• Users Online: 161
  • Print this page
  • Email this page


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 23  |  Issue : 1  |  Page : 56-61

Correlation between Clinical Dementia Rating and brain neuroimaging metrics of Alzheimer's disease: An observational study from a tertiary care institute of Eastern India


1 Associate Professor, Department of Radiodiagnosis, IPGME&R and SSKM Hospital, Kolkata, West Bengal, India
2 Assistant Professor, Department of Radiology, ICARE Institute of Medical Sciences and Research and Dr. Bidhan Chandra Roy Hospital, Haldia, West Bengal, India
3 Tutor Demonstrator, Department Community Medicine, Deben Mahata Government Medical College and Hospital, Purulia, West Bengal, India
4 Research Fellow, Department of Computer Science Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India

Date of Submission27-Jun-2021
Date of Acceptance16-Oct-2021
Date of Web Publication04-Mar-2022

Correspondence Address:
Dr. Arkaprabha Sau
Professor Colony, Panskura R.S, Purba Medinipur - 721 152, West Bengal
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/amh.amh_87_21

Rights and Permissions
  Abstract 


Context: Dementia is an already established major clinical health problem globally as well as in India. Neuroimaging, particularly magnetic resonance imaging (MRI), has an established role to support a clinical diagnosis of Alzheimer's disease (AD) by identifying certain brain atrophy patterns.
Aims: The aim of this study was to assess clinico-radiological utility of neuroimaging biomarkers of AD.
Settings and Design: This was an observational study with cross-sectional design with 36 patients of AD in a tertiary care hospital in Eastern India.
Materials and Methods: Patients with AD in the age group of 55–90 years were subjected to MRI brain examination as per protocol at the Department of Radiodiagnosis, IPGMER and SSKM Hospital, Kolkata. The GCA (Global Cortical Atrophy) score, mesial temporal atrophy (MTA) score, Fazekas grading score, and Koedam score were determined and these MRI parameters were correlated statistically with Clinical Dementia Rating (CDR) score for further analysis.
Statistical Analysis Used: IBM SPSS Statistics Version 20 was used as a statistical tool, and Spearman's rho coefficient was used as statistical test for correlation, and P < 0.05 was considered as statistically significant.
Results: Average hippocampal volumes showed a significant negative correlation with CDR depicting a clinical deterioration, can be used as probable marker of brain atrophy. This is reinforced by the findings of significant positive correlations of CDR with GCA, MTA, and Koedam's score.
Conclusions: It is proposed that global cortical atrophy, MTA, and Koedam score can be used as a basic screening imaging biomarker of AD in the population with dementia.

Keywords: Alzheimer's disease, Alzheimer's disease neuroimaging initiative protocol, Clinical Dementia Rating, dementia, magnetic resonance imaging


How to cite this article:
Chakraborty S, Mandal S, Kundu S, Sau A. Correlation between Clinical Dementia Rating and brain neuroimaging metrics of Alzheimer's disease: An observational study from a tertiary care institute of Eastern India. Arch Ment Health 2022;23:56-61

How to cite this URL:
Chakraborty S, Mandal S, Kundu S, Sau A. Correlation between Clinical Dementia Rating and brain neuroimaging metrics of Alzheimer's disease: An observational study from a tertiary care institute of Eastern India. Arch Ment Health [serial online] 2022 [cited 2022 Oct 5];23:56-61. Available from: https://www.amhonline.org/text.asp?2022/23/1/56/339122




