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Year : 2019  |  Volume : 40  |  Issue : 1  |  Page : 17-23

White matter hyperintensities in elderly patients with late-onset and early-onset depression: a comparative study

1 Department of Psychiatry, Kasr Al-Ainy, Faculty of Medicine, Cairo University, Cairo, Egypt
2 Professor of Radiology, Faculty of Medicine, Cairo University, Cairo, Egypt

Date of Submission13-Aug-2018
Date of Acceptance03-Sep-2018
Date of Web Publication9-May-2019

Correspondence Address:
Maged A Gomaa
Department of Psychiatry, Kasr Al-Ainy Faculty of Medicine, Cairo University, El-Manial, Cairo, 12151
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ejpsy.ejpsy_19_18

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Background Depression in late life is associated with subtle irreversible cognitive impairment, subtle structural brain damage, and a high frequency of white matter and other lesions, on visualization with MRI.
Objectives The objective of this article is to explore the role of white matter hyperintensities (WMH) in elderly patients with depression. A total of 80 elderly patients with depression were recruited from the Geriatric Outpatient Clinic of Psychiatry and Addiction Prevention Hospital, Kasr Al-Ainy, Cairo University. They were divided into two groups according to the age of onset of depression: late-onset group [late-onset depression (LOD)] and early-onset group (early-onset depression).
Patients and methods This was a hospital-based, cross-sectional, comparative study with consecutive referral. The patients were subjected to MRI of the brain to assess WMH using modified Fazekas score, Hamilton depression rating scale, and Addenbrooke’s cognitive examination revised.
Results The mean total score of modified Fazekas score is significantly higher in the LOD group than in the early-onset depression group, and the periventricular WMH are significantly higher in the LOD.
Conclusion WMH are more associated with LOD in elderly patients.

Keywords: cognitive impairment, early-onset depression, late-onset depression, neuroimaging, white matter hyperintensities

How to cite this article:
Hashem AH, Nasreldin HM, Gomaa MA, Khalaf OO, Ismail A. White matter hyperintensities in elderly patients with late-onset and early-onset depression: a comparative study. Egypt J Psychiatr 2019;40:17-23

How to cite this URL:
Hashem AH, Nasreldin HM, Gomaa MA, Khalaf OO, Ismail A. White matter hyperintensities in elderly patients with late-onset and early-onset depression: a comparative study. Egypt J Psychiatr [serial online] 2019 [cited 2023 Oct 1];40:17-23. Available from: https://new.ejpsy.eg.net//text.asp?2019/40/1/17/257846

  Introduction Top

There is no consensus on the age cutoff to distinguish between late-onset and early-onset disorders; however, in the UK, 60 years of age is the most commonly used cutoff (Baldwin and O’Brien, 2002).

There is an increasing evidence of the presence of significant atrophic changes, both cortical and subcortical, implicated in the pathophysiology of elderly depression. Volumetric analysis and cortical mapping found evident structural changes, especially in frontal and temporo-parietal regions, supporting the neurodegenerative hypothesis of late-onset depression (LOD) (Ballmaier et al., 2008).

LOD shows unique clinical characteristics, as it is less likely to exhibit family history of psychiatric disorders or psychiatric comorbidities yet more likely to be associated with medical comorbidities, cognitive impairment, and the development of dementia (Lim et al., 2012).

Depression in later life was associated with subtle structural brain damage and a high frequency of white matter lesions (WMLs), visualized by MRI, which are associated with subtle irreversible cognitive impairment. The significance of those lesions was unclear, but Baldwin suggested a link with cerebrovascular disease (Baldwin and Tomenson, 1995).

MRI and T2-weighted imaging were used to show that white matter hyperintensities are bright foci seen in the parenchyma of the brain of geriatric depressed patients compared with controls (Greenwald et al., 1996; Herrmann et al., 2008). Because white matter hyperintensities (WMH) are associated with age, they should increase in number and size over time (Warren et al., 2003).

The association between LOD and cognitive impairment has been previously studied. Researchers found more significant decreased cortical thickness in anterior cingulate cortex, medial orbitofrontal cortex, dorsolateral prefrontal cortex, and superior and middle temporal cortex in patients with LOD compared with normal healthy controls (Lim et al., 2008).

However, many neuroimaging studies have highlighted the association between LOD and demyelinating lesions, such as WMHs, and supported the role of cardiovascular risk factors in the development of depression and WMHs, adding more evidence to the vascular depression hypothesis (Khalaf et al., 2015).

