qap.bib

% 16598643 
@Article{magnotta2006,
   Author="Magnotta, V. A.  and Friedman, L. ",
   Title="{{M}easurement of {S}ignal-to-{N}oise and {C}ontrast-to-{N}oise in the f{B}{I}{R}{N} {M}ulticenter {I}maging {S}tudy}",
   Journal="J Digit Imaging",
   Year="2006",
   Volume="19",
   Number="2",
   Pages="140--147",
   Month="Jun",
   Abstract={The ability to analyze and merge data across sites, vendors, and field strengths depends on one's ability to acquire images with the same image quality including image smoothness, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). SNR can be used to compare different magnetic resonance scanners as a measure of comparability between the systems. This study looks at the SNR and CNR ratios in structural fast spin-echo T2-weighted scans acquired in five individuals across ten sites that are part of Functional Imaging Research of Schizophrenia Testbed Biomedical Informatics Research Network (fBIRN). Different manufacturers, field strengths, gradient coils, and RF coils were used at these sites. The SNR of gray matter was fairly uniform (41.3-43.3) across scanners at 1.5 T. The higher field scanners produced images with significantly higher SNR values (44.5-108.7 at 3 T and 50.8 at 4 T). Similar results were obtained for CNR measurements between gray/white matter at 1.5 T (9.5-10.2), again increasing at higher fields (10.1-28.9 at 3 T and 10.9 at 4 T).}
}

% 9533590 
@Article{atkinson1997,
   Author="Atkinson, D.  and Hill, D. L.  and Stoyle, P. N.  and Summers, P. E.  and Keevil, S. F. ",
   Title="{{A}utomatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion}",
   Journal="IEEE Trans Med Imaging",
   Year="1997",
   Volume="16",
   Number="6",
   Pages="903--910",
   Month="Dec",
   Abstract={We present the use of an entropy focus criterion to enable automatic focusing of motion corrupted magnetic resonance images. We demonstrate the principle using illustrative examples from cooperative volunteers. Our technique can determine unknown patient motion or use knowledge of motion from other measures as a starting estimate. The motion estimate is used to compensate the acquired data and is iteratively refined using the image entropy. Entropy focuses the whole image principally by favoring the removal of motion induced ghosts and blurring from otherwise dark regions of the image. Using only the image data, and no special hardware or pulse sequences, we demonstrate correction for arbitrary rigid-body translational motion in the imaging plane and for a single rotation. Extension to three-dimensional (3-D) and more general motion should be possible. The algorithm is able to determine volunteer motion well. The mean absolute deviation between algorithm and navigator-echo-determined motion is comparable to the displacement step size used in the algorithm. Local deviations from the recorded motion or navigator-determined motion are explained and we indicate how enhanced focus criteria may be derived. In all cases we were able to compensate images for patient motion, reducing blurring and ghosting.}
}

@Article{Friedman2006,
   Author="Friedman, L.  and Glover, G. H. ",
   Title="{{R}educing interscanner variability of activation in a multicenter f{M}{R}{I} study: controlling for signal-to-fluctuation-noise-ratio ({S}{F}{N}{R}) differences}",
   Journal="Neuroimage",
   Year="2006",
   Volume="33",
   Number="2",
   Pages="471--481",
   Month="Nov"
}

