LIMBIC STRUCTURES IN CHRONIC DEPRESSION: A STUDY...

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This study was supported by a subcontract (PI: Dan Iosifescu) to NIH grant U54 LM008748 (PI: Isaac Kohane) LIMBIC STRUCTURES IN CHRONIC DEPRESSION: A STUDY USING PRE-EXISTING CLINICAL AND MRI DATA Wouter S. Hoogenboom, MS 1, 2 , Roy H. Perlis, MD 1 , Jordan W. Smoller, MD, ScD 1 , Qing Zeng-Treitler, PhD 3 , Vivian S. Gainer, MS 4 , Shawn N. Murphy, MD, PhD 4 , Susanne E. Churchill, PhD 5 , Isaac Kohane, MD, PhD 5 , Martha E. Shenton, PhD 2, 6 , and Dan V. Iosifescu, MD, MS 1 from the 1 Depression Clinical and Research Program, Massachusetts General Hospital & Harvard Medical School, the 2 Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital & Harvard Medical School, the 3 Decision Systems Group, Brigham and Women’s Hospital, the 4 Laboratory of Computer Science, Massachusetts General Hospital & Harvard Medical School, the 5 i2b2 National Center for Biomedical Computing, Brigham and Women’s Hospital, and the 6 Clinical Neuroscience Division, Laboratory of Neuroscience, VA Boston Healthcare System, Brockton Division & Harvard Medical School CONCLUSIONS REFERENCES 1. Nierenberg A.A., & Alpert A.J. (2000). Depressive breakthrough. Psychiatr. Clin. North Am. 23(4): 731-42. 2. Sheline Y.I., Gado, M.H., & Kraemer, H.C. (2003). Untreated depression and hippocampal volume loss. Am J Psychiatry. 160(8):1516-8. 3. Caetano S.C. et al. (2006). Smaller cingulate volumes in unipolar depressed patients, Biological Psychiatry. 59: 702–706. 4. Frodl T.S., Koutsouleris N., Bottlender R., et al. (2008). Depression-related variation in brain morphology over 3 years: effects of stress? Arc Gen Psychiatry. 65(10):1156-65. 5. Fennema-Notestine C. et al. (2007). Feasibility of multi-site clinical structural neuroimaging studies of aging using legacy data. Neuroinformatics. 5(4):235-45. 6. Zeng Q.T., Goryachev S., Weiss S., et al. (2006). Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Medical Informatics and Decision Making. 6(30). 7. Fischl B. et al. (2004). Automatically parcellating the human cerebral cortex. Cereb. Cortex. 14: 11- 22. 8. Fischl, B. et al. (2002). Whole brain segmentation: automated labeling of neuroanatomical structure in the human brain. Neuron. 33: 341-355. . 9. Desikan R.S. et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 31(3):968-80. 10. Koo M.S., Levitt J.J., Salisbury D.F., et al. (2008). A cross-sectional and longitudinal magnetic resonance imaging study of cingulate gyrus gray matter volume abnormalities in first-episode schizophrenia and first-episode affective psychosis. Arch Gen Psychiatry. 65(7): 746-60. i2b2: Informatics for Integrating Biology & the Bedside. [http://www.i2b2.org/] Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital. [http://pnl.bwh.harvard.edu/] For more information please contact Wouter Hoogenboom: [email protected] METHODS RESULTS INTRODUCTION TABLE 3. Absolute and relative volume measures of limbic structures. 0.50 0.012 ± 0.004 0.20 ± 0.06 0.013 ± 0.004 0.24 ± 0.08 0.010 ± 0.005 0.17 ± 0.08 Right ANOVA Unremitted (n = 6) Improvement (n = 6) Remission (n = 8) 0.77 0.42 ± 0.07 7.11 ± 1.48 0.43 ± 0.06 7.85 ± 1.07 0.45 ± 0.09 7.41 ± 1.38 Total 0.41 0.24 ± 0.06 3.99 ± 1.08 0.20 ± 0.04 3.66 ± 0.73 0.23 ± 0.04 3.80 ± 0.73 Left Anterior cingulate cortex (ACC) 0.18 0.18 ± 0.02 3.11 ± 0.48 0.23 ± 0.04 4.19 ± 0.67 0.22 ± 0.05 3.61 ± 0.74 Right a Remission > Unremitted, p = 0.026 (LSD), and Improvement > Unremitted, p = 0.017 (LSD). 0.015 ± 0.007 0.10 ± 0.02 0.15 ± 0.05 0.07 ± 0.01 0.07 ± 0.01 0.086 ± 0.02 0.084 ± 0.01 0.24 ± 0.03 0.23 ± 0.04 relative 0.015 ± 0.008 0.14 ± 0.02 0.14 ± 0.03 0.07 ± 0.02 0.05 ± 0.01 0.086 ± 0.01 0.083 ± 0.02 0.25 ± 0.05 0.25 ± 0.04 relative 0.015 ± 0.006 0.14 ± 0.03 0.16 ± 0.04 0.08 ± 0.02 0.07 ± 0.02 0.099 ± 0.01 0.093 ± 0.01 0.25 ± 0.03 0.26 ± 0.04 relative 1.00 0.034 a 0.50 0.92 0.11 0.11 0.32 0.69 0.54 1.45 ± 0.30 1.49 ± 0.07 1.54 ± 0.17 Left 1.51 ± 0.42 1.55 ± 0.16 1.65 ± 0.24 Right 4.07 ± 0.78 4.51 ± 0.37 4.13 ± 0.50 Right 4.01 ± 0.73 4.48 ± 0.72 4.24 ± 0.66 Left absolute absolute absolute Hippocampus 1.19 ± 0.12 