Adaptive analysis of fMRI data

Friman, Ola; Borga, Magnus; Lundberg, Peter; Knutsson, Hans

doi:10.1016/s1053-8119(03)00077-6

Cited by 189 publications

(184 citation statements)

References 13 publications

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“…methods have become more flexible [15], that multivariate statistical tests have more complicated null distributions than univariate tests [16,17,18] and that more computing power is available [19,20], it may be time for the fMRI community to consider non-parametric statistical methods. A permutation test, for example, does not assume Gaussian data, a constant noise variance or a constant smoothness (and the smoothness does not need to be estimated from the data).…”

Section: Discussionmentioning

confidence: 99%

Empirically investigating the statistical validity of SPM, FSL and AFNI for single subject fMRI analysis

Eklund

Nichols

Andersson

et al. 2015

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

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The software packages SPM, FSL and AFNI are the most widely used packages for the analysis of functional magnetic resonance imaging (fMRI) data. Despite this fact, the validity of the statistical methods has only been tested using simulated data. By analyzing resting state fMRI data (which should not contain specific forms of brain activity) from 396 healthy controls, we here show that all three software packages give inflated false positive rates (4%-96% compared to the expected 5%). We isolate the sources of these problems and find that SPM mainly suffers from a too simple noise model, while FSL underestimates the spatial smoothness. These results highlight the need of validating the statistical methods being used for fMRI.

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Section: Discussionmentioning

confidence: 99%

Empirically investigating the statistical validity of SPM, FSL and AFNI for single subject fMRI analysis

Eklund

Nichols

Andersson

et al. 2015

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

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“…Responses to the experimental paradigms were then detected by time series analysis using a linear model in which each component of the experimental design was convolved separately with two g variate functions (peak responses at 4 and 8 s, respectively) to permit variability in the hemodynamic delay. The method of Friman et al 32 was used to constrain model fits to those deemed physiologically plausible. Following computation of the model fit, a goodness of fit statistic was computed.…”

Section: Discussionmentioning

confidence: 99%

To discard or not to discard: the neural basis of hoarding symptoms in obsessive-compulsive disorder

et al. 2008

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Preliminary neuroimaging studies suggest that patients with the 'compulsive hoarding syndrome' may be a neurobiologically distinct variant of obsessive-compulsive disorder (OCD) but further research is needed. A total of 29 OCD patients (13 with and 16 without prominent hoarding symptoms) and 21 healthy controls of both sexes participated in two functional magnetic resonance imaging experiments consisting of the provocation of hoarding-related and symptom-unrelated (aversive control) anxiety. In response to the hoarding-related (but not symptom-unrelated) anxiety provocation, OCD patients with prominent hoarding symptoms showed greater activation in bilateral anterior ventromedial prefrontal cortex (VMPFC) than patients without hoarding symptoms and healthy controls. In the entire patient group (n = 29), provoked anxiety was positively correlated with activation in a frontolimbic network that included the anterior VMPFC, medial temporal structures, thalamus and sensorimotor cortex. Negative correlations were observed in the left dorsal anterior cingulate gyrus, bilateral temporal cortex, bilateral dorsolateral/medial prefrontal regions, basal ganglia and parieto-occipital regions. These results were independent from the effects of age, sex, level of education, state anxiety, depression, comorbidity and use of medication. The findings are consistent with the animal and lesion literature and several landmark clinical features of compulsive hoarding, particularly decision-making difficulties. Whether the results are generalizable to hoarders who do not meet criteria for OCD remains to be investigated.

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“…The weighted sum of these two convolutions providing the best fit to the time series at each voxel was calculated, and a goodness of fit statistic (SSQ ratio) was computed at each voxel. 27 The SSQ ratio is defined as the quotient between the sum of squares of residuals under constrained model (assuming there is no activation) and the sum of squares of residuals for the complete model. The SSQ ratio distribution under the null hypothesis (of no activation) was obtained by using wavelet-based permutation.…”

Section: Discussionmentioning

confidence: 99%

Cognitive control associated with irritability induction: an autobiographical recall fMRI study

Cerqueira

Almeida

Sato

et al. 2010

Rev. Bras. Psiquiatr.

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Objective: Despite the relevance of irritability emotions to the treatment, prognosis and classification of psychiatric disorders, the neurobiological basis of this emotional state has been rarely investigated to date. We assessed the brain circuitry underlying personal script-driven irritability in healthy subjects (n = 11) using functional magnetic resonance imaging. Method: Blood oxygen level-dependent signal changes were recorded during auditory presentation of personal scripts of irritability in contrast to scripts of happiness or neutral emotional content. Self-rated emotional measurements and skin conductance recordings were also obtained. Images were acquired using a 1,5T magnetic resonance scanner. Brain activation maps were constructed from individual images, and betweencondition differences in the mean power of experimental response were identified by using cluster-wise nonparametric tests. Results: Compared to neutral scripts, increased blood oxygen level-dependent signal during irritability scripts was detected in the left subgenual anterior cingulate cortex, and in the left medial, anterolateral and posterolateral dorsal prefrontal cortex (cluster-wise p-value < 0.05). While the involvement of the subgenual cingulate and dorsal anterolateral prefrontal cortices was unique to the irritability state, increased blood oxygen level-dependent signal in dorsomedial and dorsal posterolateral prefrontal regions were also present during happiness induction. Conclusion: Irritability induction is associated with functional changes in a limited set of brain regions previously implicated in the mediation of emotional states. Changes in prefrontal and cingulate areas may be related to effortful cognitive control aspects that gain salience during the emergence of irritability. Descriptors IntroductionSeveral imaging studies in healthy human subjects using positron emission tomography (PET) or functional magnetic resonance imaging (fMRI) have been conducted during the provocation of specific emotional states, including happiness, sadness, fear, anger, anxiety, guilt or disgust. These studies have demonstrated the involvement of multi-focal brain circuits in emotional processing, frequently involving portions of the frontal and temporal neocortices, anterior cingulate gyrus, medial temporal structures, amygdala, insula and the basal ganglia.

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Adaptive analysis of fMRI data

Cited by 189 publications

References 13 publications

Empirically investigating the statistical validity of SPM, FSL and AFNI for single subject fMRI analysis

Empirically investigating the statistical validity of SPM, FSL and AFNI for single subject fMRI analysis

To discard or not to discard: the neural basis of hoarding symptoms in obsessive-compulsive disorder

Cognitive control associated with irritability induction: an autobiographical recall fMRI study

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