Alzheimer’s Imaging Consortium Poster Presentations: IC-P lobes or the pre- and post-central gyri. Based on these results, a summary ROI composed of the frontal and temporal lobes but excluding the sensory/motor areas was generated which obtains complete separation between the two groups. Figure 1 shows a boxplot of stiffness. Conclusions: It has been previously reported that global brain stiffness is decreased in subjects with AD [1]. We now report a specific pattern of fronto-temporal stiffness reduction in FTD, expanding the diagnostic utility of this novel biomarker of neurodegenerative disease.References: [1] Murphy et al. JMRI 2011. 34(3): 494.
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MULTIMODAL SUPPORT VECTOR MACHINE FOR AUTOMATED DETECTION OF FUNCTIONAL AND STRUCTURAL DISCONNECTION IN ALZHEIMER’S DISEASE
Martin Dyrba1, Michel Grothe1, Thomas Kirste2, Stefan Teipel3, 1German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany; 2University of Rostock, Rostock, Germany; 3University Medicine Rostock and DZNE Rostock, Rostock, Germany. Contact e-mail:
[email protected] Background: Recent imaging studies showed alterations of the functional connectivity between spatially segregated brain regions in Alzheimer’s disease (AD), which may be related to declining fiber integrity of the underlying white matter (WM) tracts. Based on correlation matrices of local co-activation in resting-state functional MRI (rs-fMRI) data, graph-theoretical analysis allows reconstructing and quantifying the topology of the functional networks of the brain. Diffusion tensor imaging (DTI) data allows in vivo assessment of structural WM fiber tract integrity. We applied machine learning techniques to automatically detect the patterns of functional and structural disconnection in AD. Methods: We used rsfMRI and DTI data acquired from 30 subjects with clinically probable AD and 30 healthy controls (HC). Both groups were matched for age, gender, and years of education, and all subjects were right-handed. All functional scans were time shift corrected, realigned, frequency filtered, and normalized to standard MNI space. A functional atlas was used to parcellate the brain into spatially segregated units. Functional connectivity networks were derived from inter-regional correlations of spontaneous lowfrequency signal fluctuations in the rs-fMRI time series, and graph-theoretical metrics were calculated from the co-activation correlation matrices. In addition, structural connectivity networks were obtained using DTIbased tractography. To this data we applied a multiple kernel Support Vector Machine (SVM) classifier and validated our results using the tenfold cross validation procedure. Results: The results of these analyses will be published at the conference. We expect that network metrics derived from functionally and structurally defined connectivity networks will exhibit overlapping but partly independent properties. Thus, the multimodal classification approach is expected to show superior accuracy for detection of AD when compared to using one modality alone. Conclusions: Combined assessment of rs-fMRI and DTI data allows a more comprehensive characterization of disconnection in AD, which may be exploited as a promising imaging biomarker of the disease. IC-P-179
MYELIN INTEGRITY, COGNITIVE FUNCTION AND HYPERTENSION IN MCI AND ALZHEIMER’S DISEASE
Athene Lee1, Julia Rao2, Steven van Huiden3, Stephen Correia2, Jonathan O’Muircheartaigh4, Sean Deoni4, Stephen Salloway4, Paul Malloy4, 1Alpert Medical School of Brown Univerity, Providence, Rhode Island, United States; 2Alpert Medical School of Brown University, Providence, Rhode Island, United States; 3Vrije Universiteit, 1081 HV Amsterdam, Netherlands; 4Brown University, Providence, Rhode Island, United States. Contact e-mail:
[email protected] Background: Declines in white matter integrity including white matter hyperintensities (WMH), alterations in diffusion-tensor imaging (DTI) metrics , and myelin degradation, have been associated with cognitive decline in aging and Alzheimer’s disease (AD). Recent research shows ele-