1.31 ± 0.21 1.23 ± 0.26 Right Rostral 2.50 ± 0.96 2.48 ± 0.61 2.69 ± 0.72 Left 1.73 ± 0.54 2.64 ± 0.54 2.28 ± 0.45 Right Subgenual 0.25 ± 0.10 0.28 ± 0.15 0.24 ± 0.09 Left 1.24 ± 0.19 0.90 ± 0.12 1.10 ± 0.37 Left Caudal Amygdala OBJECTIVES To test whether chronic unremitted MDD is associated with changes in limbic structures compared to MDD subjects improving with treatment or to those achieving full remission. To use NLP for identifying patient cohorts. To use existing clinical and MRI data collected as part of routine clinical treatment. Chronic unremitted depression may be associated with changes in brain morphology, in particular volume reductions in the right rostral anterior cingulate, compared to depressed individuals who achieve remission. Such changes in brain morphology may play a role in the pathophysiology of chronic unremitted depression. Our findings support the feasibility of using neuroimaging data collected during routine clinical treatment as a promising and cost- effective alternative for answering specific research questions. STUDY DESIGN Natural Language Processing (NLP) tools were used to identify cohorts of MDD subjects. N = 20 MDD subjects (see table 1) were meeting criteria as outlined in figure 2. 1.5-T coronal T1-weighted brain MRIs, acquired at Massachusetts General Hospital since 2000 or later. All patients on long-term anti-depressant regimens. Major depression is chronic and recurrent A large proportion of patients with major depressive disorder (MDD) do not improve adequately with antidepressant treatment or experience depressive relapse and recurrence after initial improvement (Nierenberg & Alpert, 2000). Morphological changes in limbic structures For example: Sheline et al. (2003) found that hippocampal volume loss is associated with the length of untreated depression. Caetano et al. (2006) reported reduced cingulate volumes in unmedicated MDD vs. controls. In a longitudinal study of MDD vs. controls, Frodl et al. (2008) found widespread decrease of gray matter density in limbic and frontal cortical brain regions, including hippocampus, amygdala, and anterior cingulate. Applications of advances in biomedical computing Meaningful re-analyses of existing (legacy) data may enable cost-effective quantitative clinical and imaging studies (Fennema-Notestine et al., 2007). Natural Language Processing (NLP) tools can be used to extract large amounts of specific clinical data from electronic medical records (Zeng et al., 2006). Automated brain-segmentation software, such as Freesurfer, can perform subcortical segmentation (Fischl et al., 2002), and parcellation of surface areas (Fischl et al., 2004), that is proven to be fast, and anatomically valid and reliable (Desikan et al., 2006). i2b2 3D MODELS OF REGIONS OF INTEREST Figure 3. Coronal view with 3DSlicer generated models of Freesurfer segmentation. Figure 4. Medial view of right hemisphere. Freesurfer reconstruction of pial matter with an overlay of anterior cingulate parcellation. Amygdala Hippocampus Caudal anterior cingulate Rostral anterior cingulate Subgenual anterior cingulate Volume distributions across groups NS 620 ± 158 587 ± 55 508 ± 73 Total white matter (mL) NS 189 ± 26 196 ± 21 193 ± 21 Subcortical GM (mL) NS 400 ± 123 425 ± 65 436 ± 46 Neocortical GM (mL) NS 1726 ± 186 1858 ± 378 1660 ± 184 Intra Cranial Volume (ICV) NS 589 ± 131 621 ± 84 629 ± 57 Total gray matter (mL) NS a 3/3 (50%) 4/2 (33%) 4/4 (50%) Gender M/F (% female) TABLE 1. Sample characteristics. Note: Means ± SD or n (%) a Chi-square NS 46 ± 25 45 ± 17 39 ± 13 Age (yrs) ANOVA p-values Unremitted (n=6) (UR) Improvement (n=6) (IMP) Remitted (n=8) (REM) amygdala hippocampus Figure 1. Visualization and segmentation tools. Left: 3DSlicer interface with T1-scan in different cross-sectional views. Right: FreeSurfer subcortical segmentation and surface parcellation. TABLE 2. Reliability between 3DSlicer and FreeSurfer (manual vs. automatic) Right Left Right Left 0.95 0.72 Amygdala 0.85 ICV 0.94 Total GM 0.94 Total WM 0.87 Hippocampus 0.85 Intra-class correlation coefficient (ICC)* *p-values < 0.05 ROI (n=10) Figure 2. Patient enrollment flowchart.

