Cognition in MS: an Overview
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Cognition in MS: An Overview John DeLuca, Ph.D. Senior Vice President for Research Kessler Foundation West Orange, New Jersey, USA Professor, Department of Physical Medicine & Rehabilitation Department of Neurology Rutgers, New Jersey Medical School Newark, New Jersey, USA Disclosures • Advisory board for Biogen IDEC • Speaker, Sanofi - Genzyme • Grant funding, Biogen IDEC • Grant funding, EMD Serono • Grant funding, NMSS • Grant funding, CMSC Overview • Cognition in MS – impact on daily life – assessment – brain imaging parameters • Cognitive Rehabilitation • Exercise and Medication • Conclusions Charcot (1868) Cognitive experience of patients with MS : “a marked enfeeblement of the memory; conceptions are formed slowly …” MS - Historical • By 1960’s, medical students taught – cognitive change not characteristic of MS • Early 1970’s: cognitive impairment in about 3% • Today, cognitive impairments up to 65% in MS Cognitive Deficits in MS • Information processing speed/ efficiency • Learning and Memory • Executive functions • planning, organization, initiation • Perceptual processing Cognitive Impairment in MS 60 50 40 30 20 percent impaired 10 0 cognitive domain Chiaravalloti & DeLuca, 2008, Lancet Neurol The frequency of cognitive impairment tends to increase over MS course Adapted from Ruano et al (2017), MSJ Examine MS symptoms prior to clinical onset (or dx) Prospectively investigate potential signs of preclinical MS activity Cognitive performance of all Norwegian men born 1950-1995 who underwent conscription between 18-19yo Linked this list to the Norwegian MS registry 924 MS cases 19,530 HC Men who later dx with MS had lower cognitive scores at conscription Mean stanine score (SEM) p<.03 Cognitive sx may be an early sign of MS Men who developedControls PPMS PPMS up 20 years later had lower cognitive scores at conscription Stanine range 1-9; mean = 5, sd =2 Cortese et al, (2016) Annals Neurol Evolution of Cognitive Impairment in MS over 10 year period Initial 4 year 10 year Testing follow-up follow-up Cognitive impairment None 74% 51% 44% Mild 8% 33% 34% Moderate 18% 16% 22% Amato et al, (2001), Archives Neurol Some Factors which affect Cognition in MS Disease Course RR < SP Duration of disease Sometimes Physical Disability Not always Fatigue Not really Depression It may, not always Stress It may, not always Gray Matter atrophy Strong predictor Gender Males at increased risk Smoking yes Ambient temperature Worse in summer Cannabis yes Cognitive Impairment and Everyday Life Cognitive Problems and Everyday Life Functioning • Cognitive deficits in MS have been shown to negatively affect daily life including: – Employment –Driving – Social and vocational activities – Household activities – Sexual functioning – Family activities – Overall QOL – Increased psychiatric illness • Beyond physical disability alone Rao et al., (1991); Schultheis et al, (2001) VR-Driving System Schultheis et al (2001) Neurology: Schultheis et al., Arch. PM&R, 2002 59% not working 5 years post dx 97 employed MS DES- deteriorated employment At 3.5year f/up, 45% declined in employment status SES- stable employment Morrow et al. [Clin Neuropsychologist, 2011] Processing Speed can affect Higher‐order Cognition Processing Speed • Symbol Digit Modalities Test (SDMT: Oral Version) • HCs faster than persons with MS 2 (F = 14.95, p < .001, ηp = .17) • Consistent with MS-related processing speed deficits documented throughout the MS literature. Leavitt et al (2014), Rehab Psychol Executive Function: Inhibition Color Word Interference Test (D-KEFS: Inhibition Trial) HCs outperformed persons with MS 2 (F = 14.95, p < .001, ηp = .17) HOWEVER Group differences disappeared when controlling for the speed aspect of the task (Color Naming) 2 (F = 0.01, p > .5, ηp = .00) Leavitt et al (2014), Rehab Psychol Executive Function: Switching Trail Making Test (D-KEFS: Number-Letter Switching) HCs outperformed persons with MS 2 (F = 6.87, p = .01, ηp = .08) HOWEVER Group differences disappeared when controlling for the speed aspect of the task (Letter Sequencing Trial) 2 (F = 0.16, p > .5, ηp = .00) Leavitt et al (2014), Rehab Psychol Processing Speed Symbol Digit Modalities Test (SDMT: Oral Version) R = -.52, p < .001 Brain atrophy is associated with slower processing speed. Leavitt et al (2014), Rehab Psychol Executive Function: Inhibition Color Word Interference Test (D-KEFS: Inhibition Trial) r = .40, p < .01 Brain atrophy is associated with worse “Stroop” performance HOWEVER rp = .13, p > .1 Group differences disappeared when controlling for the speed aspect of the task (Color Naming) Leavitt et al (2014), Rehab Psychol Executive Function: Switching Trail Making Test (D-KEFS: Number-Letter Switching) r = .40,r = .36,p < .01p = .01 Brain atrophy is associated with worse Trail Making performance HOWEVER Group differences disappeared when controlling for the speed aspect of the task (Letter Seq.) rp = -.22, p > .1 Leavitt et al (2014), Rehab Psychol Conclusions • Processing Speed may underlie many of the cognitive problems What are the Radiologic Parameters that Correlate with Cognition? Cognition and MRI • Numerous studies have consistently shown a clear relationship between cognition and MRI metrics • MS with cognitive impairment vs without – WM and GM lesions – WM and GM atrophy – Global, cortical, subcortical atrophy • Advanced imaging sensitive to cognitive dysfunction – DTI, MRS, MTR • Functional imaging sensitive to cognitive dysfunction – Task related activation and connectivity – Resting state activation and connectivity Cognition and MRI • Gray matter atrophy correlates with cognition and – predicts long term outcome 13 years later (Filippi et al, Neurology, 2013) – predicts conversion from CIS to definite MS (Dalton et al., 2004) • Early cognitive impairment – predict conversion RR to PMS (Pittieri et al, 2016) – predict disability progression (Pittieri et al, 2016) – Predict cortical thinning 8 years later (Pittieri et al, 2016) • Regional atrophy may be particular useful in cognition – Thalamus (Benedict et al., 2013; Debernard et al., 2015) – Hippocampus (Koenig et al., 2014; Sacco et al., 2015; Sicotte et al., 2008) • Functional imaging metrics have been useful in cognitive rehabilitation studies – fMRI, functional connectivity Cognitive Reserve Impact on Cognition and Imaging in MS Cognitive Reserve Hypothesis Persons with higher lifetime intellectual enrichment can better withstand disease- related neuropathology without suffering cognitive impairment or dementia, likely due to more efficient neurocognitive processing. Stern et al., JINS 2002;8:448-460. Stern et al., Cereb Cortex 2005;15:394-402. MRI accounts for 17-27% of variance in cognition (Pinter et al, 2015, Neuroimage: Clinical) Does Cognitive Reserve Moderate the Relationship between Brain Imaging and Cognitive status in multiple sclerosis? Cognitive Reserve in MS Sumowski et al., J. Clin. Exp. Neuropsyc. 2009 Sumowski et al., J. Int. Neuropsych. Soc. 2009 Assessing Cognitive Impairment in MS Objective vs Self-report Measures of Cognitive Impairment and Everyday life CMSC Member Survey on Cognitive Screening Practices Respondents (N=207) to CMSC member survey asking, “How are patients screened for cognitive problems/changes at your practice?” Respondents were instructed to check all that apply. 29% formal testing 52% not assessed 19% self report How Assess Cognition? • Neurologist assessment – No greater than chance (Peyser, 1982; Romero et al, 2015) – Clinical interview and neurological exam not sufficient • Patient Self report – Predicts emotional distress – Not objective cognitive impairment • Neuropsychological Evaluation (gold standard) • Correlated with brain imaging • Predicts everyday life activity –Employment – Cooking –Driving – Internet functional tasks (book airline ticket) –Other ADL’s Validity of Patient Self-Report Self-report Self-report Neuropsychologic DOES NOT DOES performance correlate with: correlate with: DOES correlate with: • Everyday life • Emotional functional activity distress • Everyday life • Neuropsychological functional performance activity Goverover Y, et al. Arch Phys Med Rehabil. 2005;86:2303-2308. Kalmar JH, et al. Neuropsychology. 2008;22:442-449. Cognitive Batteries MACFIMS BRB NINDS MSCOG BICAMS PS SDMT SDMT SDMT SDMT SDMT PASAT PASAT PASAT PASAT Verbal CVLT2 SRT CVLT2 SRT CVLT2 Memory Visual BVMT-R 10/36 test BVMT-R BVMT-R BVMT-R memory Expressive COWA COWA COWA Language Executive DKEFS DKEFS functions Sort Sort Spatial JLOT JLOT processing BRB – Brief Repeatable Battery California Verbal Learning Test Second Edition [CVLT2] Carrot Sweater Trial 1 __/16 Hammer Baseball Trial 2 __/16 Football Trial 3 __/16 Chisel Trial 4 __/16 Pants Brief Visuospatial Memory Test Revised [BVMTR] Beans Trial 5 __/16 Shoes Total Learning __/80 Screwdriver Basketball Corn Trial 1 __/12 Delayed Recall __/16 Saw Trial 2 __/12 Delayed Recognition __ Golf Trial 3 __/12 Dress Total Learning __/36 Lettuce Symbol Digit Modalities Test Delayed Recall __/12 Many Studies Show PS Best Predictor of Future Decline SDMT (not PASAT) was best predictor of cognitive deterioration (Amato et al, 2010) Similar results in others studies (Deloire et al, 2010) What Should be Routinely Done Clinically? Recommendations for Cognitive Screening and Management in Multiple Sclerosis Care Kalb R, Beier M, Benedict RHB, Charvet L, Costello K, Feinstein A, Gingold J, Goverover Y, Halper J, Harris C, Kostich L, Krupp L, Lathi E, LaRocca N, Thrower B, DeLuca J • Purpose: promote understanding of cognitive impairment