% 17636563 
@Article{friedman2008,
   Author="Friedman, L.  and Stern, H.  and Brown, G. G.  and Mathalon, D. H.  and Turner, J.  and Glover, G. H.  and Gollub, R. L.  and Lauriello, J.  and Lim, K. O.  and Cannon, T.  and Greve, D. N.  and Bockholt, H. J.  and Belger, A.  and Mueller, B.  and Doty, M. J.  and He, J.  and Wells, W.  and Smyth, P.  and Pieper, S.  and Kim, S.  and Kubicki, M.  and Vangel, M.  and Potkin, S. G. ",
   Title="{{T}est-retest and between-site reliability in a multicenter f{M}{R}{I} study}",
   Journal="Hum Brain Mapp",
   Year="2008",
   Volume="29",
   Number="8",
   Pages="958--972",
   Month="Aug",
   Abstract={In the present report, estimates of test-retest and between-site reliability of fMRI assessments were produced in the context of a multicenter fMRI reliability study (FBIRN Phase 1, www.nbirn.net). Five subjects were scanned on 10 MRI scanners on two occasions. The fMRI task was a simple block design sensorimotor task. The impulse response functions to the stimulation block were derived using an FIR-deconvolution analysis with FMRISTAT. Six functionally-derived ROIs covering the visual, auditory and motor cortices, created from a prior analysis, were used. Two dependent variables were compared: percent signal change and contrast-to-noise-ratio. Reliability was assessed with intraclass correlation coefficients derived from a variance components analysis. Test-retest reliability was high, but initially, between-site reliability was low, indicating a strong contribution from site and site-by-subject variance. However, a number of factors that can markedly improve between-site reliability were uncovered, including increasing the size of the ROIs, adjusting for smoothness differences, and inclusion of additional runs. By employing multiple steps, between-site reliability for 3T scanners was increased by 123\%. Dropping one site at a time and assessing reliability can be a useful method of assessing the sensitivity of the results to particular sites. These findings should provide guidance toothers on the best practices for future multicenter studies.}
}

% 19526493 
@Article{mortamet2009,
   Author="Mortamet, B.  and Bernstein, M. A.  and Jack, C. R.  and Gunter, J. L.  and Ward, C.  and Britson, P. J.  and Meuli, R.  and Thiran, J. P.  and Krueger, G. ",
   Title="{{A}utomatic quality assessment in structural brain magnetic resonance imaging}",
   Journal="Magn Reson Med",
   Year="2009",
   Volume="62",
   Number="2",
   Pages="365--372",
   Month="Aug",
   Abstract={MRI has evolved into an important diagnostic technique in medical imaging. However, reliability of the derived diagnosis can be degraded by artifacts, which challenge both radiologists and automatic computer-aided diagnosis. This work proposes a fully-automatic method for measuring image quality of three-dimensional (3D) structural MRI. Quality measures are derived by analyzing the air background of magnitude images and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring, and ghosting. The method has been validated on 749 3D T(1)-weighted 1.5T and 3T head scans acquired at 36 Alzheimer's Disease Neuroimaging Initiative (ADNI) study sites operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the ADNI quality control center (taken as the reference standard). The derived quality indices are independent of the MRI system used and agree with the reference standard quality ratings with high sensitivity and specificity ($\geq85\%$). The proposed procedures for quality assessment could be of great value for both research and routine clinical imaging. It could greatly improve workflow through its ability to rule out the need for a repeat scan while the patient is still in the magnet bore.}
}

% 22019881 
@Article{power2012,
   Author="Power, J. D.  and Barnes, K. A.  and Snyder, A. Z.  and Schlaggar, B. L.  and Petersen, S. E. ",
   Title="{{S}purious but systematic correlations in functional connectivity {M}{R}{I} networks arise from subject motion}",
   Journal="Neuroimage",
   Year="2012",
   Volume="59",
   Number="3",
   Pages="2142--2154",
   Month="Feb",
   Abstract={Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.}
}

% 8812068 
@Article{cox1996,
   Author="Cox, R. W. ",
   Title="{{A}{F}{N}{I}: software for analysis and visualization of functional magnetic resonance neuroimages}",
   Journal="Comput. Biomed. Res.",
   Year="1996",
   Volume="29",
   Number="3",
   Pages="162--173",
   Month="Jun",
   Abstract={A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described. The software can color overlay neural activation maps onto higher resolution anatomical scans. Slices in each cardinal plane can be viewed simultaneously. Manual placement of markers on anatomical landmarks allows transformation of anatomical and functional scans into stereotaxic (Talairach-Tournoux) coordinates. The techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described. Facilities are provided for several types of statistical analyses of multiple 3D functional data sets. The programs are written in ANSI C and Motif 1.2 to run on Unix workstations.}
}

% 12377157 
@Article{jenkinson2002,
   Author="Jenkinson, M.  and Bannister, P.  and Brady, M.  and Smith, S. ",
   Title="{{I}mproved optimization for the robust and accurate linear registration and motion correction of brain images}",
   Journal="Neuroimage",
   Year="2002",
   Volume="17",
   Number="2",
   Pages="825--841",
   Month="Oct",
   Abstract={Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.}
}