Transcript of LIMBIC STRUCTURES IN CHRONIC DEPRESSION: A STUDY...

Page 1: LIMBIC STRUCTURES IN CHRONIC DEPRESSION: A STUDY …pnl.bwh.harvard.edu/pub/pdfs/Hoogenboom_Mysell2009.pdfFor more information please contact Wouter Hoogenboom: whoogenboom@partners.org

This study was supported by a subcontract (PI: Dan Iosifescu) to NIH grant U54 LM008748 (PI: Isaac Kohane)

LIMBIC STRUCTURES IN CHRONIC DEPRESSION: A STUDY USING PRE-EXISTING CLINICAL AND MRI DATA

Wouter S. Hoogenboom, MS1, 2, Roy H. Perlis, MD1, Jordan W. Smoller, MD, ScD1, Qing Zeng-Treitler, PhD3, Vivian S. Gainer, MS4, Shawn N. Murphy, MD, PhD4, Susanne E. Churchill, PhD5, Isaac Kohane, MD, PhD5, Martha E. Shenton, PhD2, 6, and Dan V. Iosifescu, MD, MS1

from the 1Depression Clinical and Research Program, Massachusetts General Hospital & Harvard Medical School, the 2Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital & Harvard Medical School, the 3Decision Systems Group, Brigham and Women’s Hospital, the 4Laboratory of Computer Science, Massachusetts General Hospital & Harvard Medical School, the 5i2b2 National Center for Biomedical Computing, Brigham and Women’s Hospital, and the 6Clinical Neuroscience Division, Laboratory of Neuroscience, VA Boston Healthcare System, Brockton Division & Harvard Medical School

CONCLUSIONS

REFERENCES1. Nierenberg A.A., & Alpert A.J. (2000). Depressive breakthrough. Psychiatr. Clin. North Am.