Several lines of evidence suggest that there is a relationship between vascular factors and late-life depression. Both symptomatic and silent brain infarcts are associated with subsequent depression (Henning et al., 2004). Moreover, there is evidence to suggest that cerebrovascular disease, especially ischemic small-vessel disease, may be a factor in the pathogenesis of late-onset geriatric major depression (Michael et al., 2005).

Those with risk factors for atherosclerosis disease including hypertension, diabetes, and hyperlipidemia are suspected to be at a higher risk of vascular depression in late life (Sneed et al., 2007).

The evidence for cerebrovascular disease playing an important role in the pathogenesis of late life depression is abundant. WMH, which are surrogate markers for cerebrovascular damage, are frequently increased in elderly depressed patients, especially in those with LOD compared with early-onset depression (EOD) (Baldwin et al., 2005; Herrman et al., 2008).

Smith and Nichols (2009) suggested a vascular basis for WMH disrupting fiber tracts within frontostriatal circuits. Because of their involvement in the regulation of mood, disruption of frontostriatal circuits may lead to a disconnection syndrome that corresponds to the clinical and neuropsychological profile of late-life depression (Lenze et al., 1999).

However, the differentiation between EOD and LOD has not been thoroughly studied, although some studies found that EOD is associated with more severe depressive illness whereas LOD is associated with more neurological changes and cognitive impairment (Sachs-Ericsson et al., 2013).

  Hypothesis Top

WMLs in elderly patients are associated with LOD.

  Aim Top

The aim of this article is to investigate the association between WMH using MRI in elderly patients with LOD and cognitive impairment and to study the role of vascular risk factors in elderly depressed patients.

  Patients and methods Top


This was a hospital-based comparative study with consecutive referral. A total of 80 elderly patients with major depressive disorder according to Diagnostic and statistical manual of mental disorders (4th ed., text revision) DSM-IV TR diagnostic criteria participated in this study. All patients were drug free. They were recruited from Geriatric Psychiatry Outpatient Clinic of Psychiatry and Addiction Prevention Hospital, Faculty of Medicine, Cairo University. They were divided equally into two groups according to the onset of depression: elderly depressed patients with LOD, who had the first depressive episode at/or after the age of 60 years (LOD group), and elderly depressed patients with EOD, who had the first depressive episode before the age of 60 years (EOD group) (Baldwin and O’Brien, 2002). The two groups were matched for age, sex, level of education, and social and occupational status.

Procedures were explained to all patients, and they all consented to participate in this study. Any patient with gross neurological disorders, cerebrovascular stroke, sensory deficits, history of recent administration of electroconvulsive therapy in the previous 3 months or substance abuse, depression secondary to medications or medical illness in addition patients diagnosed with dementia were excluded from the sample by using Global Deterioration Scale (Reisberg et al., 1982), which is a clinician-rated instrument to stage the degree of cognitive decline. In addition, patients with pacemakers, aneurysm clips, heart valve replacements, neurostimulators, cochlear implants, metal fragments in the eye, metal foreign bodies, and magnetic dental implants are excluded.

Ethical approval

All procedures performed in the current study involving human participants were in accordance with the ethical standards of the National Research Committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Cases were subjected only to clinical assessment, cognitive assessment, and imaging procedures after having a written consent (explaining nature of the disease, objective of the study, and their right to refusal to participate in the current study) from them. No experimental trials were done on patients.


All patients were subjected to clinical and neurological assessment and laboratory investigation. Structured clinical interview for DSM-IV axis I disorders was applied (First et al., 2002). It is a semistructured interview used to determine DSM-IV TR axis I disorders (major mental disorders). Mood disorders section was used. The severity of depression was measured using Hamilton depression rating scale (HDRS) (Hamilton, 1980). It is 17-item reliable tool to assess the severity of depression. Those scored from 8 to 13 were considered having mild depression, whereas those scored from 14 to 18 were considered having moderate depression, meanwhile, scores from 19 to 22 were considered severe depression and more than or equal to 23 were categorized as very severe depression.

Addenbrooke’s cognitive examination revised (Mioshi et al., 2006), Arabic version (Qassem et al., 2015).

It is composed of six components evaluating separate cognitive domains. A maximum score of 100 was weighed as follows: attention (18), memory (26), verbal fluency (14), language (26), and visuospatial abilities (16). Higher scores indicated better cognitive functioning.


All patients were examined in the MR Unit of Radiology Department, Cairo University Hospitals (Kasr Al-Ainy). The MRI study was performed on a 1.5-T (Gyroscan T10 NT; Philips, Andover, Massachusetts, USA).

