Understanding Individual Differences within Large-scale Brain Networks across Cognitive Contexts

Historically, human neuroimaging has studied brain regions “activated” during behavior and how they differ between groups of people. This approach has improved our understanding of healthy and disordered brain function, but has two key shortcomings. First, focusing on brain activation restricts how we understand the brain, ignoring vital, behind-the-scenes processing. In the past decade, the focus has shifted to communication between brain regions, or connectivity, revealing networks that exhibit subtle, consistent differences across behaviors and diagnoses. Without activation-focused research’s constraints, connectivity-focused neuroimaging research more comprehensively assesses brain function. Second, focusing on group differences ignores substantial within-group heterogeneity and often imposes false dichotomies. Recent findings show that brain network variability within an individual is nearly as great as across a group. Altogether, this illustrates a need for understanding individual variability in brain networks and how it relates to behavior. Therefore, I have developed a pipeline for investigating individual differences in brain connectivity, adapting robust statistical methods to address unique challenges of neuroimaging data analysis. Here, I describe this pipeline and apply it to two datasets. First, I explore between-individual variability in brain connectivity underlying intelligence and academic performance to better understand factors contributing to student success. Second, I assess the relative contributions of stress, sleep, and hormones to within-individual variability in brain connectivity across the menstrual cycle to illuminate little-studied phenomena affecting the everyday lives of half the population. Finally, I introduce a novel signal processing workflow for cleaning electrophysiological measures of bodily stress and arousal in neuroimaging research

Neural Systems Underlying RDoC Social Constructs: An Activation Likelihood Estimation Meta-Analysis

Neuroscientists have sought to identify the underlying neural systems supporting social processing that allow interaction and communication, forming social relationships, and navigating the social world. Through the use of NIMH’s Research Domain Criteria (RDoC) framework, we evaluated consensus among studies that examined brain activity during social tasks to elucidate regions comprising the “social brain”. We examined convergence across tasks corresponding to the four RDoC social constructs, including Affiliation and Attachment, Social Communication, Perception and Understanding of Self, and Perception and Understanding of Others. We performed a series of coordinate-based meta-analyses using the activation likelihood estimate (ALE) method. Meta-analysis was performed on whole-brain coordinates reported from 864 fMRI contrasts using the NiMARE Python package, revealing convergence in medial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, temporoparietal junction, bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. Additionally, four separate RDoC-based meta-analyses revealed differential convergence associated with the four social constructs. These outcomes highlight the neural support underlying these social constructs and inform future research on alterations among neurotypical and atypical populations

Cooperating yet distinct brain networks engaged during naturalistic paradigms: A meta-analysis of functional MRI results

Cognitive processes do not occur by pure insertion and instead depend on the full complement of co-occurring mental processes, including perceptual and motor functions. As such, there is limited ecological validity to human neuroimaging experiments that use highly controlled tasks to isolate mental processes of interest. However, a growing literature shows how dynamic, interactive tasks have allowed researchers to study cognition as it more naturally occurs. Collective analysis across such neuroimaging experiments may answer broader questions regarding how naturalistic cognition is biologically distributed throughout the brain. We applied an unbiased, data-driven, meta-analytic approach that uses k-means clustering to identify core brain networks engaged across the naturalistic functional neuroimaging literature. Functional decoding allowed us to, then, delineate how information is distributed between these networks throughout the execution of dynamical cognition in realistic settings. This analysis revealed six recurrent patterns of brain activation, representing sensory, domain-specific, and attentional neural networks that support the cognitive demands of naturalistic paradigms. Though gaps in the literature remain, these results suggest that naturalistic fMRI paradigms recruit a common set of networks that that allow both separate processing of different streams of information and integration of relevant information to enable flexible cognition and complex behavior

Meta-analytic evidence for a core problem solving network across multiple representational domains

Problem solving is a complex skill engaging multi-stepped reasoning processes to find unknown solutions. The breadth of real-world contexts requiring problem solving is mirrored by a similarly broad, yet unfocused neuroimaging literature, and the domain-general or context-specific brain networks associated with problem solving are not well understood. To more fully characterize those brain networks, we performed activation likelihood estimation meta-analysis on 280 neuroimaging problem solving experiments reporting 3,166 foci from 1,919 individuals across 131 papers. The general map of problem solving revealed broad fronto-cingulo-parietal convergence, regions similarly identified when considering separate mathematical, verbal, and visuospatial problem solving domain-specific analyses. Conjunction analysis revealed a common network supporting problem solving across diverse contexts, and difference maps distinguished functionally-selective sub-networks specific to task type. Our results suggest cooperation between representationally specialized sub-network and whole-brain systems provide a neural basis for problem solving, with the core network contributing general purpose resources to perform cognitive operations and manage problem demand. Further characterization of cross-network dynamics could inform neuroeducational studies on problem solving skill development

