scholarly journals Enhanced reinstatement of naturalistic event memories due to hippocampal-network-targeted stimulation

2020 ◽  
Author(s):  
Melissa Hebscher ◽  
James E. Kragel ◽  
Thorsten Kahnt ◽  
Joel L. Voss
Keyword(s):  
Specific Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Occipital Cortex ◽  
Brain Regions ◽  
Network Location ◽  
Hippocampal Activity ◽  
Spatially Distributed ◽  
Posterior Parietal ◽  
Hippocampal Network

AbstractEpisodic memory involves the reinstatement of distributed patterns of brain activity present when events were initially experienced. The hippocampus is thought to coordinate reinstatement via its interactions with a network of brain regions, but this hypothesis has not been causally tested in humans. The current study directly tested the involvement of the hippocampal network in reinstatement using network-targeted noninvasive stimulation. We measured reinstatement of multi-voxel patterns of fMRI activity during encoding and retrieval of naturalistic video clips depicting everyday activities. Reinstatement of video-specific activity patterns was robust in posterior-parietal and occipital areas previously implicated in event reinstatement. Theta-burst stimulation targeting the hippocampal network increased videospecific reinstatement of fMRI activity patterns in occipital cortex and improved memory accuracy relative to stimulation of a control out-of-network location. Furthermore, stimulation targeting the hippocampal network influenced the trial-by-trial relationship between hippocampal activity during encoding and later reinstatement in occipital cortex. These findings implicate the hippocampal network in the reinstatement of spatially distributed patterns of event-specific activity, and identify a role for the hippocampus in encoding complex naturalistic events that later undergo cortical reinstatement.

scholarly journals Visual Number Beats Abstract Numerical Magnitude: Format-dependent Representation of Arabic Digits and Dot Patterns in Human Parietal Cortex

2015 ◽  
Vol 27 (7) ◽  
pp. 1376-1387 ◽  
Author(s):  
Jessica Bulthé ◽  
Bert De Smedt ◽  
Hans P. Op de Beeck
Keyword(s):  
Activity Pattern ◽  
Numerical Cognition ◽  
Brain Activity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Numerical Magnitude ◽  
Visual Objects ◽  
Arabic Digits ◽  
The One ◽  
Object Cognition

In numerical cognition, there is a well-known but contested hypothesis that proposes an abstract representation of numerical magnitude in human intraparietal sulcus (IPS). On the other hand, researchers of object cognition have suggested another hypothesis for brain activity in IPS during the processing of number, namely that this activity simply correlates with the number of visual objects or units that are perceived. We contrasted these two accounts by analyzing multivoxel activity patterns elicited by dot patterns and Arabic digits of different magnitudes while participants were explicitly processing the represented numerical magnitude. The activity pattern elicited by the digit “8” was more similar to the activity pattern elicited by one dot (with which the digit shares the number of visual units but not the magnitude) compared to the activity pattern elicited by eight dots, with which the digit shares the represented abstract numerical magnitude. A multivoxel pattern classifier trained to differentiate one dot from eight dots classified all Arabic digits in the one-dot pattern category, irrespective of the numerical magnitude symbolized by the digit. These results were consistently obtained for different digits in IPS, its subregions, and many other brain regions. As predicted from object cognition theories, the number of presented visual units forms the link between the parietal activation elicited by symbolic and nonsymbolic numbers. The current study is difficult to reconcile with the hypothesis that parietal activation elicited by numbers would reflect a format-independent representation of number.

scholarly journals Rapid and independent memory formation in the parietal cortex

2016 ◽  
Vol 113 (46) ◽  
pp. 13251-13256 ◽  
Author(s):  
Svenja Brodt ◽  
Dorothee Pöhlchen ◽  
Virginia L. Flanagin ◽  
Stefan Glasauer ◽  
Steffen Gais ◽  
...  
Keyword(s):  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Brain Activity ◽  
Memory Formation ◽  
Memory Representation ◽  
New Information ◽  
Hippocampal Activity ◽  
Posterior Parietal ◽  
Functional Brain Activity ◽  
Spatial Locations

