Neuroimaging meta-analysis for consensus and discovery

Project status: Complete

Data-driven meta-analytic clustering

Abstract

Although most often used to assess agreement or convergence across published studies, neuroimaging meta-analysis is a powerful tool for uncovering disparate, recurrent patterns of brain activation from the literature. By applying unsupervised clustering approaches to a dataset of 3D stereotaxic brain activation coordinates, we uncovered multiple activation patterns underlying two very different concepts in the neuroimaging literature: naturalistic fMRI paradigms and reward processing studies. Naturalistic paradigms employ dynamic stimuli that demanded continuous, real-time integration of dynamic streams of information and have been used to study a wide range of behaviors and cognitive concepts. Across 110 neuroimaging papers, we uncovered 6 recurrent patterns of brain activation, related to sensory input, attentional control, and domain-specific processing (i.e., language, affect, and spatial processing). On the other hand, reward processing is a more cohesive domain, with various external influences and internal facets. Here, meta-analytic clustering identified seven recurrent patterns of brain activation from 176 published neuroimaging studies. These patterns reflect specialized processing involved in predicting value and processing emotional, external, and internal influences, concurrent with accepted reward learning theories.

Manuscripts

1.
Bottenhorn KL, Flannery JS, Boeving ER, Riedel MC, Eickhoff SB, Sutherland MT, et al. Cooperating yet distinct brain networks engaged during naturalistic paradigms: A meta-analysis of functional MRI results. Netw Neurosci. 2018 Oct 1;3(1):27–48. DOI: 10.1162/netn_a_00050 Code NeuroVault Paper
2.
Flannery JS, Riedel MC, Bottenhorn KL, Poudel R, Salo T, Hill-Bowen LD, et al. Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms. Cogn Affect Behav Neurosci. 2020 Apr 1;20(2):215–35. DOI: 10.3758/s13415-019-00763-7 Code NeuroVault Paper

Presentations

1.
Bottenhorn KL Naturalistic paradigms in fMRI research: An ALE meta-analysis. Poster presented at: 22nd Annual Meeting of the Organization for Human Brain Mapping; 2016 Jun; Geneva, Switzerland. DOI: 10.5281/zenodo.7932052
2.
Flannery JS, Riedel MC, Poudel R, Salo T, Bottenhorn KL, Hill L, Laird AR, Sutherland MT. Meta-analytic clustering dissociates activation and behavior profiles across reward processing data. Oral presentation presented at: 22nd Annual Meeting of the Organization for Human Brain Mapping; 2016 Jun; Geneva, Switzerland
3.
Yanes J, Bottenhorn KL, Salo T, Riedel M, Laird AR, Robinson J. (2019, May). Data mining reveals discrete neurobiological systems that contribute to pain processing. Poster presented at the annual meeting of the Social and Affective Neuroscience Society in Miami, FL, USA.

Theory-driven meta-analysis for domain-specific processing

Neuroimaging meta-analysis is often applied in a more theory-driven manner to assess nuances in brain activation related to different facets of specific cognitive domains. Across a domain (e.g., social processing, problem solving), differences in experimental paradigm or statistical contrasts between studies can uncover different aspects of neural processing that would otherwise be lumped together in the literature or seemingly unrelated to other papers due to authors' phrasing. However, manual curation of meta-analytic corpora can be applied to apply domain-specific knowlege to group like papers according to underlying theory (e.g., the NIH Research Domain Criteria, RDoC, for social processing).

Manuscripts

1.
Pintos Lobo R, Bottenhorn KL, Riedel MC, Toma AI, Hare MM, Smith DD, et al. Neural systems underlying RDoC social constructs: An activation likelihood estimation meta-analysis. Neuroscience & Biobehavioral Reviews. 2023 Jan 1;144:104971. DOI: 10.1016/j.neubiorev.2022.104971 Code
2.
Bartley JE, Boeving ER, Riedel MC, Bottenhorn KL, Salo T, Eickhoff SB, et al. Meta-analytic evidence for a core problem solving network across multiple representational domains. Neurosci Biobehav Rev. 2018 Sep;92:318–37. DOI: 10.1016/j.neubiorev.2018.06.009

Functional neuroanatomy via meta-analysis

By selecting published papers for a meta-analysis not by the paper's topic or methods, but by the brain activation coordinates reported in the paper, meta-analysis can be used to assess functional neuroanatomy. For example, selecting papers that report coordinates within a region (e.g., hippocampus, hypothalamus) and meta-analyzing their results can provide insight into that region's functional connections throughout the brain (i.e., meta-analytic coactivation mapping, MACM) and its intrinsic functional organization (i.e., meta-analytic coactivation-based parcellation, CBP).

My undergraduate research mentor, Jennifer Robinson, and used meta-analytic coactivation-based parcellation, in addition to resting-state functional and diffusion-weighted magnetic resonance imaging at 7T to delineate the functional topography of the human hippocampus. In my undergraduate work, I applied meta-analytic coactivation mapping to assess the functional connectivity of the human hypothalamus.

Manuscripts

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Robinson JL, Barron DS, Kirby LAJ, Bottenhorn KL, Hill AC, Murphy JE, et al. Neurofunctional topography of the human hippocampus. Hum Brain Mapp. 2015 Sep 9;36(12):5018–37.

Presentations

1.
Bottenhorn KL, Robinson JL. Functional connectivity of the human hypothalamus using meta-analytic connectivity modeling. Poster presented at: Auburn University Research Week; 2014 Apr; Auburn, AL, USA. DOI: 10.5281/zenodo.7931840
2.
Bottenhorn KL, Robinson JL. Functional connectivity of the human hypothalamus using meta-analytic connectivity modeling. Poster presented at: 20th Annual Meeting of the Organization for Human Brain Mapping; 2014 Jun; Hamburg, Germany. DOI: 10.5281/zenodo.7931818