  Introduction Top


Dementia is an already established major clinical health problem in globally as well as in India. Alzheimer's disease (AD) constitutes a major share of dementia patients globally. Alzheimer's type of dementia causes significant mortality and morbidity generally due to coexisting or other superimposed diseases. Prevalence of AD in India is not well established given the vast geographical extent of the country and population heterogeneity. Study by Shaji et al. reports the prevalence of dementia is 33.6/1000 population with ADs being the most common type (54%).[1] Another study by Chandra et al. in 1998 found it to be as low as 1.07% in Ballabgarh area of Northern India.[2] Hence, there is a need to accurately determine the burden of AD in Indian population. AD has been considered clinically homogenous and characterized by progressive memory impairment followed by global cognitive decline. The clinical diagnosis of AD depends largely on the presenting cognitive profile. The defining features of AD are progressive deficits in memory and other aspects of cognition. The deficits result in reduced ability to perform daily activities, and most AD patients will become totally dependent on others unless they die of other causes first. At present, AD is diagnosed mainly by clinical assessment of mental status along with biochemical markers with added information obtained from neuroimaging.[3] The mental status examination is usually done by Mini–Mental State Examination and Clinical Dementia Rating (CDR) scale. Neuroimaging, particularly magnetic resonance imaging (MRI), has an established role to support a clinical diagnosis of AD by identifying certain atrophy patterns. AD is typically associated with medial temporal lobe atrophy (MTA), hippocampal volume loss, and parietal atrophy. In this present study, MRI brain was done to the AD patients already staged by the CDR scale, and the MRI findings of MTA, hippocampal atrophy, and parietal lobe atrophy were measured and compared with clinical staging of the AD.[3],[4] The main diagnostic criteria of AD are clinical and imaging parameters. Clinical parameters have significant subjective components. Imaging studies help in both subjective and objective assessment of morbid anatomy of AD by using various measurements as detailed above. The first step in assessment of burden of AD in dementia population is validation and correlation of established clinical scorings for diagnosis of AD, i.e., CDR scale with established neuroimaging metrics such as global cortical atrophy, medial temporal atrophy, and Koedam score in our population. Hence, we have planned to assess the clinico-radiological utility of neuroimaging biomarkers of AD and correlate CDR of AD with brain neuroimaging metrics in a tertiary care hospital in Eastern India.

Objectives

  1. The objective of this study was to correlate the established clinical severity scoring system (CDR) of AD with some simple neuroimaging biomarkers (GCA, MTA, Koedam's, and Fazekas score) in dementia protocol MRI of brain
  2. To look for any correlation between worsening CDR score with worse neuroimaging biomarkers



  Materials And Methods Top


This was a hospital-based observational study with cross-sectional design that included 36 patients of AD. All the patients in the age group of 55–90 years, irrespective of gender, who attended Outpatient Department of Neuromedicine, at Bangur Institute of Neurology (BIN), Kolkata, between April 1, 2014 and March 31, 2015 and were clinically diagnosed with AD were considered as study population. After clinical diagnosis of AD, for determination of severity of dementia, patients were assessed by CDR scale at the Department of Neuromedicine, BIN, Kolkata, by a standard questionnaire over online CDR calculator developed by the National Alzheimer Coordinating Center (NACC).[5] Then, they were subjected to MRI brain examination as per protocol (detailed below) at the Department of Radiodiagnosis, IPGMER and SSKM Hospital, Kolkata. After considering, the exclusion criteria such as (a) any significant neurologic disease other than AD (NINDS-AIREN criteria was used to rule out vascular dementia), (b) the presence of cardiac pacemakers, intracranial neurostimulators, aneurysm clips, artificial heart valves, cochlear implants, metal fragments or metallic foreign objects in the eyes, and skin or body, (c) patients not willing to be part of the study, and (d) patients having general contraindications (e.g., claustrophobia) for MRI, total 36 patients of AD were recruited for this study. The MRI of brain was carried out in a 3.0T MRI scanner, (WIPRO GE healthcare made, model Signa 3.0T HDxt) using 16 channel, Head, Neck, Spine (HNS) array coil, FRU ref 5338925, (Make GE Healthcare). MRI Brain image acquisition Protocol (AD Neuroimaging Initiative Protocol) is detailed below.[6]

  1. Gradient echo (GRE) axial
  2. Spoiled gradient-refocussed echo (SPGR) three-dimensional (3D) whole brain - sagittal acquisition of isotopic dataset followed by multiplanar reformations
  3. T2 fluid-attenuation inversion recovery (FLAIR) axial
  4. Spin echo T2 axial.


