scholarly journals An Omics-Inspired Elastic Net Approach Drastically Improves Out-of-Sample Prediction and Regional Inference of Task-Based fMRI

2020 ◽  
Author(s):  
Narun Pornpattananangkul ◽  
Adam Bartonicek ◽  
Yue Wang ◽  
Argyris Stringaris
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Large Scale ◽  
Brain Regions ◽  
Elastic Net ◽  
Polygenic Scores ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Univariate Approach ◽  
Do So

AbstractPredicting individual differences in cognitive processes is crucial, but the ability of task-based fMRI to do so remains dubious, despite decades of costly research. We tested the ability of working-memory fMRI in predicting working-memory, using the Adolescent Brain Cognitive Development (n = 4,350). The conventionally-used mass-univariate approach led to poor out-of-sample prediction (Mean r = .1-.12). However, the multivariate ‘Elastic Net’, which draws information across brain regions, enhanced out-of-sample prediction (r = .47) by several folds. The Elastic Net also enabled us to predict cognitive performance from various tasks collected outside of the scanner, highlighting its generalizability. Moreover, using an omics-inspired approach, we combined Elastic Net with permutation, allowing us to statistically infer which brain regions contribute to individual differences while accounting for collinearity. Accordingly, our framework can build an easy-to-interpret predictive fMRI model that transfers knowledge learned from large-scale datasets to smaller samples, akin to polygenic scores in genomics.

scholarly journals Is baseline pupil size related to cognitive ability? Yes (under proper lighting conditions).

2020 ◽  
Author(s):  
Jason S. Tsukahara ◽  
Randall W Engle
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Ability ◽  
Pupil Size ◽  
Large Scale ◽  
Fluid Intelligence ◽  
Functional Organization ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Lighting Conditions

We found that individual differences in baseline pupil size correlated with fluid intelligence and working memory capacity. Larger pupil size was associated with higher cognitive ability. However, other researchers have not been able to replicate our 2016 finding – though they only measured working memory capacity and not fluid intelligence. In a reanalysis of Tsukahara et al. (2016) we show that reduced variability on baseline pupil size will result in a higher probability of obtaining smaller and non-significant correlations with working memory capacity. In two large-scale studies, we demonstrated that reduced variability in baseline pupil size values was due to the monitor being too bright. Additionally, fluid intelligence and working memory capacity did correlate with baseline pupil size except in the brightest lighting conditions. Overall, our findings demonstrated that the baseline pupil size – working memory capacity relationship was not as strong or robust as that with fluid intelligence. Our findings have strong methodological implications for researchers investigating individual differences in task-free or task-evoked pupil size. We conclude that fluid intelligence does correlate with baseline pupil size and that this is related to the functional organization of the resting-state brain through the locus coeruleus-norepinephrine system.

A Large-scale Neurocomputational Model of Task-oriented Behavior Selection and Working Memory in Prefrontal Cortex

2006 ◽  
Vol 18 (2) ◽  
pp. 242-257 ◽  
Author(s):  
George L. Chadderdon ◽  
Olaf Sporns
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Large Scale ◽  
Memory Task ◽  
Brain Regions ◽  
Synaptic Activity ◽  
Dynamic State ◽  
Memory Representation ◽  
Behavioral Repertoire ◽  
Behavior Selection

The prefrontal cortex (PFC) is crucially involved in the executive component of working memory, representation of task state, and behavior selection. This article presents a large-scale computational model of the PFC and associated brain regions designed to investigate the mechanisms by which working memory and task state interact to select adaptive behaviors from a behavioral repertoire. The model consists of multiple brain regions containing neuronal populations with realistic physiological and anatomical properties, including extrastriate visual cortical regions, the inferotemporal cortex, the PFC, the striatum, and midbrain dopamine (DA) neurons. The onset of a delayed match-to-sample or delayed nonmatch-to-sample task triggers tonic DA release in the PFC causing a switch into a persistent, stimulus-insensitive dynamic state that promotes the maintenance of stimulus representations within prefrontal networks. Other modeled prefrontal and striatal units select cognitive acceptance or rejection behaviors according to which task is active and whether prefrontal working memory representations match the current stimulus. Working memory task performance and memory fields of prefrontal delay units are degraded by extreme elevation or depletion of tonic DA levels. Analyses of cellular and synaptic activity suggest that hyponormal DA levels result in increased prefrontal activation, whereas hypernormal DA levels lead to decreased activation. Our simulation results suggest a range of predictions for behavioral, single-cell, and neuroimaging response data under the proposed task set and under manipulations of DA concentration.

scholarly journals Complementary Correlational Profiles between Slow Wave Brain Oscillations and Working Memory Capacity

2020 ◽  
pp. 1-4
Author(s):  
Eddy J. Davelaar ◽  
Eddy J. Davelaar
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Spectral Power ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Brain Regions ◽  
Cognitive Resources ◽  
Operation Span ◽  
Eyes Closed ◽  
Electrophysiological Recordings

