ECTRIMS eLearning

Functional and structural correlates of computerized processing speed in multiple sclerosis
Author(s): ,
M Shaw
Affiliations:
Neurology, NYU Langone Medical Center, New York
,
E Bartlett
Affiliations:
Stony Brook Medicine, Stony Brook, NY, United States
,
C Schwarz
Affiliations:
Neurology, NYU Langone Medical Center, New York
,
M Kasschau
Affiliations:
Neurology, NYU Langone Medical Center, New York
,
L Ijaz
Affiliations:
Stony Brook Medicine, Stony Brook, NY, United States
,
L Krupp
Affiliations:
Neurology, NYU Langone Medical Center, New York
,
C Delorenzo
Affiliations:
Stony Brook Medicine, Stony Brook, NY, United States
L Charvet
Affiliations:
Neurology, NYU Langone Medical Center, New York
ECTRIMS Learn. Charvet L. 09/16/16; 145801; P1117
Leigh E. Charvet
Leigh E. Charvet
Contributions
Abstract

Abstract: P1117

Type: Poster

Abstract Category: Pathology and pathogenesis of MS - Neuropsychology

Cognitive impairment affects more than half of all individuals with multiple sclerosis (MS), although its cause remains unclear. Cognitive processing speed is the earliest and most common area to be affected. Recent technological advances have allowed for the development of computer-based assessments of cognitive processing speed that are sensitive, reliable, and resistant to practice effects. We sought to identify the physiological mechanism underlying cognitive processing speed impairment in MS using these advanced measurement approaches, in combination with neuroimaging measures. Relapsing-Remitting MS (RRMS) (n=20, 19.3±2.7 years of age, 55% female) and healthy control (n=26, 20.0±3.9 years of age, 69% female) participants completed the Cogstate Brief Battery information processing tasks and an MRI scan. MRI sequences included a T1-weighted anatomical scan and 64-direction diffusion tensor imaging (DTI). Regions of interest delineation and cortical thickness estimations were automatically performed using Freesurfer analysis software. Regional labels were also used to calculate region-wise mean DTI fractional anisotropy (FA), a measure of white matter integrity The Cogstate information processing measures successfully differentiated between RRMS and HC cohorts, with a z score of -0.93±1.01 for MS and -0.35±0.61 for HC, p=0.03. Further, information processing speed was differentially associated with frontal lobe FA (r=-0.550, P=0.02) in the RRMS sample and insular cortical thickness (r=-0.481, p=0.02) in the HC sample. Studies looking at both region-wise and tractography-based FA have reported frontal lobe FA reductions to be associated with cognitive impairment in RRMS. We have shown that computer-based information assessments of cognitive processing speed are not only sensitive in RRMS samples, but are also able to predict variability in frontal lobe FA in the RRMS brain.

Disclosure: Michael Shaw: Nothing to dislcose



Elizabeth Bartlett: Nothing to disclose



Colleen Schwarz: Nothing to disclose



Margaret Kasschau: Nothing to disclose



Laraib Ijaz: Nothing to disclose



Lauren Krupp: received consulting fees from Novartis, Biogen, Redhill science, served on the DSMB for Pfizer, Sanofi, on the steering committee of Novartis, received royalties from Janseen, Eisai, Abbvie, Amicus, and received research support from the Department of Defense, Biogen, Novartis, National Multiple Sclerosis Society, and the Lourie Foundation.



Christine DeLorenzo: Nothing to disclose



Leight Charvet: Consultant for Biogen

Abstract: P1117

Type: Poster

Abstract Category: Pathology and pathogenesis of MS - Neuropsychology

Cognitive impairment affects more than half of all individuals with multiple sclerosis (MS), although its cause remains unclear. Cognitive processing speed is the earliest and most common area to be affected. Recent technological advances have allowed for the development of computer-based assessments of cognitive processing speed that are sensitive, reliable, and resistant to practice effects. We sought to identify the physiological mechanism underlying cognitive processing speed impairment in MS using these advanced measurement approaches, in combination with neuroimaging measures. Relapsing-Remitting MS (RRMS) (n=20, 19.3±2.7 years of age, 55% female) and healthy control (n=26, 20.0±3.9 years of age, 69% female) participants completed the Cogstate Brief Battery information processing tasks and an MRI scan. MRI sequences included a T1-weighted anatomical scan and 64-direction diffusion tensor imaging (DTI). Regions of interest delineation and cortical thickness estimations were automatically performed using Freesurfer analysis software. Regional labels were also used to calculate region-wise mean DTI fractional anisotropy (FA), a measure of white matter integrity The Cogstate information processing measures successfully differentiated between RRMS and HC cohorts, with a z score of -0.93±1.01 for MS and -0.35±0.61 for HC, p=0.03. Further, information processing speed was differentially associated with frontal lobe FA (r=-0.550, P=0.02) in the RRMS sample and insular cortical thickness (r=-0.481, p=0.02) in the HC sample. Studies looking at both region-wise and tractography-based FA have reported frontal lobe FA reductions to be associated with cognitive impairment in RRMS. We have shown that computer-based information assessments of cognitive processing speed are not only sensitive in RRMS samples, but are also able to predict variability in frontal lobe FA in the RRMS brain.

Disclosure: Michael Shaw: Nothing to dislcose



Elizabeth Bartlett: Nothing to disclose



Colleen Schwarz: Nothing to disclose



Margaret Kasschau: Nothing to disclose



Laraib Ijaz: Nothing to disclose



Lauren Krupp: received consulting fees from Novartis, Biogen, Redhill science, served on the DSMB for Pfizer, Sanofi, on the steering committee of Novartis, received royalties from Janseen, Eisai, Abbvie, Amicus, and received research support from the Department of Defense, Biogen, Novartis, National Multiple Sclerosis Society, and the Lourie Foundation.



Christine DeLorenzo: Nothing to disclose



Leight Charvet: Consultant for Biogen

By clicking “Accept Terms & all Cookies” or by continuing to browse, you agree to the storing of third-party cookies on your device to enhance your user experience and agree to the user terms and conditions of this learning management system (LMS).

Cookie Settings
Accept Terms & all Cookies