European Psychiatry 29 (2014) 226–232
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Original article
White matter hyperintensities and cognitive performance in adult patients with bipolar I, bipolar II, and major depressive disorders T. Kieseppa¨ a,*,b, R. Ma¨ntyla¨ c,d, A. Tuulio-Henriksson e,f, K. Luoma c, O. Mantere a,b, M. Ketokivi g, M. Holma a,h, P. Jylha¨ a,b, T. Melartin a,b, K. Suominen a,h, M. Vuorilehto a, E. Isometsa¨ a,b,i a
Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, 00300 Helsinki, Finland Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland c HUS Medical Imaging Center, Helsinki University Central Hospital, Helsinki, Finland d Hyvinka¨a¨ Hospital, Hyvinka¨a¨, Finland e Social Insurance Institution, Research Department, Helsinki, Finland f Department of Behavioral Sciences, University of Helsinki, Helsinki, Finland g Operations and Technology Department, IE Business School, Madrid, Spain h Department of Psychiatry, City of Helsinki, Helsinki, Finland i Institute of Clinical Medicine, University of Helsinki, Helsinki, Finland b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 1 July 2013 Received in revised form 26 August 2013 Accepted 26 August 2013 Available online 28 October 2013
Purpose: We evaluate for the first time the associations of brain white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) with neuropsychological variables among middle-aged bipolar I (BPI), II (BPII) and major depressive disorder (MDD) patients and controls using a path model. Methods: Thirteen BPI, 15 BPII, 16 MDD patients, and 21 controls underwent brain MRI and a neuropsychological examination. Two experienced neuroradiologists evaluated WMHs on the MRI scans. We constructed structural equation models to test the strength of the associations between deep WMH (DWMH) grade, neuropsychological performance and diagnostic group. Results: Belonging in the BPI group as opposed to the control group predicted higher DWMH grade (coefficient estimate 1.13, P = 0.012). The DWMH grade independently predicted worse performance on the Visual Span Forward test (coefficient estimate 0.48, P = 0.002). Group effects of BPI and MDD were significant in predicting poorer performance on the Digit Symbol test (coefficient estimate 5.57, P = 0.016 and coefficient estimate 5.66, P = 0.034, respectively). Limitations: Because of the small number of study subjects in groups, the negative results must be considered with caution. Conclusions: Only BPI patients had an increased risk for DWMHs. DWMHs were independently associated with deficits in visual attention. ß 2013 Elsevier Masson SAS. All rights reserved.
Keywords: Unipolar depression Mania and bipolar disorder MRI Neuroscience other (neuropsychology)
1. Introduction The major affective disorders, bipolar I disorder (BPI) and recurrent major depressive disorder (MDD), are associated with structural brain abnormalities [23]. One of the most consistent findings, although not universal, has been the increased occurrence of white matter hyperintensities (WMHs) in bipolar disorder (BP) patients and elderly unipolar patients compared with healthy controls [23,29]. A recent meta-analysis [23] showed that BP patients had more than a 2-fold increase in rates of deep white matter
* Corresponding author. Tel.: +358 44 5454291. E-mail addresses: tuula.kieseppa@thl.fi, tuula.kieseppa@hus.fi (T. Kieseppa¨). 0924-9338/$ – see front matter ß 2013 Elsevier Masson SAS. All rights reserved. http://dx.doi.org/10.1016/j.eurpsy.2013.08.002
hyperintensities compared with MDD patients, and MDD patients had only moderate increase compared with healthy controls. Only few studies have separately analyzed BPI and bipolar II disorder (BPII) patients, but it seems that WMHs might be more common in BPI patients than in BPII patients [1,46]. Some differences in white matter water diffusivity have been found between BPI and BPII patients [17,27], and between BP and MDD patients [49]. WMHs most likely indicate a decrease in the density of white matter due to, for example demyelination, atrophy of the neuropil, and ischemia-associated microangiopathy [35]. The occurrence of WMHs is non-specific, and in the general population their prevalence in adults aged 82 years ranges from 11–21% to 64– 94% [13,51]. The role of hyperintensities in the pathogenesis, pathophysiology, and treatment of mood disorders remains
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unclear. Several studies of MDD patients report an association between WMHs and severity of depression or poor response to treatment [8,18,19]. A study of 19 BP patients showed that deep frontal WMHs were associated with more hospitalizations [11]. Another study revealed more subcortical WMHs in poor outcome BP patients (7/15) than good outcome BP patients (1/14) [33]. A study showed a positive linear trend by familiality and type of affectedness in mean total WMH volume between BPI patients with psychotic features, BPI patients without psychotic features, BPII patients, unaffected family members, and healthy controls [46]. A systematic review and meta-analysis [9] of prospective longitudinal magnetic resonance imaging (MRI) studies of WMHs in both the general elderly population and high-risk populations reported a suggestive association of increased occurrence of WMHs with a decline in global cognitive performance, executive function, and processing speed. The impact of psychiatric disorders was not investigated. Many studies have shown subtle neuropsychological deficits in elderly individuals with WMHs [4,42,52]. Patients with MDD and BP have deficits in several cognitive domains, and dysfunctions may remain in remitted phases of the disorders [2,3,37,39]. Based on neuropsychological studies, differences may exist between BPI and BPII patients [27,31, 43,45] and between BP and MDD patients [3,16] in terms of neurocognitive functioning including attention, executive functioning and memory. Differences between BPI and BPII in neurocognition have been suggested to be associated with white matter abnormalities [27]. WMHs may reflect such defects in white matter structure and integration that impair cognitive functioning. To our knowledge, no studies have examined the relationship between WMHs and neuropsychological performance in young or middle-aged MDD patients. In a study on the relation of cognitive functioning with WMHs in patients with BP or schizophrenia and in healthy volunteers, no differences in cognitive performance were found between patients with or without WMHs [24]. In this study, we evaluate for the first time the associations of brain WMHs on MRI with neuropsychological variables among middle-aged BPI, BPII, and MDD patients and controls using a path model. We hypothesized that patients with major mood disorder would have significantly more WMHs than controls, and BPI patients would have more WMHs than BPII or MDD patients. We further hypothesized that WMHs would, in part, mediate the influence of illness type on possible differences in neurocognitive performance. 2. Subjects and methods 2.1. Participants Eligible and consenting patients from three clinical cohorts, Jorvi Bipolar Study, Vantaa Depression Study, and Vantaa Primary Care Depression Study [20,30,32,50], were invited to participate in a detailed investigation comprising a neuropsychological examination and structural brain imaging. Complete descriptions of the cohorts and study methodologies are given in the original papers. Patients in our study had either DSM-IV BP or DSM-IV MDD confirmed using the Structural Clinical Interview for DSM-IV (SCID-I/P) [44]. Patients with possible confounding neurological or vascular diseases (any diagnosed disorder like hypertension or occurrence of stroke), head injuries, current substance use disorders, withdrawal symptoms from substances, neurological abnormalities due to substances, or serious nephropathies were excluded. The final sample included 13 BPI, 15 BPII, and 16 MDD patients. Twelve of the patients were inpatients and the rest were outpatients. All patients filled in the Beck Depression Inventory