Previous evidence indicates that the brain stores memory in two complementary systems, allowing both rapid plasticity and stable representations at different sites. For memory to be established in a long-lasting neocortical store, many learning repetitions are considered necessary after initial encoding into hippocampal circuits. To elucidate the dynamics of hippocampal and neocortical contributions to the early phases of memory formation, we closely followed changes in human functional brain activity while volunteers navigated through two different, initially unknown virtual environments. In one condition, they were able to encode new information continuously about the spatial layout of the maze. In the control condition, no information could be learned because the layout changed constantly. Our results show that the posterior parietal cortex (PPC) encodes memories for spatial locations rapidly, beginning already with the first visit to a location and steadily increasing activity with each additional encounter. Hippocampal activity and connectivity between the PPC and hippocampus, on the other hand, are strongest during initial encoding, and both decline with additional encounters. Importantly, stronger PPC activity related to higher memory-based performance. Compared with the nonlearnable control condition, PPC activity in the learned environment remained elevated after a 24-h interval, indicating a stable change. Our findings reflect the rapid creation of a memory representation in the PPC, which belongs to a recently proposed parietal memory network. The emerging parietal representation is specific for individual episodes of experience, predicts behavior, and remains stable over offline periods, and must therefore hold a mnemonic function.

scholarly journals The value of what’s to come: neural mechanisms coupling prediction error and reward anticipation

2019 ◽  
Author(s):  
Kiyohito Iigaya ◽  
Tobias U. Hauser ◽  
Zeb Kurth-Nelson ◽  
John P. O’Doherty ◽  
Peter Dayan ◽  
...  
Keyword(s):  
Decision Making ◽  
Information Seeking ◽  
Brain Activity ◽  
Well Being ◽  
Brain Regions ◽  
Functional Coupling ◽  
Hippocampal Activity ◽  
Neural Underpinnings ◽  
To Come ◽  
Human Participants

Having something to look forward to is a keystone of well-being. Anticipation of a future reward, like an upcoming vacation, can often be more gratifying than the very experience itself. Theories of anticipation have described how it induces behaviors ranging from beneficial information-seeking through to harmful addiction. However, it remains unclear how neural systems compute an attractive value from anticipation, instead of from the reward itself. To address this gap, we administered a decision-making task to human participants that allowed us to analyze brain activity during receipt of information predictive of future pleasant outcomes. Using a computational model of anticipatory value that captures participants’ decisions, we show that an anticipatory value signal is orchestrated by influences from three brain regions. Ventromedial prefrontal cortex (vmPFC) tracks the value of anticipation; dopaminergic midbrain responds to information that enhances anticipation, while sustained hippocampal activity provides a functional coupling between these regions. This coordinating function of the hippocampus is consistent with its known role in episodic future thinking. Our findings shed new light on the neural underpinnings of anticipation’s influence over decision-making, while also unifying a range of phenomena associated with risk and time-delay preference.

scholarly journals FFA and OFA encode distinct types of face identity information

2020 ◽  
Author(s):  
Maria Tsantani ◽  
Nikolaus Kriegeskorte ◽  
Katherine Storrs ◽  
Adrian Lloyd Williams ◽  
Carolyn McGettigan ◽  
...  
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Image Encoding ◽  
Low Level ◽  
Social Traits ◽  
Face Area ◽  
Face Similarity ◽  
And Gender ◽  
High Level

