title: Loss of fornix white matter volume as a predictor of cognitive impairment in cognitively normal elderly individuals.
abstract: OBJECTIVE
  Magnetic resonance imaging markers of incipient cognitive decline among healthy elderly individuals have become important for both clarifying the biological underpinnings of dementia and clinically identifying healthy individuals at high risk of cognitive decline. Even though the role of hippocampal atrophy is well known in the later stages of decline, the ability of fornix-hippocampal markers to predict the earliest clinical deterioration is less clear.
  
  
  OBJECTIVE
  To examine the involvement of the hippocampus-fornix circuit in the very earliest stages of cognitive impairment and to determine whether the volumes of fornix white matter and hippocampal gray matter would be useful markers for understanding the onset of dementia and for clinical intervention.
  
  
  METHODS
  A longitudinal cohort of cognitively normal elderly participants received clinical evaluations with T1-weighted magnetic resonance imaging and diffusivity scans during repeated visits over an average of 4 years. Regression and Cox proportional hazards models were used to analyze the relationships between fornix and hippocampal measures and their predictive power for incidence and time of conversion from normal to impaired cognition.
  
  
  METHODS
  A cohort of community-recruited elderly individuals at the Alzheimer Disease Center of the University of California, Davis.
  
  
  METHODS
  A total of 102 cognitively normal elderly participants, with an average age of 73 years, recruited through community outreach using methods designed to enhance ethnic diversity.
  
  
  METHODS
  Our preliminary hypothesis was that fornix white matter volume should be a significant predictor of cognitive decline among normal elderly individuals and that fornix measures would be associated with gray matter changes in the hippocampus.
  
  
  RESULTS
  Fornix body volume and axial diffusivity were highly significant predictors (P = .02 and .005, respectively) of cognitive decline from normal cognition. Hippocampal volume was not significant as a predictor of decline but was significantly associated with fornix volume and diffusivity (P = .004).
  
  
  CONCLUSIONS
  This could be among the first studies establishing fornix degeneration as a predictor of incipient cognitive decline among healthy elderly individuals. Predictive fornix volume reductions might be explained at least in part by clinically silent hippocampus degeneration. The importance of this finding is that white matter tract measures may become promising candidate biomarkers for identifying incipient cognitive decline in a clinical setting, possibly more so than traditional gray matter measures.
authors.affiliation: Imaging of Dementia and Aging Laboratory, Department of Neurology, University of California, Davis.
authors.forename: Evan
authors.initials: E
authors.lastname: Fletcher
authors.forename: Mekala
authors.initials: M
authors.lastname: Raman
authors.forename: Philip
authors.initials: P
authors.lastname: Huebner
authors.forename: Amy
authors.initials: A
authors.lastname: Liu
authors.forename: Dan
authors.initials: D
authors.lastname: Mungas
authors.forename: Owen
authors.initials: O
authors.lastname: Carmichael
authors.forename: Charles
authors.initials: C
authors.lastname: DeCarli
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology*; D001284:Atrophy / Q000473:pathology; D003072:Cognition Disorders / Q000175:diagnosis*; D015331:Cohort Studies; D056324:Diffusion Tensor Imaging; D005260:Female; D005625:Frontal Lobe / Q000473:pathology*; D006624:Hippocampus / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D009483:Neuropsychological Tests; D017063:Outcome Assessment, Health Care; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D011569:Psychiatric Status Rating Scales; D012044:Regression Analysis
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