Brain activity during reading The effects of exposure duration and task

Background.Brain-derived neurotrophic factor (BDNF) is an important regulator of synaptogenesis and synaptic plasticity underlying learning. However, a relationship between circulating BDNF levels and brain activity during learning has not been demonstrated in humans. Reduced brain BDNF levels are found in schizophrenia and functional neuroimaging studies of probabilistic association learning in schizophrenia have demonstrated reduced activity in a neural network that includes the prefrontal and parietal cortices and the caudate nucleus. We predicted that brain activity would correlate positively with peripheral BDNF levels during probabilistic association learning in healthy adults and that this relationship would be altered in schizophrenia.Method.Twenty-five healthy adults and 17 people with schizophrenia or schizo-affective disorder performed a probabilistic association learning test during functional magnetic resonance imaging (fMRI). Plasma BDNF levels were measured by enzyme-linked immunosorbent assay (ELISA).Results.We found a positive correlation between circulating plasma BDNF levels and brain activity in the parietal cortex in healthy adults. There was no relationship between plasma BDNF levels and task-related activity in the prefrontal, parietal or caudate regions in schizophrenia. A direct comparison of these relationships between groups revealed a significant diagnostic difference.Conclusions.This is the first study to show a relationship between peripheral BDNF levels and cortical activity during learning, suggesting that plasma BDNF levels may reflect learning-related brain activity in healthy humans. The lack of relationship between plasma BDNF and task-related brain activity in patients suggests that circulating blood BDNF may not be indicative of learning-dependent brain activity in schizophrenia.

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Contributions of Brain Function and Structure to Three Different Domains of Cognitive Control in Normal Aging

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01685 ◽

2021 ◽

pp. 1-22

Author(s):

Jenny R. Rieck ◽

Giulia Baracchini ◽

Cheryl L. Grady

Keyword(s):

Working Memory ◽

Cognitive Control ◽

Task Performance ◽

Brain Structure ◽

Functional Activity ◽

Brain Activity ◽

Diffusion Imaging ◽

Normal Aging ◽

Control Mechanisms ◽

And Task

Cognitive control involves the flexible allocation of mental resources during goal-directed behavior and comprises three correlated but distinct domains—inhibition, shifting, and working memory. The work of Don Stuss and others has demonstrated that frontal and parietal cortices are crucial to cognitive control, particularly in normal aging, which is characterized by reduced control mechanisms. However, the structure–function relationships specific to each domain and subsequent impact on performance are not well understood. In the current study, we examined both age and individual differences in functional activity associated with core domains of cognitive control in relation to fronto-parietal structure and task performance. Participants ( N = 140, aged 20–86 years) completed three fMRI tasks: go/no-go (inhibition), task switching (shifting), and n-back (working memory), in addition to structural and diffusion imaging. All three tasks engaged a common set of fronto-parietal regions; however, the contributions of age, brain structure, and task performance to functional activity were unique to each domain. Aging was associated with differences in functional activity for all tasks, largely in regions outside common fronto-parietal control regions. Shifting and inhibition showed greater contributions of structure to overall decreases in brain activity, suggesting that more intact fronto-parietal structure may serve as a scaffold for efficient functional response. Working memory showed no contribution of structure to functional activity but had strong effects of age and task performance. Together, these results provide a comprehensive and novel examination of the joint contributions of aging, performance, and brain structure to functional activity across multiple domains of cognitive control.

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Effect of luminance exposure duration, and task complexity on reaction time.

Journal of Experimental Psychology ◽

10.1037/h0021741 ◽

1965 ◽

Vol 69 (4) ◽

pp. 393-400 ◽

Cited By ~ 22

Author(s):

Jaques Kaswan ◽

Stephen Young

Keyword(s):

Reaction Time ◽

Exposure Duration ◽

Task Complexity ◽

And Task

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Atypical Spatial Working Memory and Task-General Brain Activity in Adolescents with a Family History of Alcoholism

Alcoholism Clinical and Experimental Research ◽

10.1111/j.1530-0277.2012.01948.x ◽

2012 ◽

Vol 37 (3) ◽

pp. 390-398 ◽

Cited By ~ 18

Author(s):

Kristen L. Mackiewicz Seghete ◽

Anita Cservenka ◽

Megan M. Herting ◽

Bonnie J. Nagel

Keyword(s):

Working Memory ◽

Family History ◽

Spatial Working Memory ◽

Brain Activity ◽

History Of ◽

And Task

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Quasi-periodic patterns of brain activity in individuals with Attention-Deficit/Hyperactivity Disorder

10.1101/435347 ◽

2018 ◽

Author(s):

Anzar Abbas ◽

Yasmine Bassil ◽

Shella Keilholz

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Functional Connectivity ◽

Attention Deficit ◽

Brain Activity ◽

Spatiotemporal Pattern ◽

Periodic Patterns ◽

Default Mode ◽

Analysis Techniques ◽

Hyperactivity Disorder ◽

And Task

Individuals with attention-deficit/hyperactivity disorder have been shown to have disrupted functional connectivity in the default mode and task positive networks. Traditional fMRI analysis techniques that focus on static changes in functional connectivity have been successful in identifying differences between healthy controls and individuals with ADHD. However, such analyses are unable to explain the mechanisms behind the functional connectivity differences observed. Here, we study dynamic changes in functional connectivity in individuals with ADHD through investigation of quasi-periodic patterns (QPPs). QPPs are reliably recurring low-frequency spatiotemporal patterns in the brain linked to infra-slow electrical activity. They have been shown to contribute to functional connectivity observed through static analysis techniques. We find that QPPs contribute to functional connectivity specifically in regions that are disrupted during ADHD. Individuals with ADHD also show differences in the spatiotemporal pattern observed within the QPPs. This difference results in a weaker contribution of QPPs to functional connectivity in the default mode and task positive networks. We conclude that quasi-periodic patterns provide insight into the mechanisms behind functional connectivity differences seen in individuals with ADHD. This allows for a better understanding of the etiology of the disorder and development of effective treatments.

