Volume 74, No.5: 2022 Siriraj Medical Journal
https://he02.tci-thaijo.org/index.php/sirirajmedj/index
323
Original Article
SMJ
Priyam Agarwal, M.D., Adya Kinkar Panda, M.D., Satyaswarup Jena, M.D., SSG Mohapatra, M.D.
Department of Radio Diagnosis, IMS and SUM Hospital, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, India.
Correlation of Cerebral Atrophy and White Matter
Hyperintensity Burden in MRI with Clinical
Cognitive Decline
ABSTRACT
Objective: Dementia is a disease of gradual memory and cognitive loss that aects an individual’s day-to-day
activities and is caused by permanent brain damage. Majority of patients are from the elderly population and only
2 to 10 % of aected population is less than 65 years.
Materials and Methods: We obtained a correlation of severity of white matter hyperintensity (WMH) burden in
MRI with severity of clinically assessed cognitive decline. And also analysed the severity of cerebral atrophy in MRI
with severity of clinically assessed cognitive decline.
Results: In our study Fazekas scoring for WMHs showed a sensitivity of 87.5% and specicity of 83.3% on correlation
with clinical cognitive decline assessed by ADAS-Cog. Also, MTA scale for cerebral atrophy showed a sensitivity
of 72% and specicity of 88% on correlation with clinical cognitive decline assessed by ADAS-Cog. Signicant
P-value have been obtained for both the above visual rating scales of MRI (Fazekas and MTA) by linear regression,
on correlation with clinically assessed cognitive decline.
Conclusion: White matter disease assessed by Fazekas scale and cerebral atrophy by MTA scale on MRI brain
correlated well with cognitive decline clinically assessed by neuropsychological tests.
Keywords: Cerebral atrophy; hyperintensity; MRI; clinical cognitive decline (Siriraj Med J 2022; 74: 323-329)
Corresponding author: Satyaswarup Jena
E-mail: docsatyaswarup@gmail.com
Received 30 November 2021 Revised 16 March 2022 Accepted 27 March 2022
ORCID ID: https://orcid.org/0000-0002-4591-2623
http://dx.doi.org/10.33192/Smj.2022.39
All material is licensed under terms of
the Creative Commons Attribution 4.0
International (CC-BY-NC-ND 4.0)
license unless otherwise stated.
INTRODUCTION
Dementia is a syndrome of progressive cognitive
and memory decline aecting an individual in his daily
activities, due to irreversible neuronal damage. Dementia
is predominantly a disease of the elderly population
and only 2 to 10 % of aected population is less than
65 years.
1
According to an estimate, number of people
aected by dementia will be reaching over 81 million by
the year 2040 globally, doubling in every 20 years. ere
will be approximately 150 million elderly individuals
(those aged over 60 years) constituting about 12.30%
of total population by 2025 in India.
2-4
e structure and function of the brain change
as people become older. Memory, attention, executive
cognitive function, language, and visuospatial ability
are just a few of the cognitive functions that degrade as
people become older.
5
ere is grey matter and white
matter volume loss.
6
Areas more prone to grey matter
volume reduction are the prefrontal cortex and medial
temporal lobe containing the hippocampus.
7,8
e frontal
lobe’s corpus callosum and white matter experienced the
most dramatic volume reductions.
8,9
e white matter
tract’s integrity deteriorates with age, as seen by MRI
diusion tensor imaging.
10
As individuals age, the number
Volume 74, No.5: 2022 Siriraj Medical Journal
https://he02.tci-thaijo.org/index.php/sirirajmedj/index
324
and length of dendrites decreases, as does the loss of
dendritic spines and axons, as well as there is signicant
loss of synapses.
11
e loss of synapses is an important
structural sign of ageing.
12,13
Cognitive decline is most commonly diagnosed
clinically. Clinical tests such as the Mini Mental State
Examination (MMSE), the Alzheimer Disease Assessment
Scale (ADAS cog), and the Verbal Fluency Test, Cognitive
Abilities Screening Instrument, Clock drawing test etc.
are oen used. Risk factors of Dementia on Magnetic
resonance imaging (MRI) include brain atrophy, cerebral
microhaemorrhages, and cerebral small vessel disease.