% 23499792 
@Article{yan2013,
   Author="Yan, C. G.  and Cheung, B.  and Kelly, C.  and Colcombe, S.  and Craddock, R. C.  and Di Martino, A.  and Li, Q.  and Zuo, X. N.  and Castellanos, F. X.  and Milham, M. P. ",
   Title="{{A} comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics}",
   Journal="Neuroimage",
   Year="2013",
   Volume="76",
   Pages="183--201",
   Month="Aug",
   Abstract={Functional connectomics is one of the most rapidly expanding areas of neuroimaging research. Yet, concerns remain regarding the use of resting-state fMRI (R-fMRI) to characterize inter-individual variation in the functional connectome. In particular, recent findings that "micro" head movements can introduce artifactual inter-individual and group-related differences in R-fMRI metrics have raised concerns. Here, we first build on prior demonstrations of regional variation in the magnitude of framewise displacements associated with a given head movement, by providing a comprehensive voxel-based examination of the impact of motion on the BOLD signal (i.e., motion-BOLD relationships). Positive motion-BOLD relationships were detected in primary and supplementary motor areas, particularly in low motion datasets. Negative motion-BOLD relationships were most prominent in prefrontal regions, and expanded throughout the brain in high motion datasets (e.g., children). Scrubbing of volumes with FD>0.2 effectively removed negative but not positive correlations; these findings suggest that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact. We also examined the ability of motion correction strategies to eliminate artifactual differences related to motion among individuals and between groups for a broad array of voxel-wise R-fMRI metrics. Residual relationships between motion and the examined R-fMRI metrics remained for all correction approaches, underscoring the need to covary motion effects at the group-level. Notably, global signal regression reduced relationships between motion and inter-individual differences in correlation-based R-fMRI metrics; Z-standardization (mean-centering and variance normalization) of subject-level maps for R-fMRI metrics prior to group-level analyses demonstrated similar advantages. Finally, our test-retest (TRT) analyses revealed significant motion effects on TRT reliability for R-fMRI metrics. Generally, motion compromised reliability of R-fMRI metrics, with the exception of those based on frequency characteristics - particularly, amplitude of low frequency fluctuations (ALFF). The implications of our findings for decision-making regarding the assessment and correction of motion are discussed, as are insights into potential differences among volume-based metrics of motion.}
}

% 23705677 
@Article{saad2013,
   Author="Saad, Z. S.  and Reynolds, R. C.  and Jo, H. J.  and Gotts, S. J.  and Chen, G.  and Martin, A.  and Cox, R. W. ",
   Title="{{C}orrecting brain-wide correlation differences in resting-state {F}{M}{R}{I}}",
   Journal="Brain Connect",
   Year="2013",
   Volume="3",
   Number="4",
   Pages="339--352",
   Abstract={Brain function in "resting" state has been extensively studied with functional magnetic resonance imaging (FMRI). However, drawing valid inferences, particularly for group comparisons, is fraught with pitfalls. Differing levels of brain-wide correlations can confound group comparisons. Global signal regression (GSReg) attempts to reduce this confound and is commonly used, even though it differentially biases correlations over brain regions, potentially leading to false group differences. We propose to use average brain-wide correlations as a measure of global correlation (GCOR), and examine the circumstances under which it can be used to identify or correct for differences in global fluctuations. In the process, we show the bias induced by GSReg to be a function only of the data's covariance matrix, and use simulations to compare corrections with GCOR as covariate to GSReg under various scenarios. We find that unlike GSReg, GCOR is a conservative approach that can reduce global variations, while avoiding the introduction of false significant differences, as GSReg can. However, as with GSReg, one cannot escape the interaction effect between the grouping variable and GCOR covariate on effect size. While GCOR is a complementary measure for resting state-FMRI applicable to legacy data, it is a lesser substitute for proper level-I denoising. We also assess the applicability of GCOR to empirical data with motion-based subject grouping and compare group differences to those using GSReg. We find that, while GCOR reduced correlation differences between high and low movers, it is doubtful that motion was the sole driver behind the differences in the first place.}
}

