minimization without higher 24)to n Wa Subiects and Tasks.Stanford Univ nth。U the). Center ard.Their ages ranged age aled a larg h mild to mild data ere rmo thy 21.T es) s(5 Data Analysis The follo tially ident thy yo ma nivers R For each the moh the eory() red subj The flashing vere eigh of o type (singl for task-activ 0rks(27.29.1 ha during resting MR at using aithough metho s 3. tim 2.008 time.tic olution ifth the r ise hickn runs f nin eneral Ele hen ed to most .ND15-Ts nner with an asymmetrics pin orks have be det in low an high-frequency signal (0.1 H) 6 were use components .90 ork wa ents ubie struct a series of three to fou core of ninu component in which this differ (the cho time T.2 msec I's time s Data Processing .stanford University data.Data were prepro e critical nod in the default-mode network (20).we used the PCC 463 www.pnas.org/cgi/doi/10.1073/ Greicius et al.University data), allowing us to compare the default-mode network detected with ICA to that detected with the ROI approach. An essentially identical ICA approach was then applied to the three subject groups from Washington University (referred to below as Washington University data). Further details regarding methods are available in the original publica￾tions (20, 21). Subjects and Tasks. Stanford University data. Fourteen healthy right￾handed subjects (seven females) participated in this study after giving informed consent in accordance with Stanford Universi￾ty’s Institutional Review Board. Their ages ranged from 18 to 25 years, with a mean age of 21.2 years. For the resting-state scan, subjects were instructed simply to keep their eyes closed and not to think of anything in particular. The scan lasted for 4 min. Washington University data. Thirteen subjects with very mild to mild AD, 14 healthy elderly subjects, and 14 healthy young subjects were scanned during a simple sensory-motor paradigm (21). The AD subjects (six males) ranged in age from 68 to 83 (mean age 77.2). The elderly subjects (five males) ranged in age from 66 to 89 (mean age 74.9). The 14 healthy young subjects (five male) ranged in age from 18 to 24 (mean age 21.1). The subjects with dementia were scanned at a relatively early stage of their illness: 8 of the 13 AD patients had a Clinical Dementia Rating (CDR) of 0.5, placing them in the ‘‘very mild’’ category, whereas the other five had a CDR score of 1, placing them in the ‘‘mild’’ category (22). The basic task required subjects to respond with a button press when a stimulus was presented. The stimulus, a flashing check￾erboard, was presented for 1.5 sec either singly or in a paired sequential presentation with a 5.36-sec gap between presenta￾tions. Each run, A–D, consisted of 15 trials, pseudorandomly intermixed so that there were eight trials of one type (single or paired presentation) and seven trials of the other type per run. Subjects completed four runs. One AD subject had data only for three of four runs. Imaging Methods. Stanford University data. Functional images were acquired on a 3-T General Electric Signa scanner using a standard whole-head coil. The following spiral pulse sequence parameters were used: repeat time, 2,000 ms; echo time, 30 ms; flip angle, 80°; and one interleave. To aid in the localization of functional data, a high-resolution T1-weighted spoiled grass gradient recalled 3D MRI sequence with the following param￾eters was used: 124 coronal slices; 1.5-mm thickness; no skip; repeat time, 11 ms; echo time, 2 ms; and flip angle, 15°. The images were reconstructed as a 124
256
256 matrix with a 1.5
0.9
0.9-mm spatial resolution. The structural scans were acquired on a 1.5-T General Electric Signa scanner. Washington University data. Functional images were acquired on a Siemens (Iselin, NJ) 1.5-T scanner with an asymmetric spin-echo sequence sensitive to blood oxygenation level-dependent (BOLD) contrast. The following parameters were used: repeat time (TR), 2.68 sec; 3.75
3.75-mm in-plane resolution; T2* evolution time, 50 ms;
, 90°. Whole-brain volumes were ac￾quired with 16 contiguous 8-mm-thick axial oblique slices (par￾allel to the plane connecting the anterior and posterior commi￾sures). Each functional run lasted 5.5 min. High-resolution structural images were acquired in a series of three to four separate T1-weighted MP-RAGE anatomic images with the following parameters: resolution 1
1
1.25 mm; TR, 9.7 msec; echo time, 4 msec; flip angle, 10°; TI, 20 msec; TD, 500 msec. Data Processing. Stanford University data. Data were preprocessed and analyzed by using SPM99 (23) (www.fil.ion.ucl.ac.ukspm). Images were corrected for movement by using least-squares minimization without higher-order corrections for spin history and normalized (24) to stereotaxic coordinates of Talairach and Tournoux (25). Images were then resampled every 2 mm by using sinc interpolation and smoothed with a 4-mm Gaussian kernel to decrease spatial noise. Statistical maps were superimposed on a group average of the normalized high-resolution T1-weighted images and cluster lo￾cations interpreted by using known neuroanatomical landmarks. Washington University data. Raw structural and functional data were received from the fMRI Data Center maintained at Dartmouth College (Hanover, NH). Functional data were then movement-corrected, normalized, and smoothed as described with the Stanford University data above. Examination of the smoothed images revealed a large spike artifact that contami￾nated each time point in one elderly subject’s data. This subject’s data were removed from subsequent analyses. Statistical maps were superimposed on a group average of the normalized high-resolution T1-weighted images and cluster lo￾cations interpreted using known neuroanatomical landmarks. Data Analysis. The following steps were done essentially identi￾cally, except where noted, on data from Stanford University and Washington University. Independent component analysis. For each subject, the smoothed normalized fMRI images were concatenated across time to form a single 4D image. For the Washington University data, the first two time points were eliminated to allow for equilibration of the magnetic field (this is done at the acquisition stage at Stanford University). For each subject, the 4D dataset was then analyzed with FSL MELODIC ICA software (www.fmrib.ox.ac.ukfsl melodic2index.html). ICA is a statistical technique that sepa￾rates a set of signals into independent uncorrelated and non￾Gaussian spatiotemporal components (26). When applied to the T2* signal of fMRI, ICA allows not only for the removal of high￾and low-frequency artifacts (27, 28), but also for isolation of task-activated neural networks (27, 29). Most recently, ICA has been used to identify low-frequency neural networks that are active during resting-state (visual fixation) fMRI data (30). There is no consensus, as yet, on how to choose the optimal number of components, although methods to do so are in development (31). We chose to have the analysis output 24 components for the Stanford University data and 31 components for the Washington University data (approximately one-fourth to one-fifth the number of timepoints in the respective scans). The ICA software could not converge on 31 components in a total of 11 runs from nine subjects (two AD, two elderly, five young). For these runs, the number of components generated ranged from 22 to 42. Selection of the best-fit component. An automated two-step process was then used to select the component in each subject that most closely matched the default-mode network. First, because func￾tional connectivity networks have been detected in low￾frequency ranges (32), a frequency filter was applied to remove any components in which high-frequency signal (0.1 Hz) constituted 50% or more of the total power in the Fourier spectrum. Next, a template of the default-mode network was used to select the ‘‘best-fit’’ of the remaining low-frequency components in each subject. To do this, we developed a non￾linear template-matching procedure that involved taking the average z score of voxels falling within the template minus the average z score of voxels outside the template and selecting the component in which this difference (the goodness of fit) was the greatest. z scores here reflect the degree to which a given voxel’s time series correlates with the time series corresponding to the specific ICA component. A different template was used for selecting the best-fit com￾ponents in the two data sets. Because the PCC appears to be a critical node in the default-mode network (20), we used the PCC 4638 www.pnas.orgcgidoi10.1073pnas.0308627101 Greicius et al.