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Predicting Individual Variability in Task-Evoked Brain Activity in Schizophrenia

10.1101/2020.05.25.114603 ◽

2020 ◽

Author(s):

Niv Tik ◽

Abigail Livny ◽

Shachar Gal ◽

Karny Gigi ◽

Galia Tsarfaty ◽

...

Keyword(s):

Functional Connectivity ◽

Prediction Model ◽

Resting State ◽

Brain Activity ◽

Strong Association ◽

Brain Activation ◽

Individual Variability ◽

Resting State Functional Connectivity ◽

Successful Prediction ◽

And Task

AbstractBACKGROUNDPatients suffering from schizophrenia demonstrate abnormal brain activity, as well as alterations in patterns of functional connectivity assessed by functional magnetic resonance imaging (fMRI). Previous studies in healthy participants suggest a strong association between resting-state functional connectivity and task-evoked brain activity that could be detected at an individual level, and show that brain activation in various tasks could be predicted from task-free fMRI scans. In the current study we aimed to predict brain activity in patients diagnosed with schizophrenia, using a prediction model based on healthy individuals exclusively. This offers novel insights regarding the interrelations between brain connectivity and activity in schizophrenia.METHODSWe generated a prediction model using a group of 80 healthy controls that performed the well-validated N-back task, and used it to predict individual variability in task-evoked brain activation in 20 patients diagnosed with schizophrenia.RESULTSWe demonstrated a successful prediction of individual variability in the task-evoked brain activation based on resting-state functional connectivity. The predictions were highly sensitive, reflected by high correlations between predicted and actual activation maps (Median = 0.589, SD = 0.193) and specific, evaluated by a Kolomogrov-Smirnov test (D = 0.25, p < 0.0001).CONCLUSIONSA Successful prediction of brain activity from resting-state functional connectivity highlights the strong coupling between the two. Moreover, our results support the notion that even though resting-state functional connectivity and task-evoked brain activity are frequently reported to be altered in schizophrenia, the relations between them remains unaffected. This may allow to generate task activity maps for clinical populations without the need the actually perform the task.

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Quantifying differences between passive and task-evoked intrinsic functional connectivity in a large-scale brain simulation

10.1101/250894 ◽

2018 ◽

Author(s):

Antonio Ulloa ◽

Barry Horwitz

Keyword(s):

Computational Model ◽

Resting State ◽

Short Term Memory ◽

Brain Activity ◽

Brain Regions ◽

Intrinsic Activity ◽

Task Execution ◽

Short Term ◽

Visual Short Term Memory ◽

And Task

AbstractEstablishing a connection between intrinsic and task-evoked brain activity is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from non-task neural elements while giving us the effects of task execution on non-task regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step towards elucidating the intrinsic versus task-evoked connection. Here we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our Large-Scale Neural Modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of ten subjects performing passive fixation (PF), passive viewing (PV) and delay match-to-sample (DMS) tasks. We used the simulated BOLD fMRI time-series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain functional connectivity during task execution. Our results represent a step towards establishing a connection between intrinsic and task-related brain activity.Author SummaryStudies of resting-state conditions are popular in neuroimaging. Participants in resting-state studies are instructed to fixate on a neutral image or to close their eyes. This type of study has advantages over traditional task-based studies, including its ability to allow participation of those with difficulties performing tasks. Further, a resting-state neuroimaging study reveals intrinsic activity of participants’ brains. However, task-related brain activity may change this intrinsic activity, much as a stone thrown in a lake causes ripples on the water’s surface. Can we measure those activity changes? To answer that question, we merged a computational model of visual short-term memory (task regions) with an anatomical model incorporating major connections between brain regions (non-task regions). In a computational model, unlike real data, we know how different regions are connected and which regions are doing the task. First, we simulated neuronal and neuroimaging activity of both task and non-task regions during three conditions: passive fixation (baseline), passive viewing, and visual short-term memory. Then, applying graph theory to the simulated neuroimaging of non-task regions, we computed differences between the baseline and the other conditions. Our results show that we can measure changes in non-task regions due to brain activity changes in task-related regions.

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External drivers of BOLD signal’s non-stationarity

10.1101/2021.09.07.459325 ◽

2021 ◽

Author(s):

Arian Ashourvan ◽

Sérgio Pequito ◽

Maxwell Bertolero ◽

Jason Z. Kim ◽

Danielle S. Bassett ◽

...

Keyword(s):

Computational Models ◽

Linear Time ◽

Brain Activity ◽

External Input ◽

Imaging Data ◽

External Stimulation ◽

Cortical Input ◽

Linear Time Invariant ◽

Human Connectome Project ◽

And Task

AbstractA fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. Together, we provide evidence that this embodied brain activity model offers information about the structure and dimensionality of the BOLD signal’s external drivers and shines light on likely external sources contributing to the BOLD signal’s non-stationarity.

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