% 23774715 
@Article{dimartino2014,
   Author="Di Martino, A.  and Yan, C. G.  and Li, Q.  and Denio, E.  and Castellanos, F. X.  and Alaerts, K.  and Anderson, J. S.  and Assaf, M.  and Bookheimer, S. Y.  and Dapretto, M.  and Deen, B.  and Delmonte, S.  and Dinstein, I.  and Ertl-Wagner, B.  and Fair, D. A.  and Gallagher, L.  and Kennedy, D. P.  and Keown, C. L.  and Keysers, C.  and Lainhart, J. E.  and Lord, C.  and Luna, B.  and Menon, V.  and Minshew, N. J.  and Monk, C. S.  and Mueller, S.  and Muller, R. A.  and Nebel, M. B.  and Nigg, J. T.  and O'Hearn, K.  and Pelphrey, K. A.  and Peltier, S. J.  and Rudie, J. D.  and Sunaert, S.  and Thioux, M.  and Tyszka, J. M.  and Uddin, L. Q.  and Verhoeven, J. S.  and Wenderoth, N.  and Wiggins, J. L.  and Mostofsky, S. H.  and Milham, M. P. ",
   Title="{{T}he autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism}",
   Journal="Mol. Psychiatry",
   Year="2014",
   Volume="19",
   Number="6",
   Pages="659--667",
   Month="Jun",
   Abstract={Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.}
}

% 25977800 
@Article{zuo2014,
   Author="Zuo, X. N.  and Anderson, J. S.  and Bellec, P.  and Birn, R. M.  and Biswal, B. B.  and Blautzik, J.  and Breitner, J. C.  and Buckner, R. L.  and Calhoun, V. D.  and Castellanos, F. X.  and Chen, A.  and Chen, B.  and Chen, J.  and Chen, X.  and Colcombe, S. J.  and Courtney, W.  and Craddock, R. C.  and Di Martino, A.  and Dong, H. M.  and Fu, X.  and Gong, Q.  and Gorgolewski, K. J.  and Han, Y.  and He, Y.  and He, Y.  and Ho, E.  and Holmes, A.  and Hou, X. H.  and Huckins, J.  and Jiang, T.  and Jiang, Y.  and Kelley, W.  and Kelly, C.  and King, M.  and LaConte, S. M.  and Lainhart, J. E.  and Lei, X.  and Li, H. J.  and Li, K.  and Li, K.  and Lin, Q.  and Liu, D.  and Liu, J.  and Liu, X.  and Liu, Y.  and Lu, G.  and Lu, J.  and Luna, B.  and Luo, J.  and Lurie, D.  and Mao, Y.  and Margulies, D. S.  and Mayer, A. R.  and Meindl, T.  and Meyerand, M. E.  and Nan, W.  and Nielsen, J. A.  and O'Connor, D.  and Paulsen, D.  and Prabhakaran, V.  and Qi, Z.  and Qiu, J.  and Shao, C.  and Shehzad, Z.  and Tang, W.  and Villringer, A.  and Wang, H.  and Wang, K.  and Wei, D.  and Wei, G. X.  and Weng, X. C.  and Wu, X.  and Xu, T.  and Yang, N.  and Yang, Z.  and Zang, Y. F.  and Zhang, L.  and Zhang, Q.  and Zhang, Z.  and Zhang, Z.  and Zhao, K.  and Zhen, Z.  and Zhou, Y.  and Zhu, X. T.  and Milham, M. P. ",
   Title="{{A}n open science resource for establishing reliability and reproducibility in functional connectomics}",
   Journal="Sci Data",
   Year="2014",
   Volume="1",
   Pages="140049",
   Abstract={Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals' resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.}
}

% 21897815 
@Article{gorgolewski2011,
   Author="Gorgolewski, K.  and Burns, C. D.  and Madison, C.  and Clark, D.  and Halchenko, Y. O.  and Waskom, M. L.  and Ghosh, S. S. ",
   Title="{{N}ipype: a flexible, lightweight and extensible neuroimaging data processing framework in python}",
   Journal="Front Neuroinform",
   Year="2011",
   Volume="5",
   Pages="13",
   Abstract={Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.}
}