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vated blood pressure is associated with disrupted white matter integrity (Salat et al., 2012). The current study examines whether hypertension affects the association between myelin integrity in normal-appearing white matter (NAWM) and cognitive function in patients with mild cognitive impairment (MCI) and AD. Methods: Among 30 participants (aged 55-87) with either MCI or AD, 12 were normotensive and 18 were hypertensive and treated with at least one anti-hypertensive agent. The groups did not differ by age. Participants underwent cognitive testing and brain MRI including DTI and mcDESPOT, a new method of quantifying myelin integrity (Deoni et al., 2008). Myelin integrity is represented as a metric called myelin water fraction (MWF; higher values indicate greater integrity). We calculated 4 DTI metrics: mean, axial, and radial diffusivity, and fractional anisotropy. Cognitive test scores were summarized into attention, processing speed, executive, and memory composite scores. Results: In the whole group, MWF but not DTI metrics in NAWM was significantly correlated with age (r ¼ -0.58, p < 0.01). The hypertensive and normotensive groups did not differ significantly by volume measures (NAWM, WMH, or grey matter volume), white matter or myelin integrity (DTI metrics or MWF), or cognitive performance. In the hypertensive group, there were no significant correlations between cognitive composite scores and either MWF or DTI metrics. In the normotensive group, however, MWF was significantly correlated with attention and processing speed composite measures (all r > 0.64, p < 0.035) but not with either executive or memory composites. The association remained significant after controlling for age. By contrast, DTI metrics did not correlate with any of the composite measures. Conclusions: We found that myelin integrity in NAWM is associated with attention and processing speed in MCI and AD patients among normotensives but not hypertensives. Hypertension in MCI and AD may mask or disrupt associations between myelin integrity and cognitive functions seen in normotensive individuals. IC-P-180
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POSTMORTEM STUDY OF HIPPOCAMPUS SUBFIELDS AND LAYERS AT 7T MR
Mohammad Yazdanie1, Yulin Ge2, Youssef Zaim Wadghiri1, Mony De Leon3, Thomas Wisniewski4, 1New York University, New York, New York, United States; 2NYU Langone Medical Center, New York, New York, United States; 3NYU, New York, New York, United States; 4New York University School of Medicine, New York, New York, United States. Contact e-mail:
[email protected] Background: Atrophy of the hippocampus is a key pathological hallmark of Alzheimer’s disease (AD). An interest of subfields of hippocampal imaging has emerged in recent years due to the advent of ultra-high field MR. This work was to evaluate the imaging parameters on human postmortem brain at 7T MR using 3D susceptibility-sensitivity imaging (SWI) with enhanced tissue susceptibility contrast to better identify these layers and hippocampal subfields that are not available on conventional MR in order to better understand the transition of the hippocampus in AD as disease progresses. Methods: Imaging was performed on a 7.0T Siemens MAGNETOM using a 24-element phased array head coil. Post-mortem brain specimens of the hippocampus were obtained from 3 patients (mean: 72.264.3 years) with clinically diagnosed AD and 4 age-matched healthy controls (71.465.2 years). Coronal brain slices were preserved and fixed in 2% agar for this study. High resolution 3D SWI was obtained with isotropic voxel size 150w320mm. For imaging optimization to better visualize amyloid plaques, we varied TR, TE, BW and flip angle from 30-100ms, 12-36ms, 60-140Hz/ pixel and 10-40 ; respectively. The SWI filtered phase images were used (multiplication factor of 4 w 8) to enhance susceptibility contrast in the SWI images. Results: With optimal SWI parameters TR/TE/FA of 80ms/ at 7T, Figure 1 exemplifies the excellent image contrast for visu20ms/30IS alization of hippocampal layers (Fig A) and subfields (Fig B) in an elderly post-mortem brain without AD, specifically for cell types/layers: (1) Alveus; (2) Stratum Oriens; (3) Stratum Pyramidale; (4) Stratum Radiatum; (5) Stratum Lacunosum; (6) Stratum Moleculare; and for Hippocampal Formation subfields: (1) Hippocampal Head; (2, 2’) Dentate Gyrus, (3, 3’) Cornu Ammonis (CA1), (4) CA2, (5) CA3, (6) Pre-Subiculum/ Subiculum, (7) Para-
