Memory recovery in relation to default mode network impairment and neurite density during brain tumor treatment

Rafael Romero-Garcia PhD1, John Suckling PhD1,2,3, Mallory Owen MBChB1, Moataz Assem MBBCh, PhD4, Rohitashwa Sinha BMBS, FRCS5, Pedro Coelho6, Emma Woodberry PhD7, Stephen J. Price MBBS, PhD5, Amos Burke MD, PhD8, Thomas Santarius MD, PhD5,9, Yaara Erez PhD4, and Michael G. Hart MBChB, PhD5
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  • 1 Department of Psychiatry, University of Cambridge;
  • | 2 Behavioural and Clinical Neuroscience Institute, University of Cambridge;
  • | 3 Cambridge and Peterborough NHS Foundation Trust, Cambridge;
  • | 4 MRC Cognition and Brain Sciences Unit, University of Cambridge;
  • | 5 Department of Neurosurgery, Addenbrooke’s Hospital, Cambridge;
  • | 6 Neurophys Limited, Cambridge;
  • | 7 Department of Neuropsychology, Cambridge University Hospitals NHS Foundation Trust, Cambridge;
  • | 8 Department of Paediatric Haematology, Oncology, and Palliative Care, Addenbrooke’s Hospital, Cambridge; and
  • | 9 Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridgeshire, United Kingdom
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OBJECTIVE

The aim of this study was to test brain tumor interactions with brain networks, thereby identifying protective features and risk factors for memory recovery after resection.

METHODS

Seventeen patients with diffuse nonenhancing glioma (ages 22–56 years) underwent longitudinal MRI before and after surgery, and during a 12-month recovery period (47 MRI scans in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span, and backward digit span. Using structural MRI and neurite orientation dispersion and density imaging (NODDI) derived from diffusion-weighted images, the authors estimated lesion overlap and neurite density, respectively, with brain networks derived from normative data in healthy participants (somatomotor, dorsal attention, ventral attention, frontoparietal, and default mode network [DMN]). Linear mixed-effect models (LMMs) that regressed out the effect of age, gender, tumor grade, type of treatment, total lesion volume, and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery.

RESULTS

Memory recovery was not significantly associated with either the tumor location based on traditional lobe classification or the type of treatment received by patients (i.e., surgery alone or surgery with adjuvant chemoradiotherapy). Nonlocal effects of tumors were evident on neurite density, which was reduced not only within the tumor but also beyond the tumor boundary. In contrast, high preoperative neurite density outside the tumor but within the DMN was associated with better memory recovery (LMM, p value after false discovery rate correction [Pfdr] < 10−3). Furthermore, postoperative and follow-up neurite density within the DMN and frontoparietal network were also associated with memory recovery (LMM, Pfdr = 0.014 and Pfdr = 0.001, respectively). Preoperative tumor and postoperative lesion overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr = 0.002 and Pfdr < 10−4, respectively).

CONCLUSIONS

Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumors and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patients’ memory recovery.

ABBREVIATIONS

DMN = default mode network; DTI = diffusion tensor imaging; FA = fractional anisotropy; Fval = F (statistic) value derived from the LMM; LMM = linear mixed-effect model; MDT = multidisciplinary team; NODDI = neurite orientation dispersion and density imaging; OCS = Oxford Cognitive Screen; Pfdr = p value after false discovery rate correction.

Supplementary Materials

    • Figures S1 and S2 and Table S1 (PDF 869 KB)

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