% 21081879 
@Article{giannelli2010,
   Author="Giannelli, M.  and Diciotti, S.  and Tessa, C.  and Mascalchi, M. ",
   Title="{{C}haracterization of {N}yquist ghost in {E}{P}{I}-f{M}{R}{I} acquisition sequences implemented on two clinical 1.5 {T} {M}{R} scanner systems: effect of readout bandwidth and echo spacing}",
   Journal="J Appl Clin Med Phys",
   Year="2010",
   Volume="11",
   Number="4",
   Pages="3237",
   Abstract={In EPI-fMRI acquisitions, various readout bandwidth (BW) values are used as a function of gradients' characteristics of the MR scanner system. Echo spacing (ES) is another fundamental parameter of EPI-fMRI sequences, but the employed ES value is not usually reported in fMRI studies. Nyquist ghost is a typical EPI artifact that can degrade the overall quality of fMRI time series. In this work, the authors assessed the basic effect of BW and ES for two clinical 1.5 T MR scanner systems (scanner-A, scanner-B) on Nyquist ghost of gradient-echo EPI-fMRI sequences. BW range was: scanner-A, 1953-3906 Hz/pixel; scanner-B, 1220-2894 Hz/pixel. ES range was: scanner-A, scanner-B: 0.75-1.33 ms. The ghost-to-signal ratio of time series acquisition (GSRts) and drift of ghost-to-signal ratio (DRGSR) were measured in a water phantom. For both scanner-A (93\% of variation) and scanner-B (102\% of variation) the mean GSRts significantly increased with increasing BW. GSRts values of scanner-A did not significantly depended on ES. On the other hand, GSRts values of scanner-B significantly varied with ES, showing a downward trend (81\% of variation) with increasing ES. In addition, a GSRts spike point at ES = 1.05 ms indicating a potential resonant effect was revealed. For both scanners, no significant effect of ES on DRGSR was revealed. DRGSR values of scanner-B did not significantly vary with BW, whereas DRGSR values of scanner-A significantly depended on BW showing an upward trend from negative to positive values with increasing BW. GSRts and DRGSR can significantly vary with BW and ES, and the specific pattern of variation may depend on gradients performances, EPI sequence calibrations and functional design of radiofrequency coil. Thus, each MR scanner system should be separately characterized. In general, the employment of low BW values seems to reduce the intensity and temporal variation of Nyquist ghost in EPI-fMRI time series. On the other hand, the use of minimum ES value might not be entirely advantageous when the MR scanner is characterized by gradients with low performances and suboptimal EPI sequence calibration.}
}

%18839484 
@Article{Shrout79,
   Author="Shrout, PE.  and Fleiss, JL.",
   Title="{{I}ntraclass correlations: uses in assessing rater reliability}",
   Journal="Psychol Bull",
   Year="1979",
   Volume="86",
   Pages="420-428"
}

@Article{VanEssen2012,
   Author="Van Essen, D. C.  and Ugurbil, K. ",
   Title="{{T}he future of the human connectome}",
   Journal="Neuroimage",
   Year="2012",
   Volume="62",
   Number="2",
   Pages="1299--1310",
   Month="Aug"
}

@Article{Glasser2013,
   Author="Glasser, M. F.  and Sotiropoulos, S. N.  and Wilson, J. A.  and Coalson, T. S.  and Fischl, B.  and Andersson, J. L.  and Xu, J.  and Jbabdi, S.  and Webster, M.  and Polimeni, J. R.  and Van Essen, D. C.  and Jenkinson, M. ",
   Title="{{T}he minimal preprocessing pipelines for the {H}uman {C}onnectome {P}roject}",
   Journal="Neuroimage",
   Year="2013",
   Volume="80",
   Pages="105--124",
   Month="Oct"
}

@MISC{Nichols2013,
   author =       {Nichols, Thomas},
   title =        {Notes on Creating a Standardized Version of {D}{V}{A}{R}{S}},
   month =        {September},
   year =         {2013},
   url = {\url{http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/scripts/fsl/standardizeddvars.pdf}},
   note =         {[\url{http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/scripts/fsl/standardizeddvars.pdf}; last viewed Dec 4, 2015]},
 }


