عوامل تعیین کننده تئوری عملکرد ذهن در بیماری آلزایمر: یک مطالعه داده کاوی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|129263||2017||27 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Cortex, Volume 88, March 2017, Pages 8-18
Whether theory of mind (ToM) is preserved in Alzheimer's disease (AD) remains a controversial subject. Recent studies have showed that performance on some ToM tests might be altered in AD, though to a lesser extent than in behavioural-variant Frontotemporal Dementia (bvFTD). It is however, unclear if this reflects a genuine impairment of ToM or a deficit secondary to the general cognitive decline observed in AD. Aiming to investigate the cognitive determinants of ToM performance in AD, a data-mining study was conducted in 29 AD patients then replicated in an independent age-matched group of 19 AD patients to perform an independent replication of the results. 44 bvFTD patients were included as a comparison group. All patients had an extensive neuropsychological examination. Hierarchical clustering analyses showed that ToM performance clustered with measures of executive functioning (EF) in AD. ToM performance was also specifically correlated with the executive component extracted from a principal component analysis. In a final step, automated linear modelling conducted to determine the predictors of ToM performance showed that 48.8% of ToM performance was significantly predicted by executive measures. Similar findings across analyses were observed in the independent group of AD patients, thereby replicating our results. Conversely, ToM impairments in bvFTD appeared independent of other cognitive impairments. These results suggest that difficulties of AD patients on ToM tests do not reflect a genuine ToM deficit, rather mediated by general (and particularly executive) cognitive decline. They also suggest that EF has a key role in mental state attribution, which support interacting models of ToM functioning. Finally, our study highlights the relevancy of data-mining statistical approaches in clinical and cognitive neurosciences.