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LANGUAGE FLUENCY PREDICTS RESTING STATE NETWORK CONNECTIVITY PATTERN

      Background

      Alzheimer’s disease (AD) is often recognized as a disconnection disorder in which pathophysiological changes lead to reduced communication and coordination among regions important for cognition. Therefore, brain connectomic studies designed to examine disruptions of connectivity in AD have become increasingly common. We used resting-state fMRI (rsfMRI) in conjunction with connectomics to assess the relationship of cognitive variables associated with AD with brain network connectivity. Data from two separate cohorts were analyzed.

      Methods

      Cohort1 included 74 older adult participants from the Indiana Alzheimer’s disease Center, classified as cognitively normal (CN, 29), subjective cognitive decline (SCD, 22), mild cognitive impairment (MCI, 12), and AD (11). Cohort2 was a replicate sample of 58 older adult participants from the Indiana Memory and Aging study (CN, 13; SCD, 16; MCI, 21; AD, 8). Subjects underwent baseline rsfMRI; image data were processed with an in-house pipeline according to Power et al. [1]. Functional connectivity (FC) matrices were generated, which included FC data from 278 functionally-derived gray matter regions [2]. A data-driven connectivity approach (connICA) [3] was employed to extract independent FC patterns and how much each FC-pattern was present in each subject (weights). FC pattern weights were used as the dependent variable in a multilinear regression model with cognitive variables as predictors (Cognitive Complaint [4] and Cognitive Change [5] Index scores, episodic memory, executive function, animal fluency, and composite language fluency scores), with inclusion of nuisance variables.

      Results

      Both datasets revealed a prominent resting state network pattern, as reported in Contreras et al [6]. In both cohorts, the RSN pattern was positively associated with animal and composite language fluency scores. Both language fluency measures were predictive of RSN pattern (p<.005, Table 1) demonstrating that participants with lower language fluency scores had lower FC within the canonical RSN pattern.

      Conclusions

      Deficient performance on language fluency tests may be a good predictor of aberrant brain connectivity in early stages of AD. [1] Power et al(2014)Neuroimage; [2]Shen et al(2011)NeuroImage [3]Amico et al(2016)NeuroImage [4] Saykin et al(2006)Neurology [5] Rattanabannakit et al(2016)J Alzheimer’s Dis [6] Contreras et al(2017)Alzheimers&Dementia.
      Tabled 1
      Model testedF-value for modelP-value for modelβ-coefficientsP-value for predictor
      Cohort 13.170.0196*
      Age----0.0000.46
      Sex----0.0040.17
      Education-----0.0010.06
      Animal Fluency----0.0010.00*
      Cohort 24.700.0035--
      Age-----0.0010.07
      Sex----0.0090.07
      Education-----0.0000.84
      Animal Fluency----0.0010.00*
      Cohort 13.73.0087-
      Age----0.0000.89
      Sex----0.0040.20
      Education-----0.0010.27
      Composite Language Fluency Score----0.0040.00*
      Cohort 24.50.0044--
      Age-----0.0010.01
      Sex----0.0080.12
      Education----0.0000.97
      Composite Language Fluency Score----0.0030.01*