Working memory involves a range of functions, including maintenance of information and processing that information undisturbed by distraction. Neuroscientific studies have observed critical contributions from frontal and parietal brain regions during processing of cognitive demanding tasks. However, less is known about individual differences in the resting state and their association with working memory capacity. In this study, electrophysiological recordings were taken from thirty volunteers in eyes closed and eyes open conditions after completing the automated version of the operation span task. The results reveal two clusters of correlations: a midline-theta cluster and a parieto-temporal alpha cluster. The theta and alpha clusters have a negative and a positive correlation with operation span performance, respectively. These results are interpreted as individual differences in cognitive preparedness, with the centro-parietal region being critical in switching between outward and inward attention, with the balance of theta and alpha spectral power at Pz indicating to where cognitive resources are directed.

scholarly journals Switching Poisson Gamma Dynamical Systems

2020 ◽  
Author(s):  
Wenchao Chen ◽  
Bo Chen ◽  
Yicheng Liu ◽  
Qianru Zhao ◽  
Mingyuan Zhou
Keyword(s):  
Dynamical Systems ◽  
Latent Variables ◽  
Large Scale ◽  
Complex Dynamic ◽  
Distributed Parameters ◽  
Proposed Model ◽  
Model Complex ◽  
Out Of Sample ◽  
Complex Sequences ◽  
Out Of Sample Prediction

We propose Switching Poisson gamma dynamical systems (SPGDS) to model sequentially observed multivariate count data. Different from previous models, SPGDS assigns its latent variables into mixture of gamma distributed parameters to model complex sequences and describe the nonlinear dynamics, meanwhile, capture various temporal dependencies. For efficient inference, we develop a scalable hybrid stochastic gradient-MCMC and switching recurrent autoencoding variational inference, which is scalable to large scale sequences and fast in out-of-sample prediction. Experiments on both unsupervised and supervised tasks demonstrate that the proposed model not only has excellent fitting and prediction performance on complex dynamic sequences, but also separates different dynamical patterns within them.

scholarly journals Individual Differences in Memory and Attention Processes in Prospective Remembering

2021 ◽  
Author(s):  
Hunter Ball ◽  
Elizabeth A. Wiemers ◽  
Gene Brewer
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Prospective Memory ◽  
Latent Variable ◽  
Structural Equation ◽  
Large Scale ◽  
Equation Modeling ◽  
Attention And Memory ◽  
Retrospective Memory ◽  
Prospective Remembering

Successful prospective memory (PM) involves not only detecting that an environmental cue requires action (i.e., prospective component), but also retrieval of what is supposed to be done at the appropriate moment (i.e., retrospective component). The current study examined the role of attention and memory during PM tasks that placed distinct demands on detection and retrieval processes. Using a large-scale individual differences design, participants completed three PM tasks that placed high demands on detection (but low demands on retrieval) and three tasks that placed high demands on retrieval (but low demands on detection). Additionally, participants completed three attention control, retrospective memory, and working memory tasks. Latent variable structural equation modeling showed that the prospective and retrospective components of PM were jointly influenced by multiple cognitive abilities. Critically, attention and retrospective memory fully mediated the relation between working memory and prospective memory. Furthermore, only attention uniquely predicted PM detection, whereas only retrospective memory uniquely predicted PM retrieval. These findings highlight the value of independently assessing different PM components and suggest that both attention and memory abilities must be considered to fully understand the dynamic processes underlying prospective remembering.

scholarly journals Dynamic reconfiguration of frontal brain networks during executive cognition in humans

2015 ◽  
Vol 112 (37) ◽  
pp. 11678-11683 ◽  
Author(s):  
Urs Braun ◽  
Axel Schäfer ◽  
Henrik Walter ◽  
Susanne Erk ◽  
Nina Romanczuk-Seiferth ◽  
...  
Keyword(s):  
Working Memory ◽  
Cognitive Function ◽  
Large Scale ◽  
Brain Networks ◽  
Dynamic Network ◽  
Dynamic Reconfiguration ◽  
Brain Regions ◽  
Evolving Networks ◽  
Network Flexibility ◽  
Frontal Systems

The brain is an inherently dynamic system, and executive cognition requires dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization of these reconfiguration processes during cognitive function in humans remains elusive. Here, we use a series of techniques developed in the field of “dynamic network neuroscience” to investigate the dynamics of functional brain networks in 344 healthy subjects during a working-memory challenge (the “n-back” task). In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks. This reconfiguration, which characterizes “network flexibility,” employs transient and heterogeneous connectivity between frontal systems, which we refer to as “integration.” Frontal integration predicted neuropsychological measures requiring working memory and executive cognition, suggesting that dynamic network reconfiguration between frontal systems supports those functions. Our results characterize dynamic reconfiguration of large-scale distributed neural circuits during executive cognition in humans and have implications for understanding impaired cognitive function in disorders affecting connectivity, such as schizophrenia or dementia.

