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. Author manuscript; available in PMC: 2010 May 1.
Published in final edited form as: J Affect Disord. 2008 Sep 20;115(1-2):171. doi: 10.1016/j.jad.2008.07.024

Blood pressure and white matter integrity in geriatric depression

Matthew J Hoptman a,c, Faith M Gunning-Dixon d, Christopher F Murphy d, Babak A Ardekani b,c, Jan Hrabe b, Kelvin O Lim e, Glenda R Etwaroo d, Dora Kanellopoulos d, George S Alexopoulos d
PMCID: PMC2820874  NIHMSID: NIHMS109137  PMID: 18805589

Abstract

Background

Cerebrovascular disease may increase vulnerability to geriatric depression, a syndrome often accompanied by frontal-subcortical lesions. High blood pressure is a risk factor for cerebrovascular disease and white matter changes. This study examined whether and in which brain regions blood pressure is associated with compromised white matter integrity in elderly depressed patients.

Methods

We studied the association between blood pressure and white matter integrity assessed by diffusion tensor imaging (fractional anisotropy, FA) in 41 older patients with major depression. Correlations between FA and blood pressure, after controlling for age, were examined with a voxelwise analysis.

Limitations

This study did not employ a healthy control group. Moreover, the relatively small sample size precluded a comparison of patients with and without hypertension.

Results

Significant associations between FA and blood pressure were detected throughout the anterior cingulate and in multiple frontostriatal and frontotemporal regions.

Conclusions

Compromised frontal-striatal white matter integrity may be the anatomical background through which blood pressure confers vulnerability to depression.

Keywords: geriatric depression, Cardiovascular risk, diffusion tensor imaging

Introduction

We previously proposed that cerebrovascular disease predisposes to some geriatric depressive syndromes (Alexopoulos et al., 1997;Krishnan et al., 1997). This view was based on the high prevalence of depression in patients with vascular risk factors or diseases and the presence of cerebrovascular lesions in many individuals with geriatric depression. The mechanisms by which cerebrovascular risk factors predispose to geriatric depression are unclear. However, clinical, neuroimaging, and neuropathology findings suggest that depressed elders have abnormalities in frontal-subcortical circuitry (Alexopoulos et al., 2002a;O’Brien et al., 1998;Steffens et al., 2001). Moreover, recent findings suggest that microstructural white matter abnormalities in frontal-subcortical areas predict poor or slow response of geriatric depression to an antidepressant (Alexopoulos, Kiosses, Choi, Murphy, and Lim, 2002a).

Abnormal blood pressure is a risk factor for stroke and white matter hyperintensities (WMH) (Dufouil et al., 2001;Firbank et al., 2007;van Dijk et al., 2004). Although there is some evidence that high blood pressure may increase vulnerability to geriatric depression (Niu et al., 2008), it has been unclear whether elevated blood pressure preferentially impairs the brain structures implicated in geriatric depression. For this reason, the current study focuses on the relationship of blood pressure and white matter integrity in depressed patients to explore whether high blood pressure is associated with microstructural abnormalities in frontal-subcortical white matter regions. To this end, we performed a voxelwise analysis of diffusion tensor imaging (DTI), which provides a measure of white matter integrity.

Method

Participants

Patients were 41 adults aged 60 -86 (mean: 70.1, SD: 6.3) recruited at a University-based Geriatric Psychiatry clinic who were enrolled in an escitalopram treatment trial. The female to male ratio was 1.56. They had received education for 7-22 years (mean: 16.1, SD: 3.0). Their depression was of moderate severity (HDRS mean: 23.8 SD: 4.0) and their MiniMental State Examination scores ranged from 24-30 (mean: 28.3, SD: 1.6).

