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

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.

Contralateral cortical organisation of information in visual short-term memory: Evidence from lateralized brain activity during retrieval

2012 ◽  
Vol 50 (8) ◽  
pp. 1748-1758 ◽  
Author(s):  
Ulysse Fortier-Gauthier ◽  
Nicolas Moffat ◽  
Roberto Dell'Acqua ◽  
John J. McDonald ◽  
Pierre Jolicœur
Keyword(s):  
Short Term Memory ◽  
Brain Activity ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory

scholarly journals Modulation of alpha power at encoding and retrieval tracks the precision of visual short-term memory

2014 ◽  
Vol 112 (11) ◽  
pp. 2939-2945 ◽  
Author(s):  
E. Poliakov ◽  
M. G. Stokes ◽  
M. W. Woolrich ◽  
D. Mantini ◽  
D. E. Astle
Keyword(s):  
Short Term Memory ◽  
Brain Activity ◽  
Neural Mechanism ◽  
Alpha Power ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Power Enhancement ◽  
Oscillatory Brain Activity ◽  
Initial Processing

Our ability to hold information in mind is strictly limited. We sought to understand the relationship between oscillatory brain activity and the allocation of resources within visual short-term memory (VSTM). Participants attempted to remember target arrows embedded among distracters and used a continuous method of responding to report their memory for a cued target item. Trial-to-trial variability in the absolute circular accuracy with which participants could report the target was predicted by event-related alpha synchronization during initial processing of the memoranda and by alpha desynchronization during the retrieval of those items from VSTM. Using a model-based approach, we were also able to explore further which parameters of VSTM-guided behavior were most influenced by alpha band changes. Alpha synchronization during item processing enhanced the precision with which an item could be retained without affecting the likelihood of an item being represented per se (as indexed by the guessing rate). Importantly, our data outline a neural mechanism that mirrors the precision with which items are retained; the greater the alpha power enhancement during encoding, the greater the precision with which that item can be retained.

scholarly journals A shared representation of order between encoding and recognition in visual short-term memory

2016 ◽  
Author(s):  
Kristjan Kalm ◽  
Dennis Norris
Keyword(s):  
Short Term Memory ◽  
Activity Patterns ◽  
Positional Information ◽  
Brain Regions ◽  
Order Information ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Recognition Phase ◽  
The Individual

AbstractMost complex tasks require people to bind individual stimuli into a sequence in short term memory (STM). For this purpose information about the order of the individual stimuli in the sequence needs to be in active and accessible form in STM over a period of few seconds. Here we investigated how the temporal order information is shared between the presentation and response phases of an STM task. We trained a classification algorithm on the fMRI activity patterns from the presentation phase of the STM task to predict the order of the items during the subsequent recognition phase. While voxels in a number of brain regions represented positional information during either presentation and recognition phases, only voxels in the lateral prefrontal cortex (PFC) and the anterior temporal lobe (ATL) represented position consistently across task phases. A shared positional code in the ATL might reflect verbal recoding of visual sequences to facilitate the maintenance of order information over several seconds.

scholarly journals Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

2017 ◽  
Author(s):  
Giri P. Krishnan ◽  
Oscar C. González ◽  
Maxim Bazhenov
Keyword(s):  
Computational Model ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Ion Concentration ◽  
Brain State ◽  
The Brain

AbstractResting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

Expectations impact short-term memory through changes in connectivity between attention- and task-related brain regions

Cortex ◽  
2016 ◽  
Vol 78 ◽  
pp. 1-14 ◽  
Author(s):  
Christopher Sinke ◽  
Katarina Forkmann ◽  
Katharina Schmidt ◽  
Katja Wiech ◽  
Ulrike Bingel
Keyword(s):  
Short Term Memory ◽  
Brain Regions ◽  
Short Term ◽  
Term Memory ◽  
And Task

scholarly journals The neural basis of precise visual short-term memory for complex recognisable objects

2016 ◽  
Author(s):  
Michele Veldsman ◽  
Daniel J. Mitchell ◽  
Rhodri Cusack
Keyword(s):  
Short Term Memory ◽  
Brain Regions ◽  
Core Network ◽  
Neural Basis ◽  
Short Term ◽  
Term Memory ◽  
Photographic Images ◽  
Visual Short Term Memory ◽  
Stimulus Generation ◽  
The Impact

AbstractRecent evidence suggests that visual short-term memory (VSTM) capacity estimated using simple objects, such as colours and oriented bars, may not generalise well to more naturalistic stimuli. More visual detail can be stored in VSTM when complex, recognisable objects are maintained compared to simple objects. It is not yet known if it is recognisability that enhances memory precision, nor whether maintenance of recognisable objects is achieved with the same network of brain regions supporting maintenance of simple objects.We used a novel stimulus generation method to parametrically warp photographic images along a continuum, allowing separate estimation of the precision of memory representations and the number of items retained. The stimulus generation method was also designed to create unrecognisable, though perceptually matched, stimuli, to investigate the impact of recognisability on VSTM. We adapted the widely-used change detection and continuous report paradigms for use with complex, photographic images.Across three functional magnetic resonance imaging (fMRI) experiments, we demonstrated greater precision for recognisable objects in VSTM compared to unrecognisable objects. This clear behavioural advantage was not the result of recruitment of additional brain regions, or of stronger mean activity within the core network. Representational similarity analysis revealed greater variability across item repetitions in the representations of recognisable, compared to unrecognisable complex objects. We therefore propose that a richer range of neural representations support VSTM for complex recognisable objects.

The short-term dynamics within a network of connections is creative

2003 ◽  
Vol 26 (6) ◽  
pp. 752-753
Author(s):  
William A. Phillips
Keyword(s):  
Short Term Memory ◽  
Brain Regions ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Sensory Storage

Although visual long-term memory (VLTM) and visual short-term memory (VSTM) can be distinguished from each other (and from visual sensory storage [SS]), they are embodied within the same modality-specific brain regions, but in very different ways: VLTM as patterns of connectivity and VSTM as patterns of activity. Perception and VSTM do not “activate” VLTM. They use VLTM to create novel patterns of activity relevant to novel circumstances.

Visual short term memory related brain activity predicts mathematical abilities.

2017 ◽  
Vol 31 (5) ◽  
pp. 535-545
Author(s):  
Aubrée Boulet-Craig ◽  
Philippe Robaey ◽  
Karine Lacourse ◽  
Karim Jerbi ◽  
Victor Oswald ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Brain Activity ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Mathematical Abilities

Distinguishing between lateralized and nonlateralized brain activity associated with visual short-term memory: fMRI, MEG, and EEG evidence from the same observers

NeuroImage ◽  
2010 ◽  
Vol 53 (4) ◽  
pp. 1334-1345 ◽  
Author(s):  
Nicolas Robitaille ◽  
René Marois ◽  
Jay Todd ◽  
Stephan Grimault ◽  
Douglas Cheyne ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Brain Activity ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory

scholarly journals What forms the chunks in a subject's performance? Lessons from the CHREST computational model of learning

2001 ◽  
Vol 24 (1) ◽  
pp. 128-129 ◽  
Author(s):  
Peter C.R. Lane ◽  
Fernand Gobet ◽  
Peter C-H. Cheng
Keyword(s):  
Computational Model ◽  
Computational Models ◽  
Short Term Memory ◽  
Memory Capacity ◽  
Theoretical Framework ◽  
Alternative Technique ◽  
Specific Task ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory

Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3–4 looms. An application to learning from diagrams illustrates different identifiable forms of chunk.

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