Integrated brain imaging center
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Integrated Brain Imaging Center. Structural Imaging. BIC. Mission. To develop and apply semi-quantitative and quantitative MRI Imaging and Image Analysis Techniques. UW Epidemiological Studies.

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Integrated brain imaging center

Integrated Brain Imaging Center

Structural Imaging




To develop and apply semi-quantitative and quantitative MRI Imaging and Image Analysis Techniques

Uw epidemiological studies

UW Epidemiological Studies

  • We are the imaging core for a large number of NIH funded multicenter longitudinal cohort studies to identify imaging risk factors for stroke and dementia

  • Cardiovascular Health Study – 18 years; 5888 subjects, identified risk predictors for stroke, USA

  • Atherosclerotic Risks in the Community – 13 years, 2000 subjects, USA

  • Strong Brain Study, 1000 subjects, USA

  • SABRE Stroke and Dementia Cohort, 2000 subjects, England

Integrated brain imaging center


5888 participants


(4 Centers)

Medicare sample


Baseline “risk factor” assessment

Stroke and TIA

The cardiovascular health study

The Cardiovascular Health Study

  • Assess numerous characteristics at baseline and annually

  • Blood pressure, weight, FEV1, presence of atrial fibrillation, ABI, smoking

  • EKG, echocardiography, carotid ultrasound


Surveillance of subsequent hospitalizations for event accrual

Integrated brain imaging center


  • The challenge - occurrence of the endpoint, stroke and TIA, was relatively infrequent,

    • 6%

  • Diminished the power to detect correlations

  • Concern was that this study would require an increase in sample size or prolonged period of events accrual

    • $$$$$$


Integrated brain imaging center


  • We proposed that cerebral MRI could be used to detect a surrogate endpoint for stroke

    • Less expensive

    • Increase power to detect correlates and risk factors

    • Avoid “loss to follow-up” bias

Imaging epidemiology

Imaging Epidemiology

  • Appropriateness of the surrogate

    • Clinical evidence demonstrated a correspondence between clinical stroke and MRI detected areas of infarction

    • Preliminary evidence - distribution of MRI infarcts corresponded with classic histopathologic studies of C. Miller Fisher

Imaging epidemiology1

Imaging Epidemiology

  • Rationale

    • Clinical experience- detect MRI infarcts that did not have a clinical correlate …. imaging enables sub-clinical detection of disease



  • CHS participants underwent cerebral MRI

    • Pittsburgh, Pennsylvania Winston Salem, North Caroline

    • Sacramento, California Hagerstown, Maryland

  • acquired image data transferred to the MRI Reading Center

Methods mri reading center

Methods -MRI Reading Center

  • interpreted in a standardized protocol

    • blinded to all clinical information including age, gender, cognitive function and race

Methods grading systems

Methods -Grading Systems

  • Infarcts were mapped to 23 anatomic locations

    • Size

    • Number



  • MRI exquisitively sensitive in the detection of subclinical cerebrovascular disease

  • infarct-like lesions in 36% of the population

  • 6 fold increase in power to detect associations by incorporating imaging



  • Enabled identification of correlates most commonly associated with stroke

  • Epidemiological study value

    • identified the correlates to address and individual most likely to benefit

    • Improved cost and outcome

Risk factor identification

Risk Factor Identification

  • Another frequently identified cerebral MRI finding was T2-weighted white matter hyperintense (T2W WMH) regions

Methods grading systems1

Methods -Grading Systems

  • white matter signal changes of each individual assessed on a semi-quantitative ten-point WMG (0-9) scale

  • pre-defined visual standards of 8 reference cases

  • total extent of periventricular and subcortical white matter signal abnormality on spin density weighted images

  • increased from no or barely detectable changes (grades 0 and 1 respectively) to almost all white matter involved (grade 9)

Manolio TA, Stroke 1994

Risk factor association

Risk Factor Association

  • Demonstrated an equally strong correlation to known markers of atherosclerotic disease

A new type of risk predictor

A new type of risk predictor

  • Demonstrated imaging good outcome measure

  • ? Value of imaging subclinical markers as predictors of disease risk

  • Hypothesis-T2W WMH is a significant subclinical predictor of stroke risk

Methods event ascertainment

Methods - Event Ascertainment

  • incident stroke the outcome of interest

  • participants without prevalent disease at the time of the year five MRI scan were considered “at-risk” for stroke

