Main

Depression is one of the most common mental disorders in the elderly (Alexopolous et al. 1988; Beck and Koenig 1996; Blazer et al. 1987; Blazer 1989, 1994; Koenig et al. 1993; Oxman et al. 1987; Parmalee et al. 1989; Ruegg et al. 1988; Sherbourne et al. 1994). The prevalence of major depressive disorder (MDD) is estimated to be approximately 1-2 percent in community settings and strikingly higher in inpatient and long term care settings (Blazer et al. 1987; Koenig et al. 1993; Parmalee et al. 1989). The prevalence of other clinically significant forms of depression, that do not meet the severity criteria for MDD, lies between 5 and 15 percent in all major clinical settings (Beck and Koenig 1996; Blazer et al. 1987, Blazer 1994; Parmalee et al. 1989). Anatomical and physiological neuroimaging approaches have been utilized to study the neurobiological correlates of MDD and minor depression in the elderly (Coffey et al. 1990; Coffey et al. 1993; Greenwald et al. 1996; Krishnan et al. 1988; Krishnan 1993; Kumar et al. 1993; Kumar et al. 1997a; Lesser et al. 1994; Sackheim et al. 1993; Sheline et al. 1996). Glucose hypometabolism and reductions in blood flow have been demonstrated in neocortical and subcortical regions in elderly patients with MDD when compared with non-depressed controls using positron emission tomography (PET), single photon emission computed tomography (SPECT) and Xenon 133 inhalation techniques (Kumar et al. 1993; Lesser et al. 1994; Sackheim et al. 1993). Using magnetic resonance imaging (MRI), investigators have demonstrated neuroanatomical abnormalities including smaller focal brain volumes and larger high intensity lesion volumes in patients with both late-life major and minor depression when compared with non-depressed controls (Coffey et al. 1990; Coffey et al. 1993; Greenwald et al. 1996; Krishnan et al. 1988; Krishnan 1993; Kumar et al. 1997a, 1997b, 1998; Sheline et al. 1996).

Despite these observations, the precise nature and extent of the neuroanatomical changes in elderly patients with MDD remains unresolved (Jeste et al. 1988; Morris and Rapoport 1990).

Late-life MDD is also consistently associated with medical comorbidity (Berkman et al 1986; Gierz and Jeste 1993; Katz et al. 1994; Katz 1996; Lacro and Jeste 1994; Rodin and Voshart 1986). A broad spectrum of medical disorders including cardiovascular, musculoskeletal, gastrointestinal, pulmonary and metabolic disturbances are associated with both major and minor forms of depression (Alexopolous et al. 1988; Katz et al. 1994; Koenig et al. 1993; Lacro and Jeste 1994). Despite this association, the relationship between neuroanatomical abnormalities and medical disorders in late-life and their relative contributions to the pathophysiology of mood disorders remain unknown.

In an earlier report, we demonstrated that the odds of developing MDD in late-life increased with overall medical burden and larger whole brain cerebrospinal fluid (CSF) volume (Kumar et al. 1997a). However, only global measures of whole brain CSF and ventricular volumes were obtained in that study and these served as indirect measures of brain atrophy. In addition, estimates of high intensity lesions that were used in our analyses were qualitative in nature. Consequently, no statement was made about regional brain volume differences between groups and the role of more precise quantitative estimates of atrophy and high intensity lesion volumes in the development of MDD.

The principal objective of our current study was to examine the relative roles of atrophy, high intensity lesions and medical disorders in the development of MDD in the elderly. We were further interested in clarifying the relationship between quantitative neuroanatomic measures and objective clinical indices such as specific cerebrovascular risk factors and reliable measures of overall medical burden. Such an approach, we believe, may provide us with insights into the mechanisms/pathways that may be involved in the pathophysiology of late-life MDD.

