Biology subjects

Salo, T.

Publications and source records attributed to Salo, T..

45 records · Page 3Linked to original sources

bioRxiv2020

What executive function network is that? An image-based meta-analysis of network labels

The current state of label conventions used to describe brain networks related to executive functions is highly inconsistent, leading to confusion among researchers regarding network labels. Visually similar networks are referred to by different labels, yet these same labels are used to distinguish networks within studies. We performed a literature review of fMRI studies and identified nine frequently-used labels that are used to describe topographically or functionally similar neural networks: central executive network (CEN), cognitive control network (CCN), dorsal attention network (DAN), executive control network (ECN), executive network (EN), frontoparietal network (FPN), working memory network (WMN), task positive network (TPN), and ventral attention network (VAN). Our aim was to meta-analytically determine consistency of network topography within and across these labels. We hypothesized finding considerable overlap in the spatial topography among the neural networks associated with these labels. An image-based meta-analysis was performed on 158 group-level statistical maps (SPMs) received from authors of 69 papers listed on PubMed. Our results indicated that there was very little consistency in the SPMs labeled with a given network name. We identified four clusters of SPMs representing four spatially distinct executive function networks. We provide recommendations regarding label nomenclature and propose that authors looking to assign labels to executive function networks adopt this template set for labeling networks.

neuroscience↗

bioRxiv2020

The cue-reactivity paradigm: An ensemble of networks driving attention and cognition when viewing drug-related and natural-reward stimuli

BackgroundThe cue-reactivity paradigm is a widely adopted neuroimaging probe engendering brain activity linked with attentional, affective, and reward processes following presentation of appetitive stimuli. Given the multiple mental operations invoked, we sought to decompose cue-related brain activity into constituent components employing emergent meta-analytic techniques when considering drug and natural reward-related cues. MethodsWe conducted multiple coordinate-based meta-analyses delineating common and distinct brain activity convergence across cue-reactivity studies (N=196 articles) involving drug (n=133) or natural reward-related (n=63) visual stimuli. Subsequently, we characterized the connectivity profiles of identified brain regions by using them as seeds in task-independent and task-dependent functional connectivity analyses. Using hierarchical clustering on these connectivity profiles, we grouped cue-related brain regions into subnetworks. Functional decoding was then employed to characterize mental operations linked with each subnetwork. ResultsAcross all studies, pooled activity convergence was observed in the striatum, amygdala, thalamus, cingulate, insula, and multiple frontal, parietal, and occipital regions. Drug-distinct convergence (drug>natural) was observed notably in the posterior cingulate cortex (PCC), dorsolateral prefrontal cortex (dlPFC), and temporal and parietal regions, whereas distinct natural reward convergence (natural>drug) was observed in thalamic, insular, orbitofrontal, and occipital regions. Hierarchical clustering using each regions connectivity profiles identified six subnetworks, involving: 1) occipital and thalamic (lateral geniculate nucleus) regions functionally linked with early visual processing, 2) occipital-temporal regions associated with higher level visual association, 3) parietal-frontal regions linked with cognitive control mechanisms, 4) posterior and ventral insula as well as anterior cingulate cortex (ACC) functionally linked with salient event detection, 5) nucleus accumbens, PCC, precuneus, ACC, and thalamus (mediodorsal) associated with subjective valuation, and 6) bilateral amygdalae, orbitofrontal, and dorsal insula regions linked with affective processes. ConclusionsThese outcomes suggest multifaceted brain activity during the cue-reactivity paradigm can be decomposed into more elemental processes and indicate that while drugs of abuse usurp the brains natural reward processing system, some regions appear distinctly related to drug cue-reactivity (e.g., PCC, dlPFC).

neuroscience↗

bioRxiv2020

Common and distinct brain activity associated with risky and ambiguous decision-making

Two often-studied forms of uncertain decision-making (DM) are risky-DM (outcome probabilities known) and ambiguous-DM (outcome probabilities unknown). While DM in general is associated with activation of several brain regions, previous neuroimaging efforts suggest a dissociation between activity linked with risky and ambiguous choices. However, the common and distinct neurobiological correlates associated with risky- and ambiguous-DM, as well as their specificity when compared to perceptual-DM (as a control condition), remains to be clarified. We conducted multiple meta-analyses on neuroimaging results from 151 studies to characterize common and domain-specific brain activity during risky-, ambiguous-, and perceptual-DM. When considering all DM tasks, convergent activity was observed in brain regions considered to be consituents of the canonical salience, valuation, and executive control networks. When considering subgroups of studies, risky-DM (vs. perceptual-DM) was linked with convergent activity in the striatum and anterior cingulate cortex (ACC), regions associated with reward-related processes (determined by objective functional decoding). When considering ambiguous-DM (vs. perceptual-DM), activity convergence was observed in the lateral prefrontal cortex and insula, regions implicated in affectively-neutral mental processes (e.g., cognitive control and behavioral responding; determined by functional decoding). An exploratory meta-analysis comparing brain activity between substance users and non-users during risky-DM identified reduced convergent activity among users in the striatum, cingulate, and thalamus. Taken together, these findings suggest a dissociation of brain regions linked with risky- and ambiguous-DM reflecting possible differential functionality and highlight brain alterations potentially contributing to poor decision-making in the context of substance use disorders.