23(4): 731-42.2. Sheline Y.I., Gado, M.H., & Kraemer, H.C. (2003). Untreated depression and hippocampal

volume loss. Am J Psychiatry. 160(8):1516-8.3. Caetano S.C. et al. (2006). Smaller cingulate volumes in unipolar depressed patients,

Biological Psychiatry. 59: 702–706. 4. Frodl T.S., Koutsouleris N., Bottlender R., et al. (2008). Depression-related variation in brain

morphology over 3 years: effects of stress? Arc Gen Psychiatry. 65(10):1156-65.5. Fennema-Notestine C. et al. (2007). Feasibility of multi-site clinical structural neuroimaging

studies of aging using legacy data. Neuroinformatics. 5(4):235-45.6. Zeng Q.T., Goryachev S., Weiss S., et al. (2006). Extracting principal diagnosis, co-morbidity

and smoking status for asthma research: evaluation of a natural language processing system. BMC Medical Informatics and Decision Making. 6(30).

7. Fischl B. et al. (2004). Automatically parcellating the human cerebral cortex. Cereb. Cortex. 14: 11- 22.

8. Fischl, B. et al. (2002). Whole brain segmentation: automated labeling of neuroanatomicalstructure in the human brain. Neuron. 33: 341-355. .

9. Desikan R.S. et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 31(3):968-80.

10. Koo M.S., Levitt J.J., Salisbury D.F., et al. (2008). A cross-sectional and longitudinal magnetic resonance imaging study of cingulate gyrus gray matter volume abnormalities in first-episode schizophrenia and first-episode affective psychosis. Arch Gen Psychiatry. 65(7): 746-60.

i2b2: Informatics for Integrating Biology & the Bedside. [http://www.i2b2.org/]Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital. [http://pnl.bwh.harvard.edu/]For more information please contact Wouter Hoogenboom: [email protected]

METHODS

RESULTS

INTRODUCTION

TABLE 3. Absolute and relative volume measures of limbic structures.

0.500.012 ± 0.0040.20 ± 0.060.013 ± 0.0040.24 ± 0.080.010 ± 0.0050.17 ± 0.08Right

ANOVAUnremitted (n = 6)Improvement (n = 6)Remission (n = 8)

0.770.42 ± 0.077.11 ± 1.480.43 ± 0.067.85 ± 1.070.45 ± 0.097.41 ± 1.38Total0.410.24 ± 0.063.99 ± 1.080.20 ± 0.043.66 ± 0.730.23 ± 0.043.80 ± 0.73Left

Anterior cingulate cortex (ACC)

0.180.18 ± 0.023.11 ± 0.480.23 ± 0.044.19 ± 0.670.22 ± 0.053.61 ± 0.74Right

aRemission > Unremitted, p = 0.026 (LSD), and Improvement > Unremitted, p = 0.017 (LSD).

0.015 ± 0.007

0.10 ± 0.020.15 ± 0.05

0.07 ± 0.01

0.07 ± 0.01

0.086 ± 0.020.084 ± 0.01

0.24 ± 0.030.23 ± 0.04

relative

0.015 ± 0.008

0.14 ± 0.020.14 ± 0.03

0.07 ± 0.02

0.05 ± 0.01

0.086 ± 0.010.083 ± 0.02

0.25 ± 0.050.25 ± 0.04

relative

0.015 ± 0.006

0.14 ± 0.030.16 ± 0.04

0.08 ± 0.02

0.07 ± 0.02

0.099 ± 0.010.093 ± 0.01

0.25 ± 0.030.26 ± 0.04

relative

1.00

0.034a

0.50

0.92

0.11

0.110.32

0.690.54

1.45 ± 0.301.49 ± 0.071.54 ± 0.17Left1.51 ± 0.421.55 ± 0.161.65 ± 0.24Right

4.07 ± 0.784.51 ± 0.374.13 ± 0.50Right4.01 ± 0.734.48 ± 0.724.24 ± 0.66Left

absoluteabsoluteabsoluteHippocampus

1.19 ± 0.121.31 ± 0.211.23 ± 0.26RightRostral

2.50 ± 0.962.48 ± 0.612.69 ± 0.72Left1.73 ± 0.542.64 ± 0.542.28 ± 0.45Right

Subgenual

0.25 ± 0.100.28 ± 0.150.24 ± 0.09Left

1.24 ± 0.190.90 ± 0.121.10 ± 0.37Left

Caudal

Amygdala

OBJECTIVESTo test whether chronic unremitted MDD is

associated with changes in limbic structures compared to MDD subjects improving with treatment or to those achieving full remission.