Neurofunctional topography of the human hippocampus

Much of what was assumed about the functional topography of the hippocampus was derived from a single case study over half a century ago. Given advances in the imaging sciences, a new era of discovery is underway, with potential to transform the understanding of healthy processing as well as the ability to treat disorders. Coactivation-based parcellation, a meta-analytic approach, and ultra-high field, high-resolution functional and structural neuroimaging to characterize the neurofunctional topography of the hippocampus was employed. Data revealed strong support for an evolutionarily preserved topography along the long-axis. Specifically, the left hippocampus was segmented into three distinct clusters: an emotional processing cluster supported by structural and functional connectivity to the amygdala and parahippocampal gyrus, a cognitive operations cluster, with functional connectivity to the anterior cingulate and inferior frontal gyrus, and a posterior perceptual cluster with distinct structural connectivity patterns to the occipital lobe coupled with functional connectivity to the precuneus and angular gyrus. The right hippocampal segmentation was more ambiguous, with plausible 2- and 5-cluster solutions. Segmentations shared connectivity with brain regions known to support the correlated processes. This represented the first neurofunctional topographic model of the hippocampus using a robust, bias-free, multimodal approach. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc

Toward a Neurobiological Basis for Understanding Learning in University Modeling Instruction Physics Courses

Modeling Instruction (MI) for University Physics is a curricular and pedagogical approach to active learning in introductory physics. A basic tenet of science is that it is a model-driven endeavor that involves building models, then validating, deploying, and ultimately revising them in an iterative fashion. MI was developed to provide students a facsimile in the university classroom of this foundational scientific practice. As a curriculum, MI employs conceptual scientific models as the basis for the course content, and thus learning in a MI classroom involves students appropriating scientific models for their own use. Over the last 10 years, substantial evidence has accumulated supporting MI's efficacy, including gains in conceptual understanding, odds of success, attitudes toward learning, self-efficacy, and social networks centered around physics learning. However, we still do not fully understand the mechanisms of how students learn physics and develop mental models of physical phenomena. Herein, we explore the hypothesis that the MI curriculum and pedagogy promotes student engagement via conceptual model building. This emphasis on conceptual model building, in turn, leads to improved knowledge organization and problem solving abilities that manifest as quantifiable functional brain changes that can be assessed with functional magnetic resonance imaging (fMRI). We conducted a neuroeducation study wherein students completed a physics reasoning task while undergoing fMRI scanning before (pre) and after (post) completing a MI introductory physics course. Preliminary results indicated that performance of the physics reasoning task was linked with increased brain activity notably in lateral prefrontal and parietal cortices that previously have been associated with attention, working memory, and problem solving, and are collectively referred to as the central executive network. Critically, assessment of changes in brain activity during the physics reasoning task from pre- vs. post-instruction identified increased activity after the course notably in the posterior cingulate cortex (a brain region previously linked with episodic memory and self-referential thought) and in the frontal poles (regions linked with learning). These preliminary outcomes highlight brain regions linked with physics reasoning and, critically, suggest that brain activity during physics reasoning is modifiable by thoughtfully designed curriculum and pedagogy

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.Additional co-authors: Sofie Van Den Bossche, Xenia Kobeleva, Jon Haitz Legarreta, Samuel Guay, Selim Melvin Atay, Gael P. Varoquaux, Dorien C. Huijser, Malin S. Sandström, Peer Herholz, Samuel A. Nastase, AmanPreet Badhwar, Guillaume Dumas, Simon Schwab, Stefano Moia, Michael Dayan, Yasmine Bassil, Paula P. Brooks, Matteo Mancini, James M. Shine, David O’Connor, Xihe Xie, Davide Poggiali, Patrick Friedrich, Anibal S. Heinsfeld, Lydia Riedl, Roberto Toro, César Caballero-Gaudes, Anders Eklund, Kelly G. Garner, Christopher R. Nolan, Damion V. Demeter, Fernando A. Barrios, Junaid S. Merchant, Elizabeth A. McDevitt, Robert Oostenveld, R. Cameron Craddock, Ariel Rokem, Andrew Doyle, Satrajit S. Ghosh, Aki Nikolaidis, Olivia W. Stanley, Eneko Uruñuela, The Brainhack Communit

neurostuff/NiMARE: 0.2.0rc3

What's Changed Exciting New Features Remove resample argument from IBMA estimators by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/823 Add IBMAWorkflow by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/817 Make torch optional by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/836 ### Bug Fixes Addresses new RTD configuration file requirements by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/829 ### Other Changes Fix the NeuroLibre badge by @tsalo in https://github.com/neurostuff/NiMARE/pull/824 [FIX] handle null values in metadata by @jdkent in https://github.com/neurostuff/NiMARE/pull/831 Add badges and citations for Aperture Neuro article by @tsalo in https://github.com/neurostuff/NiMARE/pull/834 Remove pytorch warning message by @yifan0330 in https://github.com/neurostuff/NiMARE/pull/828 [FIX] handle index errors by @jdkent in https://github.com/neurostuff/NiMARE/pull/839 Full Changelog: https://github.com/neurostuff/NiMARE/compare/0.2.0rc2...0.2.0rc