OBJECTIVE: This paper aims to correlate the white matter changes seen in cranial Magnetic Resonance Imaging (MRI) the severity of cognitive impairment in patients diagnose Alzheimer Disease by using the conventional Cranial MRI weighted and Fluid-attenuated inversion recovery (FLAIR) images.

METHODS: All patients diagnosed with Probable Alzheimer's disease at the Memory Assessment Center in a tertiary hospital in Manila from January 2007 to September 2008 were included in the study who underwent cranial MRI (T2 weighted and FLAIR images), with ages varying from 65-87 years old using the following tests: Alzheimer's Disease Assessment Scale-Cognitive (ADASCOG), Mini-Mental Status Examination (MMSE), Boston Naming, Verbal Fluency, Word List Memory Task, Recall and Recognition, Trail making A and B and Clinical Dementia Rating (CDR). The Cranial MRI images were rated using the Age-Related White Matter Changes (ARWMC) rating scale for MRI. Analyses were done using the Statistical Package for the Social Sciences (SPSS version 14.) Correlation between continuous data was done using the Pearson correlation test while the correlation between ranked data utilized the Spearman test.

RESULT: Four patients with no white matter changes had mild AD while another two had made an AD. Among those with focal lesions, two were categorized as mild AD, another two cases as moderate and one case with severe AD. Among those with the beginning confluence of lesions, three had mild AD, two had moderate AD and one had severe AD. No case had severe age-related white matter changes. We found no significant relationship between the clinical dementia ratings (severity of AD) and the presence or absence of white matter lesions in the cranial MRI (T2 weighted and FLAIR images). 

CONCLUSION: Diffusion tensor imaging (DTI) captures neurodegenerative brain changes that traditional MRI may miss. Further studies using structural and functional imaging methodologies should be promoted to show cognitive performance and its correlation. In particular, compensatory networks in patients were shown to linearly correlate to cognitive performance. Thus, varied approaches to understanding networks could be valuable tools in developing new approaches for early diagnosis of AD and in the future, aid us in the effectivity of treatment strategies.