Estimating brain network activity through back-projection of ICA components to GLM maps

James, G. Andrew; Tripathi, Shanti P.; Kilts, Clinton D.

doi:10.1016/j.neulet.2014.01.056

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…This process was repeated for all participants, components, and task fMRI datasets. Figure S1 illustrates this process, which is validated and fully described elsewhere (James et al 2014).…”

Section: Extracting Component Activity Timecoursesmentioning

confidence: 92%

Task‐dependent recruitment of intrinsic brain networks reflects normative variance in cognition

et al. 2014

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BackgroundFunctional neuroimaging has great potential to inform clinical decisions, whether by identifying neural biomarkers of illness progression and severity, predicting therapeutic response, or selecting suitable patients for surgical interventions. Yet a persisting barrier to functional neuroimaging's clinical translation is our incomplete understanding of how normative variance in cognition, personality, and behavior shape the brain's structural and functional organization. We propose that modeling individual differences in these brain–behavior relationships is crucial for improving the accuracy of neuroimaging biomarkers for neurologic and psychiatric disorders.MethodsWe addressed this goal by initiating the Cognitive Connectome Project, which bridges neuropsychology and neuroimaging by pairing nine cognitive domains typically assessed by clinically validated neuropsychological measures with those tapped by canonical neuroimaging tasks (motor, visuospatial perception, attention, language, memory, affective processing, decision making, working memory, and executive function). To date, we have recruited a diverse sample of 53 participants (mean [SD], age = 32 [9.7] years, 31 females).ResultsAs a proof of concept, we first demonstrate that our neuroimaging task battery can replicate previous findings that task performance recruits intrinsic brain networks identified during wakeful rest. We then expand upon these previous findings by showing that the extent to which these networks are recruited by task reflects individual differences in cognitive ability. Specifically, performance on the Judgment of Line Orientation task (a clinically validated measure of visuospatial perception) administered outside of the MRI scanner predicts the magnitude of task-induced activity of the dorsal visual network when performing a direct replication of this task within the MRI scanner. Other networks (such as default mode and right frontoparietal) showed task-induced changes in activity that were unrelated to task performance, suggesting these networks to not be involved in visuospatial perception.ConclusionThese findings establish a methodological framework by which clinical neuropsychology and functional neuroimaging may mutually inform one another, thus enhancing the translation of functional neuroimaging into clinical decision making.

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Section: Extracting Component Activity Timecoursesmentioning

confidence: 92%

Task‐dependent recruitment of intrinsic brain networks reflects normative variance in cognition

et al. 2014

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“…The relationship of each component to intertemporal reward decision-making was tested by regressing component maps on each subject’s WANT>CON contrast maps (Fig. 1C) as an alternative to estimating this relationship with GLM analyses of component time series (James, Tripathi, & Kilts, 2014). The resulting contrast estimates were mean and variance normalized by converting them to z -scores.…”

Section: Methodsmentioning

confidence: 99%

Neural Systems Underlying Individual Differences in Intertemporal Decision-making

Elton¹,

Smith²,

Parrish³

et al. 2017

Journal of Cognitive Neuroscience

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Excessively choosing immediate over larger future rewards, or delay discounting (DD), associates with multiple clinical conditions. Individual differences in DD likely depend on variations in the activation of and functional interactions between networks, representing possible endophenotypes for associated disorders, including alcohol use disorders (AUDs). Numerous fMRI studies have probed the neural bases of DD, but investigations of large-scale networks remains scant. We addressed this gap by testing whether activation within large-scale networks during “Now/Later” decision-making predicts individual differences in DD. To do so, we scanned 95 social drinkers (18–40 years; 50 females) using fMRI during hypothetical choices between small monetary amounts available “today” or larger amounts available later. We identified neural networks engaged during Now/Later choice using independent component analysis (ICA) and tested the relationship between component activation and degree of DD. The activity of two components during Now/Later choice correlated with individual DD rates: a temporal lobe network positively correlated with DD, while a frontoparietal-striatal network negatively correlated with DD. Activation differences between these networks predicted individual differences in DD and their negative correlation during Now/Later choice suggests functional competition. A generalized psychophysiological interactions (gPPI) analysis confirmed a decrease in their functional connectivity during decision-making. The functional connectivity of these two networks negatively correlates with alcohol-related harm, potentially implicating these networks in AUDs. These findings provide novel insight into the neural underpinnings of individual differences in impulsive decision making with potential implications for addiction and related disorders in which impulsivity is a defining feature.

