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|NI FEATURE: THE EDITORIAL DEBATE I-- PROS AND CONS
|Year : 2018 | Volume
| Issue : 2 | Page : 328-329
Differentiating mild cognitive impairment from normal cognition and frank dementia utilizing structural changes observed on magnetic resonance imaging
Shyamal Kumar Das, Souvik Dubey
Department of Neurology, Bangur Institute of Neurosciences and the Institute of Post-Graduate Medical Education and Research (IPGMER), Kolkata, West Bengal, India
|Date of Web Publication||15-Mar-2018|
Dr. Shyamal Kumar Das
Department of Neurology, Bangur Institute of Neurosciences, Kolkata, West Bengal
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Das SK, Dubey S. Differentiating mild cognitive impairment from normal cognition and frank dementia utilizing structural changes observed on magnetic resonance imaging. Neurol India 2018;66:328-9
|How to cite this URL:|
Das SK, Dubey S. Differentiating mild cognitive impairment from normal cognition and frank dementia utilizing structural changes observed on magnetic resonance imaging. Neurol India [serial online] 2018 [cited 2020 Jan 27];66:328-9. Available from: http://www.neurologyindia.com/text.asp?2018/66/2/328/227276
Mild cognitive impairment (MCI) is a transitional phase between normal cognitive function and dementia. MCI is basically diagnosed when the cognitive score is between 1 and 2% of the lower value of age- and sex-matched normative data. MCI may often progress leading to dementia, may remain static, or may improve. The diagnosis of MCI and early dementia depends on the age- and sex-matched application of neuropsychological scales. MCI are of various subtypes, including the amnestic, the non-amnestic and the multidomain types. Particularly, the amnestic variety usually progresses to Alzheimer dementia. A community study in Eastern India reveals that the prevalence of MCI in India is around 14.89%.
In comparison to the MCI, the diagnosis of dementia depends on the structural (magnetic resonance imaging) or functional imaging modalities (single photon emission computed tomography [SPECT] or positron emission tomography [PET]), which reveals metabolic changes and hypoperfusion in the parieto- temporal association cortex.
Addition of the two softwares provide novel information that is very helpful in establishing the diagnosis of MCI: The voxel based morphometry (VBM) helps to find out the presence of generalized brain grey matter atrophy; and, the automated regional volumetry (ARV) helps to assess the extent of regional brain atrophy in both subjects suffering from MCI and dementia.
The aim of the present study by Sheela Kumari et al., was to quantify the structural changes in cognitively stable patients suffering from MCI and AD (Alzheimer dementia) compared to the individuals with no cognitive impairment, employing the quantitative volumetric magnetic voxel based resonance imaging (vMRI) analytic tools such as ARV and VBM.
The AD subjects revealed widespread cerebral atrophy in bilateral parahippocampi, amygdale, fusiform gyri, superior and middle temporal poles, inferior temporal gyri, and medial orbitofrontal regions, along with atrophy in the right precuneus and left angular regions indicating temporal lobe, frontal lobe and parietal lobe involvement.
Gray matter (GM) volumes in MCI assessed using the automated regional volumetry showed significant differences for the left hippocampus, right precuneus, and bilateral superior temporal gyri when compared to the individuals with normal cognition. Also, in patients with AD, bilateral hippocampi, precunei, superior temporal gyri, parahippocampi, right amygdala, and right superior temporal pole revealed significant grey matter (GM) volume loss when compared to that seen in controls.
Considering the MCI patients, GM volume loss was detected in the superior temporal gyrus and left hippocampus, and variably over the mesial brain regions (cuneus, precuneus, and the cingulate cortex), in comparison to individuals with normal cognition. Although the observation of hippocampal atrophy has been documented to be a marker of future progression in MCI, atrophy of the precuneus, a part of the parietal lobe, suggests the involvement of the posteromedial regions of the cortex in MCI, and in addition, to atrophy of the temporal lobe. Thus, ARV suggests a far wider involvement of the anterior and posterior brain substance, signifying a faster disease progression that may often lead to a frank dementia.
Another aspect of this study was correlation of these structural findings revealed on MRI imaging parameters with the neuropsychological assessment scales and their scores. The correlation obtained between the cognitive scores and the regional volume loss was very specific in the included patients with MCI and AD.
Thus, the assessment of patients with MCI utilizing the new software is very useful and can predict the progression of the cognitive impairment from stage of MCI to frank dementia. However, this study needs validation with a longitudinal cognitive evaluation for about 5 to 6 years, since around 10-15% patients with MCI would progress to Alzheimer's dementia, correlating with the neuroimaging findings.
For patients with MCI, the accurate prediction of the probability of their progression to AD is important from the aspect of patient care; it shall also enable the identification of participants in the clinical trial who will deteriorate rapidly in their cognitive functions, and thus, provide a setting to enable an early intervention. Biomarkers based on neuroimaging modalities could offer complementary information regarding different aspects of disease progression.
This study is well conducted and the authors have made adequate attempts to assess the preliminary results in correlating neuroimaging finding in Indian subjects with MCI with those suffering from dementia as well as with cognitively normal subjects. We agree that the study needs further validation utilizing the 3 Tesla magnetic resonance scanner, by carrying out more cross-sectional and longitudinal studies from different parts of India, considering the multiethnic character of the Indian population.
| » References|| |
Das SK, Bose P, Biswas A, Dutt A, Banerjee TK, et al
. An epidemiologic study of mild cognitive impairment in Kolkata, India. Neurology 2007:68:2019-26.
Matsud H. The role of neuroimaging in mild cognitive impairment. Neuropathology 2007;27:570-7.
Wolf H, Jelic V, Gertz H-J, Nordberg A, Julin P, Wahlund L-O. A critical discussion of the role of neuroimaging in mild cognitive impairment. Acta Neurol Scand 2003;107:(Suppl 179):52-76.
Xu L, Wu X, Li R, chen K, Long Z, Jhang J, et al
. Prediction of progressive mild cognitive impairment by multimodal neuroimaging biomarkers. J Alxhimer Disease 2016:51:1045-56.
Atanassova PA, Massaldjieva RI, Dimitrov BD, Aleksandrov AS, Semerdjieva MA, Tsvetkova SB, et al
. Early neurological and cognitive impairments in subclinical cerebrovascular disease. Neurol India 2016;64:646-55.
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