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(BDI) before neuropsychological testing. For BP patients, the Young Mania Rating Scale (YMRS) was scored by the interviewing researcher. The information of life-time tobacco smoking was collected. Twenty-one control subjects with no mental disorder were recruited from a representative general population sample from the same geographical area as the patients [22]. Exclusion criteria were same as for patients. Control subjects were interviewed with the SCID-I/P to exclude anyone with an axis I mental disorder. None of the control subjects had any psychotropic medication. All participants gave their written informed consent after the study procedures had been disclosed. The Ethics Committee of Helsinki University Central Hospital (HUCH) approved the study protocol. 2.2. Neuropsychological procedure Experienced psychologists administered a neuropsychological examination to all subjects in a fixed order. The test methods were selected to cover the fundamental cognitive functions: attention, working memory, declarative verbal memory, information processing, executive functioning, and basic ability: Digit Span Forward and Visual Span Forward from the Wechsler Memory Scale-Revised (WMS-R) [48] were used to assess auditory and visual attention, respectively. The backward condition of the WMS-R Digit Span and Visual Span tasks measured verbal and visual working memory, respectively; the California Verbal Learning Test (CVLT) [10] was selected to measure the processes of verbal learning and memory; Digit Symbol from the Wechsler Adult Intelligence Scale-Revised (WAIS-R) [47] assessed psychomotor and information processing speed; part A of the Trail Making Test (TMT) [38] measured attention and processing speed, and part B executive functioning; WAIS-R Vocabulary subtest was used as the measure of basic verbal ability. The Vocabulary test has been considered as one of the best single measures of premorbid level of functioning [25]. 2.3. MRI and evaluation of WMHs Brain MRI was performed using a 1.5 T Siemens Magnetom Symphony scanner (Siemens AG, Erlangen, Germany). Transaxial fast FLAIR images (TR = 10,000 ms; TE = 148 ms) and fast spin echo T2-weighted images were obtained (TR = 5300 ms; TE = 112 ms) with field of view (FOV) = 230 mm, matrix size = 256 256 and slice thickness 5 mm with 1 mm interslice gap. Two experienced neuroradiologists (RM and KL), who were blinded to each other and to the clinical diagnosis, evaluated WMHs on the MRI scans. The severity of WMHs was assessed separately for deep (DWMHs) and periventricular WMHs (PVHs), and only those lesions that were seen both in T2-weighted and FLAIR images were included. DWMHs were classified in four grades according to size of the lesion: 1 = small focal ( 5 mm); 2 = large focal (6–10 mm); 3 = focal confluent (11–25 mm); and 4 = diffusively confluent ( 25 mm). The number of lesions of each grade was documented. The ratings were performed separately for frontal, parietal, temporal, occipital, cerebellar, brainstem, basal ganglia, and thalamic regions. PVHs were classified separately for frontal capping, periventricular lining, and occipital capping in three groups: 0 = no change; 1 = lesions 5 mm; 2 = lesions 6– 10 mm; 3 = lesions 10 mm. There were discrepancies in 11 of 65 cases. The radiologists reviewed the 11 cases still blind to each other. Seven cases remained with some discrepancies. The finding with the best agreement of the four different measurements was approved as the final result in these cases.
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the significance of the correlation between diagnosis and WMH grade. Also, the strength of the associations between WMH grade, neuropsychological performance, and diagnostic group was tested. The model was specified as a path model with both discrete and continuous variables, so it is a model of simultaneous regression equations (Fig. 1). The models were estimated using the robust maximum likelihood (MLR) estimator available in the M-plus statistical program [34]. The advantage of M-plus is its ability to include categorical endogenous variables in the model. Specifically, the WMH grade was modeled as a discrete ordinal variable.
SEX AGE
WMH GRADE
NPT
BDI
3. Results
GROUP
3.1. Subjects Fig. 1. A path analysis allows testing the hypothesized relationships between white matter hyperintensities grade (WMH GRADE), neuropsychological test chosen for the model (NPT), Beck Depression Inventory score (BDI), and diagnosis (GROUP). GROUP includes bipolar I disorder, bipolar II disorder, major depressive disorder, and controls. Age and sex are included in the model.
The modified four-point severity rating scale of Coffey [7,28] was used to rate the WMHs. DWMHs were rated as follows: grade 0 = absent; grade 1 = one or two lesions, each with a diameter < 5 mm; grade 2 = number (n) of lesions < 10, with the largest having a diameter between 5 and 10 mm; grade 3 = n 10, or at least one lesion with a diameter > 10 mm. PVHs were also rated: grade 0 = no lesions; grade 1 = lesions 5 mm; grade 2 = lesions 6–10 mm; grade 3 = lesions > 10 mm. 2.4. Statistical analysis Differences between demographic and clinical variables among study groups were computed by Student’s t-test or Mann-Whitney U-test for continuous data (age, illness duration, duration of current episode), by Mann-Whitney U-test for ordinal data (education), and by Fisher’s exact test for categorical data (sex, work status, smoking). We constructed structural equation models (SEMs), one for each appointed neuropsychological measure (Fig. 1). Age, BDI, gender, and diagnosis were modeled as independent (exogenous) variables in association with WMH grade; thus, we controlled for the possible confounding effects of age, gender, and current mood on the severity of WMH grade, and tested for
MDD patients were somewhat older than others, and the MDD sample included more females than the control sample (P = 0.04) or the BPI sample (P = 0.0007) (Table 1). Age and sex were included in the SEM. Education level did not differ significantly between groups. Controls were more likely than patients to be employed. The duration of illness did not differ between patient groups (each P > 0.40). MDD patients had a significantly longer duration of current episode than BPI patients (P = 0.01), but not longer than BPII patients (P = 0.18). BPI and BPII samples did not differ significantly from each other in this respect (P = 0.48). Both BPII and MDD patients had higher BDI means than BPI patients. BDI was included in the SEM. In the BPI group, antidepressive medication was taken by five (42%), mood stabilizers by 11 (92%), and antipsychotic medication by six (58%) patients. In the BPII group, 11 patients (79%) took antidepressive medication, four (29%) took stabilizers, and none antipsychotics. Of the unipolar patients, 13 (81%) were on antidepressants, one (6%) also receiving antipsychotic medication. 3.2. WMHs and neuropsychological functions Results of WMH ratings by diagnostic category are shown in Table 2. These results suggest that the grade of DWMHs may vary between diagnostic groups, and DWMH grade was chosen for the path model. Neuropsychological test results are presented according to the DWMHs ratings (Table 3). The means of the Visual Span Forward and Backward tests, and the Digit Symbol test
Table 1 Demographic and clinical characteristics of bipolar I (BPI), bipolar II (BPII), and unipolar (MDD) patients, and controls.
