoma. While many studies have examined the neural substrates implicated in CRCI, most have used group-based analyses, which may mask individual differences. In the present study, we used normative analysis to examine longitudinal changes in cognitive functioning and brain morphology at the level of the individual patient. Methods Nine participants with newly diagnosed aggressive lymphoma underwent neuropsychological assessment and anatomical MR before and 6–8 weeks after chemotherapy. Cognitive test scores were converted to T- scores and classified as impaired if ≤ 30. Deviations in cortical thickness and surface area in the superior frontal gyrus (SFG) and anterior cingulate cortex (ACC) were computed at the level of the individual using the novel CentileBrain tool, with z -scores below \(\:-\) 1.96 and above 1.96 classified as infranormal and supranormal, respectively. Results Analyses revealed substantial between-subject variability over time and across outcome measures. Cognitive impairments in executive function and verbal memory were identified in three participants before and/or after chemotherapy. CentileBrain results showed seven participants had infranormal cortical thickness and/or surface area in the SFG at one or both time points, and one patient had infranormal values in the ACC. No participants exhibited supranormal values in either region at any time point. Conclusions Our findings provide a valuable foundation for developing more personalised interventions tailored to the specific cognitive and neural profiles of lymphoma survivors, paving the way for improved clinical care to alleviate and mitigate the impact of CRCI on long-term quality of life. Cancer-related cognitive impairment brain atrophy aggressive lymphoma normative analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Aggressive lymphoma is a type of cancer caused by the rapid proliferation of malignant lymphocytes, categorised as Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL). The most common is diffuse large B-cell lymphoma (DLBCL), with an estimated incidence of 2000 Australians annually. 1 Advances in diagnosis and treatments have improved survival rates, 2 with 5-year survival rates ranging between 74–95%, 3 4 and stable remissions in 68–88% of individuals. 5 However, this has led to a growing population of survivors living with the physical and neuropsychological sequelae of cancer and its treatment. Many studies have demonstrated people with aggressive non-central nervous system (non-CNS) lymphoma experience poorer physical and emotional wellbeing, compared with the general population. 6 7 Additionally, studies have shown these people experience cancer-related cognitive impairment (CRCI), 6 8 a term used to describe impaired functioning in domains including memory, attention, executive functioning, and processing speed that may arise before, during, or after cancer treatment as a consequence of the disease and its therapies. 9 While prevalence varies across studies, research indicates 13–70% of cancer survivors experience cognitive dysfunction during or after treatment. 10 These symptoms can last for years with a profound impact on cancer survivors’ quality of life, occupational and social functioning, and daily activities. 9 11 12 CRCI has been measured through subjective self-report and objective neuropsychological assessments, evaluating various cognitive functions including executive functioning, memory, attention, and processing speed. 13 14 Although little research has been conducted in people with aggressive lymphoma, a multitude of studies have been conducted in breast cancer and other non-CNS cancer groups, establishing evidence of CRCI across a wide array of cognitive domains. 9 14 15 Some literature has included people with haematological malignancies. 6 8 16–18 Cross-sectional studies have demonstrated aggressive lymphoma survivors have significantly lower subjective and/or objective cognitive functioning compared to healthy controls or normative data, even years after the completion of chemotherapy. 17 18 Recently, two longitudinal studies have been conducted, helping to disentangle the effects of cancer and chemotherapy over time. 6 8 For example, Janelsins et al. (2022) observed significant cognitive decline in tests of memory, attention and executive function in people with aggressive lymphoma from before to six months after chemotherapy, compared to healthy controls. 8 Similarly, Gates et al. (2024) found that compared to healthy controls, people with aggressive lymphoma performed significantly worse, both before and 6 to 8 weeks after chemotherapy, on measures of processing speed, executive function, learning and memory. Participants also reported their perceived cognitive impairment had a significantly greater impact on their quality of life compared to controls, and this difference was evident both before and after chemotherapy. 6 This highlights the impact of CRCI and its effect on the quality of life for people with aggressive lymphoma. Several magnetic resonance imaging (MRI) studies have attempted to elucidate the neural underpinnings of CRCI. Data have consistently shown reductions in grey matter density, alongside alterations in white matter microstructure and brain activation and connectivity in cancer survivors. 14 19–24 Most of these MRI studies have included people with breast cancer, employing a cross-sectional or retrospective design. 25–27 However, several longitudinal studies including other or mixed non-CNS cancer populations have demonstrated similar findings. 14 28–31 For example, a systematic review of 14 longitudinal studies identified moderate changes in grey matter density in frontal, parietal and temporal brain regions in non-CNS cancer populations after chemotherapy. 14 While some structural differences at baseline have been observed that could be attributed to the disease process, these changes tend to become more pronounced following chemotherapy. 19 Although the biological mechanisms are unclear, the neurotoxic side effects of chemotherapy – such as DNA damage, oxidative stress, hormonal dysregulation and inflammation – are thought to contribute to neuroanatomical alterations. 32 Indeed, longitudinal studies with a control group of people with cancer not treated with chemotherapy have shown some neuroanatomical changes are directly linked to chemotherapy rather than the cancer itself. 19 33–35 For instance, anatomical MRI studies found breast cancer survivors had reduced grey matter density, particularly in frontal regions, one month after chemotherapy, while no significant changes were evident in people not treated with chemotherapy. 19 34 Although the presence of grey matter density reductions has been consistently observed in frontal regions, the exact subregions in this lobe have varied across studies. 14 To provide a more precise understanding of these variations in anatomical location, a recent voxel-wise meta-analysis of eight studies revealed cancer survivors treated with chemotherapy exhibited significantly reduced grey matter density in the right superior frontal gyrus (SFG) and right anterior cingulate (ACC) compared to non-cancer controls and cancer survivors not treated with chemotherapy. 24 Interestingly, reduced grey matter volume in these regions has been shown to correlate with objective and subjective cognitive functioning. 14 19 26 For example, Inagaki et al. (2007) found that, in cancer survivors one year after chemotherapy, greater atrophy in the right SFG was associated with poorer visual memory and attention/concentration, while atrophy in the cingulate gyrus was associated with poorer working memory. 26 Despite these findings, only two studies, to our knowledge, have examined alterations in brain volume specifically in people with haematological malignancies. 29 30 These studies identified neuroanatomical changes from baseline to one-year post-haematological stem cell transplantation (HSCT), which involves high-dose chemotherapy. People exhibited decreased grey matter volume in the bilateral middle frontal gyrus and left caudate nucleus, 30 and reduced mean and axial diffusivity in diffuse brain regions. 29 However, these studies lacked a true baseline because they had previously received chemotherapy unrelated to the HSCT regimen. Although previous studies have advanced knowledge on grey matter volume changes in cancer populations, much less is known about the specific roles of cortical thickness and surface area as distinct measures of brain atrophy. 36–38 Cortical thickness and surface area represent different aspects of cortical structure: thickness is linked to the density of cells within cortical columns, while surface area relates to the number of these columns in a given region. 39 These two measures are shaped by different cellular processes due to their unique genetic and environmental determinants, suggesting that the factors influencing the variation in cortical thickness may differ from those affecting the variation in surface area. 39 40 Furthermore, because grey matter volume is a product of both thickness and surface area, disentangling these components can provide more biologically-specific insights into patterns of brain atrophy. 41 However, research examining cortical thickness and surface area changes in cancer populations is limited, with only three known studies using these metrics. 37 38 42 Wu et al. (2022) found, compared to healthy controls, people with lung cancer had significantly lower pre-chemotherapy cortical thickness in several regions, and it was the only measure that declined further after chemotherapy, whereas volume changes were restricted to the temporal regions and no changes were observed in surface area. 38 Conversely, Mentzelopoulos et al. (2021) identified reductions in both cortical thickness and volume across multiple brain regions after chemotherapy, 37 while Shiroishi et al. (2017) observed decreases in either cortical thickness or surface area in various regions. 42 This highlights the importance of examining cortical thickness and surface area separately, as each measure may capture unique aspects of brain atrophy that could be overlooked when focusing solely on volume changes. Whilst the above-mentioned findings have provided valuable insights into cognitive and neuroanatomical changes in cancer populations, they are based on group-level analyses which may obscure important individual differences. 20 It is important to investigate these changes using single-subject/normative analysis in order to take into account the inherent heterogeneity within these cancer populations. It is not surprising that cognitive and neuroimaging findings have been mixed, considering the variability in demographic and clinical characteristics of cancer survivors, including variables such as cancer type, disease stage, treatment regime, and number of cycles of chemotherapy. 23 This variability has been reflected in the heterogeneity of affected cognitive domains and brain regions observed across studies, 14 along with findings showing that only a subset of people exhibit cognitive decline following chemotherapy. 43–46 Therefore, single-subject analysis has the potential to reveal subtle neuroanatomical changes not detected in group studies. While there is a paucity of single-subject analysis in cancer populations, there is emerging literature using single-subject/normative analyses in psychiatric, 47 48 and neurological disorders. 49 50 For example, Allen et al. (2024) used a novel open source tool called CentileBrain, which provides a normative framework for regional brain morphometrics, developed from a sample of 37,407 healthy individuals by the ENIGMA Lifespan Group. 47 51 Using CentileBrain, they found mostly overlapping deviations in brain structure between individuals at high risk for psychosis and typically developing groups, but distinct differences in those who later developed a psychotic disorder suggesting potential early markers of conversion to psychosis. 52 Building on these findings, our study will use CentileBrain to identify deviations from typical brain structure in people with aggressive lymphoma, along with single-subject analysis to investigate changes in cognitive functioning at the level of the individual patient. The aims of our study are twofold. Firstly, we will examine changes in cognitive function using a comprehensive set of objective neuropsychological tests recommended by the International Cognition and Cancer Task force (ICCTF), 10 in people with aggressive non-CNS lymphoma before and approximately 6–8 weeks after chemotherapy compared to population norms. We hypothesise that cognitive test scores from individual participants will deviate from the population norms at both timepoints, 6 with large between-subject variability in the affected domains. Secondly, using the CentileBrain framework, we will explore alterations in cortical thickness and surface area in these participants from before and 6–8 weeks after chemotherapy. We expect the cortical thickness and surface area values from frontal regions of individual participants, including the SFG and ACC, 24 will deviate from the normative group at both timepoints. Methods Study design This study presents analyses of the MRI scans from a subsample of a larger-scale longitudinal study cognitive impacts of chemotherapy in people with aggressive lymphoma. 