SUBJECTS

Our study groups were comprised of 51 patients with late-life Unipolar MDD diagnosed using DSM IV criteria (American Psychiatric Association 1994) and 30 non-depressed controls (Table 1) . Twenty-eight of our current sample of 51 patients with MDD have been previously reported on (Kumar et al. 1997a). The controls used in this study also served as the control group in our recent MRI report on minor depression (Kumar et al 1997b) but were not part of our earlier MDD study (Kumar et al. 1997a). In order to be included in the study, the MDD patients had to meet the following: Inclusion Criteria:

  1. 1

    DSM IV criteria for MDD;

  2. 2

    A score of 15 or greater on the 17 item Hamilton Depression Rating Scale (Hamilton 1967);

  3. 3

    Mini Mental State Exam (MMSE) Scores of 24 or greater (Folstein et al 1975).

Exclusion Criteria:

  1. 1

    Clinical evidence of any other brain disorder such as stroke, transient ischemic attacks (TIA) or Parkinson's disease;

  2. 2

    History and/or mental status exam suggestive of a progressive dementia;

  3. 3

    MMSE score less than 24;

  4. 4

    History of substance abuse.

Entry criteria for controls:

  1. 1

    Age 60 or greater;

  2. 2

    Free of current and past psychopathology including substance abuse;

  3. 3

    No evidence of any brain disease such as stroke, TIA or dementia.

Table 1 Some of the Primary Demographic and Clinical Variables in and the Absolute and Normalized Brain and Lesion Volumes in Two Groups
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Psychopathology was ruled out on the basis of mental status exams and the use of the structured interview (SCID for normals) based on the diagnostic and statistical manual for psychiatric disorders–DSM IV. Patients diagnosed with depression also received a DSM based structured psychiatric interview (SCID) in addition to the standard clinical psychiatric assessment.

MDD patients were recruited from the geriatric psychiatry inpatient and ambulatory care programs connected with the Hospital of the University of Pennsylvania. After complete description of the study to the subjects, written informed consent was obtained whenever appropriate. The geropsychiatry ambulatory care program at the University of Pennsylvania is a collaborative effort with the division of geriatric medicine and combines characteristics of a primary care and tertiary referral center. Controls were recruited from the community through newspaper advertisements.

Patients and controls had several stable comorbid medical disorders including ischemic heart diseases, diabetes and arthritis. Several patients with MDD were on benzodiazepines and antidepressants at the time of the study. None of these medications were considered to contribute to the mood disorder. Thirty-five of the 51 patients with MDD had their first onset of depression at or later than age 60. The Cumulative Illness Rating Scale (CIRS) was used to quantify overall medical burden in both groups (Linn et al. 1968). The CIRS is a validated clinical instrument that categorizes disorders into 12 organ systems (gastrointestinal, cardiovascular etc.) and rates each of them along a 0-4 severity scale (0 = no illness, 4 = severe illness). The CIRS is commonly used in geriatric research to obtain a quantified estimate of overall medical burden. The American Heart Association Scale for stroke risk factors was used to quantify cerebrovascular risk factors (American Heart Association 1990; Wolf et al. 1991). This scale incorporates eight independent measures; age, presence and treatment of hypertension, diabetes, smoking, coronary vascular disease, atrial fibrillation and left ventricular hypertrophy and provides a score indicating overall risk for cerebrovascular disease. CIRS and the stroke risk factor scales were administered by board-certified psychiatrists who were supervised by a geriatric psychiatrist (AK) familiar with the use of these instruments. While both of our study groups had several comorbid medical disorders, the MDD group presented with greater overall medical burden. The MDD group was older than the control group though there was considerable overlap in age between subjects in both groups. The gender distribution was comparable in both groups.