Age, mean (SD) Gender, M/F c
Education , mean (SD) Employed, n (%) Illness duration years, mean (SD) Duration of current episode years, mean (SD) Number of depressive episodes, mean (range) Number of manic, hypomanic or mixed episodes, mean (range) Occurrence of psychotic symptoms, n (%) Beck Depression Inventory mean (SD) Young Mania Rating Scale mean (SD) a b c d
BPI n = 13
BPII n = 15
MDD n = 16
Controls n = 21
42.8 (11.1) P = 0.80a 10/3 P = 0.15b 2.9 (1.8) P = 0.70d 2 (15.4) P = 0.0002b 15.2 (10.2) 0.6 (0.5) 5.2 (1–22) 5.1 (1–20) 9 (69) 19 (10) 8 (8)
38.4 (9.1) P = 0.35 7/8 P = 1.0 3.2 (1.5) P = 0.12 3 (20.0) P = 0.002 12.8 (6.0) 1.2 (2.3) 5.3 (1–11) 1.5 (0–10) 6 (40) 26 (12) 4 (3)
48.4 (10.3) P = 0.07 2/14 P = 0.04 3.2 (1.4) P = 0.13 5 (33.3) P = 0.005 14.1 (7.9) 2.8 (3.6) 4.2 (1–10) – 1 (6) 26 (7) –
41.7 (11.3)
Student’s t-test for the difference compared with controls. Fisher’s exact test for the difference compared with controls. Education level classified according to the Structural Clinical Interview for DSM-IV in five classes, 5 being the lowest and 1 the highest. Mann-Whitney U-test for the difference compared with controls.
10/11 2.6 (1.5) 15 (71.4) – – – – – 2 (2) –
T. Kieseppa¨ et al. / European Psychiatry 29 (2014) 226–232 Table 2 Deep white matter hyperintensity (DWMH) and periventricular white matter hyperintensity (PVH) ratings in patients and controls. BPI n = 13 n (%) DWMH grade 0 4 (31) 1 4 (31) 2 3 (23) 3 2 (15)
BPII n = 15 n (%)
MDD n = 16 n (%)
Controls n = 21 n (%)
9 3 1 2
7 2 5 2
14 (67) 2 (10) 4 (19) 1 (5)
(60) (20) (7) (13)
(44) (12) (31) (12)
Table 4 Results of path analysis of the relationships between deep white matter hyperintensity (DWMH) gradea, disease groupb, sex, age, and Beck Depression Inventory (BDI). DWMH gradea Predictor
Estimate
SE
T
P
Female
1.362
0.552
2.466
0.014
Age
0.045
0.027
1.658
0.097
BDI PVH grade 0 7 (54) 1 5 (38) 2 0 3 1 (8)
9 (60) 4 (27) 2 (13) 0
4 (31) 7 (44) 4 (25) 0
10 (48) 8 (38) 2 (10) 3 (5)
BPI = bipolar type I disorder, BPII = bipolar type II disorder, MDD = major depressive disorder.
seemed to decrease logically with the DWMH ratings and were thus admitted to the path analysis.
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0.030
0.030
0.995
0.320
1.132 0.107 0.102
0.449 0.598 0.502
2.524 0.179 0.204
0.012 0.858 0.838
b
Group BPI BPII MDD
BPI = bipolar type I disorder, BPII = bipolar type II disorder, MDD = major depressive disorder. a Deep white matter hyperintensity grade is modeled as a discrete ordinal (0,1,2,3) variable. b The baseline group is the control group.
3.3. The path model
3.5. Clinical variables
The results of the path model (Fig. 1) are shown in Tables 4 and 5. Belonging in the BPI group as opposed to the control group predicted higher DWMH grade (coefficient estimate 1.13, P = 0.012) (Table 4). Neither the BPII group nor the MDD group showed this effect, but female sex predicted higher DWMH grade (coefficient estimate 1.36, P = 0.014). Table 5 presents the results of path analysis for three different neuropsychological tasks. The DWMH grade independently predicted a poorer performance on the Visual Span Forward test (coefficient estimate 0.48, P = 0.002). Age and BDI rating were also associated with the Visual Span Forward test (coefficient estimate 0.07, P = 0.000, coefficient estimate 0.04, P = 0.022, respectively). In addition, group effects of BPI and MDD were significant in predicting diminished performance on the Digit Symbol test (coefficient estimate 5.57, P = 0.016 and coefficient estimate 5.66, P = 0.043, respectively) (Table 5).
The occurrence of DWMHs was not related to the duration of illness in this sample, or to the frequency of depressive, manic, or mixed episodes. The lifetime occurrence of psychotic symptoms did not differ between DWMH-positive and -negative groups in either the BPI sample or the entire patient sample. The information of life-time smoking was available only from patients. Thirty of the patients smoked regularly or had ceased regular smoking, 14 had never smoked or smoked only occasionally. We analyzed these two groups against the occurrence of WMHs, and found no significant association (P = 0.34).
3.4. Localization of WMHs Table 6 presents the localization of DWMHs. The majority, 53% of the lesions, were found in the frontal lobe, and 25% in the parietal lobe. A further 19% were situated in the temporal lobe, and none in the occipital lobe. Two bipolar patients had DWMHs in the brainstem.
4. Discussion We hypothesized that patients with major mood disorders would have significantly more WMHs than controls, and that BPI patients would have more WMHs than BPII or MDD patients. Indeed, BPI patients had an increased risk of DWMHs, unlike BPII and MDD patients, relative to controls. We also hypothesized that WMHs would, in part, mediate the influence of illness type on differences in neurocognitive performance. DWMH grade predicted deficits in visual attention as measured by the Visual Span Forward subtest of the WMS-R, while the diagnostic group had no independent effect.
Table 3 Means and standard deviations of the neuropsychological test variables (raw scores) in four groups classified according to the rating scale of deep white matter hyperintensities (DWMHs). DWMH grade Total
0 n = 34 Mean (SD)
1 n = 11 Mean (SD)
2 n = 13 Mean (SD)
3 n=7 Mean (SD)
Vocabulary Digit Span Forward Digit Span Backward Visual Span Forward Visual Span Backward Digit Symbol Trail-Making Test A Trail-Making Test B Verbal learning CVLT Total recall Short delay memory CVLT Long delay Memory CVLT
50.76 7.18 5.68 8.56 8.18 50.50 38.11 88.76 54.53
54.54 7.00 7.09 8.36 7.55 50.45 33.45 64.91 53.18
49.15 7.54 6.46 7.15 7.46 48.23 42.23 97.69 51.92
46.00 6.71 6.14 7.14 7.29 46.00 38.29 90.71 52.57
CVLT = California Verbal Learning Test.