Alzheimer disease (AD) is characterized by atrophy in
specic brain regions, which includes the hippocampus,
Para hippocampal cortex, entorhinal cortex, inferior
parietal lobule, precuneus, and cuneus.
14,15
Microhaemorrhages, depending on their locations,
play roles in both AD-related and vascular-specific
pathology in dementia development.
16
Haemorrhages,
particularly in deep gray and white matter, are more
likely to be associated with hypertensive arteriolar disease
and are therefore considered vascular sign.
17
White
matter hyperintensities (WMHs) and lacunar infarcts
are signs of small vessel disease.
18-22
ey contribute to
vascular dementia but may also be associated with the
pathogenesis of AD.
23,24
WMHs are regions of increased
intensity on T2-weighted and FLAIR MRI sequences
that are usually assessed using rating systems based on
ocular evaluation of the lesion nature and size.
The purpose of this research is to detect the
continuing process of neurodegeneration as early as
possible, when intervention options are most viable,
as our understanding of the risk factors and treatment
of dementia has improved.
25
Our aim here is to obtain
a correlation of severity of WMH burden in MRI with
severity of clinically assessed cognitive decline and to
study severity of cerebral atrophy in MRI with severity
of clinically assessed cognitive decline.
MATERIALS AND METHODS
e proposed study was a Hospital Based Retrospective
Cross-Sectional Study and carried out in the department
of Radiodiagnosis in collaboration with the Department
of Psychiatry, IMS & SUM Hospital, Bhubaneswar, India.
e present study was done on 40 patients of age
more than 45 years with an incidental nding of T2/
FLAIR Hyperintensity & cerebral atrophy on MRI. ey
all underwent neuropsychiatric screening by MMSE
(Mini Mental State Examination) test.
26
All patients
having score less than 23 were included in the study. ey
were further evaluated by various visual rating scales like
Fazekas scale, Medial Temporal Atrophy scale (MTA
Scale) & Global Cerebral Atrophy scale (GCA scale) for
grading the cortical atrophy and WMH. Aer assessing
the atrophy and WMH in MRI, the patients were subjected
to a battery of neuropsychiatric tests like Verbal uency
test, ADAS-Cog, Montreal Cognitive Assessment test.
27
Lastly aer obtaining all the scores, a correlation between
the MRI grading of atrophy & WMH with the severity of
cognition decline assessed by neuropsychiatric tests was
established with the help of statistical analysis. Exclusion
Criteria was Post stroke patients; space occupying lesions
in brain; history of depression, psychosis and substance
use disorder excluding nicotine use; seizure disorder;
and any contraindications to MRI.
RESULTS
e maximum number of patients in our study
belonged to the age group of 65-69 years, making a total
of 20 cases out of 40 (i.e., 50% of the total). Second largest
number of patients (9 in number) belonged to the age
group 60-64 years (i.e., 22.5% of the total), followed by
4 cases in 70-74 years age group (i.e., 10% of the total)
and 3 cases in 50-59 age group. Also 10% of the cases (4
in number) were seen in >=75 years of age. e youngest
patient of the study sample was 56 years old and the
oldest patient of the study sample was 78 years old. Our
study sample had 23 females (i.e., 55% of the total) and
17 males (i.e., 45% of the total). Among them 21 were
diabetic (i.e., 52.5% of the total) & 19 were non-diabetic
(i.e., 47.5% of the total) and 25 were hypertensive i.e.,
62.5% of the total) & 15 were non-hypertensive (i.e.,
37.5% of the total). Out of the 40 patients ,14 were both
hypertensive and diabetic (i.e., 35% of the total) and 26
were either hypertensive or diabetic (i.e., 37.5% of the
total).
