Analysis of task-based functional MRI data preprocessed with fMRIPrep

Estéban, Oscar; Ćirić, Rastko; Finc, Karolina; Blair, Ross; Markiewicz, Chris; Moodie, Craig; Kent, James D.; Goncalves, Mathias; DuPré, Elizabeth; Gomez, Daniel E. P.; Ye, Zhifang; Salo, Taylor; Valabrègue, Romain; Amlien, Inge K; Liem, Franziskus; Jacoby, Nir; Stojić, Hrvoje; Cieslak, Matthew; Urchs, Sebastian; Halchenko, Yaroslav O.; Ghosh, Satrajit; Vega, Alejandro de la; Yarkoni, Tal; Wright, Jessey; Thompson, William; Poldrack, Russell A.; Gorgolewski, Krzysztof J.

doi:10.1101/694364

Cited by 31 publications

(29 citation statements)

References 53 publications

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“…However, objective neuroimaging markers in psychiatry have been elusive, and hence the impact of neuroimaging‐based approaches on pragmatic clinical decision‐making in psychiatry is so far extremely limited. Moreover, several methodological issues in the neuroimaging literature have been highlighted in recent years, including small sample sizes and the resulting power failures leading to a “replication crisis” (Button et al, 2013; Masouleh et al, 2019; Poldrack et al, 2017; Szucs & Ioannidis, 2017) as well as the dependency on analysis pipelines (Botvinik‐Nezer et al, 2020) and modest test‐retest reliability (Esteban et al, 2020), which further hinder the potential application of neuroimaging in clinical psychiatry.…”

Section: Introductionmentioning

confidence: 99%

Effect sizes of associations between neuroimaging measures and affective symptoms: A meta‐analysis

Feng

Thompson

Paulus

2021

Depression and Anxiety

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Background: The utility of brain-based biomarkers for psychiatric disorders hinges among other factors on their ability to explain a significant portion of the phenotypic variance. In particular, many small scale studies have been unable to arbitrate whether structural or functional magnetic resonance imaging has potential to be a biological marker for these disorders.Methods: This study conducted a meta-analysis to examine the relationship between study power and published effect sizes for the relationship between affective symptoms and structural or functional magnetic resonance imaging measures. The current analyses are based on 821 brain-affective symptom association effect sizes derived from 120 publications, which employed a univariate region-of-interest approach.Results: For self-assessed affective symptoms published brain imaging measures accounted for on average 8% (confidence interval: 1.6%-23%) of between-subject variation. This average effect size was based mostly on studies with small sample sizes, which have likely led to inflation of these effect size estimates.Conclusions: These findings support the conclusion that brain imaging measures currently account for a smaller proportion of the interindividual variance in affective symptoms than has been previously reported. The current findings support the need for both large-sample clinical studies and new statistical and theoretical models to more robustly capture systematic variance of brain-affective symptom relationships.

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Section: Introductionmentioning

confidence: 99%

Effect sizes of associations between neuroimaging measures and affective symptoms: A meta‐analysis

Feng

Thompson

Paulus

2021

Depression and Anxiety

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“…The majority of the steps involved in preparing and preprocessing the MRI data employed recently developed tools and workflows aimed at enhancing standardization and reproducibility of task-based fMRI studies [for a similar preprocessing pipeline, see 96]. Following successful acquisition, all study data were arranged according to the BIDS specification [97] using the tool (version 0.6.0.dev1; freely available from https://github.com/nipy/heudiconv) running inside a container [98, 99] to facilitate further analysis and sharing of the data.…”