Scans were performed during a 2-week single blind drug washout/placebo lead-in phase. All participants met DSM-IV criteria for major depression after a Structured Clinical Interview for DSM-IV (SCID-R), had a 24-item Hamilton Depression Rating Scale (Hamilton, 1960)(HDRS) of 18 or greater and were not demented (by DSM-IV criteria). Exclusion criteria included history of psychiatric disorders other than depression (except personality disorders) prior to the onset of their depression, severe or acute medical illness within 3 months preceding the study, neurological disorders (i.e., dementia or delirium, history of head trauma, Parkinson’s disease), use of drugs known to cause symptoms of depression (e.g., steroids), and MMSE < 24. All participants signed informed consents approved by Institutional Review Boards.

Blood Pressure

Seated blood pressure was measured with a sphygmomanometer after the participant sat resting for 5 minutes. Three measurements were taken at one-week intervals. The average diastolic blood pressure (DBP) and systolic blood pressure (SBP) across measurements were the independent variables.

MRI

Scanning took place on the 1.5 T Siemens Vision Scanner (Erlangen, Germany) at Nathan Kline Institute’s Center for Advanced Brain Imaging. Patients received an magnetization prepared rapidly acquired gradient echo (MPRAGE) T1-weighted scan (TR =11.6 ms, TE = 4.9 ms, TI = 1017.6 ms, matrix = 256 × 256, FOV = 320 mm, NEX = 1, slice thickness = 1.25 mm, 172 slices, no gap), as well as a turbo dual spin echo scan (TR = 5000 ms, TE = 22/90 ms, matrix = 256 × 256, FOV = 240 mm, slice thickness = 5, 26 slices, no gap), and a DTI scan (TR = 6000 ms, TE = 100 ms, matrix = 128 × 128, FOV = 320, NEX = 7, slice thickness = 5 mm, 19 slices, no gap) acquired in an oblique axial plane parallel to the anterior commissure – posterior commissure line. The b-value for the DTI scan was 1000 s/mm2. Eight diffusion sensitization directions were used (Jones et al., 1999).

Postprocessing

Fractional anisotropy (FA) was calculated using AFNI’s (Cox, 1996) nonlinear computation algorithm 3dDWItoDT, which assures positive definite matrices. The FA images were transformed into Talairach space using methods described elsewhere (Ardekani et al., 2003).

A T1-weighted template was created from a scan of a subject whose intracranial volume was the closest to the mean for the first 11 patients and then put into Talairach space using AFNI (Cox, 1996). The skulls for these scans were stripped using FSL’s BET (http://www.fmrib.ox.ac.uk/fsl/bet/index.html). The volumes of skull-stripped brains were computed in MEDx (Sensor Systems, Sterling, VA). To increase the representativeness of this template to the study population, we iteratively registered the T1-weighted images from 101 subjects ranging in age from 60 to 86 years. Images were masked for white matter to reduce the number of comparisons.

Data Analysis

Because FA is negatively correlated with age (Pfefferbaum et al., 2000;Salat et al., 2005), we computed partial correlations between both diastolic and systolic BP and FA on a voxelwise basis, controlling for age. Partial correlations were tested for significance using t-tests. To reduce Type I error, we used the thresholding method described by Baudewig and colleagues (Baudewig et al., 2003). This approach finds clusters of voxels (100 mm3) each with significant group differences (p < 0.05) and then applies the constraint that one of the voxels in the cluster must be significant at p < 0.001. The thresholded correlation maps were superimposed onto the T1-weighted template using AFNI software (Cox, 1996).

Results

The mean diastolic blood pressure ranged from 61.3 to 91.3 mm Hg (mean: 73.6, SD: 6.8 Hg). Mean systolic blood pressure ranged from 107.3 to 160.7 mm Hg (mean: 129.1; SD: 13.4)

High diastolic blood pressure was significantly associated with low FA (negative correlations) bilaterally in the dorsal anterior cingulate, left dorsal cingulate and inferior frontal gyri, and right dorsomedial prefrontal white matter (Figure 1). Negative correlations were observed in the white matter adjacent to the lentiform nucleus and putamen, left insula, left thalamic white matter, bilateral middle temporal gyrus, right superior temporal gyrus, and the right inferior parietal lobule. Diastolic blood pressure was positively correlated with FA in left precentral gyrus, inferior parietal lobule, and precuneus white matter. Similar results, although somewhat more variable, were found for systolic blood pressure.