MRI METHODS

MRI scans were obtained on a 1.5 tesla GE scanner with head coil in planes parallel to the canthomeatal line. Both T2 and proton density weighted images were obtained in all subjects (TR = 3000, TE = 30 and 80 msec) and the images were displayed on a 256x256 matrix, with pixel size of 0.86 mm and field of view of 22 cm (Kohn et al. 1991; Kumar et al. 1994). The size of the individual voxels was 0.37 mm cubed and about 275 voxels approximated 1 cc in volume. Axial slices were 5mm thick and contiguous. A segmentation program developed within the Department of Radiology at the University of Pennsylvania and previously used to examine neuroanatomical changes in subjects with major depression, schizophrenia and dementia of the Alzheimer type (DAT) was used in our image analysis (Kohn et al. 1991; Kumar et al. 1994). The temporal lobe outlined in our analysis included both lateral and mesial temporal structures and the frontal cortex did not include the sensorimotor region (Cowell et al. 1994). Details of the technique used to segment brain from cerebrospinal fluid (CSF) and the anatomic boundaries and landmarks used to delineate the frontal and temporal lobes have been described previously (Cowell et al. 1994, See Appendix 1). Manual tracing of all the neuroanatomical boundaries was done by one rater (ZJ) who was blind to the clinical status of all subjects. Good inter-rater reliability for all regions (intraclass correlation coefficient 0.90) was established between this rater and others who apply identical methods of regional neuroanatomical analyses to the study of neuropsychiatric disorders (Cowell et al. 1994; Kumar et al. 1994). Quantitative measures of total brain and high intensity lesion volumes were obtained together with estimates of frontal and temporal lobe volumes. Absolute brain and lesion volumes were additionally normalized to the intracranial volume (brain plus CSF) to correct for individual differences in head size.

FUZZY VOXEL APPROACH

To detect and quantify the volume of high intensity signals, a novel method of object delineation based on “fuzzy connectedness” was utilized (Udupa 1994; Udupa et al. 1994a; Udupa and Samarasekera 1996). An object such as a high intensity signal lesion is considered to be a fuzzily connected entity wherein every pair of voxels has a “strength of connectivity” associated with it utilized (Udupa 1994; Udupa et al. 1994a; Udupa and Samarasekera 1996). This strength is a global property which determines how voxels hang together in a fuzzy way to form an object (Figure 1). The power of this method compared to other segmentation methods comes from the consideration of an object as fuzzily defined as well as a fuzzily connected entity. A software system called 3DVIEWNIX in which this method was implemented was utilized in all image processing. This method requires two essential steps: In the first stage, minimal operator interaction is required and the signals are automatically detected by the computer (Udupa et al. 1994a; Udupa and Samarasekera 1996). A few false positives may be included at this stage. In the second stage, the operator accepts/rejects individual high intensity signals with the click of a mouse button, in response to the systems display of each 3-dimensional lesion. The decision to accept/reject individual lesions was made by a board-certified geriatric psychiatrist (AK) experienced in neuroanatomical imaging. The fuzzy connectedness approach eventually provides whole brain high intensity signal volume in absolute voxel numbers that can be converted to cubic centimeters and normalized to total intracranial volume. This system has been tested extensively and can be used with good inter-rater reliability (intraclass correlation coefficients of 0.80).

Figure 1
figure 1

The left image shows high intensity lesions as they appear on a proton density image. The image on the right shows the same lesion after the fuzzy voxel lesion detection methodis applied

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STATISTICS

A linear regression was used to compare absolute and normalized measures of brain and lesion volumes between the two groups after controlling for the potential effects of age and gender. In a secondary analysis, we also introduced total CIRS scores as an additional covariate in our analysis in order to examine the influence of overall medical comorbidity, if any, on potential differences in brain and/or lesion volume between the two groups. A logistic regression was used to determine the influence of individual measures such as brain volume, lesion volume, overall medical burden and cerebrovascular risk factors on the odds ratio for the prevalence of existing MDD. The odds ratio from a logistic regression relating prediction to the prevalence of existing MDD, is sometimes referred to as the prevalence odds ratio (Seigel and Greenhouse 1973) (OR for MDD). The logistic regression was done in two steps: we first estimated the effects of each of these measures in increasing the OR for MDD (while controlling for intracranial volume in the case of brain and lesion volumes). In the next stage, we estimated the effects of the same measures while covarying for the other three variables. This provides us with an estimate of the ability of each of these measures to independently influence the odds for MDD. A partial Spearman correlation coefficient (after controlling for age) was used to examine the relationships between the various neuroanatomical and clinical indices of medical and cerebrovascular risk.