Patients assessed for cognitive decline by MMSE (as
the screening tool) showed about 22 of them (i.e., 55%
of the total) had mild cognitive impairment; followed
by 14 subjects who had moderate cognitive impairment
(i.e., 35% of the total). Severe cognitive impairment
was seen in only 4 study samples (10% of the total). On
Fazekas visual rating scale, about 22 of them had mild
WMH (grade 1) i.e., 55% of the total and about 14 of
them had moderate WMH (grade 2) i.e, 35% of the total.
Severe WMH (grade 3) was seen in 4 patients i.e, 10%
of the total (Fig 1). Assessment on MTA scale showed
no atrophy (Score 0) in 2 patients, i.e, 5% of the total.
Score 1 atrophy was seen in 16 patients (i.e, 40% of the
total). Score 2 atrophy was seen in 12 patients (i.e., 30%
of the total). Score 3 atrophy was seen in 8 (i.e., 20% of
the total) and Score 4 atrophy was seen in 2 (i.e., 5%
Agarwal et al.
Volume 74, No.5: 2022 Siriraj Medical Journal
https://he02.tci-thaijo.org/index.php/sirirajmedj/index
325
Original Article
SMJ
Fig 1. Grade of FAZEKAS scale; A,B) grade-1; C,D) grade-2; E,F) grade-3
of the total). Score >=2 was considered abnormal in
less than 75 years of age and Score >=3 was considered
abnormal in more than 75 years of age.
Out of 40 patients,Grade 0 ( no atrophy) is seen
in 22 patients in GCA scale, i.e., 55%of the total. Grade
1 atrophy is seen in 14 patients (i.e., 40% of the total).
Grade 2 atrophy is seen in 4 patients (i.e., 10% of the
total). However ,no cases show severe atrophy. Since
GCA scoring showed inconclusive result in terms of
assesment of severity of cognitive decline we use only
MTA scoring for further assessment of sensivity and
specicity. Further assessment for cognitive decline
by neuropsychiatric test ADAS-Cog showed about
22 of them (i.e., 55 % of the total) had mild cognitive
impairment, followed by 18 subjects who had moderate
to severe cognitive impairment (i.e., 45% of the total).
Volume 74, No.5: 2022 Siriraj Medical Journal
https://he02.tci-thaijo.org/index.php/sirirajmedj/index
326
e Sensitivity of Fazekas scoring in the assessment
of severity of cognitive decline was 87.5%.e Specicity
of Fazekas scoring in the assessment of severity of
cognitive decline was 83.3%. P-value for fazeka scale
correlation with clinical cognitive decline is 0.000013,
which is signicant. e Sensitivity of MTA scoring
in the assessment of severity of cognitive decline was
72%. e Specicity of MTA scoring in the assessment
of severity of cognitive decline was 88%. P-value of
medial temporal lobe atrophy correlation with clinical
cognitive decline by linear regression is 0.0006, which is
signicant. e Sensitivity of Combined Medial temporal
atrophy (MTA) and Fazekas scoring in the assessment
of severity of cognitive decline is 66.6%. e Specicity
of Combined Medial temporal atrophy and Fazekas
scoring in the assessment of severity of cognitive decline
is 87.5% (Tables 1&2).
DISCUSSION
Dementia is a disease of elderly seen generally in
age of more than 65 years. In the present study, the mean
age of the participants was 66.15 years and 70% of the
participants were more than 65 years of age. Aging is a
very important risk factor for dementia as with advancing
age the incidence of dementia increases exponentially
between ages 65 to 90 and doubles approximately every
5 years.
28
With aging there is loss of grey as well as
white matter volume manifesting as cerebral atrophy.
In neurons, structural abnormalities include a decrease
in the number, length and amount of dendrites, as well
as an increase in segmental demyelination axons and
a significant loss of synapses. Progressive dementia
is also observed in patients having low serum vit B12
level, patients with carcinomatous meningitis, patients
undergoing neurosurgical procedures; and among the
caregivers of dementia patients.
29-35
TABLE 1. Result of FAZEKAS, MTA scale and combined FAZEKAS+MTA scale in assessment of severity of cognitive
decline.
TABLE 2. Sensitivity and specicity of FAZEKAS, MTA scale and combined FAZEKAS+MTA scale in assessment
of severity of cognitive decline.
