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Default Network Index: a novel, clinically-informative marker of memory status in multiple sclerosis
Author(s): ,
V. Leavitt
Affiliations:
Neurology, Department of Neurology
,
C. Habeck
Affiliations:
Neurology, Department of Neurology
,
Q. Razlighi
Affiliations:
Columbia University
,
C. Riley
Affiliations:
Neurology, Columbia University, New York, NY, United States
,
G. Tosto
Affiliations:
Columbia University
K. Buyukturkoglu
Affiliations:
Neurology, Columbia University, New York, NY, United States
ECTRIMS Online Library. Leavitt V. Oct 12, 2018; 228969; P1129
Victoria Leavitt
Victoria Leavitt
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Abstract: P1129

Type: Poster Sessions

Abstract Category: Pathology and pathogenesis of MS - MRI and PET

Introduction: Despite the rapidly growing body of literature describing altered default network (DN) functional connectivity (FC) measured with resting-state functional magnetic resonance imaging (fMRI) in multiple sclerosis (MS) and its links to a wide range of cognitive and non-cognitive symptoms, our field has yet to produce a clinically useful tool leveraging this information. This may be due to 1) lack of specificity in selecting scientifically justified outcomes, and 2) challenges to developing a standardizable / reliable set of clinical criteria using fMRI. Here, we restricted our focus to memory and derived a novel, within-patient summary metric of DN FC: DN index (DNI), defined as the ratio of mean anterior to posterior DN FC.
Methods: One hundred MS patients from the MEM CONNECT cohort received fMRI scans and completed comprehensive cognitive batteries. Using predefined neuroanatomical regions to define the default network (DN), we quantified mean anterior FC (aDN), posterior FC (pDN), and total DN FC. A novel summary metric, DN Index (DNI), was calculated as the within-subject ratio of mean aDN over pDN. Partial correlations assessed the association of DNI to memory/non-memory cognitive function (as well as mood and fatigue). Regression modelling evaluated DNI for predicting memory over-and-above demographics and traditional MRI disease markers (atrophy, lesion load). Analysis of variance tested whether DNI distinguishes memory-impaired from non-memory impaired patients.
Results: DNI was associated with memory (rp= .273, p= .012), but not non-memory cognition, mood, or fatigue. DNI predicted memory function over-and-above demographics and structural MRI metrics, contributing 9.5% to total variance explained by the model. DNI distinguished memory-impaired from non-memory impaired patients [F(1, 99)= 6.486, p=.012].
Conclusions: Our results support: 1) a domain-specific approach to understanding the functional importance of DNFC alterations and for developing a clinically meaningful marker to predict memory impairment in MS; and 2) adoption of DNI as a clinically useful summary metric capturing changes across the DN that may hold specificity for predicting future memory function.
Disclosure: This work was funded in part by the National Multiple Sclerosis Society (RG-4810a1/1T to VML). Victoria M. Leavitt receives compensation for consulting work for Healios, Inc. Claire S. Riley receives compensation for consulting and advisory work for Celgene, Genentech, Genzyme, Teva Neuroscience, TG Therapeutics.

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