53 54 All participants provided written informed consent. Ethical approval was granted by the Human Research Ethics Committees of Austin Health (HREC 55582/Austin-2019) and Deakin University (DUHREC 2024-024). Participants We included nine participants (7 male, 2 female) aged 29–78 years ( M = 60.00, SD = 14.75) recruited from a specialised haematology department at Austin Health in Melbourne, Australia. Participants had to meet the following inclusion criteria: (i) aged over 18 years; (ii) newly diagnosed with HL, DLBCL, Burkitt lymphoma, transformed follicular lymphoma, or grade 3B follicular lymphoma; (iii) treatment naïve at time of enrolment and scheduled to undergo standard cancer treatment; (iv) fluent in English; and (v) a documented Eastern Cooperative Oncology Group (ECOG) performance status \(\:\le\:\) 2. Exclusion criteria included the following: (i) lymphomatous central nervous system involvement; (ii) prior or planned cranial radiotherapy; (iii) a life expectancy < 12 months; (iv) any medical condition that could compromise adherence or lead to prolonged hospitalisation; (v) a documented history of past or current substance abuse; or (vi) major psychiatric disorder (e.g., schizophrenia). Table 1 provides a summary of the clinical and demographic characteristics of participants. Insert Table 1 : Summary of Demographic and Clinical Characteristics Procedure Participants underwent MRI scans and neuropsychological assessments at two time points: (i) pre-chemotherapy at the time of diagnosis; and (ii) approximately 6–8 weeks post-chemotherapy completion. The pre-chemotherapy MRI scans were performed on average 12.9 days (SD = 9.8, range 3 to 29 days) from date of cancer diagnosis. The pre-chemotherapy neuropsychological assessments were performed on average 13 days (SD = 10.9, range 0 to 29 days) from date of diagnosis. The post-chemotherapy MRI scans were performed on average 7.8 weeks (SD = 4.7, range 3 to 15 weeks) from date of chemotherapy completion. The post-chemotherapy neuropsychological assessments were performed on average 9 weeks (SD = 7.3, range 3 to 26 weeks) from date of chemotherapy completion, which is in line with standard clinical practice. Neuropsychological assessment A series of neuropsychological tests were used to assess cognitive functioning in line with recommendations by the ICCTF (Wefel et al., 2011). The cognitive domains included: (i) attention/working memory tested via the Digit Span of the Weschler Adult Intelligence Scale-Revised (WAIS-R), 55 (ii) information processing speed via the Trail Making Test (TMT) Part A, 56 (iii) executive function via the TMT Part B, 56 and the Stroop Colour and Word Test (SCWT), 57 (iv) verbal learning and memory via the Hopkins Verbal Learning Test-Revised (HVLT-R), 58 and (v) verbal fluency via the Controlled Oral Word Association Test (COWAT). 59 See Table 2 for a detailed overview of the neuropsychological tests. Insert Table 2 : Overview of Neuropsychological Test Battery MRI data acquisition MRI acquisition was performed using a 3T Siemens Magnetom Skyra scanner with a 64-channel phased-array head coil at Austin Health. The study was part of a larger-scale multimodal MRI study, 53 60 including anatomical MRI scans (T1-weighted and T2 FLAIR), diffusion-weighted imaging (DWI), and resting-state fMRI. Our study focused on the anatomical T1-weighted MRI scans acquired using a three-dimensional magnetisation prepared rapid gradient echo (MPRAGE) with the following parameters: 1 mm isotropic voxel; repetition time (TR) = 2300 ms; echo time (TE) = 2.98 ms; voxel size = 1.0 mm 3 ; field of view (FOV) = 256 mm; flip angle = 9°; 192 slices; 1.0 mm slice thickness; 256 x 256 matrix; and acquisition time (TA) = 5’43”. The MRI scans were conducted at two timepoints (see procedure); however, one participant withdrew from the post-chemotherapy MRI scan due to distress related to disease progression. MRI data processing We performed a quality assessment of the raw scans using an in-house protocol. Specifically, all T1-weighted images were visually inspected using Mango Viewer (Version 4.0.1) for motion-related artefacts, such as ghosting and striping, and other quality-related issues, such as signal inhomogeneity and susceptibility artefact. All scans passed the quality assessment. Structural images were processed and analysed using the FreeSurfer image analysis suite Version 7.4.1 ( http://surfer.nmr.mgh.harvard.edu ). This software performs a series of automated steps, including: (1) skull stripping; (2) Talairach transformation; (3) segmentation of subcortical white matter and deep grey matter structures; (4) cortical surface reconstruction; (5) parcellation of the cerebral cortex based on the Desikan-Killiany atlas; 61 and (6) calculation of cortical thickness and surface area within these regions. 62 Quality assurance of the registration and segmentation was undertaken by visual inspection using the ENIGMA protocol available at https://enigma.ini.usc.edu/protocols/imaging-protocols/ . No participants were excluded from the analyses. Regions of interest Regions of interest (ROIs) were chosen based on prior literature, showing these cortical regions to be vulnerable in people with cancer. 14 20 23 24 Specifically, we selected the following six cortical regions from the Desikan-Killiany atlas, 61 the right and left caudal part of the ACC, the right and left rostral part of the ACC, and the right and left SFG (see Fig. 1 for cortical ROIs). For each ROI, cortical thickness and surface area were calculated. Insert Fig. 1 : Cortical regions of interest Statistical analyses For the normative analyses of the neuropsychological test scores (Aim 1), raw test scores were converted to T- scores using published normative data, adjusted for age, gender, and education. Participants were classified as “impaired” on a particular cognitive test if their T -score was ≤ 30, corresponding to 2 standard deviations below the normative mean. 10 For the normative analyses of brain morphology (Aim 2), we used the CentileBrain framework, developed by the ENIGMA Lifespan Group. 47 Specifically, we used the CentileBrain online portal ( https://centilebrain.org ) to compute normative deviation metrics for cortical thickness and surface area in our regions of interest (caudal and rostral ACC, and SFG). This tool generated brain regional z -scores in our participants and compared them with a large sample of 37,407 healthy controls. 47 Per prior research, 52 brain regional z -scores below − 1.96 (5th percentile) and above 1.96 (95th percentile were classified as “infranormal” and “supranormal”, respectively. Results As shown in Figs. 2 , 3 , and 4 , we observed substantial between-subject variability in the results over time and across outcome measures in the nine participants. Impaired cognitive functioning was noted in the domains of executive functioning and verbal learning and memory. Infranormalities were more pronounced for cortical thickness than surface area and were more evident in the SFG compared to the ACC. Below, the results for each participant are presented as individual cases. Insert Fig. 2 : Cortical Thickness Z-scores Pre- and Post-Chemotherapy Insert Fig. 3 : Cortical Surface Area Z-scores Pre- and Post-Chemotherapy Insert Fig. 4 : Neuropsychological Test T-scores Pre- and Post-Chemotherapy Participant 1 A 47-year-old male diagnosed with stage 2A HL, treated with 6 cycles of the ABVD (adriamycin, bleomycin, vinblastine, and dacarbazine) chemotherapy regimen. Pre-chemotherapy, impaired cognitive function was observed in executive functioning (SCWT word, T = 25; colour, T = 30). Post-chemotherapy, impaired cognitive function persisted in executive functioning (SCWT colour, T = 29) and became evident in verbal learning and memory (HVLT retention, T = 27). Infranormal cortical thickness values in the left and right SFG were observed both pre-chemotherapy ( z = -2.43, p = .015; z = -2.93, p = .003) and post-chemotherapy ( z = -2.08, p = .038; z = -2.56, p = .010). Participant 2 A 66-year-old male diagnosed with stage 1A Grade 3B follicular lymphoma, treated with 6 cycles of the R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone) chemotherapy regimen. He scored within the normal range on all cognitive tests both pre- and post-chemotherapy. Z -scores of the six brain ROIs were also all within the normal range at both timepoints. Participant 3 A 69-year-old male diagnosed with stage 1A DLBCL, who underwent 7 cycles of the R-CHOP & 2 cycles of the high dose IT MTX (intrathecal methotrexate) chemotherapy regimen. He scored within the normal range on all cognitive tests at both timepoints. However, pre-chemotherapy, he exhibited infranormal cortical thickness values in the left and right SFG ( z = -2.00, p = .046; z = -2.22, p = .026). Post chemotherapy, cortical thickness values of the bilateral SFG normalised and were within normal range. However, the surface area of the right SFG was identified as infranormal ( z = -2.12, p = .034). Participant 4 A 78-year-old female diagnosed with stage 4A DLBCL, treated with 6 cycles of the Mini R-CHOP chemotherapy regimen. She scored within the normal range on all cognitive tests at both timepoints. However, pre-chemotherapy, cortical thickness of the right SFG was identified as infranormal ( z = -1.98, p = .048). An MRI scan was not administered post-chemotherapy due to disease progression. Participant 5; A 72-year-old male diagnosed with stage 1A DLBCL, received 2 cycles of the R-CHOP chemotherapy regimen. He demonstrated impaired cognitive function in verbal learning and memory both pre-chemotherapy (HVLT delayed recall, T = 19; retention, T = 19) and post-chemotherapy (HVLT delayed recall, T = 19; retention, T = 19). Pre-chemotherapy, all brain regional z-scores were within the normal range. However, post-chemotherapy, surface area of the right SFG was identified as infranormal ( z = -2.17, p = .030). Participant 6 : A 29-year-old male diagnosed with stage 4B HL, who underwent 4 cycles of the Esc-BEACOPP (escalated BEACOPP: bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisolone) chemotherapy regimen. He scored within the normal range on all cognitive tests at both timepoints. Pre-chemotherapy, z -scores of the six brain ROIs were also all within the normal range. However, infranormal cortical thickness values were evident in both the left and right SFG ( z = -2.10, p = .036; z = -2.20, p = .028) post-chemotherapy. Participant 7 A 60-year-old male diagnosed with stage 2A DLBCL, treated with 4 cycles of the R-CHOP chemotherapy regimen. Pre-chemotherapy, he demonstrated impaired cognitive function in verbal learning and memory (HVLT delayed recall, T = 30). Post-chemotherapy, his verbal learning and memory function had normalised, and all cognitive test scores were within the normal range. Pre-chemotherapy, the cortical thickness of the right caudal ACC ( z = -1.98, p = .048) and right SFG ( z = -2.19, p = .029), as well as the surface area of the right rostral ACC, were identified as infranormal ( z = -1.99, p = .047). Post-chemotherapy, the cortical thickness of the right caudal ACC normalised, however, the cortical thickness of the right SFG ( z = -3.17, p = .002) and surface area of the right rostral ACC ( z = -1.98, p = .048) remained infranormal. Additionally, the cortical thickness of the left SFG was identified as infranormal ( z = -2.65, p = .008). Participant 8 A 63-year-old female diagnosed with stage 1A DLBCL, who received 3 cycles of the R-CHOP chemotherapy regimen. She scored within the normal range on all cognitive tests both pre- and post-chemotherapy. Z -scores of the six brain ROIs were also all within the normal range at both timepoints. Participant 9 A 56-year-old male diagnosed with stage 2B DLBCL, treated with 3 cycles of the R-CHOP chemotherapy regimen. He scored within the normal range on all cognitive tests at both timepoints. However, pre-chemotherapy, the surface area in the left SFG was identified as infranormal ( z = -2.11, p = .035). Post-chemotherapy, values of the surface area of the left SFG normalised and were within normal range. However, the surface area of the right SFG was identified as infranormal ( z = -1.99, p = .047). Discussion Our novel study employed single-subject analysis to examine longitudinal changes in cognitive functioning and brain morphology in people with aggressive lymphoma. As hypothesised, impaired cognitive functioning and infranormal cortical thickness and surface area were observed, with considerable variability across participants and time points. Despite this heterogeneity, some commonalities were observed. Specifically, impaired cognitive functioning was observed in the domains of executive functioning and verbal learning and memory (in a small subset of patients). Additionally, infranormalities were more pronounced for cortical thickness than surface area, and more evident in the SFG compared to the ACC. The finding that impaired cognitive functioning was mainly observed in tests of executive functioning and verbal learning and memory, contrasts with prior research on people with aggressive lymphoma, which have implicated a broader range of domains, including processing speed, attention/working memory, and verbal fluency. 6 8 17 This inconsistency may be attributed to the stringent criteria we used to define cognitive impairment (ICCTF guidelines) resulting in mild impairments in other domains going undetected. Supporting this notion, a meta-analysis of 13 studies found that only executive function and memory (from seven cognitive domains in total) showed evidence of impaired functioning in cancer populations treated with chemotherapy. 