Figure 1.

Figure 1

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Map of correlations between diastolic blood pressure and FA for patients with geriatric depression. Blue indicates negative correlations and yellow indicates positive correlations. Significant correlation clusters are superimposed on a high-resolution T1-weighted image in Talairach space. Every 4th slice is shown, slices are from left to right hemisphere.

Discussion

The principal finding of this study is that high blood pressure is associated with microstructural white matter abnormalities (lower FA) in the dorsal anterior cingulate and multiple frontostriatal and frontotemporal white matter regions. To our knowledge, this is the first study demonstrating a relationship between blood pressure and microstructural white matter impairment in these areas in depressed elderly patients. Our findings are consistent with the observation that WMH of depressed patients are primarily located in frontal subcortical areas; most of these abnormalities are due to ischemic changes (Thomas et al., 2002).

Clinical and neuroimaging findings suggest a relationship between fronto-striatal dysfunction and geriatric depression. Disorders compromising frontal pathways, including vascular dementia, Parkinson’s disease, and Huntington’s disease are more likely to result in depression than cortical dementias (Sobin et al., 1997). Executive dysfunction, a disturbance resulting from compromised integrity of frontal structures and their connections, is common in geriatric depression (Lockwood et al., 2002;Nebes et al., 2001) and persists after improvement of mood-related symptoms (Murphy et al., 2004;Nebes et al., 2003). Elderly depressed patients with executive dysfunction have pronounced psychomotor retardation, reduced interest in activities and pronounced disability (Alexopoulos, Meyers, Young, Campbell, Silbersweig, and Charlson, 1997;Alexopoulos et al., 2002b;Krishnan, Hays, and Blazer, 1997), a clinical presentation resembling medial frontal lobe syndromes. Structural neuroimaging studies have documented reduced volumes of the anterior cingulate, the orbitofrontal cortex and the gyrus rectus in geriatric depression (Ballmaier et al., 2004). Moreover, hyperintensities in frontal subcortical structures are prevalent in geriatric depression (Coffey et al., 1990;Krishnan, Hays, and Blazer, 1997;Steffens et al., 1999). Assuming that frontal circuitry compromise constitutes a vulnerability factor to geriatric depression (Alexopoulos et al., 2005), our findings suggest that depression in patients with high diastolic pressure is in part mediated by compromise in frontostriatal white matter.

Positive correlations between blood pressure and FA were also found, primarily in posterior regions. These results are difficult to interpret. Assuming that frontal compromise is one of the anatomical abnormalities predisposing to depression (Alexopoulos, 2001;Krishnan et al., 2004), one may speculate that high white matter integrity in posterior regions offers insufficient protection from depression. Another possibility is that these findings reflect high anisotropy due to loss of crossing fibers.

This study has several limitations. First, there was no normal control group. Second, the small sample size allowed only relationships with large effect sizes to be identified and prevented comparisons between subjects with and without hypertension. Third, the large number of comparisons, even after applying a white matter mask to the images, increased the risk of Type I error. Finally, the impact of crossing fibers on FA cannot be addressed in the current study.

If confirmed, these findings would suggest that frontal-subcortical dysfunction may be one of the mechanisms by which high blood pressure, a known vascular risk factor, confers vulnerability to geriatric depression. On a clinical level, the relationship of blood pressure to frontal-subcortical white matter compromise suggests that patients with diastolic hypertension should be examined for clinical syndromes contributed by frontal-subcortical dysfunction, including depression and executive function impairment.

Table 1.

Location of significant correlations between fractional anisotropy and mean diastolic blood pressure (threshold: 100 contiguous voxels all significant at p < .05, with at least one voxel significant at p < .001).