Clinical (ADAS-COG) / Moderate to Percantage Mild cognitive Percantage
FAZEKAS severe cognitive decline (<2)
decline (>=2)
Moderate to severe cognitive decline 14 4
Mild cognitive decline 2 20
Clinical(ADAS-COG)/MTA
Moderate to severe cognitive decline 16 2
Mild cognitive decline 6 16
Clinical(ADAS-COG)/MTA+FAZEKAS
Moderate to severe cognitive decline 16 2
Mild cognitive decline 8 14
FAZEKAS MTA scale Combined FAZEKAS+MTA
Sensitivity 87.5% 72% 66.6%
Specicity 83.3% 88% 87.5%
Agarwal et al.
Volume 74, No.5: 2022 Siriraj Medical Journal
https://he02.tci-thaijo.org/index.php/sirirajmedj/index
327
Original Article
SMJ
e research group had a modest female majority
with 57.5 percent of the participants being female. It
has been proposed that due to loss of estrogen or other
hormonal changes in association with other factors,
there is increased risk in postmenopausal women, and
estrogen replacement therapy has been shown to reduce
the risk of AD in them.
36
Diabetes and hypertension are important risk
factors for dementia. In the study, 52.5% (21 out of 40
study subjects) were diabetic and 62.5% (25 out of 40)
were hypertensive. Insulin receptors are abundant in
cognition-related brain areas, as well as in the blood-
brain barrier.
37
Insulin resistance reduces the quantity
of glucose that enters the brain in diabetic individuals,
resulting in neuronal damage.
38
It’s also been suggested
that a hyperglycemic state in the brain leads to the
development of glycated end products, which may lead
to neuroinammation.
38
High SBP has been linked to
decreased regional and overall brain sizes;
39-43
as well
as brain volume declines over time.
44
When compared
to normotensive people, hypertension people’s brains
have more amyloid plaques, atrophy, and neurobrillary
tangles.
45,46
Sustained rises in blood pressure may cause
cerebral vascular remodeling and, as a consequence,
cognitive impairment. Hypertension causes endothelial
dysfunction, which disrupts the microvasculature’s
coordinated connection of neurons, glia, and cerebral
blood ow.
47
e majority of research individuals exhibited mild
cognitive impairment (22 out of 40) and 45 percent had
moderate to severe cognitive impairment, according to
the MMSE and ADAS-COG assessments. It could be
because our study group had the mean age of 66.1 years
and majority of them were under the age group 60-70
years. According to the study conducted by Sengupta
et al, 2014, Cognitive impairment among elderly people
in India out of 268 total patients, 60% had mild cognitive
impairment as the maximum number of the patients
in the study were less than 70 years of age. e patients
with increasing age had moderate to severe cognitive
decline and lesser MMSE scores.
48
In our study on assessment by Fazekas Scale for
WMHs, 55% of the study subjects were rated as grade
1, suggesting that these subjects had mild cognitive
decline. About 45% of the subjects were rated as grade
2 or grade 3 suggesting that these subjects had moderate
to severe cognitive decline. WMH is associated with
cognitive decline, especially in the domains of attention,
executive function, and processing speed.
49,50
Hypoxic
injury caused by atherosclerosis-induced hypoperfusion
has been suggested as a possible etiological factor.
51
On
correlation of data obtained by neuropsychological test
ADAS-Cog and Fazekas scoring, we found sensitivity of
87.5% and specicity of 83.3%. Linear regression analysis
between Fazekas and ADAS-Cog showed a P-value of
< 0.001, which is highly signicant. In the Landmark
LADIS study, it was shown that the baseline severe white
matter changes had an association with worse scores on
MMSE and ADAS-Cog.
52
WMH has also been connected
to poor performance on global cognitive evaluations,
executive abilities, speed and motor control, attention,
naming, and vasoconstriction praxis, and is an independent
predictor of dementia and cognitive decline.