13 This suggests these areas may be more consistently impacted across studies, while other domains may exhibit more subtle changes. In particular, verbal learning and memory may be vulnerable to CRCI, as suggested by our finding that these were the most frequently impaired cognitive domains in a subset of our patients (participants 1, 5, and 7). However, despite this small subgroup, this is supported by other studies of people with haematological malignancies, 29 63 which reported verbal learning and memory are especially prone to impairment both before and after treatment. For instance, Correa et al. (2016) found that people exhibited significantly poorer verbal memory performance compared to healthy controls both prior to and one year after undergoing hematopoietic stem cell transplantation (HSCT) – which involves a high-dose chemotherapy regimen – whereas other cognitive domains were only impacted at one year post-HSCT. 29 Similarly, Syrjala et al. (2011) found that, while other cognitive functions like attention and processing speed showed signs of recovery, verbal recall remained persistently impaired from 80 days to five years post-HSCT. 63 Collectively, this suggests that verbal learning and memory may be uniquely susceptible to impairment in this population due to the combined effects of the cancer and the cumulative impact of neurotoxic effects of chemotherapy. Providing insight into the potential neural underpinnings of CRCI, we observed infranormalities in the SFG and ACC. This aligns with an extensive body of research demonstrating neuroanatomical alterations in frontal regions of non-CNS cancer populations. 23 24 64 Nonetheless, while the majority of our participants showed infranormalities in the SFG, only one patient was found to exhibit infranormalities in the ACC (who also showed impaired cognitive function in verbal learning and memory). This is intriguing, particularly in light of the meta-analysis by Niu et al. (2021) which found reductions in grey matter density in the right ACC and SFG among cancer populations treated with chemotherapy across all non-CNS cancer studies, 24 whereas only the right SFG in their sub-analysis of breast cancer studies. This suggests alterations in frontal regions may vary, with the SFG being more consistently affected, while changes in the ACC may be more variable depending on cancer-type, patient characteristics or treatment factors. Similarly, infranormalities were more pronounced for cortical thickness than surface area, suggesting the former may be more susceptible to chemotherapy or cancer-related factors. MRI research measuring cortical thickness and surface area is limited, with most studies focusing on grey matter volume or density. However, a recent study by Wu et al. (2022) assessed cortical thickness, surface area and volume in people with lung cancer both before and 2–4 months after chemotherapy. 38 They observed significantly lower cortical thickness in the frontal, temporal, and parietal regions of participants pre-chemotherapy, whereas significant volume reductions were limited to the temporal regions, and no differences were found in surface area. Furthermore, only cortical thickness showed a significant decline post-chemotherapy, while surface area and volume remained unchanged. 38 This suggests that grey matter volume changes noted in the broader literature may be largely attributable to cortical thickness rather than surface area. This is plausible given cortical thickness and surface area are influenced by different cellular processes and genetic origins, 39 40 and grey matter volume is the product of both measures. 41 Thus, our findings extend prior research indicating that changes in grey matter volume may be largely driven by alterations in cortical thickness rather than surface area, underscoring the importance of examining these variables separately to understand the mechanisms involved in cancer and its treatment. Despite some overlap in the type of impairments and infranormalities observed, there was large variability in cognitive functioning and neuroanatomical trajectories over time. Some participants exhibited cognitive impairments or infranormalities before chemotherapy which later normalised 6–8 weeks post chemotherapy, others showed impairments only after chemotherapy, and some displayed both. Prior research has similarly noted diversity in cognitive outcomes over time. 65 For instance, Jansen et al. (2008) found that 33% of breast cancer participants exhibited significant cognitive decline following chemotherapy, while 17% showed notable improvements in cognitive functioning. 65 Such patterns highlight the person-specific evolution of CRCI and related brain changes, which may be influenced by a complex interplay of factors, including pre-existing vulnerabilities, treatment effects, and recovery mechanisms. Clinically, the heterogeneity found across participants and time underscores the importance of individualised approaches or normative analyses in managing CRCI. Unlike group-level analyses, which may overlook between-subject variability, single-subject analysis allows for the precise characterisation of cognitive and neuroanatomical changes at the individual level, capturing the unique trajectories of decline and recovery people may experience. 20 This is crucial in the context of CRCI, given the high variability in impairments and neuroanatomical changes observed in our study; some participants exhibited deficits in specific domains, such as executive function or verbal memory, while others did not show any impairment. Using these individualised profiles, clinicians can develop interventions specifically designed to address each person's unique needs. Indeed, evidence has shown that group-based programs, such as a cognitive rehabilitation program, 66 and a mindfulness-based stress reduction program, 67 can significantly improve cognitive functioning in cancer survivors who have undergone chemotherapy. Our results suggest tailoring such programs to individual needs may improve outcomes further. Furthermore, our study demonstrates the value of utilising CentileBrain when assessing people with cancer. The program provides clinicians with information on any structural brain changes at the level of the individual. This information could be helpful in early identification of those at higher risk of CRCI, enabling more timely and tailored interventions to mitigate or alleviate cognitive decline. 68 Nonetheless, there are several limitations in this study that should be addressed. Firstly, the follow-up period was limited to approximately 6–8 weeks after chemotherapy (with some variability in the timing of post-treatment assessments). This time period may have missed the detection of longer-term cognitive or neuroanatomical alterations that could develop or resolve. This is particularly relevant given that grey matter volume in the SFG has been shown to be positively associated with time since chemotherapy, suggesting this region may gradually recover. 24 Furthermore, McDonald et al. (2010) reported that while some brain regions showed signs of recovery between one month and one year post-chemotherapy, other regions did not, highlighting the variability in recovery across different brain areas. 69 Similarly, cognitive functions like verbal recall and executive functioning have been reported to remain impaired up to five years post-treatment in cancer survivors. 63 70 Therefore, future research should extend follow-up to at least one-year post-chemotherapy, to better capture the long-term cognitive and neuroanatomical changes in survivors of aggressive lymphoma, given their increasing long-term survivorship. Nonetheless, this study is unique in obtaining pre-cancer treatment data in this under-researched cancer population, offering critical insights into the early effects of aggressive lymphoma and its treatment. A second limitation of this study was the reliance on objective measures of cognitive function. 10 Subjective reports, such as patient-reported cognitive concerns (e.g., the Functional Assessment of Cancer Therapy-Cognitive Function [FACT-Cog] scale) 71 may provide more sensitive and early indicators of cognitive changes. This is especially relevant for high-functioning individuals who often report cognitive concerns before objective measures detect a decline in cognitive functioning. 72 Thus, future research should incorporate both objective and subjective assessments to sufficiently capture the complexities of patient experiences beyond what objective tests alone can reveal. 10 Finally, because of our strong, anatomical, a-priori hypothesis, our neuroimaging analysis was restricted to specific regions of interest thereby potentially neglecting other brain regions that may be affected by cancer and its treatment. This is an important limitation because research has shown cognitive changes in cancer populations are not confined to these (frontal) regions alone. For example, Li and Caeyenberghs (2018) found reduced grey matter density in other areas of the frontal lobe, such as the middle frontal gyrus, as well as in temporal and parietal regions, suggesting these areas may also play a role in CRCI. 14 Future research should therefore include a range of brain regions to provide a more comprehensive understanding of the neural underpinnings of CRCI. Conclusion This study represents a significant advancement in understanding CRCI in people with aggressive lymphoma by using single-subject analysis to explore cognitive function and neuroanatomical changes from pre- to post-chemotherapy. Unlike traditional group-based studies, this method allowed for the detailed examination of individual variability, revealing diverse trajectories of cognitive impairment and brain structural alterations that may otherwise be missed. The use of normative data from CentileBrain further enhanced the sensitivity of detecting subtle changes in cortical thickness and surface area, offering a more biological-specific understanding of the neural underpinnings of CRCI. The study's focus on both cortical thickness and surface area is particularly novel, highlighting the differential impacts of chemotherapy on these measures and underscoring the need to consider both in future research. Moreover, the inclusion of pre-treatment data provides a deeper understanding of the early neuroanatomical and cognitive changes that may occur for this population. Collectively, these insights provide a valuable foundation for developing more personalised interventions tailored to the specific cognitive and neural profiles of lymphoma survivors, paving the way for improved clinical care to alleviate and mitigate the impact of CRCI on long-term quality of life. Declarations Author contributions: PG contributed all aspects of study design and collection of MRI data as part of her PhD study. She and was involved in development of research questions, methodology, data analysis and the overall preparation and writing of the manuscript. CD contributed to the literature reviews and study design, was involved in all aspects of data analysis and the overall preparation and writing of the manuscript. She undertook this research as part of her Honours thesis. KC was CD Honours primary supervisor and contributed to the original concept for this study and participated in all aspects of the design, research questions, methodology, data analysis, manuscript preparation and revision. JD contributed to study design and was involved in data analysis and the overall preparation and writing of the manuscript. HD, VD and CW contributed to study design and writing of the manuscript. All authors have been involved in preparing this manuscript and have read and approved the final version. Funding: This study was supported by a non-restricted educational grant from Celgene Pty Ltd to support the costs associated with the acquisition of the MRI data. This study was also supported by a PhD scholarship provided to PG by the Olivia Newton-John Cancer Wellness and Research Centre through the Victorian Cancer Agency Data availability: De-identified data supporting the findings of this study are available from the corresponding author upon request. Code availability: NA Ethics approval: Ethical approval has been granted by Austin Health Human Rights Ethics Committee (HREC) in Victoria Australia. This study was conducted in compliance with the principles of the Declaration of Helsinki (2013) and the principles of Good Clinical Practice and the Australian National Statement on Ethical Conduct in Human Research. Trial registration number: Australian New Zealand Clinical Trials Registry ACTRN12619001649101 on 26 th November 2019. Conflict of interest: The authors have declared no conflicts of interest. Consent to participate: Informed consent was obtained from all individual participants included in the study. Consent for publication: Participants signed informed consent regarding publishing their de-identified data. Acknowledgements: We thank the thirty participants who volunteered their time to this study. We also acknowledge and thank Professor Mei Krishnasamy and Associate Professor Karla Gough who were supervisors of P Gates PhD study who contributed their expertise. References Australian Institute of Health and Welfare. Cancer data in Australia 2019 [Available from: https://www.aihw.gov.au/reports/cancer/cancer-data-in-australia/contents/summary. Wright F, Hapgood G, Loganathan A, et al. Relative survival of patients with lymphoma in Queensland according to histological subtype. 