Introduction: White matter hyperintensity (WMH) is associated with cognitive impairment, although the clinical significance of WMH remains unclear. We aimed to elucidate the clinical significance of WMH volume and whether a fully automated quantitative analysis of WMH would be an effective marker of cognitive function. Methods: Patients with suspected cognitive impairment were retrospectively examined. Clinical data, including patient information, neuropsychological examinations, diagnoses of dementia disorders, and fluid-attenuated inversion recovery (FLAIR) images, were collected. Patient information included sex, age, and educational level. Neuropsychological examinations included the Mini-Mental State Examination (MMSE) and Japanese version of the Montreal Cognitive Assessment (MoCA-J). WMH volumes were analyzed from FLAIR images using a fully automatic analysis software. The relationship between WMH volume and clinical data was investigated. Results: WMH volume was analyzed using 889 FLAIR cases. The WMH volume did not differ significantly between the sexes. WMH volume showed a positive correlation with age. Multiple comparison tests showed no significant difference in WMH volume between junior high school and high school graduates, but all other differences were significant. Multiple comparison tests revealed significant differences in mean WMH volume among all groups in the classified MMSE. The Mann-Whitney U test revealed significant differences in WMH volume between the two groups. Multiple comparison tests revealed significant differences in WMH volume among all the groups of classified diagnostic results. Conclusion: Quantitative analysis of WMH volume from FLAIR images may provide useful information for dementia treatment and may be effective as a new marker in cognitive function examinations.

1.
Vernooij
MW
,
Ikram
MA
,
Tanghe
HL
,
Vincent
AJPE
,
Hofman
A
,
Krestin
GP
, et al
.
Incidental findings on brain MRI in the general population
.
N Engl J Med
.
2007
;
357
(
18
):
1821
8
.
2.
Fazekas
F
,
Chawluk
JB
,
Alavi
A
,
Hurtig
HI
,
Zimmerman
RA
.
MR signal abnormalities at 1.5 T in Alzheimerʼs dementia and normal aging
.
AJR Am J Roentgenol
.
1987
;
149
(
2
):
351
6
.
3.
Fazekas
F
,
Niederkorn
K
,
Schmidt
R
,
Offenbacher
H
,
Horner
S
,
Bertha
G
, et al
.
White matter signal abnormalities in normal individuals: correlation with carotid ultrasonography, cerebral blood flow measurements, and cerebrovascular risk factors
.
Stroke
.
1988
;
19
(
10
):
1285
8
.
4.
Fazekas
F
,
Barkhof
F
,
Wahlund
LO
,
Pantoni
L
,
Erkinjuntti
T
,
Scheltenset
P
, et al
.
CT and MRI rating of white matter lesions
.
Cerebrovasc Dis
.
2002
;
13
(
Suppl 2
):
31
6
.
5.
Lin
J
,
Wang
D
,
Lan
L
,
Fan
Y
.
Multiple factors involved in the pathogenesis of white matter lesions
.
Biomed Res Int
.
2017
;
2017
:
1
9
.
6.
Black
S
,
Gao
F
,
Bilbao
J
.
Understanding white matter disease: imaging-pathological correlations in vascular cognitive impairment
.
Stroke
.
2009
;
40
(
3 Suppl l
):
S48
52
.
7.
Smith
EE
.
Leukoaraiosis and stroke
.
Stroke
.
2010
;
41
(
10 Suppl l
):
S139
143
.
8.
Imaizumi
T
,
Inamura
S
,
Nomura
T
.
The severities of white matter lesions possibly influence the recurrences of several stroke types
.
J Stroke Cerebrovasc Dis
.
2014
;
23
(
7
):
1897
902
.
9.
Henneman
WJ
,
Sluimer
JD
,
Cordonnier
C
,
Baak
MME
,
Scheltens
P
,
Barkhof
F
, et al
.
MRI biomarkers of vascular damage and atrophy predicting mortality in a memory clinic population
.
Stroke
.
2009
;
40
(
2
):
492
8
.
10.
De Groot
JC
,
De Leeuw
FE
,
Oudkerk
M
,
Van Gijn
J
,
Hofman
A
,
Jolles
J
, et al
.
Periventricular cerebral white matter lesions predict rate of cognitive decline
.
Ann Neurol
.
2002
;
52
(
3
):
335
41
.
11.