neuroscience↗

bioRxiv2019

Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms

Reward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we utilized emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants. We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution which represented dissociable patterns of convergent brain activity across reward processing tasks. Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value (MAG-1 & MAG-2) and processing a variety of emotional (MAG-3), external (MAG-4 & MAG-5), and internal (MAG-6 & MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.

neuroscience↗

bioRxiv2019

Sex and pedagogy influences in learning-related reorganization of brain activity

Physics is a challenging academic pursuit in which university students regularly struggle to achieve success. Female students tend to perform negatively on introductory physics conceptual assessments compared to their male peers; however, active-learning classroom curricula are known to broadly improve performance on these tests. Here, we used fMRI to delineate physics-related brain activity in 107 students and probed for changes following a semester of active-learning or lecture-based physics instruction. Large-scale reorganization of brain activity accompanying learning occurred in a mixed frontoparietal and default mode network. Sex differences were observed in frontoparietal, default mode, and primary visual areas before and after instruction. Regions showing significant pedagogy, sex, and time interactions were revealed during physics retrieval, suggesting the type of class students complete may influence sex differences in how students retrieve information. These results reveal potentially elucidating sex and pedagogy differences underlying the neural mechanisms supporting physics learning.

neuroscience↗

bioRxiv2019

Analysis of task-based functional MRI data preprocessed with fMRIPrep

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time-consuming, error-prone, and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure (BIDS) to standardize both the input datasets --MRI data as stored by the scanner-- and the outputs --data ready for modeling and analysis--, fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

bioinformatics↗

bioRxiv2019

Brain activity links performance in science reasoning with conceptual approach

Understanding how students learn is crucial for helping them succeed. We examined brain function in 107 undergraduate students during a task known to be challenging for many students - physics problem solving - to characterize underlying neural mechanisms and determine how these support comprehension and proficiency. Further, we applied module analysis to response distributions, defining groups of students who answered using similar physics conceptions, and probed for brain differences linked with different conceptual approaches. We found integrated executive, attentional, visual motion, and default mode brain systems cooperate to achieve sequential and sustained physics-related cognition. While accuracy alone did not predict brain function, dissociable brain patterns were observed when students solved problems using different physics conceptions, and increased success was linked to conceptual coherence. Our analyses demonstrate that episodic associations and control processes operate in tandem to support physics reasoning, offering insight into effective classroom practices to promote student success.

neuroscience↗

bioRxiv2019

Sex differences in brain correlates of STEM anxiety

Anxiety is known to dysregulate the salience, default mode, and central executive networks of the human brain, yet this phenomenon has not been fully explored across the STEM learning experience, where anxiety can impact negatively academic performance. Here, we evaluated anxiety and large-scale brain connectivity in 101 undergraduate physics students. We found sex differences in STEM-related but not clinical anxiety, with longitudinal increases in science anxiety observed for both female and male students. Sex-specific impacts of STEM anxiety on brain connectivity emerged, with male students exhibiting distinct inter-network connectivity for STEM and clinical anxiety and female students demonstrating no significant within-sex correlations. Anxiety was negatively correlated with academic performance in sex-specific ways at both pre-and post-instruction. Moreover, math anxiety in male students mediated the relation between default mode-salience connectivity and course grade. Together, these results reveal complex sex differences in the neural mechanisms driving how anxiety impacts STEM learning.

neuroscience↗

bioRxiv2019

Ontological Dimensions of Cognitive-Neural Mappings

The growing literature reporting results of cognitive-neural mappings has increased calls for an adequate organizing ontology, or taxonomy, of these mappings. This enterprise is non-trivial, as relevant dimensions that might contribute to such an ontology are not yet agreed upon. We propose that any candidate dimensions should be evaluated on their ability to explain observed differences in functional neuroimaging activation patterns. In this study, we use a large sample of task-based functional magnetic resonance imaging (task-fMRI) results and a data-driven strategy to identify these dimensions. First, using a data-driven dimension reduction approach and multivariate distance matrix regression (MDMR), we quantify the variance among activation maps that is explained by existing ontological dimensions. We find that task paradigm categories explain the most variance among task-activation maps than other dimensions, including latent cognitive categories. Surprisingly, study ID, or the study from which each activation map was reported, explained close to 50% of the variance in activation patterns. Using a clustering approach that allows for overlapping clusters, we derived data-driven latent activation states, associated with re-occurring configurations of the canonical fronto-parietal/salience, sensory-motor, and default mode network activation patterns. Importantly, with only four data-driven latent dimensions, one can explain greater variance among activation maps than all conventional ontological dimensions combined. These latent dimensions may inform a data-driven cognitive ontology, and suggest that current descriptions of cognitive processes and the tasks used to elicit them do not accurately reflect activation patterns commonly observed in the human brain.

neuroscience↗