Nearest GM Location1,2 Size Talairach
Coordinate3 		t-value5
Negative Correlations
Anterior cingulate (bilateral) 1573 −14, 22, 18 −2.57
L dorsal cingulate gyrus
(BA32)		 279 −11, 18, 38 −2.52
L dorsal cingulate gyrus
(BA24)		 161 −17, −8, 38 −2.52
R medial frontal gyrus 150 14, 41, 20 −2.66
L inferior frontal gyrus 383 −40, 15, 19 −2.66
L inferior frontal gyrus 110 −22, 23, −8 −2.51
L lentiform nucleus (PLIC) 517 −30, −20, −1 −2.63
R lentiform nucleus (ALIC) 916 15, 8, 1 −2.51
L BA13/insula 169 37, 16, 18 −2.50
L thalamus 210 −14, −4, 9 −2.40
L middle temporal gyrus 182 −40, −48, 6 −2.39
L middle temporal gyrus 126 −32, 41, 0 −2.49
R middle temporal gyrus 577 39, −50, 8 −2.53
R superior temporal gyrus
(arcuate fasciculus)		 122 36, −53, 23 −2.46
R inferior parietal lobule 292 44, −35, 36 −2.72
Positive Correlations
L precentral gyrus 360 −35, −12, 37 2.51
L inferior parietal lobule 110 −52, −32, 30 2.59
L precuneus 105 −20, −61, 32 2.78
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1

Notes: GM=Gray Matter

2

L = Left, R = Right

3

Talairach coordinate of maximum of correlation cluster

4

BA=Brodmann Area, ALIC = anterior limb of the internal capsule, PLIC=posterior limb of the internal capsule

5

mean of cluster.

ACKNOWLEDGMENTS

We thank Laurie Nash, MA, Margaret E. Bloomer, BS, Susan N. Boyer, MA, and Jessica Shields, BA, for their assistance in patient recruitment and study coordination, and Raj Sangoi, RT(R)MR for his work as chief MR technologist.

ROLE OF FUNDING SOURCE This work was supported by NIMH Grants RO1 MH65653 (GSA), K23 MH067702 (CFM), P30 MH68638 (GSA), and K23 MH074818 (FMG-D) and by the Sanchez Foundation. The funding sources had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

Footnotes

CONFLICT OF INTEREST Dr. Alexopoulos has received research grants by Forest Pharmaceuticals, Inc. and Cephalon and participated in scientific advisory board meetings of Forest Pharmaceuticals, Novartis, and Sanofi-Aventis. He has given lectures supported by Forest, Cephalon, Bristol Meyers Squibb, Janssen, Pfizer, Glaxo, and Lilly and has received support by Comprehensive Neuroscience, Inc. for the development of treatment guidelines in late-life psychiatric disorders. All other authors declare that they have no conflicts of interest.