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[published Online First: 2003/11/13] Tables Table 1: Summary of Demographic and Clinical Characteristics Participant ID Age in years Sex Years of formal education Diagnosis Stage Chemotherapy regime and number of cycles Participant 1 47 Male 16 HL 2A ABVD x 6 Participant 2 66 Male 14 Grade 3B follicular 1A R-CHOP x 6 Participant 3 69 Male 12 DLBCL 1A R-CHOP x 6 and high dose IT MTX x 2 Participant 4 78 Female 11 DLBCL 4A Mini R-CHOP x 6 Participant 5 72 Male 18 DLBCL 1A R-CHOP x 2 Participant 6 29 Male 14 HL 4B Esc-BEACOPP x 4 Participant 7 60 Male 11 DLBCL 2A R-CHOP x 4 Participant 8 63 Female 13 DLBCL 1A R-CHOP x 3 Participant 9 56 Male 13 DLBCL 2B R-CHOP x 3 Note. HL = Hodgkin lymphoma; DLBCL = diffuse large B cell lymphoma; ABVD = adriamycin, bleomycin, vinblastine, and dacarbazine; R-CHOP = rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone; IT MTX = intrathecal methotrexate; Esc-BEACOPP = bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisolone. Table 2: Overview of Neuropsychological Test Battery Test Cognitive domain Instruction for participant Dependent variable(s) WAIS-R DS Attention/ working memory Recall visually presented numbers of increasing length in the same or reverse order. Total correct responses TMT-A Information processing speed Connect 25 numbered circles in ascending order quickly and accurately. Time to complete (seconds) TMT-B Executive function Connect 24 alternating numbers and letters in alternating ascending order quickly and accurately. Time to complete (seconds) SCWT Executive function Read colour words in black ink, colours of patches and the ink colour of incongruent words. Time (seconds) for each condition and interference score HVLT-R Verbal learning and memory Recall 12 verbally presented words over three trials, followed by a delayed recall and a 24-word recognition task to identify target words from distractors. Total and delayed recall, percentage retained and recognition/ discrimination COWAT Verbal fluency Generate words, orally or in writing, from a given category or starting with a specific letter in 60 seconds Number of verbal categories, verbal letters and total written words Note. WAIS-R DS = Weschler Intelligence Scale-Revised DS; TMT-A = Trail Making Test-Part A; TMT-B = Trail Making Test-Part B; SCWT = Stroop Colour Word Test; HVLT-R = Hopkins Verbal Learning Test-Revised; COWAT = Controlled Oral Word Association Test. Additional Declarations No competing interests reported. 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Medial view of the left and right hemispheres showing the cortical parcellation of the regions of interest based on the Desikan-Killiany atlas (Desikan et al., 2006), processed using FreeSurfer image analysis suite (Version 7.4.1; http://surfer.nmr.mgh.harvard.edu). SFG = superior frontal gyrus; ACC = anterior cingulate cortex.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5969242/v1/d6d1378c7257e852245c99e5.png"},{"id":75710271,"identity":"669a0e76-a70b-42f7-bde2-fb7448b698d2","added_by":"auto","created_at":"2025-02-07 10:55:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCortical Thickness Z-scores Pre- and Post-Chemotherapy. \u003c/strong\u003eNote. P1–P9 represent participants 1–9. T1 = pre-chemotherapy; T2 = post-chemotherapy. The shaded area is the normal z-score range (-1.96–1.96). A–F represent scores for each ROI: A = left caudal ACC, B = right caudal ACC, C = left rostral ACC, D = right rostral ACC, E = left SFG, F = right SFG. ACC = Anterior Cingulate Cortex; SFG = Superior Frontal Gyrus.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5969242/v1/e6de1c5514634ed50f91746d.png"},{"id":75708990,"identity":"41d80ff2-30a5-45b6-a522-62344746fc97","added_by":"auto","created_at":"2025-02-07 10:47:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":183042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCortical Surface Area Z-scores Pre- and Post-Chemotherapy. \u003c/strong\u003eNote. P1–P9 represent participants 1–9. T1 = pre-chemotherapy; T2 = post-chemotherapy. The shaded area is the normal z-score range (-1.96–1.96). A–F represent scores for each ROI: A = left caudal ACC, B = right caudal ACC, C = left rostral ACC, D = right rostral ACC, E = left SFG, F = right SFG. ACC = Anterior Cingulate Cortex; SFG = Superior Frontal Gyrus.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5969242/v1/7e6322fc0517e3b0b2e4c7fa.png"},{"id":75709000,"identity":"efc56978-e415-409c-bd5c-b91db832eafb","added_by":"auto","created_at":"2025-02-07 10:47:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":150074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeuropsychological Test T-scores Pre- and Post-Chemotherapy. \u003c/strong\u003eNote. P1–P9 represent participants 1–9. T1 = pre-chemotherapy; T2 = post-chemotherapy. The shaded area is the normal range (\u0026gt; 30). A–E represent scores for each cognitive test: A = COWAT; B = TMT; C = SCWT; D = WAIS-R Digit Span; E = HVLT-R.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5969242/v1/49762ce1ad8df4146d2c3fd9.png"},{"id":75944386,"identity":"88cdeb91-4f41-484e-b2f0-98cd793d3be6","added_by":"auto","created_at":"2025-02-10 20:01:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2150826,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5969242/v1/90b681e1-9dd8-4511-8c55-d546c72a8049.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive impairment and brain atrophy in patients with newly diagnosed aggressive lymphoma undergoing standard chemotherapy: a normative analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAggressive lymphoma is a type of cancer caused by the rapid proliferation of malignant lymphocytes, categorised as Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL). The most common is diffuse large B-cell lymphoma (DLBCL), with an estimated incidence of 2000 Australians annually.\u003csup\u003e1\u003c/sup\u003e Advances in diagnosis and treatments have improved survival rates,\u003csup\u003e2\u003c/sup\u003e with 5-year survival rates ranging between 74\u0026ndash;95%,\u003csup\u003e3 4\u003c/sup\u003e and stable remissions in 68\u0026ndash;88% of individuals.\u003csup\u003e5\u003c/sup\u003e However, this has led to a growing population of survivors living with the physical and neuropsychological sequelae of cancer and its treatment.\u003c/p\u003e \u003cp\u003eMany studies have demonstrated people with aggressive non-central nervous system (non-CNS) lymphoma experience poorer physical and emotional wellbeing, compared with the general population.\u003csup\u003e6 7\u003c/sup\u003e Additionally, studies have shown these people experience cancer-related cognitive impairment (CRCI),\u003csup\u003e6 8\u003c/sup\u003e a term used to describe impaired functioning in domains including memory, attention, executive functioning, and processing speed that may arise before, during, or after cancer treatment as a consequence of the disease and its therapies.\u003csup\u003e9\u003c/sup\u003e While prevalence varies across studies, research indicates 13\u0026ndash;70% of cancer survivors experience cognitive dysfunction during or after treatment.\u003csup\u003e10\u003c/sup\u003e These symptoms can last for years with a profound impact on cancer survivors\u0026rsquo; quality of life, occupational and social functioning, and daily activities.\u003csup\u003e9 11 12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCRCI has been measured through subjective self-report and objective neuropsychological assessments, evaluating various cognitive functions including executive functioning, memory, attention, and processing speed.\u003csup\u003e13 14\u003c/sup\u003e Although little research has been conducted in people with aggressive lymphoma, a multitude of studies have been conducted in breast cancer and other non-CNS cancer groups, establishing evidence of CRCI across a wide array of cognitive domains.\u003csup\u003e9 14 15\u003c/sup\u003e Some literature has included people with haematological malignancies.\u003csup\u003e6 8 16\u0026ndash;18\u003c/sup\u003e Cross-sectional studies have demonstrated aggressive lymphoma survivors have significantly lower subjective and/or objective cognitive functioning compared to healthy controls or normative data, even years after the completion of chemotherapy.\u003csup\u003e17 18\u003c/sup\u003e Recently, two longitudinal studies have been conducted, helping to disentangle the effects of cancer and chemotherapy over time.\u003csup\u003e6 8\u003c/sup\u003e For example, Janelsins et al. (2022) observed significant cognitive decline in tests of memory, attention and executive function in people with aggressive lymphoma from before to six months after chemotherapy, compared to healthy controls.\u003csup\u003e8\u003c/sup\u003e Similarly, Gates et al. (2024) found that compared to healthy controls, people with aggressive lymphoma performed significantly worse, both before and 6 to 8 weeks after chemotherapy, on measures of processing speed, executive function, learning and memory. Participants also reported their perceived cognitive impairment had a significantly greater impact on their quality of life compared to controls, and this difference was evident both before and after chemotherapy.\u003csup\u003e6\u003c/sup\u003e This highlights the impact of CRCI and its effect on the quality of life for people with aggressive lymphoma.\u003c/p\u003e \u003cp\u003eSeveral magnetic resonance imaging (MRI) studies have attempted to elucidate the neural underpinnings of CRCI. Data have consistently shown reductions in grey matter density, alongside alterations in white matter microstructure and brain activation and connectivity in cancer survivors.\u003csup\u003e14 19\u0026ndash;24\u003c/sup\u003e Most of these MRI studies have included people with breast cancer, employing a cross-sectional or retrospective design.\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e However, several longitudinal studies including other or mixed non-CNS cancer populations have demonstrated similar findings.\u003csup\u003e14 28\u0026ndash;31\u003c/sup\u003e For example, a systematic review of 14 longitudinal studies identified moderate changes in grey matter density in frontal, parietal and temporal brain regions in non-CNS cancer populations after chemotherapy.\u003csup\u003e14\u003c/sup\u003e While some structural differences at baseline have been observed that could be attributed to the disease process, these changes tend to become more pronounced following chemotherapy.\u003csup\u003e19\u003c/sup\u003e Although the biological mechanisms are unclear, the neurotoxic side effects of chemotherapy \u0026ndash; such as DNA damage, oxidative stress, hormonal dysregulation and inflammation \u0026ndash; are thought to contribute to neuroanatomical alterations.\u003csup\u003e32\u003c/sup\u003e Indeed, longitudinal studies with a control group of people with cancer not treated with chemotherapy have shown some neuroanatomical changes are directly linked to chemotherapy rather than the cancer itself.\u003csup\u003e19 33\u0026ndash;35\u003c/sup\u003e For instance, anatomical MRI studies found breast cancer survivors had reduced grey matter density, particularly in frontal regions, one month after chemotherapy, while no significant changes were evident in people not treated with chemotherapy.\u003csup\u003e19 34\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlthough the presence of grey matter density reductions has been consistently observed in frontal regions, the exact subregions in this lobe have varied across studies.\u003csup\u003e14\u003c/sup\u003e To provide a more precise understanding of these variations in anatomical location, a recent voxel-wise meta-analysis of eight studies revealed cancer survivors treated with chemotherapy exhibited significantly reduced grey matter density in the right superior frontal gyrus (SFG) and right anterior cingulate (ACC) compared to non-cancer controls and cancer survivors not treated with chemotherapy.\u003csup\u003e24\u003c/sup\u003e Interestingly, reduced grey matter volume in these regions has been shown to correlate with objective and subjective cognitive functioning.\u003csup\u003e14 19 26\u003c/sup\u003e For example, Inagaki et al. (2007) found that, in cancer survivors one year after chemotherapy, greater atrophy in the right SFG was associated with poorer visual memory and attention/concentration, while atrophy in the cingulate gyrus was associated with poorer working memory.\u003csup\u003e26\u003c/sup\u003e Despite these findings, only two studies, to our knowledge, have examined alterations in brain volume specifically in people with haematological malignancies.\u003csup\u003e29 30\u003c/sup\u003e These studies identified neuroanatomical changes from baseline to one-year post-haematological stem cell transplantation (HSCT), which involves high-dose chemotherapy. People exhibited decreased grey matter volume in the bilateral middle frontal gyrus and left caudate nucleus,\u003csup\u003e30\u003c/sup\u003e and reduced mean and axial diffusivity in diffuse brain regions.\u003csup\u003e29\u003c/sup\u003e However, these studies lacked a true baseline because they had previously received chemotherapy unrelated to the HSCT regimen.\u003c/p\u003e \u003cp\u003eAlthough previous studies have advanced knowledge on grey matter volume changes in cancer populations, much less is known about the specific roles of cortical thickness and surface area as distinct measures of brain atrophy.\u003csup\u003e36\u0026ndash;38\u003c/sup\u003e Cortical thickness and surface area represent different aspects of cortical structure: thickness is linked to the density of cells within cortical columns, while surface area relates to the number of these columns in a given region.