Cognitive and anatomical data in a healthy cohort of adults

We present data from a sample of 190 healthy adults including assessments of 4 cognitive factor scores, 12 cognitive tests, and 115 MRI-assessed neuroanatomical variables (cortical thicknesses, cortical and sub-cortical volumes, fractional anisotropy, and radial diffusivity). These data were used in estimating underlying sources of individual variation via independent component analysis (Watson et al., In press) [25].


Subject area
Neuroscience More specific subject area

Anatomical Neuroimaging
Type of data Brief description of any pretreatment of samples

Experimental features
Multi-modal MRI collection prior to a large cognitive training intervention.

Value of the data
These data characterize individual variation across demographic, neuroanatomical, and cognitive factors.
These provide a useful model of individual variation that can be used to control for individual differences.
The relationship between these data and other neuroimaging (such as resting state) and cognitive data remains unexplored and would be a fruitful area of collaboration.
These data can be used to estimate patterns of joint variance across and within different neuroimaging and behavioral methods.
These patterns can be used to test specific cognitive-anatomical linkages.

Data
The data (Supplementary Table 1) includes cognitive and anatomical variables collected prior to a large, multi-modal cognitive training study [25]. They include: a) Demographic measures (i.e., age, sex, and education). b) Cardiovascular fitness measures. c) 4 cognitive factors estimated via structural equation modeling [15]. d) Scores from the battery of 12 cognitive tests used to estimate these factors. e) 35 cortical thickness estimates and volume estimates for these same regions. f) 11 sub-cortical volumetric estimates. g) Total brain and total intracranial volume estimates. h) 7 estimates of ventricular size. i) 5 estimates of corpus callosum. j) 12 estimates of fractional anisotropy and in matter tracts. k) 12 estimates of radial diffusivity in white matter tracts.

Demographics
The 190 participants consisted of 85 females, and 105 males. The age range in our sample was 18-44 years, with a median of 22 years, and a mean of 24.3 years. The mean educational level of the participants was "some college" (i.e., median score 3, mean score 3.6) as reported on a scale from 1 to 5, where 1 denoted "less than a high school diploma", 2 denoted "high school diploma or equivalent", 3 denoted "some college", 4 denoted "college degree", and 5 denoted "post-graduate education."

Aerobic fitness assessment
Maximal oxygen consumption (VO 2max ) was measured using a computerized indirect calorimetry system (ParvoMedics True Max 2400) and a modified Balke protocol [1] with averages for oxygen uptake (VO2) and respiratory exchange ratio (RER) assessed every 20 s. Participants ran on a motordriven treadmill at a constant speed, with 2.0% increases in grade every two minutes until volitional exhaustion. The raw value was adjusted for body size, age, and gender to produce a VO 2max percentile score.

Cognitive tests and factor scores
Participants received a battery of 12 cognitive tests designed to estimate underlying latent variables corresponding to cognitive constructs (see Table 1). The four latent variables of interest were fluid intelligence (gf), working memory (wm), executive function (ef), and episodic memory (em). Each of these latent variables was measured with three cognitive tests as follows. Fluid intelligence (gf) was measured by the BOMAT, number series, and letter sets tests [3,4,7]. Working memory (wm) was measured by the reading, rotation, and symmetry span tests [8,23]. Executive function (ef) was measured by the Garavan, Keep Track, and Stroop tests [14,22,26]. Episodic memory (em) was measured by immediate free recall, words, pictures and paired associates tests [23,24,9]. Using a structural equation modeling approach [15], across the larger sample of 518 participants, we extracted estimates of the four cognitive construct latent variables (i.e., gf, wm, ef, em). Because Garavan and Stroop produce error scores, while all others are measures of accuracy, we inverted these two values (i.e., multiplied by À 1) in order to ensure all cognitive variables had the same sign.

Structural MRI protocol
High resolution T1-weighted brain images were acquired using a 3D MPRAGE (Magnetization Prepared Rapid Gradient Echo Imaging) protocol with 192 contiguous axial slices, collected in ascending fashion parallel to the anterior and posterior commissures, echo time (TE) ¼2.32 ms, repetition time (TR) ¼ 1900 ms, field of view (FOV) ¼230 mm, acquisition matrix 256 mm Â 256 mm,

Automated volumetrics, cortical thickness estimates, and white-matter tractography
Automated brain tissue segmentation and reconstruction of the T1-weighted structural MRI images were performed using the standard recon-all processing pipeline in FreeSurfer, version 5.2.0 (Released May, 2013; http://surfer-nmr.mgh.harvard.edu/). This produced estimates of 1) cortical thickness, 2) cortical volumes, 3) sub-cortical volumes, 4) ventricles, and 5) corpus callosum [5,6,[10][11][12][13]. Segmentations and tractography were manually checked for errors. Estimates in the left and right hemispheres were summed to produce bilateral estimates, and all values were converted to z-scores to control for differences in scale. A complete list of estimated structures appears in Table 1. Free-Surfer produced automated segmentation that closely approximates hand tracing, but like all segmentation procedures may introduce systematic bias.
The diffusion tensor imaging estimates for fractional anisotropy (FA) and radial diffusivity (RD) data was analyzed using tract-based spatial statistics in FSL [19][20][21]. This pipeline involves fitting a tensor model to the raw diffusion data using fMRIDB's diffusion toolbox, and non-brain tissues were removed using FSL's brain extraction tool. All subjects' FA data were then aligned into a common space using the nonlinear registration tool FNIRT [18,2]. Next, the mean FA image was created and thinned to create a mean FA skeleton that represents the centers of all tracts common to the group. Each subject's aligned FA data was then projected onto this skeleton to create an estimate of the subject-level value associated with each tract.