@Article{smith2004,
   Author="Smith, S. M.  and Jenkinson, M.  and Woolrich, M. W.  and Beckmann, C. F.  and Behrens, T. E.  and Johansen-Berg, H.  and Bannister, P. R.  and De Luca, M.  and Drobnjak, I.  and Flitney, D. E.  and Niazy, R. K.  and Saunders, J.  and Vickers, J.  and Zhang, Y.  and De Stefano, N.  and Brady, J. M.  and Matthews, P. M. ",
   Title="{{A}dvances in functional and structural {M}{R} image analysis and implementation as {F}{S}{L}}",
   Journal="Neuroimage",
   Year="2004",
   Volume="23 Suppl 1",
   Pages="S208--219"
}

@techreport{Jenkinson99FD,
  title={Measuring transformation error by RMS deviation.},
  author={M. Jenkinson},
  year={1999},
  institution={Oxford Centre for Functional Magnetic Resonance Imaging of the Brain},
  type={Internal Technical Report {TR99MJ1}}
}

@article{VanDijk2012,
title = "The influence of head motion on intrinsic functional connectivity \{MRI\} ",
journal = "NeuroImage ",
volume = "59",
number = "1",
pages = "431 - 438",
year = "2012",
note = "Neuroergonomics: The human brain in action and at work ",
issn = "1053-8119",
doi = "http://dx.doi.org/10.1016/j.neuroimage.2011.07.044",
url = "http://www.sciencedirect.com/science/article/pii/S1053811911008214",
author = "Koene R.A. Van Dijk and Mert R. Sabuncu and Randy L. Buckner",
keywords = "Functional MRI",
keywords = "BOLD",
keywords = "Connectome",
keywords = "Resting-state",
keywords = "Movement ",
abstract = "Functional connectivity \{MRI\} (fcMRI) has been widely applied to explore group and individual differences. A confounding factor is head motion. Children move more than adults, older adults more than younger adults, and patients more than controls. Head motion varies considerably among individuals within the same population. Here we explored the influence of head motion on fcMRI estimates. Mean head displacement, maximum head displacement, the number of micro movements (> 0.1 mm), and head rotation were estimated in 1000 healthy, young adult subjects each scanned for two resting-state runs on matched 3T scanners. The majority of fcMRI variation across subjects was not linked to head motion. However, head motion had significant, systematic effects on fcMRI network measures. Head motion was associated with decreased functional coupling in the default and frontoparietal control networks — two networks characterized by coupling among distributed regions of association cortex. Other network measures increased with motion including estimates of local functional coupling and coupling between left and right motor regions — a region pair sometimes used as a control in studies to establish specificity. Comparisons between groups of individuals with subtly different levels of head motion yielded difference maps that could be mistaken for neuronal effects in other contexts. These effects are important to consider when interpreting variation between groups and across individuals. "
}

@inproceedings{Gentzsch2001,
 author = {Gentzsch, Wolfgang},
 title = {Sun Grid Engine: Towards Creating a Compute Power Grid},
 booktitle = {Proceedings of the 1st International Symposium on Cluster Computing and the Grid},
 series = {CCGRID '01},
 year = {2001},
 isbn = {0-7695-1010-8},
 pages = {35--},
 url = {http://dl.acm.org/citation.cfm?id=560889.792378},
 acmid = {792378},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
} 

@article{condor2005, 
  author    = "Douglas Thain and Todd Tannenbaum and Miron Livny",
  title     = "Distributed computing in practice: the Condor experience.",
  journal   = "Concurrency - Practice and Experience",
  volume    = "17",
  number    = "2-4",
  year      = "2005",
  pages     = "323-356",
}

@incollection{pbs2001,
 author = {Jones, James Patton},
 chapter = {PBS: Portable Batch System},
 title = {Beowulf Cluster Computing with Windows},
 editor = {Sterling, Thomas},
 year = {2002},
 isbn = {0-262-69275-9},
 pages = {363--383},
 numpages = {21},
 url = {http://dl.acm.org/citation.cfm?id=571095.571114},
 acmid = {571114},
 publisher = {MIT Press},
 address = {Cambridge, MA, USA},
} 

@INPROCEEDINGS{slurm2002,
    author = {Morris A. Jette and Andy B. Yoo and Mark Grondona},
    title = {SLURM: Simple Linux Utility for Resource Management},
    booktitle = {In Lecture Notes in Computer Science: Proceedings of Job Scheduling Strategies for Parallel Processing (JSSPP) 2003},
    year = {2002},
    pages = {44--60},
    publisher = {Springer-Verlag}
}

