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Testable Hypotheses for Unbalanced Neuroimaging Data.

ABSTRACT: Unbalanced group-level models are common in neuroimaging. Typically, data for these models come from factorial experiments. As such, analyses typically take the form of an analysis of variance (ANOVA) within the framework of the general linear model (GLM). Although ANOVA theory is well established for the balanced case, in unbalanced designs there are multiple ways of decomposing the sums-of-squares of the data. This leads to several methods of forming test statistics when the model contains multiple factors and interactions. Although the Type I-III sums of squares have a long history of debate in the statistical literature, there has seemingly been no consideration of this aspect of the GLM in neuroimaging. In this paper we present an exposition of these different forms of hypotheses for the neuroimaging researcher, discussing their derivation as estimable functions of ANOVA models, and discussing the relative merits of each. Finally, we demonstrate how the different hypothesis tests can be implemented using contrasts in analysis software, presenting examples in SPM and FSL.

SUBMITTER: McFarquhar M

PROVIDER: S-EPMC4911391 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Testable Hypotheses for Unbalanced Neuroimaging Data.

McFarquhar Martyn M

Frontiers in neuroscience 20160617

Unbalanced group-level models are common in neuroimaging. Typically, data for these models come from factorial experiments. As such, analyses typically take the form of an analysis of variance (ANOVA) within the framework of the general linear model (GLM). Although ANOVA theory is well established for the balanced case, in unbalanced designs there are multiple ways of decomposing the sums-of-squares of the data. This leads to several methods of forming test statistics when the model contains mul ...[more]

PMID: 27378839

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Project description:Beliefs about the state of the world are an important influence on both normal behavior and psychopathology. However, understanding of the neural basis of belief processing remains incomplete, and several aspects of belief processing have only recently been explored. Specifically, different types of beliefs may involve fundamentally different inferential processes and thus recruit distinct brain regions. Additionally, neural processing of truth and falsity may differ from processing of certainty and uncertainty. The purpose of this study was to investigate the neural underpinnings of assessment of testable and non-testable propositions in terms of truth or falsity and the level of certainty in a belief. Functional magnetic resonance imaging (fMRI) was used to study 14 adults while they rated propositions as true or false and also rated the level of certainty in their judgments. Each proposition was classified as testable or non-testable. Testable propositions activated the DLPFC and posterior cingulate cortex, while non-testable statements activated areas including inferior frontal gyrus, superior temporal gyrus, and an anterior region of the superior frontal gyrus. No areas were more active when a proposition was accepted, while the dorsal anterior cingulate was activated when a proposition was rejected. Regardless of whether a proposition was testable or not, certainty that the proposition was true or false activated a common network of regions including the medial prefrontal cortex, caudate, posterior cingulate, and a region of middle temporal gyrus near the temporo-parietal junction. Certainty in the truth or falsity of a non-testable proposition (a strong belief without empirical evidence) activated the insula. The results suggest that different brain regions contribute to the assessment of propositions based on the type of content, while a common network may mediate the influence of beliefs on motivation and behavior based on the level of certainty in the belief.

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Testable Hypotheses for Unbalanced Neuroimaging Data.

Publications

Testable Hypotheses for Unbalanced Neuroimaging Data.

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