After acquisition of brain MRI images, the following parameters were analyzed by an experienced radiologist in the proprietary workstation (Advantage Workstation, GE healthcare):

  1. The global cortical atrophy (GCA) score It gives the mean score for cortical atrophy throughout the entire cerebrum and is based on a study by Pasquier et al. in 1996.[7] The visual assessment of GCA is based on consideration of the width of the sulci and the volume of the gyri in all lobes of the cerebrum and can also be used for the cerebellum. It is a four-point scale, where 0 = no cortical atrophy; 1 = mild atrophy, and opening of the sulci; 2 = moderate atrophy, with volume loss of the gyri; and 3 = severe atrophy, also known as end-stage, “knife-blade” atrophy
  2. Mesial temporal atrophy (MTA)score – Visual assessment of MTA was developed in the early 1990s by Philip Scheltens' team in Amsterdam.[8] The MTA score is based on a five-point grading scale. The assessment is performed on a coronal MRI image which can be angulated perpendicular to the anterior–posterior commissure (AC–PC) line or perpendicular to the long axis of the hippocampus, which is a coronal line along the brainstem axis at the level of mamillary bodies. Structures evaluated are the choroid fissure, the temporal horn of the lateral ventricle, and the hippocampal formation including the hippocampus proper, subiculum, parahippocampal gyrus, and dentate gyrus. The MTA scores range from 0 = no atrophy; to 1 = slightly increased width of the choroid fissure; 2 = increasing width of the choroid fissure, slightly increased width of the temporal horn and slightly decreasing height of hippocampus; 3 = wide open choroid fissure, increasing width of the temporal horn, and progressive decrease in hippocampal height; and 4 = end-stage atrophy with wide-open choroid fissure and temporal horns and a minimal hippocampal height
  3. Fazekas grading scale is used for quantifying white matter changes. It evaluates the microangiopathic changes in the white matter manifesting as white matter hyperintensities (WMH) in long TR sequences (T2, FLAIR) in MRI and grades those on a 4-point scale.[9] A score of 0 signifies a maximum of a single punctate WMH lesion, Fazekas 1: multiple punctate lesions, Fazekas 2: beginning confluency of lesions (bridging), and Fazekas 3: large confluent lesions. This was mainly used to rule out vascular dementia
  4. Posterior parietal atrophy score or Koedam score – This score was developed by Koedam et al. for visual assessment of atrophy of posterior parietal lobe structures and is assessed by analyzing 3D volumetric SPGR images in multiplanar format.[10]


  • Grade 0: closed sulci of parietal lobe and cuneus, no gyral atrophy
  • Grade 1: mild sulcal widening in posterior cingulate and parieto-occipital sulci, mild gyral atrophy
  • Grade 2: substantial sulcal widening, substantial parietal gyral atrophy
  • Grade 3: extreme parieto-occipital and posterior cingulate sulcal widening, knife-blade gyral atrophy.


These MRI parameters were correlated statistically with CDR scores for further analysis. This study was approved by the Institute Ethics Committee. Written informed consent was taken after proper explanation about the study process for each of the patients.


  Results Top


A total number of 36 patients were enrolled for the study after being diagnosed clinically as having Alzheimer's dementia. There were 25 (69.44%) males and 11 (30.6%) female patients. The mean age of presentation was 65.67 (standard deviation ± 5.80) years. The CDR was determined by asking questions to the patient and accompanying relatives regarding memory, orientation, judgment and problem-solving abilities, community affairs, home and hobbies and personal care maintenance habits, and putting the scores over online CDR calculator in NACC webpage (https://www.alz.washington.edu/cdrnacc.html). Thereafter, data regarding global cortical atrophy (GCA), MTA, Koedam's score for parietal atrophy, Fazekas score for white matter microangiopathic changes, and bilateral hippocampal volumes were collected following MRI study of brain of the eligible patients [Table 1]. Master chart was prepared in Microsoft Office Excel 2007 and analyzed by IBM SPSS Statistics Version 20. Confidence interval was taken as 95%, and P < 0.05 was considered as statistically significant.
Table 1: Description of the clinical/radiological attributes of dementia

