The relationship between processing speed and verbal and non-verbal new learning and memory in progressive multiple sclerosis

Author:

Chiaravalloti Nancy D1ORCID,DeLuca John1,Salter Amber2ORCID,Amato Maria Pia3ORCID,Brichetto Giampaolo4ORCID,Chataway Jeremy5,Dalgas Ulrik6ORCID,Farrell Rachel7ORCID,Feys Peter8,Filippi Massimo9ORCID,Freeman Jennifer10,Inglese Matilde11,Meza Cecilia12,Moore Nancy B13,Motl Robert W14ORCID,Rocca Maria Assunta15ORCID,Sandroff Brian M1ORCID,Cutter Gary16,Feinstein Anthony12ORCID

Affiliation:

1. Kessler Foundation, East Hanover, NJ, USA/Department of Physical Medicine Rehabilitation, Rutgers New Jersey Medical School, Newark, NJ, USA

2. Section on Statistical Planning and Analysis, Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA

3. Department NEUROFARBA, Section Neurosciences, University of Florence, Florence, Italy/IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy

4. Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy/AISM Rehabilitation Service, Italian Multiple Sclerosis Society, Genoa, Italy

5. Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK/National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK

6. Exercise Biology, Department of Public Health, Aarhus University, Aarhus, Denmark

7. Department of Neurorehabilitation, National Hospital for Neurology and Neurosurgery, London, UK/Department Neuroinflammation, UCL Queen Square Institute of Neurology, London, UK/National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK

8. REVAL, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium

9. Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy/Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy/Vita-Salute San Raffaele University, Milan, Italy

10. Faculty of Health, School of Health Professions, University of Plymouth, Devon, UK

11. Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, and Center of Excellence for Biomedical Research, University of Genoa, Genoa, Italy/IRCCS Ospedale Policlinico San Martino-IRCCS, Genoa, Italy

12. Department of Psychiatry, University of Toronto and Sunnybrook Health Sciences Centre, Toronto, ON, Canada

13. Kessler Foundation, East Hanover, NJ, USA

14. Department of Kinesiology and Nutrition, College of Applied Health Sciences, University of Illinois Chicago, Chicago, IL, USA

15. Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy/Vita-Salute San Raffaele University, Milan, Italy

16. Department of Biostatistics, The University of Alabama at Birmingham, Birmingham, AL, USA

Abstract

Objective: Processing speed (PS) deficits are the most common cognitive deficits in multiple sclerosis (MS), followed by learning and memory deficits, and are often an early cognitive problem. It has been argued that impaired PS is a primary consequence of MS, which in turn decreases learning. The current analysis examined the association between PS and learning in a large cohort of individuals with progressive MS. Methods: Baseline data from a randomized clinical trial on rehabilitation taking place at 11 centers across North America and Europe were analyzed. Participants included 275 individuals with clinically definite progressive MS (primary, secondary) consented into the trial. Results: Symbol Digit Modalities Test (SDMT) significantly correlated with California Verbal Learning Test-II (CVLT-II) ( r = 0.21, p = 0.0003) and Brief Visuospatial Memory Test–Revised (BVMT-R) ( r = 0.516, p < 0.0001). Receiver operating characteristic (ROC) analysis of the SDMT z score to distinguish between impaired and non-impaired CVLT-II performance demonstrated an area under the curve (AUC) of 0.61 (95% confidence interval (CI): 0.55–0.68) and a threshold of −1.62. ROC analysis between SDMT and BVMT-R resulted in an AUC of 0.77 (95% CI: 0.71–0.83) and threshold of −1.75 for the SDMT z score to predict impaired BVMT-R. Conclusion: Results indicate little ability beyond chance to predict CVLT-II from SDMT (61%), albeit statistically significant. In contrast, there was a 77% chance that the model could distinguish between impaired and non-impaired BVMT-R. Several potential explanations are discussed.

Funder

MS Society of Canada

Publisher

SAGE Publications

Subject

Neurology (clinical),Neurology

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