(9.42) (1.75) (1.70) (1.88) (2.25) (14.73) (21.41) (58.24) (11.76)
11.26 (3.46) 12.03 (3.42)
(10.32) (2.19) (1.92) (2.17) (2.33) (10.84) (10.76) (17.53) (12.08)
11.45 (2.50) 12.45 (1.51)
(9.72) (2.22) (2.47) (1.14) (1.61) (8.20) (14.01) (42.25) (10.54)
11.69 (2.29) 11.92 (2.69)
(6.83) (1.25) (2.12) (1.21) (1.38) (10.55) (8.38) (12.32) (9.50)
9.86 (5.08) 12.29 (1.98)
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Table 5 Results of path analysis of the relationships between neuropsychological test performance, deep white matter hyperintensity (DWMH) gradea, disease groupb, sex, age, and Beck Depression Inventory (BDI). Digit Symbol test
Visual Span Forward
Visual Span Backward
Predictor
Estimate
SE
T
P
Estimate
SE
T
P
Estimate
SE
T
P
Female
2.633
2.784
0.946
0.344
0.129
0.440
0.294
0.769
0.440
0.558
0.788
0.431
Age
0.003
0.070
0.019
3.667
0.000
0.189
0.438
0.128
1.477
0.140
0.043
0.019
2.286
0.022
0.022
0.029
0.738
0.461
Group BPI BPII MDD
5.567 0.202 5.659
2.303 1.909 2.795
2.417 0.106 2.025
0.016 0.916 0.043
0.059 0.006 0.553
0.352 0.357 0.353
0.167 0.017 1.568
0.868 0.986 0.117
0.279 0.243 0.901
0.398 0.471 0.589
0.702 0.516 1.530
0.482 0.606 0.126
DWMH gradea
0.764
1.037
0.737
0.461
0.484
0.156
3.107
0.002
0.208
0.184
1.129
0.259
BDI
0.147
2.982
0.014
0.024
0.596
0.551
b
BPI = bipolar type I disorder, BPII = bipolar type II disorder, MDD = major depressive disorder. a Deep white matter hyperintensity grade is modeled as a discrete ordinal (0,1,2,3) variable. b The baseline group is the control group.
Table 6 Localization of deep white matter hyperintensities (DWMHs). Brain area
DWMH grade 1 n (%)
DWMH grades 2-3 n (%)
Frontal Temporal Parietal Occipital Basal ganglia or brainstem
11 3 6 0 2
17 7 7 0 0
(21) (6) (12) (0) (4)
(32) (13) (13) (0) (0)
exist concerning middle-aged populations with no neurological or vascular disorders. One study reports an association between frontal WMHs and reaction time changes among females, but information processing speed was not investigated [5]. The role of WMHs affecting processing speed in mood disorders has been controversial [12,24]. In our study, BPI and MDD patients performed worse than controls on the Digit Symbol test, and age was associated with this finding. 4.1. Limitations
Our results are in accordance with the findings of a recent metaanalysis [23] showing that BP patients had increase in rates of DWMHs compared with MDD patients. BPII patients were not analyzed separately. Our study supports two previous studies of WMHs in BPI and BPII patients suggesting difference between the patient groups [1,46]. While BPI patients had an increased risk for DWMHs, DWMHs would logically be expected to be associate with manias or psychotic symptoms. Patients with schizophrenia or BPI with psychotic features have been found to have increased WMHs volumes [46,53]. However, no significant association existed between the occurrence of DWMHs and psychotic symptoms or manic episodes in our study. Increased DWMH grade together with age and BDI score predicted deficits in visual attention. The diagnostic group had no independent impact. The Visual Span subtest is a complex task involving immediate memory, maintenance memory, memory of sequence, and a motor response, including volition to initiate the response. In the Visual Span Forward task, subjects are presented a card showing eight squares and are asked to copy sequences indicated by the examiner. In this complex visual scene, one employs attention to select stimuli that are behaviorally relevant. Thus, we propose that the increased DWMH grade may affect the attentional selection preceding the volition to initiate a response. Visually controlled attentional selection appears to be mediated in part by neural synchrony between neurons in the prefrontal cortex, the posterior parietal cortex, and early visual areas [6,15]. In our study, DWMHs were localized mainly in the frontal and parietal lobes, thus potentially influencing the synchronization of neural activity and disturbing the visual attention. The DWMH grade did not show a significant effect on the Digit Symbol test, which is a measure of information processing speed. Although the association between increased prevalence of WMHs and a decline in processing speed has been shown relatively constantly among the elderly [9,36], few studies
Because of the small number of study subjects in groups, the negative results should be considered with cautious. Unfortunately there was no information about blood pressure of study subjects, and thus we were not able to analyze the effects of subclinical hypertension in occurrence of WMHs. Diagnosed vascular disorders were exclusion criteria. The data of smoking was collected only from patients, but among them it did not show significant association with WMHs. Small sample size forced us to specify a study model that is as simple as possible. However, even with the small power, we were able to uncover significant relationships. Sex and age differed in the study groups, and both female sex and age are associated with increased prevalence of WMHs [40,41]. The differences were taken into account in the statistical analyses, and the variables were included in the path model. However, the BPI group, which had an increased risk for DWMHs, had less female subjects, and the mean age in this group was lower than in other groups. Thus, the increase of DWMHs in BPI patients seems not to be caused by sex or age difference. The significance of WMHs remains obscure. Unfortunately, we did not have diffusion tensor imaging data of the whole study group, which would have enabled detecting the possible occurrence of fractional anisotropy changes in lesions reflecting latent white matter structural defects. WMHs might be result of hypoperfusion and arteriolar disease, which may be influenced by factors such as drug intake and blood pressure [26]. Adults with BPI are at higher risk of vascular hypertension [14,21]. We excluded patients with cardiovascular disorders, but we did not have information about blood pressure in the study subjects. The effects of medication would have been interesting to study, but unfortunately we had information of current medication only, and the exact doses of drugs were not available. The causal relationship of DWMHs and visual attention remains suggestive. Based on our results, we hypothesized that frontal and parietal DWMHs may influence visual attention by disturbing the
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synchrony of neural activity in the fronto-parietal cortex, but we had no measurements with which to test this. 5. Conclusion Only BPI patients had an increased risk for DWMHs relative to controls. In this middle-aged study population, DWMHs were associated with deficits in visual attention, but showed no effect on visual working memory or information processing speed. A decrease in information processing speed was found in the BPI and MDD groups, but not in the BPII group.
[20]
[21]
[22]
[23]
Disclosure of interest
[24]
The authors declare that they have no conflicts of interest concerning this article.