Click here to view


The averages of hippocampal volumes of the patients showed nonnormal distribution having median (interquartile range) of 2.17 (0.81) cc. Spearman's rho was calculated [Table 2] between CDR and each of GCA, MTA, Koedam's scale, Fazeka's score, age (in years), and average hippocampi volume (in cc) [Figure 1]. The correlation coefficient was not found to be significant with age of the patient and Fazekas score. Average hippocampal volumes showed a significant negative correlation with CDR depicting a clinical deterioration, a probable marker of brain atrophy. This is reinforced by the findings of significant positive correlations of CDR with GCA, MTA, and Koedam's score, all with increasing grades being surrogate markers of brain atrophy.
Figure 1: Correlation plot of Clinical Dementia Rating with age and radiological attributes

Click here to view
Table 2: Correlation of Clinical Dementia Score with age and radiological findings following magnetic resonance imaging brain

Click here to view



  Discussion Top


With increasing aging population in India, there is also substantial increase in different types of dementia. There is dearth in data regarding prevalence of AD among dementia patients in Eastern India. Diagnosis of AD is challenging and mainly depends on clinical and imaging biomarkers. Although there are novel biomarkers for AD in cerebrospinal fluid (Abeta42, total tau, and p-tau181), serum (A beta 42 and neurofilament light chain)[11] and imaging modalities such as structural MRI, functional MRI, positron emission tomography (Pittsburgh compound B PET), single-photon emission computed tomography, and diffusion tensor imaging but these investigations are not widely available specially in a health-care resource-constrained country like India. Structural MRI biomarkers such as global cortical atrophy score, medial temporal atrophy score, and Koedam score have been shown to correlate well with clinical categorization of dementia patients.[12],[13],[14] Simple structural MRI is available more or less adequately in urban India. Therefore, it can add value to measure and confirm the burden of AD and severity among dementia patients. In this hospital-based study, we tried to validate and correlate the structural brain MRI biomarkers of AD with CDR scores. In this study, it was found that there was significant negative statistical correlation between diminishing hippocampal volume with CDR scores. This is in concurrence to already established evidences in published literature by Bobinski et al.[15] Albert et al. also proposed that hippocampal volume may be used as a neuroimaging biomarker for early AD diagnosis.[16] It is reported that longitudinal MRI studies have shown increased rates of hippocampal volume loss in AD and mild cognitive impairment, in comparison to normal aging.[12] There was also significant positive correlation between global cortical atrophy, medial temporal atrophy, and Koedam score with CDR scale in our study population. This is of importance because though these metrics are subjective but these are less time-consuming and resource-intensive for evaluation in a conventional brain MRI. It is also proposed that global cortical atrophy, MTA, and Koedam score can be used as a basic screening imaging modality of AD in the population with dementia. In clinically or imaging-wise equivocal cases, we can proceed to more time-consuming and resource-intensive method of hippocampal volumetry for confirmation of the imaging diagnosis of AD. Volumetric evaluation of the hippocampus is preferred, but qualitative rating of MTA is a good alternative.[17] Visual assessment of MTA has been tested with excellent inter-rater (κ, 0.75–0.94) and intra-rater (κ, 0.84–0.94) reliabilities for VRS ratings of atrophy in the HPC, ERC, and PRC.[18] It has also been shown to have high sensitivity and specificity for distinguishing patients with AD from those with no cognitive impairment, and similar sensitivity and specificity levels for patients with a MCI.[4],[18],[19] After validation of the imaging biomarkers of AD in this study population of 36 patients, we intend to extend this study further to conduct a collaborative multicentric clinical and imaging study in West Bengal to further validate our findings.

[TAG:2]Conclusion [/TAG:2]

Efficient and Effective methodology for Screening of mental health Disorder among the elderly population like Alzheimer diseases is a challenging task. With advanced neuroimaging technologies it can be done using some established image-based scoring system. In this study, it is proposed that global cortical atrophy, MTA, and Koedam score can be used as a basic screening imaging biomarker of AD in the population with dementia.

