Summary metrics of memory subnetwork functional connectivity alterations in multiple sclerosis

Abstract

Background: Memory dysfunction is common in multiple sclerosis (MS); mechanistic understanding of its causes is lacking. Large-scale network resting-state functional connectivity (RSFC) is sensitive to memory dysfunction. Objective: We derived and tested summary metrics of memory network RSFC. Methods: Cognitive data and 3T magnetic resonance imaging (MRI) scans were collected from 235 MS patients and 35 healthy controls (HCs). Index scores were calculated as RSFC within (anteriority index, AntI) and between (integration index, IntI) dorsomedial anterior temporal and medial temporal memory subnetworks. Group differences in index expression were evaluated. Associations between index scores and memory/non-memory cognition were evaluated; relationships between T2 lesion volume (T2LV) and index scores were assessed. Results: Index scores were related to memory and T2LV in MS patients, who showed marginally elevated AntI relative to HC ( p = 0.06); no group differences were found for IntI. Better memory was associated with higher AntI (β = 0.15, p = 0.018) and IntI (β = 0.16, p = 0.014). No associations were found for non-memory cognition. Higher T2LV was associated with higher AntI and IntI; exploratory mediation analysis revealed significant inconsistent mediation, that is, higher index scores partially suppressed the negative association between T2LV and memory. Conclusion: Summary, within-subject metrics permit replication and circumvent challenges of traditional (incommensurate) RSFC variables to advance development of mechanistic models of memory dysfunction in MS.

Characterizing the Extended Language Network in Individuals with Multiple Sclerosis

“…Prior studies employing rsFC have generally evaluated connectivity across primary brain networks, e.g., the default-mode network, the salience network, and their constituent nodes 37,38 . Here, we employed a strategy of calculating network-level summary index scores, consistent with our prior work 18 . The advantage of summary measures is that they permit explicit tests of potential mechanisms of large-scale network reorganization to explain dysfunction within a prespecified cognitive domain.…”

“…Here, we utilized the ELN as a framework to develop a network-level model of language impairment in MS. Applying an established approach for characterizing network (re)organization of functionally specific subnetworks using resting state functional connectivity (rsFC), we derived language-specific rsFC summary measures to capture non-random patterns of network-level reorganization of the language network: within-ELN connectivity, between-ELN connectivity, segregation index (Seg-I), and anteriority index (Ant-I) 18,19 . We then tested: (a) whether distinct patterns of functional organization of the ELN are observable in pwMS compared to matched HCs; (b) whether rsFC in the ELN is associated with language function within the MS group; and (c) whether ELN summary measures are more informative for predicting language function than standard structural and functional MRI measures.…”