Patients were examined by MRI of the brain to assess WMH using modified Fazekas rating (Fazekas et al., 1987). Using axial T2 and FLAIR images, the modified Fazekas criteria described MRI hyperintensities in three regions and followed an ascending degree of severity (periventricular, deep white matter hyperintensities, and subcortical gray matter) (Fazekas et al., 1987). In addition, the total modified Fazekas score was created by summing the three ratings; from this total score, a categorical score was created: 3 or more, high and 2 or less, low.

Statistical analysis

Data were coded and entered using the statistical package SPSS, version 21 (SPSS 21, IBM, Armonk, NY, USA). Comparisons between means were done using t tests. Comparisons between quantitative variables were done using the nonparametric Mann–Whitney test (Chan, 2003a). For comparing categorical data, χ2 test was performed. Exact test was used instead when the expected frequency is less than 5 (Chan, 2003b). Correlation was done to test for linear relations between quantitative variables by Spearman’s correlation coefficient (Chan, 2003c). P values less than 0.05 were considered as statistically significant.

  Results Top

There were no statistical significant differences between both groups regarding age sex, educational level, and social status as shown in [Table 1].
Table 1 Demographic and clinical data of the two groups

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Patients with LOD had severe to very severe depression (N=22) compared with patients with EOD (N=12). The difference reached statistical significance (P=0.024), as shown in [Table 2]. Percentages of diabetes mellitus and hypertension were significantly higher in patients with LOD than patients with EOD. Patients with LOD had significantly higher degree of cognitive impairment than patients with EOD except for attention.
Table 2 Comparison of severity of depression and vascular risk assessment between the two groups

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Patients with LOD had higher statistically significant periventricular WMH (P=0.01) and modified Fazekas score (P=0.007) than patients with EOD ([Table 3]).
Table 3 Comparison between the two groups regarding the MRI findings

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In each group, there was no significant correlation between modified Fazekas scores on one side and severity of depression and degree of cognitive impairment on the other side ([Table 4]).
Table 4 Correlations between modified Fazekas score and severity of depression and degree of cognitive impairment in each group

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When taking all patients of the sample, modified Fazekas score was significantly correlated to both severity of depression and degree of cognitive impairment ([Table 5]).
Table 5 Correlations between modified Fazekas score and severity of depression and degree of cognitive impairment in both groups together

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MRI-brain findings in both LOD and EOD are shown in [Figure 1] and [Figure 2], respectively.
Figure 1 MRI-brain section in a patient from late-onset depression group. T2-flair MRI section of a patient in group A (late-onset depression) showing irregular PVWML, that is, score 3 on modified Fazekas scale for PVWM. A, anterior; L, left; P, posterior; PVWML, periventricular white matter lesions; R, right.

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Figure 2 MRI-brain section in a patient from early-onset depression group. T2-flair MRI section of a patient in group B (early-onset depression) showing smooth halo PVWML, that is, score 2 on modified Fazekas scale for PVWM, and punctate foci in DWM, that, score 1 on modified Fazekas scale for DWM. A, anterior; DWML, deep white matter lesion; L, left; P, posterior; PVWML, periventricular white matter lesions; R, right.

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  Discussion Top

The two study groups were properly matched, as there were no statistically significant differences between the two groups regarding age, sex, level of education, and social and occupational status. However, there was statistically higher severity of depression in the LOD group than in the EOD group using HDRS, which was inconsistent with Variend and Gopal (2008) and Sachs-Ericsson et al. (2013), who showed that EOD is associated with greater severity of depressive illness. This difference could be explained by the usage of different scales, as they used Montgomery-Åsberg depression rating scale, where symptoms were rated on a scale from 0 to 6, and also owing to the difference in the research design.

In this study, the significantly higher hyperintensity findings in periventricular white matter with total modified Fazekas scores strengthened the vascular hypothesis of depression in this age group. This was in line with other studies which applied MRI of the brain and showed that patients with LOD had greater severity of subcortical WMLs than those with EOD (1991) Dahabra et al., 1997; Delaloye et al., 2010; Smagula and Aizenstein, 2016; Aizenstein et al., 2016). Meanwhile, the statistically significant positive correlation between total HDRS score and modified Fazekas score when assessing the whole sample was in agreement with Cole et al. (2012) who investigated white matter integrity in patients with major depression and healthy controls. They assessed its relationship with depressive symptom severity and concluded that alterations in white matter integrity were heightened with increasing severity of depressive symptoms.

