Premium
P1‐287: NEUROIMAGING FINDINGS OF NASU‐HAKOLA DISEASE
Author(s) -
Bilgic Basar,
Gelisin Ozlem,
Guerreiro Rita,
Lohmann Ebba,
Hanagasi Hasmet Ayhan,
Gurvit Hakan,
Emre Murat
Publication year - 2014
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2014.05.527
Subject(s) - corpus callosum , frontotemporal dementia , medicine , pathology , atrophy , magnetic resonance imaging , dementia , leukoencephalopathy , white matter , neuroimaging , cerebral atrophy , temporal lobe , disease , radiology , epilepsy , psychiatry
Background: With the advent of disease-modifying therapies that target specific pathologies, accurate diagnosis of different dementias, including Alzheimer’s disease (AD) and frontal temporal lobar degeneration, is becoming increasingly important. Recent advances in brain imaging have greatly facilitated our ability to visualize and quantify pathological and neurodegenerative markers in vivo. Using different imaging techniques in a multi-modal fashionmay provide new insights into the pathophysiological processes underlying different dementias. The aim of this study was to assess patterns of cerebral blood flow, glucose metabolism and amyloid deposition in subjects with different dementia syndromes. Methods: The study included 19 subjects (see Table): 5 healthy controls, 5 with posterior cortical atrophy (PCA, visual variant of Alzheimer’s disease), 5 with progressive non-fluent aphasia (PNFA, poor delivery of spoken language) and 4 with semantic dementia (SD, loss of knowledge of word meaning). ASL data were acquired at a label inversion time of 2s alongside a 3-point inversion recovery sequence used to estimate the initial magnetisation. In addition, a high resolution T1-weighted volume was acquired and used to generate a WM/GM tissue segmentation for partial volume correction and a label image. ASL data were converted to Cerebral Blood Flow images with partial volume correction. Subjects also had FDG and AV45-PET imaging to measure glucose metabolism and amyloid deposition, respectively. All data were normalised to average cerebellum values. PET data were registered to the MRI data. Results: There was a good correspondence between patterns of cerebral blood flow (ASL) and glucose metabolism (FDGPET, Figure). ASL and FDG-PET patterns also matched atrophy patterns typically associated with these clinical syndromes, with greater involvement of posterior regions in PCA, asymmetric left temporal regions in SD, and bilateral temporal regions in PNFA. In contrast, in patients with amyloid deposition, amyloid was distributed equally across different lobes, suggesting that factors other than amyloid may drive the region-specific patterns of neurodegeneration. Interestingly, 2 PNFA patients showed evidence of amyloid deposition on their AV45-PET scans, suggesting underlying AD pathology in these patients. Conclusions: Multi-modal image analyses can provide complementary information about the pathophysiological processes underlying different dementia syndromes which may aid accurate diagnosis and tracking of disease progression.