53
MTA Scale is also used in the present study for
assessment of severity of cognitive decline by MRI. Out of
40 patients, 55 % of the total study subjects had moderate
to severe atrophy (Score >=2) and 45% had mild atrophy
(Score <2). e MTA scale shows a good correlation
with manual hippocampal assessments when utilised in
combination with cognitive function, as well as increased
clinical importance. Automated volume measurement
and volume of cortical thickness estimates have the same
sensitivity and specicity.
54,55
On correlation of data
obtained by neuropsychological test ADAS-COG and
MTA scoring, we found sensitivity 72 % and specicity
of 88%. Linear regression analysis between MTA and
ADAS-COG showed a P-value of <0.001 which is highly
signicant. In a study done by Jules j Claus et al on 1165
patients of Alzheimer disease and subjective cognitive
impairment, it was seen that optimal MTA cut-o values
for the age ranges <65, 65–74, 75–84 and ≥85 years were
≥1.0, ≥1.5, ≥ 2.0 and ≥2.0 and Corresponding sensitivity
& specicity values were 83.3%, 86.4%; 73.7%, 84.6%
and 73.7%, 76.2%, 84.0%, 62.5% respectively.
56
Limitations of the study: e data in this study came
from a cross-sectional survey and, there was no follow
up. Confounding factors like diabetes and hypertension
may have aected the results interpretation in our study
as scale of dementia will vary according to duration of
both illnesses.
CONCLUSION
Cognitive impairment, which is a typical sign of
ageing, is oen considered as a precursor to more serious
disorders like Alzheimer’s disease, dementia and depression.
e role of white matter disorders and brain shrinkage
in cognitive decline and dementia is becoming more
generally recognised. White matter disease assessed
by Fazekas scale and cerebral atrophy by MTA scale
on MRI brain correlated well with cognitive decline
clinically assessed by neuropsychological tests. In our
Volume 74, No.5: 2022 Siriraj Medical Journal
https://he02.tci-thaijo.org/index.php/sirirajmedj/index
328
study Fazekas scoring for WMHs showed a sensitivity
of 87.5% and specicity of 83.3% on correlation with
clinical cognitive decline assessed by ADAS-Cog. Also,
MTA scale for cerebral atrophy showed a sensitivity of
72% and specicity of 88% on correlation with clinical
cognitive decline assessed by ADAS-Cog. Signicant
P-value have been obtained for both the above visual rating
scales of MRI (Fazekas and MTA) by linear regression,
on correlation with clinically assessed cognitive decline.
REFERENCES
1. World Alzheimer Report 2014. Available from: URL: https://
www. alz.co.uk/research/WorldAlzheimerReport2014.pdf
2. Bose A, Shankardass MK. Growing old in India: voices reveal,
statistics speak. New Delhi: BR Publishing Corporation; 2000.
p.244-6.
3. Tantanokit T, Bosittipichet T, Leesri T. e Study of Prevalence
and Associated Factors of Dementia in the Elderly.Siriraj Med
J.2021;73(4):224-35.
4. Sukhatunga K, Phattarayuttawat S, Luchom M, Chantra J,
Chaiyasit W, Bunnagulrote K. Depression and Dementia in ai
Elderly in Urban and Rural Communities.Siriraj Med J.
1999;51(4):232-43.
5. Lezak MD, Howieson DB, Bigler ED, Tranel D. Neuropsychological
Assessment. 5th ed.New York, NY: Oxford University Press;
2012.
6. Harada CN, Natelson Love MC, Triebel KL. Normal cognitive
aging.Clin Geriatr Med.2013;29(4):737-52.
7. Raz N, Gunning FM, Head D, Dupuis JH, McQuain J, Briggs
SD, et al. Selective aging of the human cerebral cortex observed
in vivo: dierential vulnerability of the prefrontal gray matter.
Cereb Cortex.1997;7(3):268-82.
8. Salat DH, Kaye JA, Janowsky JS. Prefrontal gray and white
matter volumes in healthy aging and Alzheimer disease.Arch
Neurol.1999;56(3):338-44.