Acknowledgment

We are thankful to the Department of Neuromedicine, BIN, Kolkata, and study participants for their contribution in this scientific endeavor.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Shaji S, Bose S, Verghese A. Prevalence of dementia in an urban population in Kerala, India. Br J Psychiatry 2005;186:136-40.  Back to cited text no. 1
    
2.
Chandra V, Ganguli M, Pandav R, Johnston J, Belle S, DeKosky ST. Prevalence of Alzheimer's disease and other dementias in rural India: The Indo-US study. Neurology 1998;51:1000-8.  Back to cited text no. 2
    
3.
Jack CR Jr., Dickson DW, Parisi JE, Xu YC, Cha RH, O'Brien PC, et al. Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia. Neurology 2002;58:750-7.  Back to cited text no. 3
    
4.
Duara R, Loewenstein DA, Potter E, Appel J, Greig MT, Urs R, et al. Medial temporal lobe atrophy on MRI scans and the diagnosis of Alzheimer disease. Neurology 2008;71:1986-92.  Back to cited text no. 4
    
5.
CDR® Calculator | National Alzheimer's Coordinating Center. Available from: https://naccdata.org/data-collection/tools-calculators/cdr. [Last accessed on 2021 Jun 13].  Back to cited text no. 5
    
6.
ADNI | Alzheimer's Disease Neuroimaging Initiative. Available from: http://adni.loni.usc.edu/. [Last accessed on 2021 Jun 27].  Back to cited text no. 6
    
7.
Pasquier F, Leys D, Weerts JG, Mounier-Vehier F, Barkhof F, Scheltens P. Inter- and intraobserver reproducibility of cerebral atrophy assessment on MRI scans with hemispheric infarcts. Eur Neurol 1996;36:268-72.  Back to cited text no. 7
    
8.
Scheltens P, Launer LJ, Barkhof F, Weinstein HC, van Gool WA. Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging: Interobserver reliability. J Neurol 1995;242:557-60.  Back to cited text no. 8
    
9.
Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol 1987;149:351-6.  Back to cited text no. 9
    
10.
Koedam EL, Lehmann M, van der Flier WM, Scheltens P, Pijnenburg YA, Fox N, et al. Visual assessment of posterior atrophy development of a MRI rating scale. Eur Radiol 2011;21:2618-25.  Back to cited text no. 10
    
11.
Tarawneh R. Biomarkers: Our path towards a cure for Alzheimer disease. Biomark Insights 2020;15:1-15.  Back to cited text no. 11
    
12.
Visser PJ, Verhey FR, Hofman PA, Scheltens P, Jolles J. Medial temporal lobe atrophy predicts Alzheimer's disease in patients with minor cognitive impairment. J Neurol Neurosurg Psychiatry 2002;72:491-7.  Back to cited text no. 12
    
13.
Weiner MF, Lipton AM. Clinical Manual of Alzheimer Disease and Other Dementias. Washington, DC, USA: American Psychiatric Pub.; 2012.  Back to cited text no. 13
    
14.
Emery VO, Oxman TE. Dementia: Presentations, Differential Diagnosis, and Nosology. Baltimore, Maryland, USA: JHU Press; 2003.  Back to cited text no. 14
    
15.
Bobinski M, Wegiel J, Wisniewski HM, Tarnawski M, Bobinski M, Reisberg B, et al. Neurofibrillary pathology-correlation with hippocampal formation atrophy in Alzheimer disease. Neurobiol Aging 1996;17:909-19.  Back to cited text no. 15
    
16.
Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011;7:270-9.  Back to cited text no. 16
    
17.
Lehmann M, Koedam EL, Barnes J, Bartlett JW, Ryan NS, Pijnenburg YA, et al. Posterior cerebral atrophy in the absence of medial temporal lobe atrophy in pathologically-confirmed Alzheimer's disease. Neurobiol Aging 2012;33:627.e1-12.  Back to cited text no. 17
    
18.
Urs R, Potter E, Barker W, Appel J, Loewenstein DA, Zhao W, et al. Visual rating system for assessing magnetic resonance images: A tool in the diagnosis of mild cognitive impairment and Alzheimer disease. J Comput Assist Tomogr 2009;33:73-8.  Back to cited text no. 18
    
19.
Frisoni GB, Fox NC, Jack CR Jr., Scheltens P, Thompson PM. The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 2010;6:67-77.  Back to cited text no. 19
    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
   Abstract
  Introduction
   Materials And Me...
  Results
  Discussion
  Conclusion
   References
   Article Figures
   Article Tables

 Article Access Statistics
    Viewed716    
    Printed42    
    Emailed0    
    PDF Downloaded31    
    Comments [Add]    

Recommend this journal