Current methods and limitations for longitudinal fMRI analysis across development

Madhyastha, Tara M.; Peverill, Matthew; Koh, Natalie; McCabe, Connor; Flournoy, John C.; Mills, Kate; King, Kevin M.; Pfeifer, Jennifer H.; McLaughlin, Katie A.

doi:10.1016/j.dcn.2017.11.006

Cited by 58 publications

(64 citation statements)

References 52 publications

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“…A true FWE rate of 5% can also be achieved via nonparametric methods (Eklund et al, 2016). One piece of software, Neuropointillist, provides a flexible framework that allows researchers to use cluster computing resources for custom, voxel-wise neuroimaging analyses, which could include non-parametric tests specific to complex designs (Madhyastha et al, 2018). Other ostensible barriers to employing these methods in non-experimental designs have been overcome, such as how to handle covariates (Winkler, Ridgway, Webster, Smith, & Nichols, 2014), and how to permute data in nested designs (Winkler, Webster, Vidaurre, Nichols, & Smith 2015).…”

Section: Box 1: Spotlight On Conducting and Reporting Cluster-based Tmentioning

confidence: 99%

Improving Practices and Inferences in Developmental Cognitive Neuroscience

Flournoy¹,

Vijayakumar²,

Cheng³

et al. 2019

Preprint

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The past decade has seen growing concern about research practices in cognitive neuroscience, and psychology more broadly, that shake our confidence in many inferences in these fields. We consider how these issues affect developmental cognitive neuroscience, with the goal of progressing our field to support strong and defensible inferences from our neurobiological data. This manuscript focuses on the importance of distinguishing between confirmatory versus exploratory data analysis approaches in developmental cognitive neuroscience. Regarding confirmatory research, we discuss problems with analytic flexibility, appropriately instantiating hypotheses, and controlling the error rate given how we threshold data and correct for multiple comparisons. To counterbalance these concerns with confirmatory analyses, we present two complementary strategies. First, we discuss the advantages of working within an exploratory analysis framework, including estimating and reporting effect sizes, using parcellations, and conducting specification curve analyses. Second, we summarize defensible approaches for null hypothesis significance testing in confirmatory analyses, focusing on transparent and reproducible practices in our field. Specific recommendations are given, and templates, scripts, or other resources are hyperlinked, whenever possible.

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Section: Box 1: Spotlight On Conducting and Reporting Cluster-based Tmentioning

confidence: 99%

Improving Practices and Inferences in Developmental Cognitive Neuroscience

Flournoy¹,

Vijayakumar²,

Cheng³

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…However, given the multifactorial nature of longitudinal data sets, such analyses may be more complex in nature and can be accompanied by further confounding factors (e.g. linear or non-linear developmental trajectories depending on domain and process studied; see for instance Madhyastha et al, 2018). A potential difficulty is the differentiation of change and error in longitudinal modelling, as changes might reflect a combination of low measurement reliability and true developmental change (see for an excellent discussion of this topic).…”

Section: Methodological Challenges In Developmental Cognitive Neuroscmentioning

confidence: 99%

Opportunities for increased reproducibility and replicability of developmental neuroimaging

Klapwijk¹,

Bos²,

Tamnes³

et al. 2019

Preprint

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Recently, many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies also involve challenges such as choices about age groupings, modelling across the lifespan, the analyses of longitudinal changes, and neuroimaging data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental cognitive neuroscience, and how preregistration of studies can improve methodological rigor in the field. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.

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“…Age-related differences in connectivity parameters were further probed across data from both longitudinal timepoints using a mixed model approach in R with the package nlme (Pinheiro, Bates, DebRoy, Sarkar, & Team, 2018 Madhyastha et al, 2017). Likelihood ratio tests between the intercept-only, linear, quadratic, and cubic models were used to determine whether the age-related models with significant age parameters significantly improved model fit over the intercept-only model or the next simplest model with a significant age parameter.…”

Section: Experimental Design and Statistical Analysismentioning

confidence: 99%

Developmental maturation of the precuneus as a functional core of the default-mode network

Utevsky

Huettel

et al. 2018

Preprint

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Efforts to map the functional architecture of the developing human brain have shown that connectivity between and within functional neural networks changes from childhood to adulthood. While prior work has established that the adult precuneus distinctively modifies its connectivity during task versus rest states , it remains unknown how these connectivity patterns emerge over development. Here, we use functional magnetic resonance imaging (fMRI) data collected at two longitudinal timepoints from over 250 participants between the ages of 8 and 26 engaging in two cognitive tasks and a resting-state scan. By applying independent component analysis (ICA) to both task and rest data, we identified three canonical networks of interest -the rest-based default mode network (DMN) and the task-based left and right frontoparietal networks (LFPN, RFPN) -which we explored for developmental changes using dualregression analyses. We found systematic state-dependent functional connectivity in the precuneus, such that engaging in a task (compared to rest) resulted in greater precuneus-LFPN and precuneus-RFPN connectivity, whereas being at rest (compared to task) resulted in greater precuneus-DMN connectivity. These crosssectional results replicated across both tasks and at both developmental timepoints.Finally, we used longitudinal mixed models to show that the degree to which precuneus distinguishes between task and rest states increases with age, due to agerelated increasing segregation between precuneus and LFPN at rest. Our results highlight the distinct role of the precuneus in tracking processing state, in a manner that is both present throughout and strengthened across development.

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Current methods and limitations for longitudinal fMRI analysis across development

Cited by 58 publications

References 52 publications

Improving Practices and Inferences in Developmental Cognitive Neuroscience

Improving Practices and Inferences in Developmental Cognitive Neuroscience

Opportunities for increased reproducibility and replicability of developmental neuroimaging

Developmental maturation of the precuneus as a functional core of the default-mode network

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