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Published on: October 13, 2016
Visuospatial impairment and resting-state network correlations in Alzheimer's disease: A systematic review
Ho Yan Lai1, Andi Tri Supratno Musrah2, Kah Hui Yap3
1Department of Psychology, Faculty of Behavioral Sciences, HELP University, Selangor, Malaysia.
Background:
Alzheimer's disease (AD) exhibits distinct visuospatial impairments across its clinical subtypes, including typical amnestic AD and posterior cortical atrophy (PCA). Resting-state functional MRI (rs-fMRI) reveals disruptions in large-scale brain networks, yet the relationship between connectivity alterations and visuospatial deficits remains unclear.
Objective:
We investigated visuospatial deficits across AD subtypes and identified affected brain networks to inform clinical interventions.
Methods:
Six studies were reviewed by comparing visuospatial performance and rs-fMRI-derived functional connectivity in AD and healthy controls (HC). Effect sizes (Bonett's δ) for behavioral tasks and correlation coefficients (r) for imaging-clinical associations were synthesized.
Results:
Patients with AD exhibited significant visuospatial deficits, with moderate to large effect sizes in amnestic AD (δ = 1.0-1.5) and severe impairments in PCA (δ ≥ 3.0). Rs-fMRI revealed disrupted connectivity in three key networks: dorsal parietal-occipital networks (strongly linked to visuospatial deficits, especially in PCA), default mode network (DMN) (associated with memory but indirectly contributing to visual integration), and cerebellar-cortical circuits (implicated in early AD). Moderate correlations (r = 0.33-0.44, p < 0.05) were found between connectivity loss (DMN, cerebellar) and poor Rey-Osterrieth Complex Figure (ROCF) performance. However, some studies reported null associations, possibly due to compensatory mechanisms or methodological heterogeneity.
Conclusion:
AD subtypes exhibit distinct network dysfunction patterns, with PCA showing severe posterior network disruption and amnestic AD involving broader DMN and cerebellar degeneration. Rs-fMRI provides valuable insights into the neural basis of visuospatial deficits, suggesting potential biomarkers for subtype-specific diagnosis and intervention.
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