Section: Methodsmentioning

confidence: 99%

Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis

Wittkuhn

2020

Preprint

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Neural computations are often anatomically localized and executed on sub-second time scales. Understanding the brain therefore requires methods that offer sufficient spatial and temporal resolution. This poses a particular challenge for the study of the human brain because non-invasive methods have either high temporal or spatial resolution, but not both. Here, we introduce a novel multivariate analysis method for conventional blood-oxygen-level dependent functional magnetic resonance imaging (BOLD fMRI) that allows to study sequentially activated neural patterns separated by less than 100 ms with anatomical precision. Human participants underwent fMRI and were presented with sequences of visual stimuli separated by 32 to 2048 ms. Probabilistic pattern classifiers were trained on fMRI data to detect the presence of image-specific activation patterns in early visual and ventral temporal cortex. The classifiers were then applied to data recorded during sequences of the same images presented at increasing speeds. Our results show that probabilistic classifier time courses allowed to detect neural representations and their order, even when images were separated by only 32 ms. Moreover, the frequency spectrum of the statistical sequentiality metric distinguished between sequence speeds on sub-second versus supra-second time scales. These results survived when data with high levels of noise and rare sequence events at unknown times were analyzed. Our method promises to lay the groundwork for novel investigations of fast neural computations in the human brain, such as hippocampal replay.

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“…A 3D T1-weighted anatomical scan was acquired for registration purposes, and fMRI data were acquired using a single-shot echo-planar imaging sequence (parameters: TR/TE = 2300/30 ms, resolution = 2.3 × 2.3 × 3 mm, 39 sequential slices, FA = 80 • , dynamics = 70). Preprocessing was performed using FMRIPREP v.1.2.3 (Esteban et al, 2019(Esteban et al, , 2020 (RRID: SCR 016 216). Each T1-weighted (T1w) scan was normalized to MNI space.…”

Section: Fmrimentioning

confidence: 99%

Effects of prolonged methylphenidate treatment on amygdala reactivity and connectivity: a randomized controlled trial in stimulant treatment-naive, male participants with ADHD

et al. 2021

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Background Problems with emotional processing are widely reported in individuals with attention-deficit/hyperactivity disorder (ADHD). Although methylphenidate (MPH) effectively alleviates inattention and hyperactivity symptoms in ADHD, its effects on emotional processing and internalizing symptoms have remained elusive. While we previously found that acute MPH administration modulated neural mechanisms underlying emotional processing in an age-dependent manner, the effects of prolonged administration remained unknown. Objectives Therefore, we investigated: (i) whether prolonged MPH treatment influences neural substrates (amygdala reactivity and connectivity) of emotional processing, and (ii) whether these effects are modulated by age. Methods The “effects of Psychotropic drugs On Developing brain-MPH” (“ePOD-MPH”) randomized controlled trial was a 16-week double-blind, placebo-controlled, multi-center trial with MPH in 50 boys (10–12 years of age) and 49 men (23–40 years of age), all stimulant treatment-naive and diagnosed with ADHD. Participants performed an emotional face-matching task during functional magnetic resonance imaging. We assessed their symptoms of ADHD and internalizing symptoms at baseline, during the trial (8 weeks), and 1 week after the trial end (17 weeks). Results and Conclusions We did not find effects of prolonged MPH treatment on emotional processing, as measured by amygdala reactivity and connectivity and internalizing symptoms in this trial with stimulant treatment-naive participants. This differs from our findings on emotional processing following acute MPH administration and the effects of prolonged MPH treatment on the dopamine system, which were both modulated by age. Interestingly, prolonged MPH treatment did improve ADHD symptoms, although depressive and anxiety symptoms showed a medication-independent decrease. Furthermore, our data indicate that baseline internalizing symptoms may be used to predict MPH treatment effects on ADHD symptoms, particularly in (male) adults with ADHD.

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Analysis of task-based functional MRI data preprocessed with fMRIPrep

Cited by 31 publications

References 53 publications

Effect sizes of associations between neuroimaging measures and affective symptoms: A meta‐analysis

Effect sizes of associations between neuroimaging measures and affective symptoms: A meta‐analysis

Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis

Effects of prolonged methylphenidate treatment on amygdala reactivity and connectivity: a randomized controlled trial in stimulant treatment-naive, male participants with ADHD

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