\u003csup\u003e39\u003c/sup\u003e These two measures are shaped by different cellular processes due to their unique genetic and environmental determinants, suggesting that the factors influencing the variation in cortical thickness may differ from those affecting the variation in surface area.\u003csup\u003e39 40\u003c/sup\u003e Furthermore, because grey matter volume is a product of both thickness and surface area, disentangling these components can provide more biologically-specific insights into patterns of brain atrophy.\u003csup\u003e41\u003c/sup\u003e However, research examining cortical thickness and surface area changes in cancer populations is limited, with only three known studies using these metrics.\u003csup\u003e37 38 42\u003c/sup\u003e Wu et al. (2022) found, compared to healthy controls, people with lung cancer had significantly lower pre-chemotherapy cortical thickness in several regions, and it was the only measure that declined further after chemotherapy, whereas volume changes were restricted to the temporal regions and no changes were observed in surface area.\u003csup\u003e38\u003c/sup\u003e Conversely, Mentzelopoulos et al. (2021) identified reductions in both cortical thickness and volume across multiple brain regions after chemotherapy,\u003csup\u003e37\u003c/sup\u003e while Shiroishi et al. (2017) observed decreases in either cortical thickness or surface area in various regions.\u003csup\u003e42\u003c/sup\u003e This highlights the importance of examining cortical thickness and surface area separately, as each measure may capture unique aspects of brain atrophy that could be overlooked when focusing solely on volume changes.\u003c/p\u003e \u003cp\u003eWhilst the above-mentioned findings have provided valuable insights into cognitive and neuroanatomical changes in cancer populations, they are based on group-level analyses which may obscure important individual differences.\u003csup\u003e20\u003c/sup\u003e It is important to investigate these changes using single-subject/normative analysis in order to take into account the inherent heterogeneity within these cancer populations. It is not surprising that cognitive and neuroimaging findings have been mixed, considering the variability in demographic and clinical characteristics of cancer survivors, including variables such as cancer type, disease stage, treatment regime, and number of cycles of chemotherapy.\u003csup\u003e23\u003c/sup\u003e This variability has been reflected in the heterogeneity of affected cognitive domains and brain regions observed across studies,\u003csup\u003e14\u003c/sup\u003e along with findings showing that only a subset of people exhibit cognitive decline following chemotherapy.\u003csup\u003e43\u0026ndash;46\u003c/sup\u003e Therefore, single-subject analysis has the potential to reveal subtle neuroanatomical changes not detected in group studies. While there is a paucity of single-subject analysis in cancer populations, there is emerging literature using single-subject/normative analyses in psychiatric,\u003csup\u003e47 48\u003c/sup\u003e and neurological disorders.\u003csup\u003e49 50\u003c/sup\u003e For example, Allen et al. (2024) used a novel open source tool called CentileBrain, which provides a normative framework for regional brain morphometrics, developed from a sample of 37,407 healthy individuals by the ENIGMA Lifespan Group.\u003csup\u003e47 51\u003c/sup\u003e Using CentileBrain, they found mostly overlapping deviations in brain structure between individuals at high risk for psychosis and typically developing groups, but distinct differences in those who later developed a psychotic disorder suggesting potential early markers of conversion to psychosis.\u003csup\u003e52\u003c/sup\u003e Building on these findings, our study will use CentileBrain to identify deviations from typical brain structure in people with aggressive lymphoma, along with single-subject analysis to investigate changes in cognitive functioning at the level of the individual patient.\u003c/p\u003e \u003cp\u003eThe aims of our study are twofold. Firstly, we will examine changes in cognitive function using a comprehensive set of objective neuropsychological tests recommended by the International Cognition and Cancer Task force (ICCTF),\u003csup\u003e10\u003c/sup\u003e in people with aggressive non-CNS lymphoma before and approximately 6\u0026ndash;8 weeks after chemotherapy compared to population norms. We hypothesise that cognitive test scores from individual participants will deviate from the population norms at both timepoints,\u003csup\u003e6\u003c/sup\u003e with large between-subject variability in the affected domains. Secondly, using the CentileBrain framework, we will explore alterations in cortical thickness and surface area in these participants from before and 6\u0026ndash;8 weeks after chemotherapy. We expect the cortical thickness and surface area values from frontal regions of individual participants, including the SFG and ACC,\u003csup\u003e24\u003c/sup\u003e will deviate from the normative group at both timepoints.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study presents analyses of the MRI scans from a subsample of a larger-scale longitudinal study cognitive impacts of chemotherapy in people with aggressive lymphoma.\u003csup\u003e53 54\u003c/sup\u003e All participants provided written informed consent. Ethical approval was granted by the Human Research Ethics Committees of Austin Health (HREC 55582/Austin-2019) and Deakin University (DUHREC 2024-024).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eWe included nine participants (7 male, 2 female) aged 29\u0026ndash;78 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;60.00, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14.75) recruited from a specialised haematology department at Austin Health in Melbourne, Australia. Participants had to meet the following inclusion criteria: (i) aged over 18 years; (ii) newly diagnosed with HL, DLBCL, Burkitt lymphoma, transformed follicular lymphoma, or grade 3B follicular lymphoma; (iii) treatment na\u0026iuml;ve at time of enrolment and scheduled to undergo standard cancer treatment; (iv) fluent in English; and (v) a documented Eastern Cooperative Oncology Group (ECOG) performance status \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 2. Exclusion criteria included the following: (i) lymphomatous central nervous system involvement; (ii) prior or planned cranial radiotherapy; (iii) a life expectancy \u0026lt; 12 months; (iv) any medical condition that could compromise adherence or lead to prolonged hospitalisation; (v) a documented history of past or current substance abuse; or (vi) major psychiatric disorder (e.g., schizophrenia). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a summary of the clinical and demographic characteristics of participants.\u003c/p\u003e \u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Summary of Demographic and Clinical Characteristics\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eParticipants underwent MRI scans and neuropsychological assessments at two time points: (i) pre-chemotherapy at the time of diagnosis; and (ii) approximately 6\u0026ndash;8 weeks post-chemotherapy completion. The pre-chemotherapy MRI scans were performed on average 12.9 days (SD\u0026thinsp;=\u0026thinsp;9.8, range 3 to 29 days) from date of cancer diagnosis. The pre-chemotherapy neuropsychological assessments were performed on average 13 days (SD\u0026thinsp;=\u0026thinsp;10.9, range 0 to 29 days) from date of diagnosis. The post-chemotherapy MRI scans were performed on average 7.8 weeks (SD\u0026thinsp;=\u0026thinsp;4.7, range 3 to 15 weeks) from date of chemotherapy completion. The post-chemotherapy neuropsychological assessments were performed on average 9 weeks (SD\u0026thinsp;=\u0026thinsp;7.3, range 3 to 26 weeks) from date of chemotherapy completion, which is in line with standard clinical practice.\u003c/p\u003e\n\u003ch3\u003eNeuropsychological assessment\u003c/h3\u003e\n\u003cp\u003eA series of neuropsychological tests were used to assess cognitive functioning in line with recommendations by the ICCTF (Wefel et al., 2011). The cognitive domains included: (i) attention/working memory tested via the Digit Span of the Weschler Adult Intelligence Scale-Revised (WAIS-R),\u003csup\u003e55\u003c/sup\u003e (ii) information processing speed via the Trail Making Test (TMT) Part A,\u003csup\u003e56\u003c/sup\u003e (iii) executive function via the TMT Part B,\u003csup\u003e56\u003c/sup\u003e and the Stroop Colour and Word Test (SCWT),\u003csup\u003e57\u003c/sup\u003e (iv) verbal learning and memory via the Hopkins Verbal Learning Test-Revised (HVLT-R),\u003csup\u003e58\u003c/sup\u003e and (v) verbal fluency via the Controlled Oral Word Association Test (COWAT).\u003csup\u003e59\u003c/sup\u003e See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for a detailed overview of the neuropsychological tests.\u003c/p\u003e \u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Overview of Neuropsychological Test Battery\u003c/p\u003e\n\u003ch3\u003eMRI data acquisition\u003c/h3\u003e\n\u003cp\u003eMRI acquisition was performed using a 3T Siemens Magnetom Skyra scanner with a 64-channel phased-array head coil at Austin Health. The study was part of a larger-scale multimodal MRI study,\u003csup\u003e53 60\u003c/sup\u003e including anatomical MRI scans (T1-weighted and T2 FLAIR), diffusion-weighted imaging (DWI), and resting-state fMRI. Our study focused on the anatomical T1-weighted MRI scans acquired using a three-dimensional magnetisation prepared rapid gradient echo (MPRAGE) with the following parameters: 1 mm isotropic voxel; repetition time (TR)\u0026thinsp;=\u0026thinsp;2300 ms; echo time (TE)\u0026thinsp;=\u0026thinsp;2.98 ms; voxel size\u0026thinsp;=\u0026thinsp;1.0 mm\u003csup\u003e3\u003c/sup\u003e; field of view (FOV)\u0026thinsp;=\u0026thinsp;256 mm; flip angle\u0026thinsp;=\u0026thinsp;9\u0026deg;; 192 slices; 1.0 mm slice thickness; 256 x 256 matrix; and acquisition time (TA)\u0026thinsp;=\u0026thinsp;5\u0026rsquo;43\u0026rdquo;. The MRI scans were conducted at two timepoints (see procedure); however, one participant withdrew from the post-chemotherapy MRI scan due to distress related to disease progression.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMRI data processing\u003c/h2\u003e \u003cp\u003eWe performed a quality assessment of the raw scans using an in-house protocol. Specifically, all T1-weighted images were visually inspected using Mango Viewer (Version 4.0.1) for motion-related artefacts, such as ghosting and striping, and other quality-related issues, such as signal inhomogeneity and susceptibility artefact. All scans passed the quality assessment.\u003c/p\u003e \u003cp\u003eStructural images were processed and analysed using the FreeSurfer image analysis suite Version 7.4.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://surfer.nmr.mgh.harvard.edu\u003c/span\u003e\u003cspan address=\"http://surfer.nmr.mgh.harvard.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This software performs a series of automated steps, including: (1) skull stripping; (2) Talairach transformation; (3) segmentation of subcortical white matter and deep grey matter structures; (4) cortical surface reconstruction; (5) parcellation of the cerebral cortex based on the Desikan-Killiany atlas;\u003csup\u003e61\u003c/sup\u003e and (6) calculation of cortical thickness and surface area within these regions.\u003csup\u003e62\u003c/sup\u003e Quality assurance of the registration and segmentation was undertaken by visual inspection using the ENIGMA protocol available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://enigma.ini.usc.edu/protocols/imaging-protocols/\u003c/span\u003e\u003cspan address=\"https://enigma.ini.usc.edu/protocols/imaging-protocols/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. No participants were excluded from the analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRegions of interest\u003c/h3\u003e\n\u003cp\u003eRegions of interest (ROIs) were chosen based on prior literature, showing these cortical regions to be vulnerable in people with cancer.\u003csup\u003e14 20 23 24\u003c/sup\u003e Specifically, we selected the following six cortical regions from the Desikan-Killiany atlas,\u003csup\u003e61\u003c/sup\u003e the right and left caudal part of the ACC, the right and left rostral part of the ACC, and the right and left SFG (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for cortical ROIs). For each ROI, cortical thickness and surface area were calculated.\u003c/p\u003e \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Cortical regions of interest\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eFor the normative analyses of the neuropsychological test scores (Aim 1), raw test scores were converted to \u003cem\u003eT-\u003c/em\u003escores using published normative data, adjusted for age, gender, and education. Participants were classified as \u0026ldquo;impaired\u0026rdquo; on a particular cognitive test if their \u003cem\u003eT\u003c/em\u003e-score was \u0026le;\u0026thinsp;30, corresponding to 2 standard deviations below the normative mean.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFor the normative analyses of brain morphology (Aim 2), we used the CentileBrain framework, developed by the ENIGMA Lifespan Group.\u003csup\u003e47\u003c/sup\u003e Specifically, we used the CentileBrain online portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://centilebrain.org\u003c/span\u003e\u003cspan address=\"https://centilebrain.