References

  1. Alexopoulos GS, Kiosses D, Choi S, Murphy C, Lim K. Frontal white matter microstructure and treatment response of late-life depression: A preliminary study. Am. J. Psychiat. 2002a;159:1929–1931. doi: 10.1176/appi.ajp.159.11.1929. [DOI] [PubMed] [Google Scholar]
  2. Alexopoulos GS, Schultz SK, Lebowitz BD. Late-life depression: A model for classification. Biol Psychiatry. 2005;58:283–289. doi: 10.1016/j.biopsych.2005.04.055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alexopoulos GS. “The depression-executive dysfunction syndrome of late life”: a specific target for D3 agonists? Am. J. Geriatr. Psychiatry. 2001;9:22–29. [PubMed] [Google Scholar]
  4. Alexopoulos GS, Kiosses DN, Klimstra S, Kalayam B, Bruce ML. Clinical presentation of the “depression-executive dysfunction syndrome” of late life. Am. J. Geriatr. Psychiatry. 2002b;10:98–106. [PubMed] [Google Scholar]
  5. Alexopoulos GS, Meyers BS, Young RC, Campbell S, Silbersweig D, Charlson M. ‘Vascular depression’ hypothesis. Arch. Gen. Psychiatry. 1997;54:915–922. doi: 10.1001/archpsyc.1997.01830220033006. [DOI] [PubMed] [Google Scholar]
  6. Ardekani BA, Nierenberg J, Hoptman MJ, Javitt DC, Lim KO. MRI study of white matter diffusion anisotropy in schizophrenia. NeuroReport. 2003;14:2025–2029. doi: 10.1097/00001756-200311140-00004. [DOI] [PubMed] [Google Scholar]
  7. Ballmaier M, Toga AW, Blanton RE, Sowell ER, Lavretsky H, Peterson J, Pham D, Kumar A. Anterior cingulate, gyrus rectus, and orbitofrontal abnormalities in elderly depressed patients: an MRI-based parcellation of the prefrontal cortex. Am. J. Psychiatry. 2004;161:99–108. doi: 10.1176/appi.ajp.161.1.99. [DOI] [PubMed] [Google Scholar]
  8. Baudewig J, Dechent P, Merboldt KD, Frahm J. Thresholding in correlational analyses of magnetic resonance functional neuroimaging. Magn Reson Imaging. 2003;21:1121–1130. doi: 10.1016/j.mri.2003.08.013. [DOI] [PubMed] [Google Scholar]
  9. Coffey CE, Figiel GS, Djang WT, Weiner RD. Subcortical hyperintensity on magnetic resonance imaging: a comparison of normal and depressed elderly subjects. Am. J. Psychiatry. 1990;147:187–189. doi: 10.1176/ajp.147.2.187. [DOI] [PubMed] [Google Scholar]
  10. Cox RW. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research. 1996;29:162–173. doi: 10.1006/cbmr.1996.0014. [DOI] [PubMed] [Google Scholar]
  11. Dufouil C, de Kersaint-Gilly A, Besancon V, Levy C, Auffray E, Brunnereau L, Alperovitch A, Tzourio C. Longitudinal study of blood pressure and white matter hyperintensities: the EVA MRI Cohort. Neurology. 2001;56:921–926. doi: 10.1212/wnl.56.7.921. [DOI] [PubMed] [Google Scholar]
  12. Firbank MJ, Wiseman RM, Burton EJ, Saxby BK, O’Brien JT, Ford GA. Brain atrophy and white matter hyperintensity change in older adults and relationship to blood pressure : Brain atrophy, WMH change and blood pressure. J. Neurol. 2007;254:713–721. doi: 10.1007/s00415-006-0238-4. [DOI] [PubMed] [Google Scholar]
  13. Hamilton A. A rating scale for depression. Journal of Neurology, Neurosurgery & Psychiatry. 1960;23:56–62. doi: 10.1136/jnnp.23.1.56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Jones DK, Horsfield MA, Simmons A. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn Reson Med. 1999;42:515–525. [PubMed] [Google Scholar]
  15. Krishnan KR, Hays JC, Blazer DG. MRI-defined vascular depression. Am. J. Psychiatry. 1997;154:497–501. doi: 10.1176/ajp.154.4.497. [DOI] [PubMed] [Google Scholar]
  16. Krishnan KR, Taylor WD, McQuoid DR, MacFall JR, Payne ME, Provenzale JM, Steffens DC. Clinical characteristics of magnetic resonance imaging-defined subcortical ischemic depression. Biol. Psychiatry. 2004;55:390–397. doi: 10.1016/j.biopsych.2003.08.014. [DOI] [PubMed] [Google Scholar]