Sachdev
PS
,
Thalamuthu
A
,
Mather
KA
,
Ames
D
,
Wright
MJ
,
Wen
W
, et al
.
White matter hyperintensities are under strong genetic influence
.
Stroke
.
2016
;
47
(
6
):
1422
8
.
12.
Kertesz
A
,
Black
SE
,
Tokar
G
,
Benke
T
,
Carr
T
,
Nicholson
L
.
Periventricular and subcortical hyperintensities on magnetic resonance imaging: “Rims, caps, and unidentified bright objects”
.
Arch Neurol
.
1988
;
45
(
4
):
404
8
.
13.
Debette
S
,
Markus
HS
.
The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and metaanalysis
.
BMJ
.
2010
;
341
:
c3666
.
14.
Koga
H
,
Yuzuriha
T
,
Yao
H
,
Endo
K
,
Hiejima
S
,
Takashima
Y
, et al
.
Quantitative MRI findings and cognitive impairment among community dwelling elderly subjects
.
J Neurol Neurosurg Psychiatry
.
2002
;
72
(
6
):
737
41
.
15.
Au
R
,
Massaro
JM
,
Wolf
PA
,
Young
ME
,
Beiser
A
,
Seshadri
S
, et al
.
Association of white matter hyperintensity volume with decreased cognitive functioning: the Framingham Heart Study
.
Arch Neurol
.
2006
;
63
(
2
):
246
50
.
16.
Torgil Riise Vangberg
.
Live Eikenes, Asta K. Håberg. The effect of white matter hyperintensities on regional brain volumes and white matter microstructure, a population-based study in HUNT
.
Neuroimage
.
2019
;
203
:
1
16
.
17.
Smith
EE
,
Killiany
RJ
,
Muzikansky
A
,
Dickerson
BC
,
Svetlana
E
,
Alona
M
, et al
.
Magnetic resonance imaging white matter hyperintensities and brain volume in the prediction of mild cognitive impairment and dementia
.
Arch Neurol
.
2008
;
65
(
1
):
94
100
.
18.
Arvanitakis
Z
,
Fleischman
DA
,
Arfanakis
K
,
Leurgans
SE
,
Barnes
LL
,
Bennett
DA
.
Association of white matter hyperintensities and gray matter volume with cognition in older individuals without cognitive impairment
.
Brain Struct Funct
.
2016
;
221
(
4
):
2135
46
.
19.
Brickman
AM
,
Provenzano
FA
,
Muraskin
J
,
Manly
JJ
,
Blum
S
,
Apa
Z
, et al
.
Regional white matter hyperintensity volume, not hippocampal atrophy, predicts incident Alzheimer disease in the community
.
Arch Neurol
.
2012
;
69
(
12
):
1621
7
.
20.
Vangberg
TR
,
Eikenes
L
,
Håberg
AK
.
The effect of white matter hyperintensities on regional brain volumes and white matter microstructure, a population-based study in HUNT
.
Neuroimage
.
2019
;
203
:
116158
.
21.
Anbeek
P
,
Vincken
KL
,
van Osch
MJ
,
Bisschops
RHC
,
van der Grond
J
.
Probabilistic segmentation of white matter lesions in MR imaging
.
Neuroimage
.
2004
;
21
(
3
):
1037
44
.
22.
Jack
CR
Jr
,
O’Brien
PC
,
Rettman
DW
,
Shiung
MM
,
Xu
Y
,
Muthupillai
R
, et al
.
FLAIR histogram segmentation for measurement of leukoaraiosis volume
.
J Magn Reson Imaging
.
2001
;
14
(
6
):
668
76
.
23.
Valdés Hernández
MC
,
Morris
Z
,
Dickie
DA
,
Royle
NA
,
Muñoz Maniega
S
,
Aribisala
BS
, et al
.
Close correlation between quantitative and qualitative assessments of white matter lesions
.
Neuroepidemiology
.
2012
;
40
(
1
):
13
22
.
24.
Gouw
AA
,
van der Flier
WM
,
van Straaten
ECW
,
Pantoni
L
,
Bastos-Leite
AJ
,
Inzitari
D
, et al
.
Reliability and sensitivity of visual scales versus volumetry for evaluating white matter hyperintensity progression
.
Cerebrovasc Dis
.
2008
;
25
(
3
):
247
53
.