Regarding, correlations with modified Fazekas score as a quantitative assessment of MRI to detect WMH versus qualitative measures in other studies could limit the comparability of the results and may result in the inconsistency of the conclusions (Kim et al., 2008).

Finally, when assessing a geriatric patient having depression, clinicians should put into consideration the following clinical correlates, namely, age at onset, severity of depression, vascular risk, and MRI-brain imaging, as vascular risk changes were highly common in this age group regardless of the age at onset, and although it was more commonly observed in the late-onset group, but that did not exclude its presence in the early-onset group.

The forms of WMLs are quite variable: periventricular caps or rims or halos, subcortical multiple punctuate or patchy lesions, and partially confluent or confluent lesions. These various WMLs are often divided into two broad categories: periventricular white matter lesions (PVWMLs), which are attached to the ventricular system, and deep white matter lesions (DWMLs), which are located apart from the cerebral ventricle in subcortical white matter. As PVWMLs have been consistently associated with different clinical, histopathological, and etiological correlates from DWMLs, a possible dissimilarity in pathogenic mechanisms between PVWMLs and DWMLs may provide the clues for understanding pathophysiology of many geriatric syndromes associated with WMLs (Ki Woong et al., 2008).

  Conclusions Top

From this study, we can conclude that WMH are more associated with geriatric patients with LOD. PVWML is a more relevant imaging finding when assessing depression and cognitive decline in geriatric patients. Vascular risk factors have the most important role in the development of WML in late-onset depressed patients.


MRI of the brain must be used routinely in assessing geriatric depressed patients along with assessing vascular risk factors.

This study is better be replicated using a larger sample number, which may result in more data helping geriatric population.[34]