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to compute normative deviation metrics for cortical thickness and surface area in our regions of interest (caudal and rostral ACC, and SFG). This tool generated brain regional \u003cem\u003ez\u003c/em\u003e-scores in our participants and compared them with a large sample of 37,407 healthy controls.\u003csup\u003e47\u003c/sup\u003e Per prior research,\u003csup\u003e52\u003c/sup\u003e brain regional \u003cem\u003ez\u003c/em\u003e-scores below \u0026minus;\u0026thinsp;1.96 (5th percentile) and above 1.96 (95th percentile were classified as \u0026ldquo;infranormal\u0026rdquo; and \u0026ldquo;supranormal\u0026rdquo;, respectively.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAs shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, we observed substantial between-subject variability in the results over time and across outcome measures in the nine participants. Impaired cognitive functioning was noted in the domains of executive functioning and verbal learning and memory. Infranormalities were more pronounced for cortical thickness than surface area and were more evident in the SFG compared to the ACC. Below, the results for each participant are presented as individual cases.\u003c/p\u003e \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Cortical Thickness Z-scores Pre- and Post-Chemotherapy\u003c/p\u003e \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Cortical Surface Area Z-scores Pre- and Post-Chemotherapy\u003c/p\u003e \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Neuropsychological Test T-scores Pre- and Post-Chemotherapy\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant 1\u003c/strong\u003e \u003cp\u003eA 47-year-old male diagnosed with stage 2A HL, treated with 6 cycles of the ABVD (adriamycin, bleomycin, vinblastine, and dacarbazine) chemotherapy regimen. Pre-chemotherapy, impaired cognitive function was observed in executive functioning (SCWT word, \u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25; colour, \u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30). Post-chemotherapy, impaired cognitive function persisted in executive functioning (SCWT colour, \u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29) and became evident in verbal learning and memory (HVLT retention, \u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27). Infranormal cortical thickness values in the left and right SFG were observed both pre-chemotherapy (\u003cem\u003ez\u003c/em\u003e = -2.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.015; \u003cem\u003ez\u003c/em\u003e = -2.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003) and post-chemotherapy (\u003cem\u003ez\u003c/em\u003e = -2.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.038; \u003cem\u003ez\u003c/em\u003e = -2.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.010).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant 2\u003c/strong\u003e \u003cp\u003eA 66-year-old male diagnosed with stage 1A Grade 3B follicular lymphoma, treated with 6 cycles of the R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone) chemotherapy regimen. He scored within the normal range on all cognitive tests both pre- and post-chemotherapy. \u003cem\u003eZ\u003c/em\u003e-scores of the six brain ROIs were also all within the normal range at both timepoints.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant 3\u003c/strong\u003e \u003cp\u003eA 69-year-old male diagnosed with stage 1A DLBCL, who underwent 7 cycles of the R-CHOP \u0026amp; 2 cycles of the high dose IT MTX (intrathecal methotrexate) chemotherapy regimen. He scored within the normal range on all cognitive tests at both timepoints. However, pre-chemotherapy, he exhibited infranormal cortical thickness values in the left and right SFG (\u003cem\u003ez\u003c/em\u003e = -2.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.046; \u003cem\u003ez\u003c/em\u003e = -2.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.026). Post chemotherapy, cortical thickness values of the bilateral SFG normalised and were within normal range. However, the surface area of the right SFG was identified as infranormal (\u003cem\u003ez\u003c/em\u003e = -2.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.034).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant 4\u003c/strong\u003e \u003cp\u003eA 78-year-old female diagnosed with stage 4A DLBCL, treated with 6 cycles of the Mini R-CHOP chemotherapy regimen. She scored within the normal range on all cognitive tests at both timepoints. However, pre-chemotherapy, cortical thickness of the right SFG was identified as infranormal (\u003cem\u003ez\u003c/em\u003e = -1.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.048). An MRI scan was not administered post-chemotherapy due to disease progression.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eParticipant 5;\u003c/b\u003e A 72-year-old male diagnosed with stage 1A DLBCL, received 2 cycles of the R-CHOP chemotherapy regimen. He demonstrated impaired cognitive function in verbal learning and memory both pre-chemotherapy (HVLT delayed recall, \u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19; retention, \u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19) and post-chemotherapy (HVLT delayed recall, \u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19; retention, \u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19). Pre-chemotherapy, all brain regional z-scores were within the normal range. However, post-chemotherapy, surface area of the right SFG was identified as infranormal (\u003cem\u003ez\u003c/em\u003e = -2.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.030).\u003c/p\u003e \u003cp\u003e \u003cb\u003eParticipant 6\u003c/b\u003e: A 29-year-old male diagnosed with stage 4B HL, who underwent 4 cycles of the Esc-BEACOPP (escalated BEACOPP: bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisolone) chemotherapy regimen. He scored within the normal range on all cognitive tests at both timepoints. Pre-chemotherapy, \u003cem\u003ez\u003c/em\u003e-scores of the six brain ROIs were also all within the normal range. However, infranormal cortical thickness values were evident in both the left and right SFG (\u003cem\u003ez\u003c/em\u003e = -2.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.036; \u003cem\u003ez\u003c/em\u003e = -2.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.028) post-chemotherapy.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant 7\u003c/strong\u003e \u003cp\u003eA 60-year-old male diagnosed with stage 2A DLBCL, treated with 4 cycles of the R-CHOP chemotherapy regimen. Pre-chemotherapy, he demonstrated impaired cognitive function in verbal learning and memory (HVLT delayed recall, \u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30). Post-chemotherapy, his verbal learning and memory function had normalised, and all cognitive test scores were within the normal range. Pre-chemotherapy, the cortical thickness of the right caudal ACC (\u003cem\u003ez\u003c/em\u003e = -1.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.048) and right SFG (\u003cem\u003ez\u003c/em\u003e = -2.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.029), as well as the surface area of the right rostral ACC, were identified as infranormal (\u003cem\u003ez\u003c/em\u003e = -1.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.047). Post-chemotherapy, the cortical thickness of the right caudal ACC normalised, however, the cortical thickness of the right SFG (\u003cem\u003ez\u003c/em\u003e = -3.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002) and surface area of the right rostral ACC (\u003cem\u003ez\u003c/em\u003e = -1.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.048) remained infranormal. Additionally, the cortical thickness of the left SFG was identified as infranormal (\u003cem\u003ez\u003c/em\u003e = -2.65, p\u0026thinsp;=\u0026thinsp;.008).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant 8\u003c/strong\u003e \u003cp\u003eA 63-year-old female diagnosed with stage 1A DLBCL, who received 3 cycles of the R-CHOP chemotherapy regimen. She scored within the normal range on all cognitive tests both pre- and post-chemotherapy. \u003cem\u003eZ\u003c/em\u003e-scores of the six brain ROIs were also all within the normal range at both timepoints.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant 9\u003c/strong\u003e \u003cp\u003eA 56-year-old male diagnosed with stage 2B DLBCL, treated with 3 cycles of the R-CHOP chemotherapy regimen. He scored within the normal range on all cognitive tests at both timepoints. However, pre-chemotherapy, the surface area in the left SFG was identified as infranormal (\u003cem\u003ez\u003c/em\u003e = -2.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.035). Post-chemotherapy, values of the surface area of the left SFG normalised and were within normal range. However, the surface area of the right SFG was identified as infranormal (\u003cem\u003ez\u003c/em\u003e = -1.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.047).\u003c/p\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur novel study employed single-subject analysis to examine longitudinal changes in cognitive functioning and brain morphology in people with aggressive lymphoma. As hypothesised, impaired cognitive functioning and infranormal cortical thickness and surface area were observed, with considerable variability across participants and time points. Despite this heterogeneity, some commonalities were observed. Specifically, impaired cognitive functioning was observed in the domains of executive functioning and verbal learning and memory (in a small subset of patients). Additionally, infranormalities were more pronounced for cortical thickness than surface area, and more evident in the SFG compared to the ACC.\u003c/p\u003e \u003cp\u003eThe finding that impaired cognitive functioning was mainly observed in tests of executive functioning and verbal learning and memory, contrasts with prior research on people with aggressive lymphoma, which have implicated a broader range of domains, including processing speed, attention/working memory, and verbal fluency.\u003csup\u003e6 8 17\u003c/sup\u003e This inconsistency may be attributed to the stringent criteria we used to define cognitive impairment (ICCTF guidelines) resulting in mild impairments in other domains going undetected. Supporting this notion, a meta-analysis of 13 studies found that only executive function and memory (from seven cognitive domains in total) showed evidence of impaired functioning in cancer populations treated with chemotherapy.\u003csup\u003e13\u003c/sup\u003e This suggests these areas may be more consistently impacted across studies, while other domains may exhibit more subtle changes.\u003c/p\u003e \u003cp\u003eIn particular, verbal learning and memory may be vulnerable to CRCI, as suggested by our finding that these were the most frequently impaired cognitive domains in a subset of our patients (participants 1, 5, and 7). However, despite this small subgroup, this is supported by other studies of people with haematological malignancies,\u003csup\u003e29 63\u003c/sup\u003e which reported verbal learning and memory are especially prone to impairment both before and after treatment. For instance, Correa et al. (2016) found that people exhibited significantly poorer verbal memory performance compared to healthy controls both prior to and one year after undergoing hematopoietic stem cell transplantation (HSCT) \u0026ndash; which involves a high-dose chemotherapy regimen \u0026ndash; whereas other cognitive domains were only impacted at one year post-HSCT.\u003csup\u003e29\u003c/sup\u003e Similarly, Syrjala et al. (2011) found that, while other cognitive functions like attention and processing speed showed signs of recovery, verbal recall remained persistently impaired from 80 days to five years post-HSCT.\u003csup\u003e63\u003c/sup\u003e Collectively, this suggests that verbal learning and memory may be uniquely susceptible to impairment in this population due to the combined effects of the cancer and the cumulative impact of neurotoxic effects of chemotherapy.\u003c/p\u003e \u003cp\u003eProviding insight into the potential neural underpinnings of CRCI, we observed infranormalities in the SFG and ACC. This aligns with an extensive body of research demonstrating neuroanatomical alterations in frontal regions of non-CNS cancer populations.\u003csup\u003e23 24 64\u003c/sup\u003e Nonetheless, while the majority of our participants showed infranormalities in the SFG, only one patient was found to exhibit infranormalities in the ACC (who also showed impaired cognitive function in verbal learning and memory). This is intriguing, particularly in light of the meta-analysis by Niu et al. (2021) which found reductions in grey matter density in the right ACC and SFG among cancer populations treated with chemotherapy across all non-CNS cancer studies,\u003csup\u003e24\u003c/sup\u003e whereas only the right SFG in their sub-analysis of breast cancer studies. This suggests alterations in frontal regions may vary, with the SFG being more consistently affected, while changes in the ACC may be more variable depending on cancer-type, patient characteristics or treatment factors.\u003c/p\u003e \u003cp\u003eSimilarly, infranormalities were more pronounced for cortical thickness than surface area, suggesting the former may be more susceptible to chemotherapy or cancer-related factors. MRI research measuring cortical thickness and surface area is limited, with most studies focusing on grey matter volume or density. However, a recent study by Wu et al. (2022) assessed cortical thickness, surface area and volume in people with lung cancer both before and 2\u0026ndash;4 months after chemotherapy.