scholarly journals Activity in the Fronto-Parietal Multiple-Demand Network is Robustly Associated with Individual Differences in Working Memory and Fluid Intelligence

2017 ◽  
Author(s):  
Moataz Assem ◽  
Idan Asher Blank ◽  
Zachary Mineroff ◽  
Ahmet Ademoglu ◽  
Evelina Fedorenko
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Fluid Intelligence ◽  
Brain Lesion ◽  
Memory Task ◽  
Brain Regions ◽  
Small Sample ◽  
Level Of Activity ◽  
Language Network ◽  
Executive Abilities

AbstractNumerous brain lesion and fMRI studies have linked individual differences in executive abilities and fluid intelligence to brain regions of the fronto-parietal “multiple-demand” (MD) network. Yet, fMRI studies have yielded conflicting evidence as to whether better executive abilities are associated with stronger or weaker MD activations and whether this relationship is restricted to the MD network. Here, in a large-sample (n=216) fMRI investigation, we found that stronger activity in MD regions – functionally defined in individual participants – was robustly associated with more accurate and faster responses on a spatial working memory task performed in the scanner, as well as fluid intelligence measured independently (n=114). In line with some prior claims about a relationship between language and fluid intelligence, we also found a weak association between activity in the brain regions of the left fronto-temporal language network during an independent passive reading task, and performance on the working memory task. However, controlling for the level of MD activity abolished this relationship, whereas the MD activity-behavior association remained highly reliable after controlling for the level of activity in the language network. Finally, we demonstrate how unreliable MD activity measures, coupled with small sample sizes, could falsely lead to the opposite, negative, association that has been reported in some prior studies. Taken together, these results demonstrate that a core component of individual differences variance in executive abilities and fluid intelligence is selectively and robustly positively associated with the level of activity in the MD network, a result that aligns well with lesion studies.

scholarly journals Differential prioritization of intramaze cue and boundary information during spatial navigation across the human lifespan

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Franka Glöckner ◽  
Nicolas W. Schuck ◽  
Shu-Chen Li
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Processing ◽  
Spatial Learning ◽  
Spatial Working Memory ◽  
Working Memory Capacity ◽  
Brain Regions ◽  
Older Children ◽  
Human Lifespan ◽  
Boundary Information

AbstractSpatial learning can be based on intramaze cues and environmental boundaries. These processes are predominantly subserved by striatal- and hippocampal-dependent circuitries, respectively. Maturation and aging processes in these brain regions may affect lifespan differences in their contributions to spatial learning. We independently manipulated an intramaze cue or the environment’s boundary in a navigation task in 27 younger children (6–8 years), 30 older children (10–13 years), 29 adolescents (15–17 years), 29 younger adults (20–35 years) and 26 older adults (65–80 years) to investigate lifespan age differences in the relative prioritization of either information. Whereas learning based on an intramaze cue showed earlier maturation during the progression from younger to later childhood and remained relatively stable across adulthood, maturation of boundary-based learning was more protracted towards peri-adolescence and showed strong aging-related decline. Furthermore, individual differences in prioritizing intramaze cue- over computationally more demanding boundary-based learning was positively associated with cognitive processing fluctuations and this association was partially mediated by spatial working memory capacity during adult, but not during child development. This evidence reveals different age gradients of two modes of spatial learning across the lifespan, which seem further influenced by individual differences in cognitive processing fluctuations and working memory, particularly during aging.

scholarly journals Individual Differences in Memory and Attention Processes in Prospective Remembering

2021 ◽  
Author(s):  
Hunter Ball ◽  
Elizabeth A. Wiemers ◽  
Gene Arnold Brewer
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Prospective Memory ◽  
Latent Variable ◽  
Structural Equation ◽  
Large Scale ◽  
Equation Modeling ◽  
Attention And Memory ◽  
Retrospective Memory ◽  
Prospective Remembering

Successful prospective memory (PM) involves not only detecting that an environmental cue requires action (i.e., prospective component), but also retrieval of what is supposed to be done at the appropriate moment (i.e., retrospective component). The current study examined the role of attention and memory during PM tasks that placed distinct demands on detection and retrieval processes. Using a large-scale individual differences design, participants completed three PM tasks that placed high demands on detection (but low demands on retrieval) and three tasks that placed high demands on retrieval (but low demands on detection). Additionally, participants completed three attention control, retrospective memory, and working memory tasks. Latent variable structural equation modeling showed that the prospective and retrospective components of PM were jointly influenced by multiple cognitive abilities. Critically, attention and retrospective memory fully mediated the relation between working memory and prospective memory. Furthermore, only attention uniquely predicted PM detection, whereas only retrospective memory uniquely predicted PM retrieval. These findings highlight the value of independently assessing different PM components and suggest that both attention and memory abilities must be considered to fully understand the dynamic processes underlying prospective remembering.

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