Depression of CBF in Alzheimer
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Depression of CBF in Alzheimer's Disease and Mild Cognitive Impaired Patients Using Arterial Spin Labeling Perfusion MRI Iris Asllani 1 , Ajna Borogovac 1 , Nikolaos Scarmeas 2 , Truman R. Brown 1 , Christian Habeck 2 ,Yaakov Stern 2 ,

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Introduction

Depression of CBF in Alzheimer's Disease and Mild Cognitive Impaired Patients Using Arterial Spin Labeling Perfusion MRI

Iris Asllani1, Ajna Borogovac1, Nikolaos Scarmeas2, Truman R. Brown1, Christian Habeck2 ,Yaakov Stern2,

1 Hatch MR Research Center, Departments of Radiology and Biomedical Engineering, 2 Cognitive Neuroscience Division, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain

Columbia University, New York, NY 10032

Introduction

  • CBF Computation

  • ASL fractional signal images (M / Mc)were calculated from the normalized control and labeled EPI images

  • CBF was computed using Eqs. [1] & [2] below, taking into account the variable PLD by acquisition slice 6,7 ; PGM and PWM represent the posterior probability values obtained through segmentation of SPRG using SPM99. For ROI analysis, only voxels with P[GM] >0.8 were included, (Fig. 1).

As we continue to develop treatments for Alzheimer’s disease (AD) there is an urgent need for biomarkers that can diagnose AD as early as possible, and that can map disease progression for the purposes of testing drug efficacy. Previous studies have suggested that there is a strong link between resting cerebral blood flow (CBF) assessed by PET or SPECT and neuropathological changes of AD. However, PET or SPECT are limited by factors such as high cost, low availability, invasive nature, ionizing radiation and low spatial resolution 1,2.Arterial spin labeling (ASL) MRI is a relatively novel, non-invasive, practically feasible and cost-effective imaging technique (easily acquired during structural MRI sessions), which provides quantification of CBF in physiological units with reproducibility, resolution and contrast exceeding that obtained with PET or SPECT. Recent studies have shown that ASL can be successfully used in characterizing AD-related CBF decreases 3-5.

This ongoing study applies continuous ASL (CASL) perfusion MRI to reliably detect depression of CBF in AD population. Of great interest is the possibility of using the depression pattern of CBF as a predictor of which patients with Mild Cognitive Impairment (MCI) will subsequently develop AD. For this, we acquired ASL images from 3 groups, healthy controls (HC), MCI, and AD; CBF images were compared using voxelwise and ROI-wise statistical analysis.

Voxelwise analysis

SPM{T} map (p<0.05, corrected) from the (HC-AD) contrast is shown overlaid on the glass-brain template and slicewise on one of the subjects’ SPGR in Figs.2A and 2B, respectively. The slice for Fig.2B was chosen as representing the highest CBF difference between the two groups.

No voxels survived correction for multiple comparisons in MCI. Therefore, for comparison, in Fig.3 we show similar data for the (HC-MCI) contrast as in Fig.2 but with p<0.001, uncorrected.

The following parameters were used: longitudinal relaxation of blood, T1a = 1400 ms; blood/tissue water partition, l = 0.9 mL/g; transit time, d = 1300 ms; labeling duration, t = 2000 ms; tissue longitudinal relaxation in absence of RF, T1 = 1150 ms and 800 ms for GM and WM, respectively; T1 in presence of RF, T1rf = 750 ms and 530 ms for GM and WM, respectively [7]; PLD adjusted to account for the inter-slice acquisition time, w = (acquisition slice –1)·(64 ms) + 800 ms; labeling efficiency for CASL at 1.5T on Philips Intera, a = 0.70. The equation is valid under the assumption that t + w > d which holds true for all our acquisitions.

Eq.[1]

Fig.2

Eq.[2]

Methods

  • Subjects

  • Flow images were obtained from three groups: AD (N=12, 7 males, age 70.7± 8.1 years), MCI (N=10, 7 males, age 72.6 ± 5.9 y.o.) , and HC (N=20, 8 males, age 72.4 ± 6.9 y.o.). HC were recruited from family members and advertisements. For the AD group, the modified Mini Mental State score was 38.7 ± 11.1 and CDR 1.25 ± 0.45. IRB approved consent was obtained from all subjects.

  • Imaging acquisition

  • Single shot spin-echo EPI CASL:

    • TR/TE/FA = 4s/36ms/90º;

    • 15 slices

    • FOV=220 ×198 mm; matrix 64 × 51; slice thickness/gap = 8mm/1mm

    • labeling time = 2000ms; post-label delay = 800ms; control 250 Hz cosine modulation

    • position of labeling plane: 48 mm inferior to the center of the imaged volume

    • Alternating control/label images

  • 3DT1 SPGR images:

    • TE/TR/FA = 3 ms/34 ms/45º

    • 100 slices

    • FOV = 240 × 240 mm; matrix = 256 × 256; slice thickness/gap = 1.5mm/1mm

    • Used for anatomical alignment to MNI template, generation of tissue type probability masks and ROI selection

  • Image Preprocessing

  • ASL control and label images motion corrected to the first acquired image and registered to SPGR using SPM99

  • SPGR normalized to MNI template using SPM99

  • All images were resampled to 2x2x2 mm resolution

  • SPGR images used to generate gray matter, white matter and CSF posterior probability masks using SPM99

  • Voxelwise/ROI Analysis

  • Whole brain voxelwise comparison were done using SPM99 fixed effects

  • 17 ROI’s (Fig.1) identified from PickAtlas, both right and left. Only voxels with posterior probability of gray matter, P[GM] >0.8 were inlcuded

  • CBF for each ROI was calculated as average over voxels in the ROI

  • Results

    • ROI analysis

    • Fig.1 shows the ROI-wise CBF for HC (blue), MCI (yellow), and AD (red). Only ROIs in the left hemispheres are shown. There was no significant difference between the CBF values for left- and right-hemisphere ROIs. The largest significant change between MCI and HC was found in the pulvinar, cingulate gyrus, and middle frontal gyrus (ttwo_tailed >2.0 at =0.05, for all)

    Fig.3

    Conclusion

    We have demonstrated the feasibility of CASL MRI for detection areas of hypoperfusion associated with AD. Preliminary data from MCI indicate an early decrease in CBF with several ROIs showing significant decrease in CBF compared to HC. From Fig.1 one can appreciate the decline in CBF with cognitive performance. Future work based on longitudinal study of MCI conversion to AD is needed.

    References

    Scarmeas et al., Neuroimage 23:35-45 (2004)

    Alavi et al., J Nucl Med 34:1681-1687 (1993)

    Asllani et al., JCBFM (AOP Oct 2007)

    Johnson et al. Preclinical prediction of Alzheimer's disease using SPECT. Neurology 50:1563-1571

    Alsop et a., AD paper

    Alsop DC and Detre JA. Radiology 208:410-416 (1998)

    Wang et al., Magn Reson Med 48:242-254 (2002)

    Fig.1: ROI-wise average CBF values for HC, MCI, and AD are shown in bleu, yellow, and red, respectively. Only data from the left hemisphere are shown. Data from the right hemisphere were very similar. Bars represent ± s.d.


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