Faculty Opinions recommendation of Effect of modafinil on learning and task-related brain activity in methamphetamine-dependent and healthy individuals.

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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A putative high risk diplotype of the G72 gene is in healthy individuals associated with better performance in working memory functions and altered brain activity in the medial temporal lobe

NeuroImage ◽

10.1016/j.neuroimage.2008.12.054 ◽

2009 ◽

Vol 45 (3) ◽

pp. 1002-1008 ◽

Cited By ~ 29

Author(s):

Andreas Jansen ◽

Sören Krach ◽

Axel Krug ◽

Valentin Markov ◽

Thomas Eggermann ◽

...

Keyword(s):

Working Memory ◽

High Risk ◽

Temporal Lobe ◽

Medial Temporal Lobe ◽

Brain Activity ◽

Healthy Individuals ◽

Memory Functions

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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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Three dimensional kinematic analyses of finger movement control and association to brain activity responses – A pilot study on healthy individuals

Gait & Posture ◽

10.1016/j.gaitpost.2017.06.464 ◽

2017 ◽

Vol 57 ◽

pp. 355

Author(s):

Helena Grip ◽

Anna-Maria Johansson ◽

Jonas Selling ◽

Louise Rönnqvist ◽

Carl-Johan Boraxbekk ◽

...

Keyword(s):

Pilot Study ◽

Brain Activity ◽

Three Dimensional ◽

Movement Control ◽

Finger Movement ◽

Healthy Individuals ◽

Kinematic Analyses

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Brain activity during reading The effects of exposure duration and task

Brain ◽

10.1093/brain/117.6.1255 ◽

1994 ◽

Vol 117 (6) ◽

pp. 1255-1269 ◽

Cited By ~ 275

Author(s):

C. J. Price ◽

R. J. S. Wise ◽

J. D. G. Watson ◽

K. Patterson ◽

D. Howard ◽

...

Keyword(s):

Exposure Duration ◽

Brain Activity ◽

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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