AbstractFaces of different people elicit distinct functional MRI (fMRI) patterns in several face-selective brain regions. Here we used representational similarity analysis to investigate what type of identity-distinguishing information is encoded in three face-selective regions: fusiform face area (FFA), occipital face area (OFA), and posterior superior temporal sulcus (pSTS). We used fMRI to measure brain activity patterns elicited by naturalistic videos of famous face identities, and compared their representational distances in each region with models of the differences between identities. Models included low-level to high-level image-computable properties and complex human-rated properties. We found that the FFA representation reflected perceived face similarity, social traits, and gender, and was well accounted for by the OpenFace model (deep neural network, trained to cluster faces by identity). The OFA encoded low-level image-based properties (pixel-wise and Gabor-jet dissimilarities). Our results suggest that, although FFA and OFA can both discriminate between identities, the FFA representation is further removed from the image, encoding higher-level perceptual and social face information.

scholarly journals Neural correlates of perceiving and interpreting engraved prehistoric patterns as human production: effect of archaeological expertise.

2021 ◽  
Author(s):  
Mathilde Salagnon ◽  
Sandrine Cremona ◽  
Marc Joliot ◽  
Francesco d'Errico ◽  
Emmanuel Mellet
Keyword(s):  
Decision Making ◽  
Visual Processing ◽  
Visual Analysis ◽  
Brain Activity ◽  
Occipital Cortex ◽  
Brain Regions ◽  
Salience Network ◽  
Natural Processes ◽  
And Control ◽  
Lateral Occipital Cortex

It has been suggested that engraved abstract patterns dating from the Middle and Lower Palaeolithic served as means of representation and communication. Identifying the brain regions involved in visual processing of these engravings can provide insights into their function. In this study, brain activity was measured during perception of the earliest known Palaeolithic engraved patterns and compared to natural patterns mimicking human-made engravings. Participants were asked to categorise marks as being intentionally made by humans or due to natural processes (e.g. erosion, root etching). To simulate the putative familiarity of our ancestors with the marks, the responses of expert archaeologists and control participants were compared, allowing characterisation of the effect of previous knowledge on both behaviour and brain activity in perception of the marks. Besides a set of regions common to both groups and involved in visual analysis and decision-making, the experts exhibited greater activity in the inferior part of the lateral occipital cortex, ventral occipitotemporal cortex, and medial thalamic regions. These results are consistent with those reported in visual expertise studies, and confirm the importance of the integrative visual areas in the perception of the earliest abstract engravings. The attribution of a natural rather than human origin to the marks elicited greater activity in the salience network in both groups, reflecting the uncertainty and ambiguity in the perception of, and decision-making for, natural patterns. The activation of the salience network might also be related to the process at work in the attribution of an intention to the marks. The primary visual area was not specifically involved in the visual processing of engravings, which argued against its central role in the emergence of engraving production.

scholarly journals Measurement of Benzodiazepine Receptor Number and Affinity in Humans Using Tracer Kinetic Modeling, Positron Emission Tomography, and [11C]Flumazenil

1993 ◽  
Vol 13 (4) ◽  
pp. 656-667 ◽  
Author(s):  
Julie C. Price ◽  
Helen S. Mayberg ◽  
Robert F. Dannals ◽  
Alan A. Wilson ◽  
Hayden T. Ravert ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Specific Activity ◽  
Free Fraction ◽  
Benzodiazepine Receptor ◽  
Occipital Cortex ◽  
Compartment Model ◽  
Brain Regions ◽  
Emission Tomography ◽  
Positron Emission ◽  
Convergence Problems

