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Latent profile analysis in frontotemporal lobar degeneration and related disorders: clinical presentation and SPECT functional correlates

Author(s): Borroni Barbara | Grassi Mario | Agosti Chiara | Paghera Barbara | Alberici Antonella | Di Luca Monica | Perani Daniela | Padovani Alessandro

Journal: BMC Neurology
ISSN 1471-2377

Volume: 7;
Issue: 1;
Start page: 9;
Date: 2007;
Original page

Abstract Background Frontotemporal Lobar Degeneration (FTLD) thus recently renamed, refers to a spectrum of heterogeneous conditions. This same heterogeneity of presentation represents the major methodological limit for the correct evaluation of clinical designation and brain functional correlates. At present, no study has investigated clinical clusters due to specific cognitive and behavioural disturbances beyond current clinical criteria. The aim of this study was to identify clinical FTLD presentation, based on cognitive and behavioural profile, and to define their SPECT functional correlations. Methods Ninety-seven FTLD patients entered the study. A clinical evaluation and standardised assessment were preformed, as well as a brain SPECT perfusion imaging study. Latent Profile Analysis on clinical, neuropsychological, and behavioural data was performed. Voxel-basis analysis of SPECT data was computed. Results Three specific clusters were identified and named "pseudomanic behaviour" (LC1), "cognitive" (LC2), and "pseudodepressed behaviour" (LC3) endophenotypes. These endophenotypes showed a comparable hypoperfusion in left temporal lobe, but a specific pattern involving: medial and orbitobasal frontal cortex in LC1, subcortical brain region in LC2, and right dorsolateral frontal cortex and insula in LC3. Conclusion These findings provide evidence that specific functional-cluster symptom relationship can be delineated in FTLD patients by a standardised assessment. The understanding of the different functional correlates of clinical presentations will hopefully lead to the possibility of individuating diagnostic and treatment algorithms.
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