[25] [26]
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Original article
White matter hyperintensities and cognitive performance in adult patients with bipolar I, bipolar II, and major depressive disorders T. Kieseppa¨ a,*,b, R. Ma¨ntyla¨ c,d, A. Tuulio-Henriksson e,f, K. Luoma c, O. Mantere a,b, M. Ketokivi g, M. Holma a,h, P. Jylha¨ a,b, T. Melartin a,b, K. Suominen a,h, M. Vuorilehto a, E. Isometsa¨ a,b,i a
Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, 00300 Helsinki, Finland Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland c HUS Medical Imaging Center, Helsinki University Central Hospital, Helsinki, Finland d Hyvinka¨a¨ Hospital, Hyvinka¨a¨, Finland e Social Insurance Institution, Research Department, Helsinki, Finland f Department of Behavioral Sciences, University of Helsinki, Helsinki, Finland g Operations and Technology Department, IE Business School, Madrid, Spain h Department of Psychiatry, City of Helsinki, Helsinki, Finland i Institute of Clinical Medicine, University of Helsinki, Helsinki, Finland b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 1 July 2013 Received in revised form 26 August 2013 Accepted 26 August 2013 Available online 28 October 2013
Purpose: We evaluate for the first time the associations of brain white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) with neuropsychological variables among middle-aged bipolar I (BPI), II (BPII) and major depressive disorder (MDD) patients and controls using a path model. Methods: Thirteen BPI, 15 BPII, 16 MDD patients, and 21 controls underwent brain MRI and a neuropsychological examination. Two experienced neuroradiologists evaluated WMHs on the MRI scans. We constructed structural equation models to test the strength of the associations between deep WMH (DWMH) grade, neuropsychological performance and diagnostic group. Results: Belonging in the BPI group as opposed to the control group predicted higher DWMH grade (coefficient estimate 1.13, P = 0.012). The DWMH grade independently predicted worse performance on the Visual Span Forward test (coefficient estimate 0.48, P = 0.002). Group effects of BPI and MDD were significant in predicting poorer performance on the Digit Symbol test (coefficient estimate 5.57, P = 0.016 and coefficient estimate 5.66, P = 0.034, respectively). Limitations: Because of the small number of study subjects in groups, the negative results must be considered with caution. Conclusions: Only BPI patients had an increased risk for DWMHs. DWMHs were independently associated with deficits in visual attention. ß 2013 Elsevier Masson SAS. All rights reserved.
Keywords: Unipolar depression Mania and bipolar disorder MRI Neuroscience other (neuropsychology)
1. Introduction The major affective disorders, bipolar I disorder (BPI) and recurrent major depressive disorder (MDD), are associated with structural brain abnormalities [23]. One of the most consistent findings, although not universal, has been the increased occurrence of white matter hyperintensities (WMHs) in bipolar disorder (BP) patients and elderly unipolar patients compared with healthy controls [23,29]. A recent meta-analysis [23] showed that BP patients had more than a 2-fold increase in rates of deep white matter
* Corresponding author. Tel.: +358 44 5454291. E-mail addresses: tuula.kieseppa@thl.fi, tuula.kieseppa@hus.fi (T. Kieseppa¨). 0924-9338/$ – see front matter ß 2013 Elsevier Masson SAS. All rights reserved. http://dx.doi.org/10.1016/j.eurpsy.2013.08.002
hyperintensities compared with MDD patients, and MDD patients had only moderate increase compared with healthy controls. Only few studies have separately analyzed BPI and bipolar II disorder (BPII) patients, but it seems that WMHs might be more common in BPI patients than in BPII patients [1,46]. Some differences in white matter water diffusivity have been found between BPI and BPII patients [17,27], and between BP and MDD patients [49]. WMHs most likely indicate a decrease in the density of white matter due to, for example demyelination, atrophy of the neuropil, and ischemia-associated microangiopathy [35]. The occurrence of WMHs is non-specific, and in the general population their prevalence in adults aged 82 years ranges from 11–21% to 64– 94% [13,51]. The role of hyperintensities in the pathogenesis, pathophysiology, and treatment of mood disorders remains
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unclear. Several studies of MDD patients report an association between WMHs and severity of depression or poor response to treatment [8,18,19]. A study of 19 BP patients showed that deep frontal WMHs were associated with more hospitalizations [11]. Another study revealed more subcortical WMHs in poor outcome BP patients (7/15) than good outcome BP patients (1/14) [33]. A study showed a positive linear trend by familiality and type of affectedness in mean total WMH volume between BPI patients with psychotic features, BPI patients without psychotic features, BPII patients, unaffected family members, and healthy controls [46]. A systematic review and meta-analysis [9] of prospective longitudinal magnetic resonance imaging (MRI) studies of WMHs in both the general elderly population and high-risk populations reported a suggestive association of increased occurrence of WMHs with a decline in global cognitive performance, executive function, and processing speed. The impact of psychiatric disorders was not investigated. Many studies have shown subtle neuropsychological deficits in elderly individuals with WMHs [4,42,52]. Patients with MDD and BP have deficits in several cognitive domains, and dysfunctions may remain in remitted phases of the disorders [2,3,37,39]. Based on neuropsychological studies, differences may exist between BPI and BPII patients [27,31, 43,45] and between BP and MDD patients [3,16] in terms of neurocognitive functioning including attention, executive functioning and memory. Differences between BPI and BPII in neurocognition have been suggested to be associated with white matter abnormalities [27]. WMHs may reflect such defects in white matter structure and integration that impair cognitive functioning. To our knowledge, no studies have examined the relationship between WMHs and neuropsychological performance in young or middle-aged MDD patients. In a study on the relation of cognitive functioning with WMHs in patients with BP or schizophrenia and in healthy volunteers, no differences in cognitive performance were found between patients with or without WMHs [24]. In this study, we evaluate for the first time the associations of brain WMHs on MRI with neuropsychological variables among middle-aged BPI, BPII, and MDD patients and controls using a path model. We hypothesized that patients with major mood disorder would have significantly more WMHs than controls, and BPI patients would have more WMHs than BPII or MDD patients. We further hypothesized that WMHs would, in part, mediate the influence of illness type on possible differences in neurocognitive performance. 2. Subjects and methods 2.1. Participants Eligible and consenting patients from three clinical cohorts, Jorvi Bipolar Study, Vantaa Depression Study, and Vantaa Primary Care Depression Study [20,30,32,50], were invited to participate in a detailed investigation comprising a neuropsychological examination and structural brain imaging. Complete descriptions of the cohorts and study methodologies are given in the original papers. Patients in our study had either DSM-IV BP or DSM-IV MDD confirmed using the Structural Clinical Interview for DSM-IV (SCID-I/P) [44]. Patients with possible confounding neurological or vascular diseases (any diagnosed disorder like hypertension or occurrence of stroke), head injuries, current substance use disorders, withdrawal symptoms from substances, neurological abnormalities due to substances, or serious nephropathies were excluded. The final sample included 13 BPI, 15 BPII, and 16 MDD patients. Twelve of the patients were inpatients and the rest were outpatients. All patients filled in the Beck Depression Inventory