9. Madden DJ, Spaniol J, Costello MC, Bucur B, White LE, Cabeza
R, et al. Cerebral white matter integrity mediates adult age
dierences in cognitive performance.J Cogn Neurosci.2009;21(2):
289-302.
10. Dennis EL, ompson PM. Functional brain connectivity using
fMRI in aging and Alzheimer’s disease.Neuropsychol Rev.2014;
24(1):49-62.
11. Pannese E. Morphological changes in nerve cells during normal
aging.Brain Struct Funct.2011;216(2):85-89.
12. Terry RD, Masliah E, Salmon DP, Butters N, DeTeresa R, Hill
R, et al. Physical basis of cognitive alterations in Alzheimer’s
disease: synapse loss is the major correlate of cognitive impairment.
Ann Neurol.1991;30(4):572-80.
13. Masliah E, Mallory M, Hansen L, DeTeresa R, Terry RD.
Quantitative synaptic alterations in the human neocortex
during normal aging.Neurology.1993;43(1):192-7.
14. Galton CJ, Patterson K, Graham K, Lambon-Ralph MA, Williams
G, Antoun N, et al. Diering pattern so emporal atrophyin
Alzheimer’s disease and semantic dementia. Neurology. 2001;
57(2):216-25.
15. Reitz C, Mayeux R. Alzheimer disease: epidemiology, diagnostic
criteria, risk factors and biomarkers. Biochem Pharmacol.
2014;88(4):640-651.
16. Romero JR, Beiser A, Himali JJ, Shoamanesh A, DeCarli C,
Seshadri S. Cerebral microb leed sand risk o ncident dementia:
the Framingham Heart Study. Neurobiol Aging. 2017;54:94-99.
17. Gra-Radford J, Simino J, Kantarci K, Mosley TH Jr, Griswold ME,
Windham BG, et al. Neuroimaging correlates of cerebral
microbleeds: the ARIC Study (Atherosclerosis Risk in Communities).
Stroke. 2017;48(11):2964-72.
18. Poungvarin N, Prayoonwiwat N, Senanarong V, Chaisevikul R,
Danchaivijitr C, Nilanont Y. Siriraj Acute Stroke Unit: e
Experience of 614 Patients.Siriraj Med J.2002;54(3):151-8.
19. Kuller LH, Lopez OL, Newman A, Beauchamp NJ, Burke G,
Dulberg C, et al. Risk factors for dementiaint he cardiovascular
health cognition study. Neuroepidemiology. 2003;22(1):13-22.
20. Barnes DE, Covinsky KE, Whitmer RA, Kuller LH, Lopez
OL, Yae K. Predicting risk of dementia in older adults: the
late-life dementia risk index. Neurology. 2009;73(3):173-79.
21. Debette S, Beiser A, DeCarli C, Au R, Himali JJ, Kelly-Hayes
M, et al. Association of MRI markers of vascular brain injury
with incident stroke, mild cognitive impairment, dementia, and
mortality: the Framingham Ospring Study. Stroke. 2010;41(4):
600-6.
22. Knopman DS, Griswold ME, Lirette ST, Gottsman RF, Kantarci
K, Sharrett AR, et al. ARIC Neurocognitive Investigators. Vascular
imaging abnormalities and cognition: mediation by cortical volume
in nondemented individuals: atherosclerosis risk in communities-
neurocognitive study. Stroke. 2015;46(2):433-40.
23. Gottesman RF, Schneider AL, Zhou Y, Coresh J, Green E,
Gupta N, et al. Association between midlife vascular risk factors
and estimated brain amyloid deposition. JAMA. 2017;317(14):
1443-50.
24. Chawalparit O, Saenanarong V, Chiewvit P. CT Criteria in
Vascular Dementia: A Study in ai Population. Siriraj Med J.
2006;58(2):644-7.
25. Barkhof F, Hazewinkel M, Binnewijzend M, Smithuis R.
Dementia - Role of MRI updated version. [Internet] Alzheimer
Centre and Image Analysis Centre, Vrije Universiteit Medical
Center, Amsterdam and the Rijnland Hospital, Leiderdorp,
e Netherlands. Available from: https://radiologyassistant.
nl/neuroradiology/dementia/role-of-mri
26. Folstein MF, Folstein SE, McHugh PR. Mini-mental state: A
practical method for grading the cognitive state of patients for
the clinician. J Psychiatr Res. 1975;12:189-98.