25.
Miller
DH
,
Barkhof
F
,
Frank
JA
,
Parker
GJM
,
Thompson
AJ
.
Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance
.
Brain
.
2002
;
125
(
Pt 8
):
1676
95
.
26.
Hernández
MCV
,
Ferguson
KJ
,
Chappell
FM
,
Wardlaw
JM
.
New multispectral MRI data fusion technique for white matter lesion segmentation: method and comparison with thresholding in FLAIR images
.
Eur Radiol
.
2010
;
20
(
7
):
1684
91
.
27.
Park
K
,
Nemoto
K
,
Yamakawa
Y
,
Yamashita
F
,
Yoshida
K
,
Tamura
M
, et al
.
Cerebral white matter hyperintensity as a healthcare quotient
.
J Clin Med
.
2019
;
8
(
11
):
1823
.
28.
Okawa
R
,
Hayashi
N
,
Takahashi
T
,
Atarashi
R
,
Yasui
G
,
Mihara
B
.
Comparison of qualitative and fully automated quantitative tools for classifying severity of white matter hyperintensity
.
J Stroke Cerebrovasc Dis
.
2024
;
33
(
8
):
107772
.
29.
Ashburner
J
.
SPM: a history
.
Neuroimage
.
2012
;
62
(
2
):
791
800
.
30.
Dunn
OJ
.
Multiple comparisons among means
.
J Am Stat Assoc
.
1961
;
56
(
293
):
52
64
.
31.
Kanda
Y
.
Investigation of the freely available easy-to-use software “EZR” for medical statistics
.
Bone Marrow Transpl
.
2013
;
48
(
3
):
452
8
.
32.
Elahi
FM
,
Miller
BL
.
A clinicopathological approach to the diagnosis of dementia
.
Nat Rev Neurol
.
2017
;
13
(
8
):
457
76
.
33.
Okeh
UM
.
Statistical analysis of the application of Wilcoxon and Mann-Whitney U test in medical research studies
.
Biotechnol Mol Biol Rev
.
2009
;
4
:
128
31
.
34.
Ostertagová
E
,
Ostertag
O
,
Kováč
J
.
Methodology and application of the kruskal-wallis test
.
Appl Mech Mater
.
2014
;
611
:
115
20
.
35.
Qi
X
,
Tang
H
,
Luo
Q
,
Ding
B
,
Chen
J
,
Cui
P
, et al
.
White matter hyperintensities predict cognitive decline: a community-based study
.
Can J Neurol Sci
.
2019
;
46
(
4
):
383
8
.
36.
Ciesielska
N
,
Sokołowski
R
,
Mazur
E
,
Podhorecka
M
,
Polak-Szabela
A
,
Kędziora-Kornatowska
K
.
Is the Montreal Cognitive Assessment (MoCA) test better suited than the Mini-Mental State Examination (MMSE) in mild cognitive impairment (MCI) detection among people aged over 60
.
Psychiatr Pol
.
2016
;
50
(
5
):
1039
52
.
37.
Tsoi
KKF
,
Chan
JYC
,
Hirai
HW
,
Wong
A
,
Mok
VCT
,
Lam
LCW
, et al
.
Recall tests are effective to detect mild cognitive impairment: a systematic review and meta-analysis of 108 diagnostic studies
.
J Am Med Dir Assoc
.
2017
;
18
(
9
):
807.e17
29
.
38.
Fujiwara
Y
,
Suzuki
H
,
Yasunaga
M
,
Sugiyama
M
,
Ijuin
M
,
Sakuma
N
, et al
.
Brief screening tool for mild cognitive impairment in older Japanese: validation of the Japanese version of the Montreal Cognitive Assessment
.
Geriatr Gerontol Int
.
2010
;
10
(
3
):
225
32
.
39.
DeCarli
C
,
Miller
BL
,
Swan
GE
,
Reed
T
,
Wolf
PA
,
Carmelli
D
.
Cerebrovascular and brain morphologic correlates of mild cognitive impairment in the national heart, lung, and blood Institute twin study
.
Arch Neurol
.
2001
;
58
(
4
):
643
7
.
40.
Ihara
M
,
Yamamoto
Y
.
Emerging evidence for pathogenesis of sporadic cerebral small vessel disease
.
Stroke
.
2016
;
47
(
2
):
554
60
.
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