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(BDI) before neuropsychological testing. For BP patients, the Young Mania Rating Scale (YMRS) was scored by the interviewing researcher. The information of life-time tobacco smoking was collected. Twenty-one control subjects with no mental disorder were recruited from a representative general population sample from the same geographical area as the patients [22]. Exclusion criteria were same as for patients. Control subjects were interviewed with the SCID-I/P to exclude anyone with an axis I mental disorder. None of the control subjects had any psychotropic medication. All participants gave their written informed consent after the study procedures had been disclosed. The Ethics Committee of Helsinki University Central Hospital (HUCH) approved the study protocol. 2.2. Neuropsychological procedure Experienced psychologists administered a neuropsychological examination to all subjects in a fixed order. The test methods were selected to cover the fundamental cognitive functions: attention, working memory, declarative verbal memory, information processing, executive functioning, and basic ability: Digit Span Forward and Visual Span Forward from the Wechsler Memory Scale-Revised (WMS-R) [48] were used to assess auditory and visual attention, respectively. The backward condition of the WMS-R Digit Span and Visual Span tasks measured verbal and visual working memory, respectively; the California Verbal Learning Test (CVLT) [10] was selected to measure the processes of verbal learning and memory; Digit Symbol from the Wechsler Adult Intelligence Scale-Revised (WAIS-R) [47] assessed psychomotor and information processing speed; part A of the Trail Making Test (TMT) [38] measured attention and processing speed, and part B executive functioning; WAIS-R Vocabulary subtest was used as the measure of basic verbal ability. The Vocabulary test has been considered as one of the best single measures of premorbid level of functioning [25]. 2.3. MRI and evaluation of WMHs Brain MRI was performed using a 1.5 T Siemens Magnetom Symphony scanner (Siemens AG, Erlangen, Germany). Transaxial fast FLAIR images (TR = 10,000 ms; TE = 148 ms) and fast spin echo T2-weighted images were obtained (TR = 5300 ms; TE = 112 ms) with field of view (FOV) = 230 mm, matrix size = 256 256 and slice thickness 5 mm with 1 mm interslice gap. Two experienced neuroradiologists (RM and KL), who were blinded to each other and to the clinical diagnosis, evaluated WMHs on the MRI scans. The severity of WMHs was assessed separately for deep (DWMHs) and periventricular WMHs (PVHs), and only those lesions that were seen both in T2-weighted and FLAIR images were included. DWMHs were classified in four grades according to size of the lesion: 1 = small focal ( 5 mm); 2 = large focal (6–10 mm); 3 = focal confluent (11–25 mm); and 4 = diffusively confluent ( 25 mm). The number of lesions of each grade was documented. The ratings were performed separately for frontal, parietal, temporal, occipital, cerebellar, brainstem, basal ganglia, and thalamic regions. PVHs were classified separately for frontal capping, periventricular lining, and occipital capping in three groups: 0 = no change; 1 = lesions 5 mm; 2 = lesions 6– 10 mm; 3 = lesions 10 mm. There were discrepancies in 11 of 65 cases. The radiologists reviewed the 11 cases still blind to each other. Seven cases remained with some discrepancies. The finding with the best agreement of the four different measurements was approved as the final result in these cases.
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the significance of the correlation between diagnosis and WMH grade. Also, the strength of the associations between WMH grade, neuropsychological performance, and diagnostic group was tested. The model was specified as a path model with both discrete and continuous variables, so it is a model of simultaneous regression equations (Fig. 1). The models were estimated using the robust maximum likelihood (MLR) estimator available in the M-plus statistical program [34]. The advantage of M-plus is its ability to include categorical endogenous variables in the model. Specifically, the WMH grade was modeled as a discrete ordinal variable.
SEX AGE
WMH GRADE
NPT
BDI
3. Results
GROUP
3.1. Subjects Fig. 1. A path analysis allows testing the hypothesized relationships between white matter hyperintensities grade (WMH GRADE), neuropsychological test chosen for the model (NPT), Beck Depression Inventory score (BDI), and diagnosis (GROUP). GROUP includes bipolar I disorder, bipolar II disorder, major depressive disorder, and controls. Age and sex are included in the model.
The modified four-point severity rating scale of Coffey [7,28] was used to rate the WMHs. DWMHs were rated as follows: grade 0 = absent; grade 1 = one or two lesions, each with a diameter < 5 mm; grade 2 = number (n) of lesions < 10, with the largest having a diameter between 5 and 10 mm; grade 3 = n 10, or at least one lesion with a diameter > 10 mm. PVHs were also rated: grade 0 = no lesions; grade 1 = lesions 5 mm; grade 2 = lesions 6–10 mm; grade 3 = lesions > 10 mm. 2.4. Statistical analysis Differences between demographic and clinical variables among study groups were computed by Student’s t-test or Mann-Whitney U-test for continuous data (age, illness duration, duration of current episode), by Mann-Whitney U-test for ordinal data (education), and by Fisher’s exact test for categorical data (sex, work status, smoking). We constructed structural equation models (SEMs), one for each appointed neuropsychological measure (Fig. 1). Age, BDI, gender, and diagnosis were modeled as independent (exogenous) variables in association with WMH grade; thus, we controlled for the possible confounding effects of age, gender, and current mood on the severity of WMH grade, and tested for
MDD patients were somewhat older than others, and the MDD sample included more females than the control sample (P = 0.04) or the BPI sample (P = 0.0007) (Table 1). Age and sex were included in the SEM. Education level did not differ significantly between groups. Controls were more likely than patients to be employed. The duration of illness did not differ between patient groups (each P > 0.40). MDD patients had a significantly longer duration of current episode than BPI patients (P = 0.01), but not longer than BPII patients (P = 0.18). BPI and BPII samples did not differ significantly from each other in this respect (P = 0.48). Both BPII and MDD patients had higher BDI means than BPI patients. BDI was included in the SEM. In the BPI group, antidepressive medication was taken by five (42%), mood stabilizers by 11 (92%), and antipsychotic medication by six (58%) patients. In the BPII group, 11 patients (79%) took antidepressive medication, four (29%) took stabilizers, and none antipsychotics. Of the unipolar patients, 13 (81%) were on antidepressants, one (6%) also receiving antipsychotic medication. 3.2. WMHs and neuropsychological functions Results of WMH ratings by diagnostic category are shown in Table 2. These results suggest that the grade of DWMHs may vary between diagnostic groups, and DWMH grade was chosen for the path model. Neuropsychological test results are presented according to the DWMHs ratings (Table 3). The means of the Visual Span Forward and Backward tests, and the Digit Symbol test
Table 1 Demographic and clinical characteristics of bipolar I (BPI), bipolar II (BPII), and unipolar (MDD) patients, and controls.