Kinetic methods were used to obtain regional estimates of benzodiazepine receptor concentration ( Bmax) and equilibrium dissociation constant ( Kd) from high and low specific activity (SA) [11C]flumazenil ([11C] Ro 15-1788) positron emission tomography studies of five normal volunteers. The high and low SA data were simultaneously fit to linear and nonlinear three-compartment models, respectively. An additional inhibition study (pretreatment with 0.15 mg/kg of flumazenil) was performed on one of the volunteers, which resulted in an average gray matter K1/ k2 estimate of 0.68 ± 0.08 ml/ml (linear three-compartment model, nine brain regions). The free fraction of flumazenil in plasma ( f1) was determined for each study (high SA f1: 0.50 ± 0.03; low SA f1: 0.48 ± 0.05). The free fraction in brain ( f2) was calculated using the inhibition K1/ k2 ratio and each volunteer's mean f1 value ( f2 across volunteers = 0.72 ± 0.03 ml/ml). Three methods (Methods I–III) were examined. Method I determined five kinetic parameters simultaneously [ K1, k2, k3 (= kon f2 Bmax), k4, and kon f2/SA] with no a priori constraints. An average kon value of 0.030 ± 0.003 n M−1 min−1 was estimated for receptor-rich regions using Method I. In Methods II and III, the kon f2/SA parameter was specifically constrained using the Method I value of kon and the volunteer's values of f2 and low SA (Ci/μmol). Four parameters were determined simultaneously using Method II. In Method III, K1/ k2 was fixed to the inhibition value and only three parameters were estimated. Method I provided the most variable results and convergence problems for regions with low receptor binding. Method II provided results that were less variable but very similar to the Method I results, without convergence problems. However, the K1/ k2 ratios obtained by Method II ranged from 1.07 in the occipital cortex to 0.61 in the thalamus. Fixing the K1/ k2 ratio in Method III provided a method that was physiologically consistent with the fixed value of f2 and resulted in parameters with considerably lower variability. The average Bmax values obtained using Method III were 100 ± 25 n M in the occipital cortex, 64 ±18 n M in the cerebellum, and 38 ± 5.5 n M in the thalamus; the average Kd was 8.9 ± 1.0 n M (five brain regions).

Representation of Eye Position in the Human Parietal Cortex

2010 ◽  
Vol 104 (4) ◽  
pp. 2169-2177 ◽  
Author(s):  
Adrian L. Williams ◽  
Andrew T. Smith
Keyword(s):  
Parietal Cortex ◽  
Cortical Neurons ◽  
Posterior Parietal Cortex ◽  
Visual Stimulation ◽  
Block Design ◽  
Occipital Cortex ◽  
Brain Regions ◽  
Eye Position ◽  
Posterior Aspect ◽  
Posterior Parietal

Neurons that signal eye position are thought to make a vital contribution to distinguishing real world motion from retinal motion caused by eye movements, but relatively little is known about such neurons in the human brain. Here we present data from functional MRI experiments that are consistent with the existence of neurons sensitive to eye position in darkness in the human posterior parietal cortex. We used the enhanced sensitivity of multivoxel pattern analysis (MVPA) techniques, combined with a searchlight paradigm, to isolate brain regions sensitive to direction of gaze. During data acquisition, participants were cued to direct their gaze to the left or right for sustained periods as part of a block-design paradigm. Following the exclusion of saccade-related activity from the data, the multivariate analysis showed sensitivity to tonic eye position in two localized posterior parietal regions, namely the dorsal precuneus and, more weakly, the posterior aspect of the intraparietal sulcus. Sensitivity to eye position was also seen in anterior portions of the occipital cortex. The observed sensitivity of visual cortical neurons to eye position, even in the total absence of visual stimulation, is possibly a result of feedback from posterior parietal regions that receive eye position signals and explicitly encode direction of gaze.

scholarly journals The value of what’s to come: Neural mechanisms coupling prediction error and the utility of anticipation

2020 ◽  
Vol 6 (25) ◽  
pp. eaba3828 ◽  
Author(s):  
Kiyohito Iigaya ◽  
Tobias U. Hauser ◽  
Zeb Kurth-Nelson ◽  
John P. O’Doherty ◽  
Peter Dayan ◽  
...  
Keyword(s):  
Information Seeking ◽  
Brain Activity ◽  
Well Being ◽  
Brain Regions ◽  
Neural Systems ◽  
Functional Coupling ◽  
Hippocampal Activity ◽  
To Come ◽  
Human Participants ◽  
To Receive

