Analysis of Functional Magnetic Resonance Imaging in Python

Millman, K. Jarrod; Brett, Matthew

doi:10.1109/mcse.2007.46

Cited by 74 publications

(60 citation statements)

References 2 publications

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“…Functional images were preprocessed using tools from NiPy (Millman & Brett, 2007), AFNI (Cox, 1996), and the FMRIB software library (Smith et al, 2004). First, large transient spikes in voxel time series were interpolated downward using the AFNI 3dDespike program.…”

Section: Methodsmentioning

confidence: 99%

Modulation of reward-related neural activation on sensation seeking across development

et al. 2017

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Sensation seeking is a personality construct associated with an increased propensity for engaging in risk-taking. Associations with deleterious outcomes ranging from mental health impairments to increased mortality rates highlight important public health concerns related to this construct. Although some have suggested that increased neural responsivity to reward within the ventral striatum (e.g., nucleus accumbens) may drive sensation seeking behaviors, few studies have examined the neural mechanisms associated with stable individual differences in sensation seeking across development. To address this issue, the current study used functional magnetic resonance imaging to examine the association between neural responding to reward and stable patterns of sensation seeking across a three-year follow-up period among healthy adolescents and young adults (N = 139). Results indicated that during early adolescence (~ages 10–12), increased reactivity to reward within the nucleus accumbens (NAcc) was associated with lower levels of sensation seeking across a three-year follow-up. In middle adolescence (~ages 12–16), there was no evidence of a relationship between NAcc reactivity and sensation seeking. However, during the transition from late adolescence into adulthood (~ages 17–25), heightened reward-related reactivity in the NAcc was linked to increased sensation seeking. Findings suggest that the neural mechanisms underlying individual differences in trait-like levels of sensation seeking change from early to late adolescence.

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Section: Methodsmentioning

confidence: 99%

Modulation of reward-related neural activation on sensation seeking across development

et al. 2017

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“…In the future, these different approaches will be more easily accessible so that all researchers can more simply take advantage of the features of many programs without having to apply additional warping when translating from one program to another. Such an effort is underway in the Neuroimaging in Python (NiPy; http://neuroimaging.scipy.org/) project (Millman and Brett, 2007). …”

Section: Solutionsmentioning

confidence: 99%

Group comparisons: imaging the aging brain

Samanez-Larkin

D’Esposito

2008

Social Cognitive and Affective Neuroscience

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With the recent growth of functional magnetic resonance imaging (fMRI), scientists across a range of disciplines are comparing neural activity between groups of interest, such as healthy controls and clinical patients, children and young adults and younger and older adults. In this edition of Tools of the Trade, we will discuss why great caution must be taken when making group comparisons in studies using fMRI. Although many methodological contributions have been made in recent years, the suggestions for overcoming common issues are too often overlooked. This review focuses primarily on neuroimaging studies of healthy aging, but many of the issues raised apply to other group designs as well.

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“…The commonly used statistical approach, massively univariate, considers each voxel independently from each other using regression techniques; see Friston et al (1995) and Bullmore et al (1996). It is available in freeware packages such as FSL (see http://www.fmrib.ox.ac.uk/fsl/ and Smith et al 2004), SPM (see http://www.fil.ion.ucl.ac.uk/spm/ and Friston et al 2007), BrainVISA (see http://brainvisa.info/ and Rivière et al 2009) or NIPY (see http://nipy.sourceforge.net / and Millman and Brett 2007). The time series response at each voxel is modeled as a stationary linear filter where the finite impulse response corresponds to a model of the HRF.…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in theAnalyzeFMRIRPackage

Bordier¹,

Dojat²,

Micheaux³

2011

J. Stat. Soft.

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For statistical analysis of functional magnetic resonance imaging (fMRI) data sets, we propose a data-driven approach based on independent component analysis (ICA) implemented in a new version of the AnalyzeFMRI R package. For fMRI data sets, spatial dimension being much greater than temporal dimension, spatial ICA is the computationally tractable approach generally proposed. However, for some neuroscientific applications, temporal independence of source signals can be assumed and temporal ICA becomes then an attractive exploratory technique. In this work, we use a classical linear algebra result ensuring the tractability of temporal ICA. We report several experiments on synthetic data and real MRI data sets that demonstrate the potential interest of our R package.

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Analysis of Functional Magnetic Resonance Imaging in Python

Cited by 74 publications

References 2 publications

Modulation of reward-related neural activation on sensation seeking across development

Modulation of reward-related neural activation on sensation seeking across development

Group comparisons: imaging the aging brain

Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in theAnalyzeFMRIRPackage

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