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Looking for Neuroimaging Markers in Frontotemporal Lobar Degeneration Clinical Trials: A Multi-Voxel Pattern Analysis Study in Granulin Disease

额颞叶变性 神经影像学 体素 失智症 疾病 基于体素的形态计量学 医学 神经科学 心理学 病理 磁共振成像 放射科 痴呆 白质
作者
Enrico Premi,Franco Cauda,Tommaso Costa,Matteo Diano,Stefano Gazzina,Vera Gualeni,Antonella Alberici,Silvana Archetti,Mauro Magoni,Roberto Gasparotti,Alessandro Padovani,Barbara Borroni
出处
期刊:Journal of Alzheimer's Disease [IOS Press]
卷期号:51 (1): 249-262 被引量:39
标识
DOI:10.3233/jad-150340
摘要

In light of future pharmacological interventions, neuroimaging markers able to assess the response to treatment would be crucial. In Granulin (GRN) disease, preclinical data will prompt pharmacological trials in the future. Two main points need to be assessed: (1) to identify target regions in different disease stages and (2) to determine the most accurate functional and structural neuroimaging index to be used. To this aim, we have taken advantage of the multivariate approach of multi-voxel pattern analysis (MVPA) to explore the information of brain activity patterns in a cohort of GRN Thr272fs carriers at different disease stages (14 frontotemporal dementia (FTD) patients and 17 asymptomatic carriers) and a group of 33 healthy controls. We studied structural changes by voxel-based morphometry (VBM), functional connectivity by assessing salience, default mode, fronto-parietal, dorsal attentional, executive networks, and local connectivity by regional homogeneity, amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), degree centrality, and voxel-mirrored homotopic connectivity. In FTD patients with GRN mutation, the most predictive measure was VBM structural analysis, while in asymptomatic carriers the best predictor marker was the local connectivity measure (fALFF). Altogether, all indexes demonstrated fronto-temporo-parietal damage in GRN pathology, with widespread structural damage of fronto-parietal and temporal regions when disease is overt. MVPA could be of aid in identifying the most accurate neuroimaging marker for clinical trials. This approach was able to identify both the target region and the best neuroimaging approach, which would be specific in the different disease stages. Further studies are needed to simultaneously integrate multimodal indexes in a classifier able to trace the disease progression moving from preclinical to clinical stage of the disease.

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