\u003csup\u003e38\u003c/sup\u003e They observed significantly lower cortical thickness in the frontal, temporal, and parietal regions of participants pre-chemotherapy, whereas significant volume reductions were limited to the temporal regions, and no differences were found in surface area. Furthermore, only cortical thickness showed a significant decline post-chemotherapy, while surface area and volume remained unchanged.\u003csup\u003e38\u003c/sup\u003e This suggests that grey matter volume changes noted in the broader literature may be largely attributable to cortical thickness rather than surface area. This is plausible given cortical thickness and surface area are influenced by different cellular processes and genetic origins,\u003csup\u003e39 40\u003c/sup\u003e and grey matter volume is the product of both measures.\u003csup\u003e41\u003c/sup\u003e Thus, our findings extend prior research indicating that changes in grey matter volume may be largely driven by alterations in cortical thickness rather than surface area, underscoring the importance of examining these variables separately to understand the mechanisms involved in cancer and its treatment.\u003c/p\u003e \u003cp\u003eDespite some overlap in the type of impairments and infranormalities observed, there was large variability in cognitive functioning and neuroanatomical trajectories over time. Some participants exhibited cognitive impairments or infranormalities before chemotherapy which later normalised 6\u0026ndash;8 weeks post chemotherapy, others showed impairments only after chemotherapy, and some displayed both. Prior research has similarly noted diversity in cognitive outcomes over time.\u003csup\u003e65\u003c/sup\u003e For instance, Jansen et al. (2008) found that 33% of breast cancer participants exhibited significant cognitive decline following chemotherapy, while 17% showed notable improvements in cognitive functioning.\u003csup\u003e65\u003c/sup\u003e Such patterns highlight the person-specific evolution of CRCI and related brain changes, which may be influenced by a complex interplay of factors, including pre-existing vulnerabilities, treatment effects, and recovery mechanisms.\u003c/p\u003e \u003cp\u003eClinically, the heterogeneity found across participants and time underscores the importance of individualised approaches or normative analyses in managing CRCI. Unlike group-level analyses, which may overlook between-subject variability, single-subject analysis allows for the precise characterisation of cognitive and neuroanatomical changes at the individual level, capturing the unique trajectories of decline and recovery people may experience.\u003csup\u003e20\u003c/sup\u003e This is crucial in the context of CRCI, given the high variability in impairments and neuroanatomical changes observed in our study; some participants exhibited deficits in specific domains, such as executive function or verbal memory, while others did not show any impairment. Using these individualised profiles, clinicians can develop interventions specifically designed to address each person's unique needs. Indeed, evidence has shown that group-based programs, such as a cognitive rehabilitation program,\u003csup\u003e66\u003c/sup\u003e and a mindfulness-based stress reduction program,\u003csup\u003e67\u003c/sup\u003e can significantly improve cognitive functioning in cancer survivors who have undergone chemotherapy. Our results suggest tailoring such programs to individual needs may improve outcomes further. Furthermore, our study demonstrates the value of utilising CentileBrain when assessing people with cancer. The program provides clinicians with information on any structural brain changes at the level of the individual. This information could be helpful in early identification of those at higher risk of CRCI, enabling more timely and tailored interventions to mitigate or alleviate cognitive decline.\u003csup\u003e68\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNonetheless, there are several limitations in this study that should be addressed. Firstly, the follow-up period was limited to approximately 6\u0026ndash;8 weeks after chemotherapy (with some variability in the timing of post-treatment assessments). This time period may have missed the detection of longer-term cognitive or neuroanatomical alterations that could develop or resolve. This is particularly relevant given that grey matter volume in the SFG has been shown to be positively associated with time since chemotherapy, suggesting this region may gradually recover.\u003csup\u003e24\u003c/sup\u003e Furthermore, McDonald et al. (2010) reported that while some brain regions showed signs of recovery between one month and one year post-chemotherapy, other regions did not, highlighting the variability in recovery across different brain areas.\u003csup\u003e69\u003c/sup\u003e Similarly, cognitive functions like verbal recall and executive functioning have been reported to remain impaired up to five years post-treatment in cancer survivors.\u003csup\u003e63 70\u003c/sup\u003e Therefore, future research should extend follow-up to at least one-year post-chemotherapy, to better capture the long-term cognitive and neuroanatomical changes in survivors of aggressive lymphoma, given their increasing long-term survivorship. Nonetheless, this study is unique in obtaining pre-cancer treatment data in this under-researched cancer population, offering critical insights into the early effects of aggressive lymphoma and its treatment.\u003c/p\u003e \u003cp\u003eA second limitation of this study was the reliance on objective measures of cognitive function.\u003csup\u003e10\u003c/sup\u003e Subjective reports, such as patient-reported cognitive concerns (e.g., the Functional Assessment of Cancer Therapy-Cognitive Function [FACT-Cog] scale)\u003csup\u003e71\u003c/sup\u003e may provide more sensitive and early indicators of cognitive changes. This is especially relevant for high-functioning individuals who often report cognitive concerns before objective measures detect a decline in cognitive functioning.\u003csup\u003e72\u003c/sup\u003e Thus, future research should incorporate both objective and subjective assessments to sufficiently capture the complexities of patient experiences beyond what objective tests alone can reveal.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFinally, because of our strong, anatomical, a-priori hypothesis, our neuroimaging analysis was restricted to specific regions of interest thereby potentially neglecting other brain regions that may be affected by cancer and its treatment. This is an important limitation because research has shown cognitive changes in cancer populations are not confined to these (frontal) regions alone. For example, Li and Caeyenberghs (2018) found reduced grey matter density in other areas of the frontal lobe, such as the middle frontal gyrus, as well as in temporal and parietal regions, suggesting these areas may also play a role in CRCI.\u003csup\u003e14\u003c/sup\u003e Future research should therefore include a range of brain regions to provide a more comprehensive understanding of the neural underpinnings of CRCI.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study represents a significant advancement in understanding CRCI in people with aggressive lymphoma by using single-subject analysis to explore cognitive function and neuroanatomical changes from pre- to post-chemotherapy. Unlike traditional group-based studies, this method allowed for the detailed examination of individual variability, revealing diverse trajectories of cognitive impairment and brain structural alterations that may otherwise be missed. The use of normative data from CentileBrain further enhanced the sensitivity of detecting subtle changes in cortical thickness and surface area, offering a more biological-specific understanding of the neural underpinnings of CRCI. The study\u0026apos;s focus on both cortical thickness and surface area is particularly novel, highlighting the differential impacts of chemotherapy on these measures and underscoring the need to consider both in future research. Moreover, the inclusion of pre-treatment data provides a deeper understanding of the early neuroanatomical and cognitive changes that may occur for this population. Collectively, these insights provide a valuable foundation for developing more personalised interventions tailored to the specific cognitive and neural profiles of lymphoma survivors, paving the way for improved clinical care to alleviate and mitigate the impact of CRCI on long-term quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003ePG contributed all aspects of study design and collection of MRI data as part of her PhD study. She and was involved in development of research questions, methodology, data analysis and the overall preparation and writing of the manuscript. CD contributed to the literature reviews and study design, was involved in all aspects of data analysis and the overall preparation and writing of the manuscript. She undertook this research as part of her Honours thesis. \u0026nbsp;KC was CD Honours primary supervisor and contributed to the original concept for this study and participated in all aspects of the design, research questions, methodology, data analysis, manuscript preparation and revision. JD contributed to study design and was involved in data analysis and the overall preparation and writing of the manuscript. HD, VD and CW contributed to study design and writing of the manuscript. All authors have been involved in preparing this manuscript and have read and approved the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by a non-restricted educational grant from Celgene Pty Ltd to support the costs associated with the acquisition of the MRI data. This study was also supported by a PhD scholarship provided to PG by the Olivia Newton-John Cancer Wellness and Research Centre through the Victorian Cancer Agency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e De-identified data supporting the findings of this study are available from the corresponding author upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e NA\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e Ethical approval has been granted by Austin Health Human Rights Ethics Committee (HREC) in Victoria Australia. This study was conducted in compliance with the principles of the Declaration of Helsinki (2013) and the principles of Good Clinical Practice and the Australian National Statement on Ethical Conduct in Human Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration number:\u003c/strong\u003e Australian New Zealand Clinical Trials Registry ACTRN12619001649101 on 26\u003csup\u003eth\u003c/sup\u003e November 2019.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors have declared no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e Informed consent was obtained from all individual participants included in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Participants signed informed consent regarding publishing their de-identified data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We thank the thirty participants who volunteered their time to this study. We also acknowledge and thank Professor Mei Krishnasamy and Associate Professor Karla Gough who were supervisors of P Gates PhD study who contributed their expertise.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAustralian Institute of Health and Welfare. Cancer data in Australia 2019 [Available from: \u0026nbsp;https://www.aihw.gov.au/reports/cancer/cancer-data-in-australia/contents/summary.\u003c/li\u003e\n \u003cli\u003eWright F, Hapgood G, Loganathan A, et al. Relative survival of patients with lymphoma in Queensland according to histological subtype. \u003cem\u003eMed J Aust\u003c/em\u003e 2018;209(4):166-72. doi: 10.5694/mja17.00937 [published Online First: 2018/08/11]\u003c/li\u003e\n \u003cli\u003eJohnson PC, Yi A, Horick N, et al. 