  • “possible” events ascertained every six months

Price TR, AEP, 1993; Ives DG, An Epid 1995



  • Strokes occurred in 159

    -participants in the analysis

    incidence 12.1/ 1000 p-yr

Risk factor identification1

Risk Factor Identification

  • Using multiple logistic regression, stepwise increase in the hazard ratio

    • WMG = 2 (HR=1.5)

    • WMG = 3 (HR=2.4)

    • WMG = 4(HR=3.2)

    • WMG > 5 (HR=3.9)

  • *controlling for known stroke risk factors of age, gender, race,

  • systolic BP, diabetes, cardiovascular disease, atrial fibrillation,

  • internal carotid wall thickness and elevated creatinine

Risk factor identification2

Risk Factor Identification

  • combining WMG > 2 with known risk factors for cerebrovascular disease

    • WMG >2 with hypertension (HR = 3.4)

    • WMG > 2 with diabetes (HR = 2.4)

    • WMG >2 with history of MI (HR = 3.2)

    • WMG >2 with history of CHF (HR = 2.5)

    • WMG >2 with atrial fibrillation (HR = 7.9)

Risk factor identification3

Risk Factor Identification

  • Thus, imaging correlates, independent and in combination, were a better risk predictor than any previously identified risk factor from the Framingham Study

    • Further target the subset of individuals at greatest risk

    • Imaging decreasing cost and improving outcome

Chs cognition study mri follow up


5888 participants 65+

(4 Centers)

Medicare sample

1989-90, 1992-93

MRI Brain



2nd MRI: 2112


1581 Normal

301 MCI

195 Incident dementia

between MRI’s

35 Prevalent dementia

at first MRI

Measurement of

incidence of


CHS Cognition Study: MRI Follow-up

Integrated brain imaging center

Odds Ratio

Age at


3MSE at


HS or less

Vs. >HS


(yes vs no)



Vent. Size 5+

at 2nd MRI

Results of Logistic Regression Analysis for Risk of Alzheimer’s Disease vs. Normals (excludes MCIs) Among Normals and Cases Incident for AD at the 1st MRI Who are Prevalent at the 2nd MRI

Variables in the Model

CHS Dementia Study

Integrated brain imaging center

Results of Logistic Regression Analysis for Risk of Dementia Among Non-Demented Cases and Cases Incident at MRI 1 but Prevalent at MRI 2: Controlling for Time Between MRIs

Odds Ratio

Age at


3MSE at



(HS or less

vs. >HS)


(yes vs no)



vs. white)



vs. male)


Vent 5+

vs. <5

Inc. by 2+

grades at




MRIs (yrs)

Variables in the Model

CHS Dementia Study



  • Quantitative

    • Infarct like lesions

    • Hippocampal volumes

    • White matter ischemia

  • Semiquantive

    • White matter grade

    • Ventricular grade

    • Sulcal grade

    • Quantitative



supported by N01-HC-85079 to N01-HC-85086, N01-HC-35129 and N01-HC- 15103 from the National Heart, Lung and Blood Institute.



Wake Forest University School of Medicine: Gregory L. Burke,MD, Sharon Jackson, Curt D. Furberg, David S. Lefkowitz, Mary F.Lyles, Cathy Nunn, John Chen, Beverly Tucker, Harriet Weiler; Wake Forest University – ECG Reading Center: Pentti M. Rautaharju,

MD, PhD; University of California, Davis: John Robbins, MD, MHS; Johns Hopkins University: Linda P. Fried, MD, MPH; University of Washington Norman J Beauchamp MD MHS, JohnsHopkins University – MRI Reading Center: Nick Bryan, MD, PhD,University of Pittsburgh: Lewis H.Kuller, MD, DrPH; University of California at Irvine – Echocardiography Reading Center (baseline): Julius M. Gardin, MD; Georgetown Medical Center – Echocardiography Reading Center (followup): John S. Gottdiener, MD; New England Medical Center Boston –

Ultrasound Reading Center: Daniel H. O’Leary, MD; University of Vermont – Central Blood Analysis Laboratory: Russell P. Tracy, PhD; University of Arizona at Tucson – Pulmonary Reading Center:

Paul Enright, MD; Retinal Reading Center – University of Wisconsin: Ronald Klein, MD; University of Washington – Coordinating Center: Richard A. Kronmal, PhD; NHLBI Project Office: Jean

Olson, MD, MPH.


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