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Aizenstein HJ, Baskys A, Boldrini M, Butters MA, Diniz BS, Jaiswal MK et al. (2016). Vascular depression consensus report − a critical update. BMC Med 14:161.  Back to cited text no. 1
Baldwin RC, O’Brien J. (2002). Vascular basis of late-onset depressive disorder. British J Psychiatry 180:157–160.  Back to cited text no. 2
Baldwin RC, Tomenson B. (1995). Depression in later life. A comparison of symptoms and risk factors in early and late onset cases. British J Psychiatry, 167: 649–652.  Back to cited text no. 3
Baldwin RC, Jeffries S, Jackson A et al. (2005). Neurological findings in late-onset depressive disorder: comparison of individuals with and without depression, British J Psychiatry 186:308–313.  Back to cited text no. 4
Ballmaier M, Kumar A, Elderkin-Thompson V, Narr KL, Luders E, Thompson PM et al. (2008). Mapping callosal morphology in early- and late-onset elderly depression: an index of distinct changes in cortical connectivity. Neuropsychopharmacology 33:1528–1536.  Back to cited text no. 5
Chan YH. (2003a). Biostatistics102: Quantitative Data − Parametric & Non-parametric Tests. Singapore Med J 44:391–396.  Back to cited text no. 6
Chan YH. (2003b), Biostatistics 103: Qualitative Data −Tests of Independence. Singapore Med J 44:498-503.  Back to cited text no. 7
Chan YH. (2003c). Biostatistics 104: correlational analysis. Singapore Med J 44:614–619.  Back to cited text no. 8
Cole J, Chaddock C, Farmer A et al. (2012). White matter abnormalities and illness severity in major depressive disorder. British J Psychiatry 201:33–39.  Back to cited text no. 9
Dahabra S, Ashton CH, Bahrainian M et al. (1997). Structural and functional abnormalities in elderly patients clinically recovered from early- and late-onset depression. Biol Psychiatry 44:34–46.  Back to cited text no. 10
Delaloye C, Moy G, de Bilbao F, Baudois S, Weber K, Hofer F et al. (2010). Neuroanatomical and neuropsychological features of elderly euthymic depressed patients with early- and late-onset. J Neurol Sci 299:19–23.  Back to cited text no. 11
Fazekas F, Chawluk JB, Alavi A et al. (1987). MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. Am J Roentgenol Radium Ther Nucl Med 149: 351–356.  Back to cited text no. 12
Figiel GS, Krishnan KR, Doraiswamy PM et al. (1991). Subcortical hyperintensities on brain magnetic resonance imaging: a comparison between late age onset and early onset elderly depressed subjects. Neurobiology Aging 12:245–247.  Back to cited text no. 13
First MB, Spitzer RL, Gibbon M, Williams JBW. (2002). Structured clinical interview for DSM-IV-TR axis I disorders, research version, patient edition. (SCID-I/P). New York: Biometrics Research, New York State Psychiatric Institute.  Back to cited text no. 14
Greenwald BS, Kramer-Ginsberg E, Krishnan KR et al. (1998). Neuroanatomic localization of magnetic resonance imaging signal hyperintensities in geriatric depression. Stroke 29:613–617.  Back to cited text no. 15
Hamilton M. (1980). Rating depressive patients. J Clin Psychiatry 41:21–24.  Back to cited text no. 16
Henning T, Dijck W, Hofman A et al. (2004). Relationship Between Atherosclerosis and Late-Life Depression, Arch Gen Psychiatry 61:369–376.  Back to cited text no. 17
Herrmann L, Le Masurier M, Ebmeier K. (2008). White matter hyperintensities in late life depression: a systematic review Journal of Neurology, Neurosurg Psychiatry 79:619–624.  Back to cited text no. 18
Ki Woong K, James RM, Martha EP et al. (2008). Classification of white matter lesions on magnetic resonance imaging in the elderly. Psychiatry 15:273–280.  Back to cited text no. 19
Kim KW, MacFall JR, Payne ME. (2008). Classification of white matter lesions on magnetic resonance imaging in elderly persons. Biol Psychiatry 64:273–80.  Back to cited text no. 20
Khalaf A, Edelman K, Tudorascu D, Andreescu C, Reynolds CF, Aizenstein H. (2015). White matter hyperintensity accumulation during treatment of late-life depression. Neuropsychopharmacology 40:3027–3035.  Back to cited text no. 21
Lenze E, Cross D, McKeel D et al. (1999). White matter hyperintensities and gray matter lesions in physically healthy depressed subjects. Am J Psychiatry 156:1602–1607.  Back to cited text no. 22
Lim HK, Jung WS, Ahn KJ, Won WY, Hahn C, Lee SY et al. (2012). Regional cortical thickness and subcortical volume changes are associated with cognitive impairments in the drug-naïve patients with late-onset depression. Neuropsychopharmacology 37:838–849.  Back to cited text no. 23
Lim HK, Juh R, Pae CU, Lee BT, Yoo SS, Ryu SH et al. (2008). Altered verbal working memory process in patients with Alzeheimer’s disease, Neuropsychobiology 57:181–187.  Back to cited text no. 24
Mioshi E, Dawson K, Mitchell J. et al. (2006). The Addenbrooke’s Cognitive Examination Revised (ACE-R): A brief cognitive test battery for dementia screening. Int J Geriatr Psychiatry 21:1078–1085.  Back to cited text no. 25
Michael AR, Karen D, Mary S et al. (2005). Neuropsychological differences between late-onset and recurrent geriatric major depression, Am J Psychiatry 162:691–698.  Back to cited text no. 26
Qassem T, Khater MS, Emara T, Rasheedy D, Tawfik HM, Mohammedin AS et al. (2015). Normative data for healthy adult performance on the Egyptian–Arabic Addenbrooke’s Cognitive Examination III. Middle East Current Psychiatry 22:27–36.  Back to cited text no. 27
Reisberg B, Ferris SH, de Leon MJ. (1982). The Global Deterioration Scale for assessment of primary degenerative dementia. Am J Psychiatry 39:1136–1139.  Back to cited text no. 28
Sachs-Ericsson N, Corsentino E, Moxley J, Hames JL, Rushing NC, Sawyer K et al. (2013). A longitudinal study of differences in late- and early-onset geriatric depression: depressive symptoms and psychosocial, cognitive, and neurological functioning. Aging Ment Health 17:1–11.  Back to cited text no. 29
Smagula SF, Aizenstein HJ. (2016). Brain structural connectivity in late-life major depressive disorder. Biol Psychiatry 3:271–277.  Back to cited text no. 30
Smith SM, Nichols TE. (2009). Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage 44:83–98.  Back to cited text no. 31
Sneed JR, Kasen S, Cohen P. (2007). Early-life risk factors for lateonset depression, Int J Geriatr Psychiatry. 2:663–667.  Back to cited text no. 32
Variend H, Gopal YV. (2008). Late-onset depression: issues affecting clinical care. Adv Psych Treat 14:152–158.  Back to cited text no. 33
Warren D, David C, James R et al. (2003). White Matter Hyperintensity Progression and Late-Life Depression Outcomes, Arch Gen Psychiatry 60:1090–1096.  Back to cited text no. 34


  [Figure 1], [Figure 2]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


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