RESULTS

Our findings (Table 1) demonstrate that patients with MDD have significantly smaller normalized frontal lobe volumes when compared with the non-depressed controls after controlling for age and gender differences between the two groups. Normalized temporal lobe and whole brain volumes were also smaller in the depressed group, though the differences did not reach statistical significance. Though we present absolute and normalized measures of frontal and temporal lobe volumes for both groups (Table 1), statistical analyses for regional measures were performed only on normalized volumes. Absolute (actual number of voxels) and normalized measures of whole brain high intensity lesion volumes were significantly larger in the MDD group when compared with the controls (p = 0.034 and 0.024 respectively). The statistically significant decrease in normalized frontal volume between groups remained after we adjusted for overall medical comorbidity, in addition to the existing age and gender controls. However, the differences in lesion volumes between the MDD and control groups were no longer statistically significant after adjusting for overall medical burden.

A logistic regression (Table 2) revealed that the OR for MDD increased with a decrease in frontal lobe volume and an increase in whole brain lesion volume. Table 2 presents the OR for MDD (with 95 % confidence intervals) for each of our primary variables of interest both before (Unadjusted OR) and after covarying for the other three variables (Adjusted OR). A decrease in frontal lobe volume by 25 cc (which approximates the mean difference between groups) increased the OR by 3.088, while a frontal lobe volume decrease of 50cc increased the OR by 9.536. The influence of a smaller frontal lobe volume on the OR for MDD persisted after controlling for the other three variables (Table 2).

Table 2 The Odds Ratio for MDD with Incremental Decreases in Brain Volume and Increases in Whole Brain Lesion Volumes
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An increase in lesion volume (measured in voxels) also significantly increased OR for MDD. An increase in lesion volume of 200 voxels increased the OR by 1.904. Controlling for the other variables had an impact, albeit small, on the influence of lesion volumes on MDD—the statistical significance became insignificant (p value changed from 0.028 to 0.111), and the effect size decreased marginally after the introduction of these covariates. In order to further determine if smaller frontal lobe volumes and larger lesion volumes impacted on each other's ability to increase the OR for MDD, we estimated their individual impact on the OR while controlling for the other variable in a subanalysis. Smaller frontal lobe volumes and larger lesion volumes did not influence the ability of the other to increase the odds for depression. An increase in overall medical comorbidity (CIRS Score by a factor of 1) and cerebrovascular risk factors did not affect the OR for MDD in our sample (Table 2). To additionally clarify the relative impact of medical comorbidity and lesion volume on MDD, we also examined the influence of each measure on the OR while controlling for the other. Neither measure significantly increased the OR for MDD while controlling for the other.

There is a significant correlation (Table 3) between total lesion volume and overall medical comorbidity (r = 0.414, p = .0058). The relationship between lesion volume and cerebrovascular risk factors only approached statistical significance (r = 0.283, p = .066). Frontal lobe volume did not correlate significantly with either overall medical comorbidity or cerebrovascular risk factors. There was also no significant correlation between frontal lobe volume and estimates of whole brain lesion volume. The two primary clinical measures of medical illness, the CVRF and the CIRS were significantly correlated (r = 0.34, p = 0.025) in our study.

Table 3 Spearman (Partial) Correlation Matrix, Adjusted For Age and ICV, Demonstrating the Relationships between the Primary Neuroanatomical and Clinical Measures
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DISCUSSION

The principal finding to emerge from our study is that the odds for MDD increase significantly with a decrease in frontal lobe volume and an increase in whole brain high intensity lesion volume. Our data also suggest that atrophy and high intensity lesions may represent complementary pathways to late-life MDD in our sample: In our analyses, controlling for either variable did not impact on the ability of the other to increase the odds of developing major depression. Also, while lesion volumes are significantly correlated with overall medical burden, atrophy appears to be a relatively independent phenomenon that contributes to late-life MDD. While the MDD group also presented with smaller normalized temporal lobe and total brain volumes when compared with the control group, these differences were not statistically significant.