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Preliminary results of a longitudinal study of changes in cognitive function in breast cancer patients undergoing chemotherapy with doxorubicin and cyclophosphamide. \u003cem\u003ePsychooncology\u003c/em\u003e 2008;17(12):1189-95. doi: 10.1002/pon.1342 [published Online First: 2008/05/29]\u003c/li\u003e\n \u003cli\u003eBray VJ, Dhillon HM, Bell ML, et al. Evaluation of a Web-Based Cognitive Rehabilitation Program in Cancer Survivors Reporting Cognitive Symptoms After Chemotherapy. \u003cem\u003eJ Clin Oncol\u003c/em\u003e 2017;35(2):217-25. doi: 10.1200/JCO.2016.67.8201 [published Online First: 2017/01/06]\u003c/li\u003e\n \u003cli\u003eJohns SA, Brown LF, Beck-Coon K, et al. Randomized controlled pilot trial of mindfulness-based stress reduction compared to psychoeducational support for persistently fatigued breast and colorectal cancer survivors. \u003cem\u003eSupport Care Cancer\u003c/em\u003e 2016;24(10):4085-96. doi: 10.1007/s00520-016-3220-4 [published Online First: 2016/05/18]\u003c/li\u003e\n \u003cli\u003eNakamura ZM, Ali NT, Crouch A, et al. Impact of Cognitive Rehabilitation on Cognitive and Functional Outcomes in Adult Cancer Survivors: A Systematic Review. \u003cem\u003eSemin Oncol Nurs\u003c/em\u003e 2024:151696. doi: 10.1016/j.soncn.2024.151696 [published Online First: 2024/07/26]\u003c/li\u003e\n \u003cli\u003eMcDonald BC, Conroy SK, Ahles TA, et al. Gray matter reduction associated with systemic chemotherapy for breast cancer: a prospective MRI study. \u003cem\u003eBreast Cancer Res Treat\u003c/em\u003e 2010;123(3):819-28. doi: 10.1007/s10549-010-1088-4 [published Online First: 2010/08/07]\u003c/li\u003e\n \u003cli\u003eLa Carpia D, Liperoti R, Guglielmo M, et al. Cognitive decline in older long-term survivors from Non-Hodgkin Lymphoma: a multicenter cross-sectional study. \u003cem\u003eJ Geriatr Oncol\u003c/em\u003e 2020;11(5):790-95. doi: 10.1016/j.jgo.2020.01.007 [published Online First: 2020/02/06]\u003c/li\u003e\n \u003cli\u003eWagner L, Lai J, Cella D, et al. Chemotherapy-related cognitive deficits: development of the Fact-Cog instrument. \u003cem\u003eAnn Behav Med\u003c/em\u003e 2004;27\u003c/li\u003e\n \u003cli\u003eSaykin AJ, Ahles TA, McDonald BC. Mechanisms of chemotherapy-induced cognitive disorders: neuropsychological, pathophysiological, and neuroimaging perspectives. \u003cem\u003eSemin Clin Neuropsychiatry\u003c/em\u003e 2003;8(4):201-16. [published Online First: 2003/11/13]\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Summary of Demographic and Clinical Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5854%;\"\u003e\n \u003cp\u003eParticipant ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.10569%;\"\u003e\n \u003cp\u003eAge in years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0813%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6585%;\"\u003e\n \u003cp\u003eYears of formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.748%;\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.13008%;\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6911%;\"\u003e\n \u003cp\u003eChemotherapy regime and number of cycles\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5854%;\"\u003e\n \u003cp\u003eParticipant 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.10569%;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0813%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6585%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.748%;\"\u003e\n \u003cp\u003eHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.13008%;\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6911%;\"\u003e\n \u003cp\u003eABVD x 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5854%;\"\u003e\n \u003cp\u003eParticipant 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.10569%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0813%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6585%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.748%;\"\u003e\n \u003cp\u003eGrade 3B follicular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.13008%;\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6911%;\"\u003e\n \u003cp\u003eR-CHOP x 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5854%;\"\u003e\n \u003cp\u003eParticipant 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.10569%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0813%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6585%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.748%;\"\u003e\n \u003cp\u003eDLBCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.13008%;\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6911%;\"\u003e\n \u003cp\u003eR-CHOP x 6 and high dose IT MTX x 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5854%;\"\u003e\n \u003cp\u003eParticipant 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.10569%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0813%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6585%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.748%;\"\u003e\n \u003cp\u003eDLBCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.13008%;\"\u003e\n \u003cp\u003e4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6911%;\"\u003e\n \u003cp\u003eMini R-CHOP x 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5854%;\"\u003e\n \u003cp\u003eParticipant 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.10569%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0813%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6585%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.748%;\"\u003e\n \u003cp\u003eDLBCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.13008%;\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6911%;\"\u003e\n \u003cp\u003eR-CHOP x 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5854%;\"\u003e\n \u003cp\u003eParticipant 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.10569%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0813%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6585%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.748%;\"\u003e\n \u003cp\u003eHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.13008%;\"\u003e\n \u003cp\u003e4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6911%;\"\u003e\n \u003cp\u003eEsc-BEACOPP x 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5854%;\"\u003e\n \u003cp\u003eParticipant 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.10569%;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0813%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6585%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.748%;\"\u003e\n \u003cp\u003eDLBCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.13008%;\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6911%;\"\u003e\n \u003cp\u003eR-CHOP x 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5854%;\"\u003e\n \u003cp\u003eParticipant 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.10569%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0813%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6585%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.748%;\"\u003e\n \u003cp\u003eDLBCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.13008%;\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6911%;\"\u003e\n \u003cp\u003eR-CHOP x 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5854%;\"\u003e\n \u003cp\u003eParticipant 9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.10569%;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0813%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6585%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.748%;\"\u003e\n \u003cp\u003eDLBCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.13008%;\"\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6911%;\"\u003e\n \u003cp\u003eR-CHOP x 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote. HL = Hodgkin lymphoma; DLBCL = diffuse large B cell lymphoma; ABVD = adriamycin, bleomycin, vinblastine, and dacarbazine; R-CHOP = rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone; IT MTX = intrathecal methotrexate; Esc-BEACOPP = bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisolone.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: \u003cem\u003eOverview of Neuropsychological Test Battery\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1901%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3306%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstruction for participant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9587%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent variable(s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1901%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWAIS-R DS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3306%;\"\u003e\n \u003cp\u003eAttention/ working memory\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5207%;\"\u003e\n \u003cp\u003eRecall visually presented numbers of increasing length in the same or reverse order.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9587%;\"\u003e\n \u003cp\u003eTotal correct responses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1901%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTMT-A\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3306%;\"\u003e\n \u003cp\u003eInformation processing speed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5207%;\"\u003e\n \u003cp\u003eConnect 25 numbered circles in ascending order quickly and accurately.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9587%;\"\u003e\n \u003cp\u003eTime to complete (seconds)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1901%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTMT-B\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3306%;\"\u003e\n \u003cp\u003eExecutive function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5207%;\"\u003e\n \u003cp\u003eConnect 24 alternating numbers and letters in alternating ascending order quickly and accurately.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9587%;\"\u003e\n \u003cp\u003eTime to complete (seconds)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1901%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCWT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3306%;\"\u003e\n \u003cp\u003eExecutive function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5207%;\"\u003e\n \u003cp\u003eRead colour words in black ink, colours of patches and the ink colour of incongruent words. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9587%;\"\u003e\n \u003cp\u003eTime (seconds) for each condition and interference score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1901%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHVLT-R \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3306%;\"\u003e\n \u003cp\u003eVerbal learning and memory\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5207%;\"\u003e\n \u003cp\u003eRecall 12 verbally presented words over three trials, followed by a delayed recall and a 24-word recognition task to identify target words from distractors. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9587%;\"\u003e\n \u003cp\u003eTotal and delayed recall, percentage retained and recognition/ discrimination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1901%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOWAT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3306%;\"\u003e\n \u003cp\u003eVerbal fluency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5207%;\"\u003e\n \u003cp\u003eGenerate words, orally or in writing, from a given category or starting with a specific letter in 60 seconds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9587%;\"\u003e\n \u003cp\u003eNumber of verbal categories, verbal letters and total written words\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote. WAIS-R DS = Weschler Intelligence Scale-Revised DS; TMT-A = Trail Making Test-Part A; TMT-B = Trail Making Test-Part B; SCWT = Stroop Colour Word Test; HVLT-R = Hopkins Verbal Learning Test-Revised; COWAT = Controlled Oral Word Association Test.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer-related cognitive impairment, brain atrophy, aggressive lymphoma, normative analysis","lastPublishedDoi":"10.21203/rs.3.rs-5969242/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5969242/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eCancer-related cognitive impairment (CRCI) can impact daily-life functioning of people with aggressive lymphoma. While many studies have examined the neural substrates implicated in CRCI, most have used group-based analyses, which may mask individual differences. In the present study, we used normative analysis to examine longitudinal changes in cognitive functioning and brain morphology at the level of the individual patient.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eNine participants with newly diagnosed aggressive lymphoma underwent neuropsychological assessment and anatomical MR before and 6\u0026ndash;8 weeks after chemotherapy. Cognitive test scores were converted to \u003cem\u003eT-\u003c/em\u003escores and classified as impaired if\u0026thinsp;\u0026le;\u0026thinsp;30. Deviations in cortical thickness and surface area in the superior frontal gyrus (SFG) and anterior cingulate cortex (ACC) were computed at the level of the individual using the novel CentileBrain tool, with \u003cem\u003ez\u003c/em\u003e-scores below \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e1.96 and above 1.96 classified as infranormal and supranormal, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAnalyses revealed substantial between-subject variability over time and across outcome measures. Cognitive impairments in executive function and verbal memory were identified in three participants before and/or after chemotherapy. CentileBrain results showed seven participants had infranormal cortical thickness and/or surface area in the SFG at one or both time points, and one patient had infranormal values in the ACC. No participants exhibited supranormal values in either region at any time point.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings provide a valuable foundation for developing more personalised interventions tailored to the specific cognitive and neural profiles of lymphoma survivors, paving the way for improved clinical care to alleviate and mitigate the impact of CRCI on long-term quality of life.\u003c/p\u003e","manuscriptTitle":"Cognitive impairment and brain atrophy in patients with newly diagnosed aggressive lymphoma undergoing standard chemotherapy: a normative analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-07 10:46:57","doi":"10.21203/rs.3.rs-5969242/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e3e9a63-be69-4361-ade6-92e2697d1b04","owner":[],"postedDate":"February 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T19:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-07 10:46:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5969242","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5969242","identity":"rs-5969242","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}