Age, mean (SD) Gender, M/F c
Education , mean (SD) Employed, n (%) Illness duration years, mean (SD) Duration of current episode years, mean (SD) Number of depressive episodes, mean (range) Number of manic, hypomanic or mixed episodes, mean (range) Occurrence of psychotic symptoms, n (%) Beck Depression Inventory mean (SD) Young Mania Rating Scale mean (SD) a b c d
BPI n = 13
BPII n = 15
MDD n = 16
Controls n = 21
42.8 (11.1) P = 0.80a 10/3 P = 0.15b 2.9 (1.8) P = 0.70d 2 (15.4) P = 0.0002b 15.2 (10.2) 0.6 (0.5) 5.2 (1–22) 5.1 (1–20) 9 (69) 19 (10) 8 (8)
38.4 (9.1) P = 0.35 7/8 P = 1.0 3.2 (1.5) P = 0.12 3 (20.0) P = 0.002 12.8 (6.0) 1.2 (2.3) 5.3 (1–11) 1.5 (0–10) 6 (40) 26 (12) 4 (3)
48.4 (10.3) P = 0.07 2/14 P = 0.04 3.2 (1.4) P = 0.13 5 (33.3) P = 0.005 14.1 (7.9) 2.8 (3.6) 4.2 (1–10) – 1 (6) 26 (7) –
41.7 (11.3)
Student’s t-test for the difference compared with controls. Fisher’s exact test for the difference compared with controls. Education level classified according to the Structural Clinical Interview for DSM-IV in five classes, 5 being the lowest and 1 the highest. Mann-Whitney U-test for the difference compared with controls.
10/11 2.6 (1.5) 15 (71.4) – – – – – 2 (2) –
T. Kieseppa¨ et al. / European Psychiatry 29 (2014) 226–232 Table 2 Deep white matter hyperintensity (DWMH) and periventricular white matter hyperintensity (PVH) ratings in patients and controls. BPI n = 13 n (%) DWMH grade 0 4 (31) 1 4 (31) 2 3 (23) 3 2 (15)
BPII n = 15 n (%)
MDD n = 16 n (%)
Controls n = 21 n (%)
9 3 1 2
7 2 5 2
14 (67) 2 (10) 4 (19) 1 (5)
(60) (20) (7) (13)
(44) (12) (31) (12)
Table 4 Results of path analysis of the relationships between deep white matter hyperintensity (DWMH) gradea, disease groupb, sex, age, and Beck Depression Inventory (BDI). DWMH gradea Predictor
Estimate
SE
T
P
Female
1.362
0.552
2.466
0.014
Age
0.045
0.027
1.658
0.097
BDI PVH grade 0 7 (54) 1 5 (38) 2 0 3 1 (8)
9 (60) 4 (27) 2 (13) 0
4 (31) 7 (44) 4 (25) 0
10 (48) 8 (38) 2 (10) 3 (5)
BPI = bipolar type I disorder, BPII = bipolar type II disorder, MDD = major depressive disorder.
seemed to decrease logically with the DWMH ratings and were thus admitted to the path analysis.
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0.030
0.030
0.995
0.320
1.132 0.107 0.102
0.449 0.598 0.502
2.524 0.179 0.204
0.012 0.858 0.838
b
Group BPI BPII MDD
BPI = bipolar type I disorder, BPII = bipolar type II disorder, MDD = major depressive disorder. a Deep white matter hyperintensity grade is modeled as a discrete ordinal (0,1,2,3) variable. b The baseline group is the control group.
3.3. The path model
3.5. Clinical variables
The results of the path model (Fig. 1) are shown in Tables 4 and 5. Belonging in the BPI group as opposed to the control group predicted higher DWMH grade (coefficient estimate 1.13, P = 0.012) (Table 4). Neither the BPII group nor the MDD group showed this effect, but female sex predicted higher DWMH grade (coefficient estimate 1.36, P = 0.014). Table 5 presents the results of path analysis for three different neuropsychological tasks. The DWMH grade independently predicted a poorer performance on the Visual Span Forward test (coefficient estimate 0.48, P = 0.002). Age and BDI rating were also associated with the Visual Span Forward test (coefficient estimate 0.07, P = 0.000, coefficient estimate 0.04, P = 0.022, respectively). In addition, group effects of BPI and MDD were significant in predicting diminished performance on the Digit Symbol test (coefficient estimate 5.57, P = 0.016 and coefficient estimate 5.66, P = 0.043, respectively) (Table 5).
The occurrence of DWMHs was not related to the duration of illness in this sample, or to the frequency of depressive, manic, or mixed episodes. The lifetime occurrence of psychotic symptoms did not differ between DWMH-positive and -negative groups in either the BPI sample or the entire patient sample. The information of life-time smoking was available only from patients. Thirty of the patients smoked regularly or had ceased regular smoking, 14 had never smoked or smoked only occasionally. We analyzed these two groups against the occurrence of WMHs, and found no significant association (P = 0.34).
3.4. Localization of WMHs Table 6 presents the localization of DWMHs. The majority, 53% of the lesions, were found in the frontal lobe, and 25% in the parietal lobe. A further 19% were situated in the temporal lobe, and none in the occipital lobe. Two bipolar patients had DWMHs in the brainstem.
4. Discussion We hypothesized that patients with major mood disorders would have significantly more WMHs than controls, and that BPI patients would have more WMHs than BPII or MDD patients. Indeed, BPI patients had an increased risk of DWMHs, unlike BPII and MDD patients, relative to controls. We also hypothesized that WMHs would, in part, mediate the influence of illness type on differences in neurocognitive performance. DWMH grade predicted deficits in visual attention as measured by the Visual Span Forward subtest of the WMS-R, while the diagnostic group had no independent effect.
Table 3 Means and standard deviations of the neuropsychological test variables (raw scores) in four groups classified according to the rating scale of deep white matter hyperintensities (DWMHs). DWMH grade Total
0 n = 34 Mean (SD)
1 n = 11 Mean (SD)
2 n = 13 Mean (SD)
3 n=7 Mean (SD)
Vocabulary Digit Span Forward Digit Span Backward Visual Span Forward Visual Span Backward Digit Symbol Trail-Making Test A Trail-Making Test B Verbal learning CVLT Total recall Short delay memory CVLT Long delay Memory CVLT
50.76 7.18 5.68 8.56 8.18 50.50 38.11 88.76 54.53
54.54 7.00 7.09 8.36 7.55 50.45 33.45 64.91 53.18
49.15 7.54 6.46 7.15 7.46 48.23 42.23 97.69 51.92
46.00 6.71 6.14 7.14 7.29 46.00 38.29 90.71 52.57
CVLT = California Verbal Learning Test.