Having something to look forward to is a keystone of well-being. Anticipation of future reward, such as an upcoming vacation, can often be more gratifying than the experience itself. Theories suggest the utility of anticipation underpins various behaviors, ranging from beneficial information-seeking to harmful addiction. However, how neural systems compute anticipatory utility remains unclear. We analyzed the brain activity of human participants as they performed a task involving choosing whether to receive information predictive of future pleasant outcomes. Using a computational model, we show three brain regions orchestrate anticipatory utility. Specifically, ventromedial prefrontal cortex tracks the value of anticipatory utility, dopaminergic midbrain correlates with information that enhances anticipation, while sustained hippocampal activity mediates a functional coupling between these regions. Our findings suggest a previously unidentified neural underpinning for anticipation’s influence over decision-making and unify a range of phenomena associated with risk and time-delay preference.

SIMULATION OF HEALTHY AND EPILEPTIFORM BRAIN ACTIVITY USING CELLULAR AUTOMATA

2012 ◽  
Vol 22 (09) ◽  
pp. 1250229 ◽  
Author(s):  
VASSILIOS TSOUTSOURAS ◽  
GEORGIOS Ch. SIRAKOULIS ◽  
GEORGIOS P. PAVLOS ◽  
AGGELOS C. ILIOPOULOS
Keyword(s):  
Cellular Automata ◽  
Chaotic Behavior ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Global Features ◽  
Healthy State ◽  
Eeg Data ◽  
Spatially Distributed ◽  
Low Dimensional

In this study, we first present a modeling mechanism for the loss of neurons in limbic brain regions (epileptogenic focus) that could cause epileptic seizures by spreading the pathological dynamics from the focal to healthy brain regions. Prior work has shown that Cellular Automata (CAs) are very effective in simulating physical systems and solving scientific problems by capturing essential global features of the systems resulting from the collective effect of simple system components that interact locally. Nontrivial CAs are obtained whenever the dependence on the values at each CA site is nonlinear. Consequently, in this study, we show that brain activity in a healthy and epileptic state can be simulated by CA long-range interactions. Results from analysis of CA simulation data, as well as real electroencephalographic (EEG) data clearly show the efficiency of the proposed CA algorithm for simulation of the transition to an epileptic state. The results are in agreement with ones from previous studies about the existence of high-dimensional stochastic behavior during the healthy state and low-dimensional chaotic behavior during the epileptic state. The correspondence of the CA simulation results with the ones from real EEG data analysis implies that the spatiotemporal chaotic dynamics of the epileptic brain are similar to observed nonequilibrium phase transition processes in spatially distributed complex systems.

Discriminative Learning of Propagation and Spatial Pattern for Motor Imagery EEG Analysis

2013 ◽  
Vol 25 (10) ◽  
pp. 2709-2733 ◽  
Author(s):  
Xinyang Li ◽  
Haihong Zhang ◽  
Cuntai Guan ◽  
Sim Heng Ong ◽  
Kai Keng Ang ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Statistical Significance ◽  
Activity Patterns ◽  
Signal Propagation ◽  
Brain Regions ◽  
Computer Interface ◽  
Scalp Eeg

Effective learning and recovery of relevant source brain activity patterns is a major challenge to brain-computer interface using scalp EEG. Various spatial filtering solutions have been developed. Most current methods estimate an instantaneous demixing with the assumption of uncorrelatedness of the source signals. However, recent evidence in neuroscience suggests that multiple brain regions cooperate, especially during motor imagery, a major modality of brain activity for brain-computer interface. In this sense, methods that assume uncorrelatedness of the sources become inaccurate. Therefore, we are promoting a new methodology that considers both volume conduction effect and signal propagation between multiple brain regions. Specifically, we propose a novel discriminative algorithm for joint learning of propagation and spatial pattern with an iterative optimization solution. To validate the new methodology, we conduct experiments involving 16 healthy subjects and perform numerical analysis of the proposed algorithm for EEG classification in motor imagery brain-computer interface. Results from extensive analysis validate the effectiveness of the new methodology with high statistical significance.

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