Anticipation of temporally structured events in the brain

2020 ◽  
Author(s):  
Caroline S Lee ◽  
Mariam Aly ◽  
Christopher Baldassano
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Hidden Markov ◽  
Temporal Structure ◽  
Activity Patterns ◽  
Subjective Experiences ◽  
Movie Clip ◽  
Multiple Regions ◽  
Neural Event ◽  
The Brain

Learning about temporal structure is adaptive because it enables the generation of expectations. We examined how the brain uses experience in structured environments to anticipate upcoming events. During fMRI, individuals watched a 90-second movie clip six times. Using a Hidden Markov Model applied to searchlights across the whole brain, we identified temporal shifts between activity patterns evoked by the first vs. repeated viewings of the movie clip. In multiple regions throughout the cortex, neural activity patterns for repeated viewings shifted to preceded those of initial viewing by up to 12 seconds. This anticipation varied hierarchically in a posterior (less anticipation) to anterior (more anticipation) fashion. In a subset of these regions, neural event boundaries shifted with repeated viewing to precede subjective event boundaries by 5-7 seconds. Together, these results demonstrate a hierarchy of anticipatory signals in the human brain and link them to subjective experiences of events.

scholarly journals Anticipation of temporally structured events in the brain

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Caroline S Lee ◽  
Mariam Aly ◽  
Christopher Baldassano
Keyword(s):  
Markov Model ◽  
Human Brain ◽  
Hidden Markov Model ◽  
Neural Activity ◽  
Hidden Markov ◽  
Temporal Structure ◽  
Activity Patterns ◽  
Subjective Experiences ◽  
Movie Clip ◽  
The Brain

Learning about temporal structure is adaptive because it enables the generation of expectations. We examined how the brain uses experience in structured environments to anticipate upcoming events. During fMRI, individuals watched a 90-second movie clip six times. Using a Hidden Markov Model applied to searchlights across the whole brain, we identified temporal shifts between activity patterns evoked by the first vs. repeated viewings of the movie clip. In many regions throughout the cortex, neural activity patterns for repeated viewings shifted to precede those of initial viewing by up to 15 seconds. This anticipation varied hierarchically in a posterior (less anticipation) to anterior (more anticipation) fashion. We also identified specific regions in which the timing of the brain's event boundaries were related to those of human-labeled event boundaries, with the timing of this relationship shifting on repeated viewings. With repeated viewing, the brain's event boundaries came to precede human-annotated boundaries by 1-4 seconds on average. Together, these results demonstrate a hierarchy of anticipatory signals in the human brain and link them to subjective experiences of events.

scholarly journals On the methods for reactivation and replay analysis

2020 ◽  
Vol 375 (1799) ◽  
pp. 20190231 ◽  
Author(s):  
David Tingley ◽  
Adrien Peyrache
Keyword(s):  
Neural Activity ◽  
Temporal Structure ◽  
Activity Patterns ◽  
Large Body ◽  
Neural Data ◽  
History Of ◽  
Methodological Approaches ◽  
Neuronal Patterns ◽  
External Stimuli ◽  
The Brain

A major task in the history of neurophysiology has been to relate patterns of neural activity to ongoing external stimuli. More recently, this approach has branched out to relating current neural activity patterns to external stimuli or experiences that occurred in the past or future. Here, we aim to review the large body of methodological approaches used towards this goal, and to assess the assumptions each makes with reference to the statistics of neural data that are commonly observed. These methods primarily fall into two categories, those that quantify zero-lag relationships without examining temporal evolution, termed reactivation , and those that quantify the temporal structure of changing activity patterns, termed replay . However, no two studies use the exact same approach, which prevents an unbiased comparison between findings. These observations should instead be validated by multiple and, if possible, previously established tests. This will help the community to speak a common language and will eventually provide tools to study, more generally, the organization of neuronal patterns in the brain. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals Robust and distributed neural representation of action values

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Eun Ju Shin ◽  
Yunsil Jang ◽  
Soyoun Kim ◽  
Hoseok Kim ◽  
Xinying Cai ◽  
...  
Keyword(s):  
Neural Activity ◽  
Statistical Tests ◽  
Brain Structures ◽  
Surrogate Data ◽  
Neural Representation ◽  
Decision Variables ◽  
Multiple Regions ◽  
Serial Correlations ◽  
Action Value ◽  
The Brain

Studies in rats, monkeys, and humans have found action-value signals in multiple regions of the brain. These findings suggest that action-value signals encoded in these brain structures bias choices toward higher expected rewards. However, previous estimates of action-value signals might have been inflated by serial correlations in neural activity and also by activity related to other decision variables. Here, we applied several statistical tests based on permutation and surrogate data to analyze neural activity recorded from the striatum, frontal cortex, and hippocampus. The results show that previously identified action-value signals in these brain areas cannot be entirely accounted for by concurrent serial correlations in neural activity and action value. We also found that neural activity related to action value is intermixed with signals related to other decision variables. Our findings provide strong evidence for broadly distributed neural signals related to action value throughout the brain.