(9.42) (1.75) (1.70) (1.88) (2.25) (14.73) (21.41) (58.24) (11.76)
11.26 (3.46) 12.03 (3.42)
(10.32) (2.19) (1.92) (2.17) (2.33) (10.84) (10.76) (17.53) (12.08)
11.45 (2.50) 12.45 (1.51)
(9.72) (2.22) (2.47) (1.14) (1.61) (8.20) (14.01) (42.25) (10.54)
11.69 (2.29) 11.92 (2.69)
(6.83) (1.25) (2.12) (1.21) (1.38) (10.55) (8.38) (12.32) (9.50)
9.86 (5.08) 12.29 (1.98)
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Table 5 Results of path analysis of the relationships between neuropsychological test performance, deep white matter hyperintensity (DWMH) gradea, disease groupb, sex, age, and Beck Depression Inventory (BDI). Digit Symbol test
Visual Span Forward
Visual Span Backward
Predictor
Estimate
SE
T
P
Estimate
SE
T
P
Estimate
SE
T
P
Female
2.633
2.784
0.946
0.344
0.129
0.440
0.294
0.769
0.440
0.558
0.788
0.431
Age
0.003
0.070
0.019
3.667
0.000
0.189
0.438
0.128
1.477
0.140
0.043
0.019
2.286
0.022
0.022
0.029
0.738
0.461
Group BPI BPII MDD
5.567 0.202 5.659
2.303 1.909 2.795
2.417 0.106 2.025
0.016 0.916 0.043
0.059 0.006 0.553
0.352 0.357 0.353
0.167 0.017 1.568
0.868 0.986 0.117
0.279 0.243 0.901
0.398 0.471 0.589
0.702 0.516 1.530
0.482 0.606 0.126
DWMH gradea
0.764
1.037
0.737
0.461
0.484
0.156
3.107
0.002
0.208
0.184
1.129
0.259
BDI
0.147
2.982
0.014
0.024
0.596
0.551
b
BPI = bipolar type I disorder, BPII = bipolar type II disorder, MDD = major depressive disorder. a Deep white matter hyperintensity grade is modeled as a discrete ordinal (0,1,2,3) variable. b The baseline group is the control group.
Table 6 Localization of deep white matter hyperintensities (DWMHs). Brain area
DWMH grade 1 n (%)
DWMH grades 2-3 n (%)
Frontal Temporal Parietal Occipital Basal ganglia or brainstem
11 3 6 0 2
17 7 7 0 0
(21) (6) (12) (0) (4)
(32) (13) (13) (0) (0)
exist concerning middle-aged populations with no neurological or vascular disorders. One study reports an association between frontal WMHs and reaction time changes among females, but information processing speed was not investigated [5]. The role of WMHs affecting processing speed in mood disorders has been controversial [12,24]. In our study, BPI and MDD patients performed worse than controls on the Digit Symbol test, and age was associated with this finding. 4.1. Limitations
Our results are in accordance with the findings of a recent metaanalysis [23] showing that BP patients had increase in rates of DWMHs compared with MDD patients. BPII patients were not analyzed separately. Our study supports two previous studies of WMHs in BPI and BPII patients suggesting difference between the patient groups [1,46]. While BPI patients had an increased risk for DWMHs, DWMHs would logically be expected to be associate with manias or psychotic symptoms. Patients with schizophrenia or BPI with psychotic features have been found to have increased WMHs volumes [46,53]. However, no significant association existed between the occurrence of DWMHs and psychotic symptoms or manic episodes in our study. Increased DWMH grade together with age and BDI score predicted deficits in visual attention. The diagnostic group had no independent impact. The Visual Span subtest is a complex task involving immediate memory, maintenance memory, memory of sequence, and a motor response, including volition to initiate the response. In the Visual Span Forward task, subjects are presented a card showing eight squares and are asked to copy sequences indicated by the examiner. In this complex visual scene, one employs attention to select stimuli that are behaviorally relevant. Thus, we propose that the increased DWMH grade may affect the attentional selection preceding the volition to initiate a response. Visually controlled attentional selection appears to be mediated in part by neural synchrony between neurons in the prefrontal cortex, the posterior parietal cortex, and early visual areas [6,15]. In our study, DWMHs were localized mainly in the frontal and parietal lobes, thus potentially influencing the synchronization of neural activity and disturbing the visual attention. The DWMH grade did not show a significant effect on the Digit Symbol test, which is a measure of information processing speed. Although the association between increased prevalence of WMHs and a decline in processing speed has been shown relatively constantly among the elderly [9,36], few studies
Because of the small number of study subjects in groups, the negative results should be considered with cautious. Unfortunately there was no information about blood pressure of study subjects, and thus we were not able to analyze the effects of subclinical hypertension in occurrence of WMHs. Diagnosed vascular disorders were exclusion criteria. The data of smoking was collected only from patients, but among them it did not show significant association with WMHs. Small sample size forced us to specify a study model that is as simple as possible. However, even with the small power, we were able to uncover significant relationships. Sex and age differed in the study groups, and both female sex and age are associated with increased prevalence of WMHs [40,41]. The differences were taken into account in the statistical analyses, and the variables were included in the path model. However, the BPI group, which had an increased risk for DWMHs, had less female subjects, and the mean age in this group was lower than in other groups. Thus, the increase of DWMHs in BPI patients seems not to be caused by sex or age difference. The significance of WMHs remains obscure. Unfortunately, we did not have diffusion tensor imaging data of the whole study group, which would have enabled detecting the possible occurrence of fractional anisotropy changes in lesions reflecting latent white matter structural defects. WMHs might be result of hypoperfusion and arteriolar disease, which may be influenced by factors such as drug intake and blood pressure [26]. Adults with BPI are at higher risk of vascular hypertension [14,21]. We excluded patients with cardiovascular disorders, but we did not have information about blood pressure in the study subjects. The effects of medication would have been interesting to study, but unfortunately we had information of current medication only, and the exact doses of drugs were not available. The causal relationship of DWMHs and visual attention remains suggestive. Based on our results, we hypothesized that frontal and parietal DWMHs may influence visual attention by disturbing the
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synchrony of neural activity in the fronto-parietal cortex, but we had no measurements with which to test this. 5. Conclusion Only BPI patients had an increased risk for DWMHs relative to controls. In this middle-aged study population, DWMHs were associated with deficits in visual attention, but showed no effect on visual working memory or information processing speed. A decrease in information processing speed was found in the BPI and MDD groups, but not in the BPII group.
[20]
[21]
[22]
[23]
Disclosure of interest
[24]
The authors declare that they have no conflicts of interest concerning this article.
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