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“…Final component number was determined using the stability index (Iq), which reflect the internal stability of a component. K = 15 components was the smallest k value to meet a high Iq threshold (mean Iq, .0.95; minimum Iq, .0.90; Himberg et al, 2004;Turner et al, 2012;James et al, 2014). Qualitative follow-up examination revealed that adjacent component amounts (e.g., k = 14 and k = 16) resulted in nearly identical findings, while examinations of (1) low total components (e.g., k = 4) showed a merging of significant subcomponents (e.g., the P600 was a single component, whereas higher component amounts subdivided the P600 into subcomponents that significantly contributed to the raw data); and (2) high total components (e.g., k = 21) showed a division of relevant subcomponents so that they did not meet contribution thresholds.…”

Section: Experimental Design and Statistical Analysismentioning

confidence: 99%

Rapid Interactions of Widespread Brain Networks Characterize Semantic Cognition

et al. 2022

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Language comprehension requires the rapid retrieval and integration of contextually appropriate concepts (“semantic cognition”). Current neurobiological models of semantic cognition are limited by the spatial and temporal restrictions of single-modality neuroimaging and lesion approaches. This is a major impediment given the rapid sequence of processing steps that have to be coordinated to accurately comprehend language. Through the use of fused functional magnetic resonance imaging and electroencephalography analysis in humans (n= 26 adults; 15 females), we elucidate a temporally and spatially specific neurobiological model for real-time semantic cognition. We find that semantic cognition in the context of language comprehension is supported by trade-offs between widespread neural networks over the course of milliseconds. Incorporation of spatial and temporal characteristics, as well as behavioral measures, provide convergent evidence for the following progression: a hippocampal/anterior temporal phonological semantic retrieval network (peaking at ∼300 ms after the sentence final word); a frontotemporal thematic semantic network (∼400 ms); a hippocampal memory update network (∼500 ms); an inferior frontal semantic syntactic reappraisal network (∼600 ms); and nodes of the default mode network associated with conceptual coherence (∼750 ms). Additionally, in typical adults, mediatory relationships among these networks are significantly predictive of language comprehension ability. These findings provide a conceptual and methodological framework for the examination of speech and language disorders, with additional implications for the characterization of cognitive processes and clinical populations in other cognitive domains.SIGNIFICANCE STATEMENTThe present study identifies a real-time neurobiological model of the meaning processes required during language comprehension (i.e., “semantic cognition”). Using a novel application of fused magnetic resonance imaging and electroencephalography in humans, we found that semantic cognition during language comprehension is supported by a rapid progression of widespread neural networks related to meaning, meaning integration, memory, reappraisal, and conceptual cohesion. Relationships among these systems were predictive of individuals' language comprehension efficiency. Our findings are the first to use fused neuroimaging analysis to elucidate language processes. In so doing, this study provides a new conceptual and methodological framework in which to characterize language processes and guide the treatment of speech and language deficits/disorders.

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Estimating brain network activity through back-projection of ICA components to GLM maps

Cited by 4 publications

References 19 publications

Task‐dependent recruitment of intrinsic brain networks reflects normative variance in cognition

Task‐dependent recruitment of intrinsic brain networks reflects normative variance in cognition

Neural Systems Underlying Individual Differences in Intertemporal Decision-making

Rapid Interactions of Widespread Brain Networks Characterize Semantic Cognition

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