  17. Lockwood KA, Alexopoulos GS, Gorp W. Executive dysfunction in geriatric depression. Am. J. Psychiat. 2002;159:1119–1126. doi: 10.1176/appi.ajp.159.7.1119. [DOI] [PubMed] [Google Scholar]
  18. Murphy CF, Alexopoulos GS. Longitudinal association of initiation/perseveration and severity of geriatric depression. Am. J. Geriatr. Psychiatry. 2004;12:50–56. [PubMed] [Google Scholar]
  19. Nebes RD, Butters MA, Houck PR, Zmuda MD, Aizenstein H, Pollock BG, Mulsant BH, Reynolds CF., III Dual-task performance in depressed geriatric patients. Psychiatry Res. 2001;102:139–151. doi: 10.1016/s0165-1781(01)00244-x. [DOI] [PubMed] [Google Scholar]
  20. Nebes RD, Pollock BG, Houck PR, Butters MA, Mulsant BH, Zmuda MD, Reynolds CF., III Persistence of cognitive impairment in geriatric patients following antidepressant treatment: a randomized, double-blind clinical trial with nortriptyline and paroxetine. J. Psychiatr. Res. 2003;37:99–108. doi: 10.1016/s0022-3956(02)00085-7. [DOI] [PubMed] [Google Scholar]
  21. Niu K, Hozawa A, Awata S, Guo H, Kuriyama S, Seki T, Ohmori-Matsuda K, Nakaya N, Ebihara S, Wang Y, Tsuji I, Nagatomi R. Home blood pressure is associated with depressive symptoms in an elderly population aged 70 years and over: a population-based, cross-sectional analysis. Hypertens. Res. 2008;31:409–416. doi: 10.1291/hypres.31.409. [DOI] [PubMed] [Google Scholar]
  22. O’Brien J, Ames D, Chiu E, Schweitzer I, Desmond P, Tress B. Severe deep white matter lesions and outcome in elderly patients with major depressive disorder: follow up study. BMJ. 1998;317:982–984. doi: 10.1136/bmj.317.7164.982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Pfefferbaum A, Sullivan EV, Hedehus M, Lim KO, Adalsteinsson E, Moseley M. Age-related decline in brain white matter anisotropy measured with spatially corrected echo-planar diffusion tensor imaging. Magn Reson Med. 2000;44:259–268. doi: 10.1002/1522-2594(200008)44:2<259::aid-mrm13>3.0.co;2-6. [DOI] [PubMed] [Google Scholar]
  24. Salat DH, Tuch DS, Greve DN, van der Kouwe AJ, Hevelone ND, Zaleta AK, Rosen BR, Fischl B, Corkin S, Rosas HD, Dale AM. Age-related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiol. Aging. 2005;26:1215–1227. doi: 10.1016/j.neurobiolaging.2004.09.017. [DOI] [PubMed] [Google Scholar]
  25. Sobin C, Sackeim HA. Psychomotor symptoms of depression. Am. J. Psychiatry. 1997;154:4–17. doi: 10.1176/ajp.154.1.4. [DOI] [PubMed] [Google Scholar]
  26. Steffens DC, Conway CR, Dombeck CB, Wagner R, Tupler LA, Weiner RD. Severity of subcortical gray matter hyperintensity predicts ECT response in geriatric depression. The Journal of ECT. 2001;17:45–49. doi: 10.1097/00124509-200103000-00009. [DOI] [PubMed] [Google Scholar]
  27. Steffens DC, Helms MJ, Krishnan KR, Burke GL. Cerebrovascular disease and depression symptoms in the cardiovascular health study. Stroke. 1999;30:2159–2166. doi: 10.1161/01.str.30.10.2159. [DOI] [PubMed] [Google Scholar]
  28. Thomas AJ, O’Brien JT, Davis S, Ballard C, Barber R, Kalaria RN, Perry RH. Ischemic basis for deep white matter hyperintensities in major depression: a neuropathological study. Arch. Gen. Psychiatry. 2002;59:785–792. doi: 10.1001/archpsyc.59.9.785. [DOI] [PubMed] [Google Scholar]
  29. van Dijk EJ, Breteler MM, Schmidt R, Berger K, Nilsson LG, Oudkerk M, Pajak A, Sans S, de RM, Dufouil C, Fuhrer R, Giampaoli S, Launer LJ, Hofman A, CASCADE Consortium The association between blood pressure, hypertension, and cerebral white matter lesions: cardiovascular determinants of dementia study. Hypertension. 2004;44:625–630. doi: 10.1161/01.HYP.0000145857.98904.20. [DOI] [PubMed] [Google Scholar]

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