scholarly journals Associations of Vitamin D and Vitamin K and Their Metabolites Across Four Regions of the Human Brain: The Memory and Aging Project (MAP) (FS05-02-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Xueyan Fu ◽  
Will Patterson ◽  
Gregory Dolnikowski ◽  
Bess Dawson-Hughes ◽  
Martha Morris ◽  
...  
Keyword(s):  
Vitamin D ◽  
Human Brain ◽  
Vitamin K ◽  
Vitamin D3 ◽  
Rank Order ◽  
Temporal Cortex ◽  
Correlation Coefficients ◽  
Brain Regions ◽  
Multiple Regions ◽  
The Brain

Abstract Objectives Very little is known about the forms of vitamin D and vitamin K in the human brain. The objective of this study is to evaluate concentrations of vitamin D and vitamin K forms in human brain and their correlations across four human brain regions. Methods Vitamin D [D3, 25(OH)D and 1,25(OH)2D] and vitamin K [phylloquinone and menaquinone-4 (MK4)] concentrations were measured by LC/MS/MS and HPLC, respectively, in four brain regions from post-mortem samples obtained from participants in the Rush Memory and Aging Project (n = 130, mean age 82 yrs, 81% female). The brain regions analyzed were the mid-frontal cortex (MF) and mid-temporal cortex (MT) [two regions important for memory in Alzheimer's Disease (AD)], the cerebellum (CR, a region not affected by AD), and the anterior watershed white matter (AWS, a region associated with vascular disease). The correlations among the vitamin forms across brain regions were calculated using Spearman rank order correlation coefficients. Significance was set at P < 0.001. Results The average concentrations of vitamin D3, 25(OH)D and MK4 were 604 pg/g, 535 pg/g, and 3.4 pmol/g, respectively. 25(OH)D and MK4 were detected in >95% of the brain samples. Nearly 92% of 1,25(OH)2D and 80% of phylloquinone samples had concentrations below the limit of assay detection (LOD) 1,25(OH)2D = 20 ng/g, phylloquinone = 0.1 pmol/g). Vitamin D3 and 25(OH)D concentrations were positively correlated across all four regions (all Spearman r ≥ 0.78, P < 0.0001). The 1,25(OH)2D was significantly correlated between the MF and CR regions only (Spearman r = 0.30, P < 0.001, all other P ≥ 0.002). MK4 and PK were positively correlated across the four regions studied (MK4 all Spearman r ≥ 0.78, phylloquinone r ≥ 0.49, all P < 0.001). Conclusions To the best of our knowledge, this study is the first evaluation of the concentrations of vitamin D and vitamin K forms in multiple regions of the human brain. Overall, the vitamin D and vitamin K forms were each positively correlated across the four brain regions studied. Future studies are needed to clarify the roles of these nutrients in AD and dementia. Funding Sources National Institute of Aging.

scholarly journals Understanding the Human Brain using Brain Organoids and a Structure-Function Theory

2020 ◽  
Author(s):  
Gabriel A. Silva ◽  
Alysson R. Muotri ◽  
Christopher White
Keyword(s):  
Human Brain ◽  
Mathematical Models ◽  
Structure Function ◽  
Neural Activity ◽  
Function Theory ◽  
Mutual Benefit ◽  
Physical Constraints ◽  
Clinical Considerations ◽  
Constraint Based Modeling ◽  
The Brain

AbstractA basic neurobiology-clinical trial paradigm motivates our use of constrained mathematical models and analysis of personalized human-derived brain organoids toward predicting clinical outcomes and safely developing new therapeutics. Physical constraints imposed on the brain can guide the analyses an interpretation of experimental data and the construction of mathematical models that attempt to make sense of how the brain works and how cognitive functions emerge. Development of these mathematical models for human-derived brain organoids offer an opportunity for testing new hypotheses about the human brain. When it comes to testing ideas about the brain that require a careful balance between experimental accessibility, manipulation, and complexity, in order to connect neurobiological details with higher level cognitive properties and clinical considerations, we argue that fundamental structure-function constraints applied to models of brain organoids offer a path forward. Moreover, we show these constraints appear in canonical and novel math models of neural activity and learning, and we make the case that constraint-based modeling and use of representations can bridge to machine learning for powerful mutual benefit.

scholarly journals Learning multiple variable-speed sequences in striatum via cortical tutoring

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
James M Murray ◽  
G Sean Escola
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Pattern Generation ◽  
Cortical Circuits ◽  
Motor Sequence ◽  
Learned Behaviors ◽  
Multiple Variable ◽  
Lesion Studies ◽  
Inhibitory Circuit ◽  
The Brain

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

scholarly journals New neural activity patterns emerge with long-term learning

2019 ◽  
Vol 116 (30) ◽  
pp. 15210-15215 ◽  
Author(s):  
Emily R. Oby ◽  
Matthew D. Golub ◽  
Jay A. Hennig ◽  
Alan D. Degenhart ◽  
Elizabeth C. Tyler-Kabara ◽  
...  
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Causal Link ◽  
Neural Population ◽  
Population Activity ◽  
The Brain ◽  
Neural Population Activity

Learning has been associated with changes in the brain at every level of organization. However, it remains difficult to establish a causal link between specific changes in the brain and new behavioral abilities. We establish that new neural activity patterns emerge with learning. We demonstrate that these new neural activity patterns cause the new behavior. Thus, the formation of new patterns of neural population activity can underlie the learning of new skills.

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