27. Zainal NH, Silva E, Lim LL, Kandiah N. Psychometric Properties
of Alzheimer’s Disease Assessment Scale-Cognitive Subscale for
Mild Cognitive Impairment and Mild Alzheimer’s Disease
Patients in an Asian Context. Ann Acad Med Singap. 2016;45(7):
273-83.
28. JormAF,Jolley D. e incidence of dementia: a meta-analysis.
Neurology.1998;51(3):728-33.
29. Sotthibundhu A, Buntup D, Sanghirun C, Cherdchu K, Cheeramakara
C, Chansirikarnjan S, et al. Low Serum Vitamin B12 in Alzheimerûs
Patients as Detected by a Solid Phase Radioimmunoassay.Siriraj
Med J.2008;60(1):66-68.
30. Muangpaisan W. Rapidly Progressive Dementia due to
Carcinomatous Meningitis Associated with Gastric Cancer.
Siriraj Med J.2016;68(1):47-50.
31. Suesat H, Srinonprasert V, Limpawattana P, Nakyos S,
Poontananggul J, Jiraphorncharas C, et al. Detection of
Postoperative Cognitive Dysfunction by Telemedicine Among
Octogenarian Patients Who Underwent Minor Elective Surgery;
Agarwal et al.
Volume 74, No.5: 2022 Siriraj Medical Journal
https://he02.tci-thaijo.org/index.php/sirirajmedj/index
329
Original Article
SMJ
Prospective Cohort Study.Siriraj Med J.2022;74(2):126-33.
32. Piyapittayanan S, Segsarnviriya C, Ngamsombat C, Witthiwej
T, Cheunsuchon P, Chawalparit O. Comparison between
Dynamic Contrast-Enhanced MRI and Dynamic Susceptibility
Contrast MRI in Glioma Grading.Siriraj Med J.2017;69(6):
369-76.
33. Yossie Susanti Eka Putri, Sitthimongkol Y, Wirojratana V,
Chansatitporn N. Predictors of Depressive Symptoms among
Family Caregivers of Patients with Dementia in Java, Indonesia.
Siriraj Med J.2021;73(8):549-58.
34. Piyapittayanan S, Segsarnviriya C, Ngamsombat C, Witthiwej
T, Cheunsuchon P, Chawalparit O. Comparison between
Dynamic Contrast-Enhanced MRI and Dynamic Susceptibility
Contrast MRI in Glioma Grading.Siriraj Med J.2017;69(6):
369-76.
35. Ananwattanasuk J, Chiewvit P, Nilanont Y. Imaging Findings
of CNS Lymphoma in Siriraj Hospital.Siriraj Med J.2006;58(8):
967-72.
36. Paganini-Hill A, Henderson VW. Estrogen replacement therapy
and risk of Alzheimer’s disease. Arch Intern Med. 1996;156:
2213-17.
37. Stephen CW, Randy JS, Denis GB, Michael WS. Insulin and
the Blood-Brain Barrier. Curr Pharm Des. 2003;9:795-800.
38. Convit A. Links between cognitive impairment in insulin
resistance: An explanatory model. Neurobiol Aging. 2005;26:
31-35.
39. Leritz EC, Salat DH, Williams VJ, Schnyer DM, Rudolph JL,
Lipsitz L, et al. ickness of the human cerebral cortex is
associated with metrics of cerebrovascular health in a normative
sample of community dwelling older adults. Neuroimage. 2011;
54:2659-71.
40. Nagai M, Hoshide S, Ishikawa J, Shimada K, Kario K. Ambulatory
blood pressure as an independent determinant of brain atrophy
and cognitive function in elderly hypertension. J. Hypertens.
2008;26:1636-41.