To use NLP for identifying patient cohorts.

To use existing clinical and MRI data collected as part of routine clinical treatment.

Chronic unremitted depression may be associated with changes in brain morphology, in particular volume reductions in the right rostral anterior cingulate, compared to depressed individuals who achieve remission.

Such changes in brain morphology may play a role in the pathophysiology of chronic unremitted depression.

Our findings support the feasibility of using neuroimaging data collected during routine clinical treatment as a promising and cost-effective alternative for answering specific research questions.

STUDY DESIGN Natural Language Processing (NLP) tools were used

to identify cohorts of MDD subjects. N = 20 MDD subjects (see table 1) were meeting

criteria as outlined in figure 2. 1.5-T coronal T1-weighted brain MRIs, acquired at

Massachusetts General Hospital since 2000 or later. All patients on long-term anti-depressant regimens.

Major depression is chronic and recurrent A large proportion of patients with major depressive

disorder (MDD) do not improve adequately with antidepressant treatment or experience depressive relapse and recurrence after initial improvement (Nierenberg & Alpert, 2000).

Morphological changes in limbic structures For example:

Sheline et al. (2003) found that hippocampal volume loss is associated with the length of untreated depression.

Caetano et al. (2006) reported reduced cingulate volumes in unmedicated MDD vs. controls.

In a longitudinal study of MDD vs. controls, Frodl et al. (2008) found widespread decrease of gray matter density in limbic and frontal cortical brain regions, including hippocampus, amygdala, and anterior cingulate.

Applications of advances in biomedical computing

Meaningful re-analyses of existing (legacy) data mayenable cost-effective quantitative clinical andimaging studies (Fennema-Notestine et al., 2007).

Natural Language Processing (NLP) tools can be used to extract large amounts of specific clinical data from electronic medical records (Zeng et al., 2006).

Automated brain-segmentation software, such as Freesurfer, can perform subcortical segmentation (Fischl et al., 2002), and parcellation of surface areas (Fischl et al., 2004), that is proven to be fast, and anatomically valid and reliable (Desikan et al., 2006).

i2b2

3D MODELS OF REGIONS OF INTEREST

Figure 3. Coronal view with 3DSlicer generated models of Freesurfer segmentation.

Figure 4. Medial view of right hemisphere. Freesurfer reconstruction of pialmatter with an overlay of anterior cingulate parcellation.

Amygdala

Hippocampus

Caudal anterior cingulate

Rostral anterior cingulate

Subgenual anterior cingulate

Volume distributions across groups

NS620 ± 158587 ± 55508 ± 73Total white matter (mL)

NS189 ± 26196 ± 21193 ± 21Subcortical GM (mL)

NS400 ± 123425 ± 65436 ± 46Neocortical GM (mL)

NS1726 ± 1861858 ± 3781660 ± 184Intra Cranial Volume (ICV)

NS589 ± 131621 ± 84629 ± 57Total gray matter (mL)

NSa3/3 (50%) 4/2 (33%) 4/4 (50%) Gender M/F (% female)

TABLE 1. Sample characteristics.

Note: Means ± SD or n (%)aChi-square

NS46 ± 2545 ± 1739 ± 13 Age (yrs)

ANOVAp-values

Unremitted (n=6) (UR)

Improvement (n=6) (IMP)

Remitted (n=8) (REM)

amygdalahippocampus

Figure 1. Visualization and segmentation tools. Left: 3DSlicer interface with T1-scan in different cross-sectional views. Right: FreeSurfer subcortical segmentation and surface parcellation.

TABLE 2. Reliability between 3DSlicer and FreeSurfer(manual vs. automatic)

RightLeftRightLeft

0.95 0.72

Amygdala

0.85

ICV

0.94

Total GM

0.94

Total WM

0.87

Hippocampus

0.85Intra-class correlation coefficient (ICC)**p-values < 0.05

ROI (n=10)

Figure 2. Patient enrollment flowchart.