@INPROCEEDINGS{nifti2004,
    author = {Robert W Cox and John Ashburner and Hester Breman and Kate Fissell and Christian Haselgrove and Colin J Holmes and Jack L Lancaster and David E Rex and Stephen M Smith and Jeffrey B Woodward and Stephen C Strother},
    title = {A (Sort of) New Image Data Format Standard: {N}{I}{f}{T}{I}-1.},
    booktitle = {Proceedings of the 10th Annual Meeting of Organisation of Human Brain Mapping (2004)},
    year = {2004},
     address = {Budapest, Hungary}
}


@article{gorgolewski_brain_2016,
	title = {The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments},
	volume = {3},
	issn = {2052-4463},
	url = {http://www.nature.com/articles/sdata201644},
	doi = {10.1038/sdata.2016.44},
	urldate = {2016-08-11},
	journal = {Scientific Data},
	author = {Gorgolewski, Krzysztof J. and Auer, Tibor and Calhoun, Vince D. and Craddock, R. Cameron and Das, Samir and Duff, Eugene P. and Flandin, Guillaume and Ghosh, Satrajit S. and Glatard, Tristan and Halchenko, Yaroslav O. and Handwerker, Daniel A. and Hanke, Michael and Keator, David and Li, Xiangrui and Michael, Zachary and Maumet, Camille and Nichols, B. Nolan and Nichols, Thomas E. and Pellman, John and Poline, Jean-Baptiste and Rokem, Ariel and Schaefer, Gunnar and Sochat, Vanessa and Triplett, William and Turner, Jessica A. and Varoquaux, Gaël and Poldrack, Russell A.},
	month = jun,
	year = {2016},
	pages = {160044}
}

@misc{gorgolewski_2016_50186,
  author       = {Gorgolewski, Krzysztof J. and
                  Esteban, Oscar and
                  Burns, Christopher and
                  Ziegler, Erik and
                  Pinsard, Basile and
                  Madison, Cindee and
                  Waskom, Michael and
                  Ellis, David Gage and
                  Clark, Dav and
                  Dayan, Michael and
                  Manhães-Savio, Alexandre and
                  Notter, Michael Philipp and
                  Johnson, Hans and
                  Dewey, Blake E and
                  Halchenko, Yaroslav O. and
                  Hamalainen, Carlo and
                  Keshavan, Anisha and
                  Clark, Daniel and
                  Huntenburg, Julia M. and
                  Hanke, Michael and
                  Nichols, B. Nolan and
                  Wassermann , Demian and
                  Eshaghi, Arman and
                  Markiewicz, Christopher and
                  Varoquaux, Gael and
                  Acland, Benjamin and
                  Forbes, Jessica and
                  Rokem, Ariel and
                  Kong, Xiang-Zhen and
                  Gramfort, Alexandre and
                  Kleesiek, Jens and
                  Schaefer, Alexander and
                  Sikka, Sharad and
                  Perez-Guevara, Martin Felipe and
                  Glatard, Tristan and
                  Iqbal, Shariq and
                  Liu, Siqi and
                  Welch, David and
                  Sharp, Paul and
                  Warner, Joshua and
                  Kastman, Erik and
                  Lampe, Leonie and
                  Perkins, L. Nathan and
                  Craddock, R. Cameron and
                  Küttner, René and
                  Bielievtsov, Dmytro and
                  Geisler, Daniel and
                  Gerhard, Stephan and
                  Liem, Franziskus and
                  Linkersdörfer, Janosch and
                  Margulies, Daniel S. and
                  Andberg, Sami Kristian and
                  Stadler, Jörg and
                  Steele, Christopher John and
                  Broderick, William and
                  Cooper, Gavin and
                  Floren, Andrew and
                  Huang, Lijie and
                  Gonzalez, Ivan and
                  McNamee, Daniel and
                  Papadopoulos Orfanos, Dimitri and
                  Pellman, John and
                  Triplett, William and
                  Ghosh, Satrajit},
  title        = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in {Python}},
  month        = apr,
  year         = 2016,
  doi          = {10.5281/zenodo.50186},
  url          = {http://dx.doi.org/10.5281/zenodo.50186}
}