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P3‐225: POSTERIOR CINGULATE GABA AND GLX ARE REDUCED IN AMNESTIC MILD COGNITIVE IMPAIRMENT
Author(s) -
Riese Florian,
Gietl Anton,
Zölch Nikolaus,
Henning Anke,
O'Gorman Ruth,
Kälin Andrea M.,
Leh Sandra E.,
Buck Alfred,
Warnock Geoffrey,
Luechinger Roger,
Hock Christoph,
Kollias Spyros,
Michels Lars
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.2014.05.1316
Subject(s) - posterior cingulate , pittsburgh compound b , glutamine , medicine , creatine , biomarker , apolipoprotein e , glutamate receptor , psychology , anterior cingulate cortex , endocrinology , neuroscience , alzheimer's disease , oncology , cognition , chemistry , disease , receptor , biochemistry , amino acid
has been challenging to derive indices that are highly sensitive and specific on an individual basis. Moreover, it has been challenging to capture the heterogeneity of brain changes with disease progression in the population, as data analysis methods typically used seek a common trend of structural and functional changes in the brain, among all individuals, whereas patient subpopulations might display somewhat different brain changes, sometimes due to mixed pathologies. The recent years have seen increasing adoption of advanced analytical methods that integrate various forms of information from imaging and non-imaging biomarkers, potentially yielding more specific indices of neurodegeneration of higher predictive value. Methods: Advanced pattern analysis and machine learning methods are discussed, and demonstrated in the context of predicting progression from MCI to AD, and from cognitively normal to MCI, using structural, functional and amyloid scans. Structural imaging patterns are also examined in combination with CSF, genetic, and cognitive markers, in their ability to predict clinical progression to AD. Finally, these methods are used to capture heterogeneity in brain structural changes in AD. Results: Jointly, patterns of brain change in AD were highly specific to result to nearly perfect classification (Figure-top-left), using the SPARE-AD index. Combination between SPARE-AD, ADAS-Cog and APOE genotype provided very good prediction of conversion fromMCI to AD (Figure-bottom-left; Hazard ratio between 1 st and 4 th quartile was 17.8; CSF biomarkers didn’t provide any additional predictive value). Brain changes in AD were heterogeneous, primarily due to the presence of small vessel disease in a subpopulation (Figure-right). Additional results from normal aging, Parkinson’s Disease and Diabetes are also reviewed, indicating that AD-like patterns of brain atrophy are predictive of cognitive decline on an individual person basis. Conclusions: PatternAnalysismethods provide powerful tools for capturing subtle and complex brain changes in AD and other neurodegenerative diseases, thereby providing patient-specific markers of predictive value. They also capture heterogeneity in the population, which can potentially identify sub-groups of patients with different trajectories of clinical progression.