41. Glodzik L, Mosconi L, Tsui W, de Santi S, Zinkowski R, Pirraglia
E, et al. Alzheimer’s disease markers, hypertension, and gray
matter damage in normal elderly. Neurobiol Aging. 2012;33:
1215-27.
42. Gianaros PJ, Greer PJ, Ryan CM, Jennings JR. Higher blood
pressure predicts lower regional grey matter volume: Consequences
on short-term information processing. Neuroimage. 2006;31:
754-65.
43. Firbank MJ, Wiseman RM, Burton EJ, Saxby BK, O’Brien JT,
Ford GA. Brain atrophy and white matter hyperintensity change
in older adults and relationship to blood pressure. Brain atrophy,
WMH change and blood pressure. J Neurol. 2007;254:713-21.
44. Jennings JR, Mendelson DN, Muldoon MF, Ryan CM, Gianaros
PJ, Raz N, et al. Regional grey matter shrinks in hypertensive
individuals despite successful lowering of blood pressure. J
Hum Hypertens. 2012;26:295-305.
45. Petrovitch H, White LR, Izmirilian G, Ross GW, Havlik RJ,
Markesbery W, et al. Midlife blood pressure and neuritic
plaques, neurobrillary tangles, and brain weight at death: the
HAAS. Neurobiol. Aging. 2000;21:57-62.
46. Ashby EL, Miners JS, Kehoe PG, Love S. Eects of Hypertension
and Anti-Hypertensive Treatment on Amyloid-ß Plaque Load
and Aß-Synthesizing and Aß-Degrading Enzymes in Frontal
Cortex. J Alzheimers Dis. 2016;50:1191-203.
47. Girouard H, Iadecola C. Neurovascular coupling in the normal
brain and in hypertension, stroke, and Alzheimer disease. J
Appl Physiol. 2006;100:328-35.
48. Sengupta P, Benjamin AI, Singh Y, Grover A. Prevalence and
correlates of cognitive impairment in a north Indian elderly
population. WHO South East Asia J Public Health. 2014;3(2):
135-43.
49. Verdelho A, Madureira S, Moleiro C, Ferro JM, Santos CO,
Erkinjuntti T, et al. White matter changes and diabetes predict
cognitive decline in the elderly: the LADIS study. Neurology.
2010;75:160-7.
50. Debette S, Markus HS. e clinical importance of white matter
hyperintensities on brain magnetic resonance imaging: systematic
review and meta-analysis. BMJ. 2010;341:c3666.
51. Roman GC, Erkinjuntti T, Wallin A, Pantoni L, Chui HC.
Subcortical ischaemic vascular dementia. Lancet Neurol. 2002;
1:426-36.
52. Van der Flier WM, van Straaten EC, Barkhof F, Verdelho A,
Madureira S, Pantoni L, et al. Small vessel disease and general
cognitive function in nondisabled elderly: the LADIS study.
Stroke. 2005;36:2116-20.
53. Verdelho A, Madureira S, Ferro JM, Basile AM, Chabriat H,
Erkinjuntti T, et al. Dierential impact of cerebral white matter
changes, diabetes, hypertension and stroke on cognitive performance
among non-disabled elderly. The LADIS study. J Neurol
Neurosurg Psychiatry. 2007;78:1325-30.
54. Cavallin L, Bronge L, Zhang Y, Oksengard, AR, Wahlund LO,
Fratiglioni L, et al. Comparison between visual assessment
of MTA and hippocampal volumes in an elderly, non-demented
population. Acta Radiol. 1987;53:573-9.
55. Westman E, Cavallin L, Muehlboeck J-S, Zhang Y, Mecocci P,
Vellas B, et al. Sensitivity and specicity of medial temporal
lobe visual ratings and multivar- iate regional MRI classication
in Alzheimer’s disease. PLoS One. 2011;6:e22506.
56. Claus JJ, Staekenborg SS, Holl DC, Roorda JJ, Schuur J, Koster
P, et al. Practical use of visual medial temporal lobe atrophy
cut-o scores in Alzheimer’s disease: Validation in a large
memory clinic population. Eur Radiol. 2017;27(8):3147-55.