The frontal lobes have been implicated in a variety of behavioral and cognitive disorders including schizophrenia, obsessive compulsive disorder and frontotemporal dementia (Baxter et al. 1987; Fuster 1996; Kumar and Gottlieb 1993; Mishkin 1964; Weinberger et al. 1992). The frontal lobes occupy approximately one third of the cerebral cortex and the frontal cortex is one of the last regions of the cerebral cortex to develop both phylogenetically and ontogenetically. Both anatomically and histologically the frontal lobe is heterogeneous and the physiological functions attributed to this region reflect this heterogeneity (Fuster 1989; Fuster 1996; Kumar and Gottlieb 1993; Mishkin 1964). The frontal cortex has rich afferent and efferent connections to several limbic, subcortical and other neocortical regions (Stuss and Benson 1984). Evidence from primate and human studies indicate an important role for this region in the regulation of affect and in modulating certain cognitive functions consistent with its extensive neural connections (Fuster 1989; Fuster 1996; Mishkin 1964). In vivo neuroimaging studies corroborate preclinical observations and abnormalities in frontal anatomy and physiology have been demonstrated in several psychiatric disorders (Baxter et al. 1987; Fuster 1996; Mishkin 1964; Weinberger et al. 1992). Our findings extend these earlier observations and indicate that in addition to being smaller in patients with late-life MDD, frontal atrophy may also represent a relatively distinct pathway that compromises neural circuits and leads to MDD.

Atrophy in mood disorders may not however be restricted to the frontal region and the magnitude and extent of atrophy in MDD samples may vary depending on a number of underlying neurobiological factors (Caine et al. 1993; Coffey et al. 1993; Rabins et al. 1991; Sheline et al. 1996). Reports range from those that demonstrate a reduction in total brain volumes to others that indicate a more selective involvement in patients with MDD when compared with controls (Rabins et al. 1991; Sheline et al. 1996, Coffey et al. 1993). There are regional variations in the sensitivity of neurons to different biological insults. Hippocampal neurons are believed to be more cortisol sensitive while subcortical nuclei are well established sites of small “lacunar” infarcts in patients with long-standing vascular disease (Hastak and Hachinski 1992; Sapolsky and Pulsinelli 1985; Sapolsky et al. 1986, 1990; Schmidt et al. 1991). Hippocampal atrophy in depression may therefore be a reflection of hypercortisolemia that occurs with hypothalamic pituitary adrenal (HPA) axis dysfunction commonly observed in mood disorders (Sheline et al. 1996). While the precise causes of atrophy in patients with MDD are unknown, several neurobiological factors may be involved in this process. Reductions in the level of neurotrophins such as nerve growth factor (NGF) and alterations in molecules such as cytokines may also contribute to the atrophy observed in patients with MDD (Duman et al. 1997; Katz et al. 1994). Changes in the levels of cortisol and trophic factors are perhaps better conceptualized as an integral part of the perturbations of stress mediated pathways that are commonly encountered in depression (Duman et al. 1997; Sheline et al. 1996). The cumulative impact of these different neurobiological injuries on the brain would be to cause atrophy in sensitive regions that could collectively result in a reduction in total brain volume. The apparent discrepancy in reports of atrophy patterns in patients withMDD may more accurately reflect variability in the underlying biological processes that are selectively captured on MR imaging of specific study samples (Coffey et al. 1993; Kumar et al. 1997a; Lyness et al. 1998; Ruegg et al. 1988; Sheline et al. 1996).

The relationship between medical disorders, high intensity lesions and atrophy is complex and needs to be better elucidated. High intensity lesions occur predominantly in white matter regions of the brain and they represent a spectrum of pathological changes including ischemia, demyelination and edema (Boyko et al. 1994; Drayer 1988). Results from the Cardiovascular Health Study indicate that the frequent correlates of these lesions include age, silent stroke, hypertension and FEV1 (Longstreth et al. 1996). Other, as yet unidentified, biologic factors may also contribute to the MR signal (Ylikoski et al. 1995). In our current study, covarying for medical comorbidity eliminated the statistical significance in lesion volume, but not frontal lobe volume differences between the MDD and control groups. These findings, together with our data showing that lesion volumes correlate with measures of medical comorbidity and cerebrovascular risk factors, suggest that high intensity lesions are an integral part of the medical/vascular complex frequently observed in elderly clinical populations. In our logistic regression, controlling for overall medical comorbidity did not appreciably impact on the ability of lesions to increase the OR for depression. We interpret this finding to indicate that despite the correlations between lesions and comorbidity, other biological factors, in addition to medical illness, probably contribute to the lesions and their impact on brain structure and function. In our MDD sample, atrophy appears to be independent of both overall medical burden and cerebrovascular risk factors. However, chronic ischemic changes have been shown to lead to atrophy, predominantly in the white matter, resulting in well recognized clinical/cognitive disturbances (DeReuck et al. 1980; Fisher 1989; Huang et al. 1985; Loizou et al. 1981). The structure of subcortical nuclei may also be influenced by chronic hypertension and lacunar infarcts (Schmidt et al. 1991; Hastak and Hachinski 1992). Ischemic neurons may in turn be more sensitive to stressors including psychological ones (Duman et al. 1997). Therefore, at a more basic cellular level, atrophic and ischemic changes may interact and amplify their individual impact on brain structure and function (Duman et al. 1997).

Late-life MDD is associated with several acute and chronic medical conditions involving multiple organ systems. Acknowledging this robust association, the NIH consensus statement on the diagnosis and treatment of late-life depression states “the hallmark of depression in late-life is its association with medical comorbidity” (NIH Consensus Panel 1992). Each disorder may lead to clinical depression via a distinct set of mechanisms such as vascular, immunologic, etc. The relative contribution of these disorders to mood disturbances is likely to vary substantially in different patient groups. While vascular mechanisms may be presumed in depression following acute vascular events such as myocardial infarction (post MI depression) and cerebrovascular accidents (post stroke depression), the mechanisms responsible for mood disturbances in more stable, chronically ill populations are likely to be multifactorial (Frasure-Smith et al. 1995; Glassman and Shapiro 1998; Rabins et al. 1985; Robinson et al. 1984). The apparent relationship between vascular disease and high intensity lesions on MR images of patients with MDD, has led investigators to suggest that vascular mechanisms may be the principal ones involved in late-life MDD (Krishnan et al. 1997; Alexopolous et al. 1997). While it may be possible to identify subgroups with clinical characteristics suggestive of a vascular etiology even in general settings, a conservative interpretation of findings is warranted when study samples are recruited from heterogeneous settings where vascular disease coexists with other medical disorders. Our demonstration of a statistically significant correlation between measures of overall medical and vascular burden in our sample further corroborates our contention that in primary care/ambulatory settings, vascular disease is perhaps better conceptualized as an important part of the total comorbidity picture, than as an isolated set of mechanisms.

There are a few limitations of our current study that we would like to discuss. First, several of the relationships between neuroanatomical and clinical measures and the suggestion that atrophy and lesions may reflect distinct pathways to depression are based on statistical approaches/ correlations between these measures. While we acknowledge that correlations alone do not prove causality, our observations are consistent with established structure:function relationships in the brain. Second, this is a cross-sectional study with samples recruited largely from a University based geriatric clinic. The extent to which this sample and consequently our findings reflect community based patients with depression is therefore unclear. Also, the possibility that a subgroup of our patients with smaller frontal lobe volumes are in the preclinical phase of a degenerative disorder cannot be entirely be ruled out. Despite these caveats, our observations are the first to fully integrate quantitative neuroanatomical measures with relevant medical indices and offer an early glimpse into plausible mechanisms and pathways that contribute to mood disturbances. Our findings, albeit cross-sectional in nature, represent an important first step in elucidating the complex pathways and circuits that underlie clinical depression in the elderly.

In summary, our data indicate that atrophy and high intensity lesions represent complementary, and somewhat independent paths to late-life MDD. While these two phenomena may overlap in certain clinical situations and at a cellular level, the primary origins of the two pathways in MDD, i.e. neurodegeneration and high intensity lesions, are likely to be different, each of them arising as a consequence of fundamentally different processes. Disruption of critical corticocortical and subcorticocortical circuits by atrophy, ischemia, or a combination of mechanisms could provide a neurobiological substrate that increases vulnerability to overt clinical disturbances. When the neurobiological burden crosses a threshold, either independently or in the presence of other acute medical or psychosocial challenges, the end point may be a serious clinical disturbance like MDD. These observations have broad pathophysiological implications for mood disorders in general as they conceptually narrow the differences between medical and psychiatric illness and illustrate the complementary nature of biological mechanisms in the etiology of affective disorders.