scholarly journals Stimulation-specific information is represented as local activity patterns across the brain

2019 ◽  
Author(s):  
Amirouche Sadoun ◽  
Tushar Chauhan ◽  
Samir Mameri ◽  
Yifan Zhang ◽  
Pascal Barone ◽  
...  
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
Three Dimensional ◽  
Correlation Method ◽  
Brain Regions ◽  
Specific Information ◽  
Spatially Correlated ◽  
Face Area ◽  
Spatial Configurations ◽  
The Brain

AbstractModern neuroimaging represents three-dimensional brain activity, which varies across brain regions. It remains unknown whether activity within brain regions is organized in spatial configurations to reflect perceptual and cognitive processes. We developed a rotational cross-correlation method allowing a straightforward analysis of spatial activity patterns for the precise detection of the spatially correlated distributions of brain activity. Using several statistical approaches, we found that the seed patterns in the fusiform face area were robustly correlated to brain regions involved in face-specific representations. These regions differed from the non-specific visual network meaning that activity structure in the brain is locally preserved in stimulation-specific regions. Our findings indicate spatially correlated perceptual representations in cerebral activity and suggest that the 3D coding of the processed information is organized in locally preserved activity patterns. More generally, our results provide the first demonstration that information is represented and transmitted as local spatial configurations of brain activity.

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.

Architectural Styles as Subordinate Scene Categories

2021 ◽  
pp. 225-229
Author(s):  
Dirk B. Walther
Keyword(s):  
Magnetic Resonance Imaging ◽  
Scene Perception ◽  
Brain Activity ◽  
Brain Regions ◽  
Architectural Style ◽  
Entry Level ◽  
Face Area ◽  
Architectural Styles ◽  
Subordinate Level ◽  
The Brain

How do the brains of experts and non-experts represent entry-level and subordinate-level categories of buildings and places? In the study reviewed in this chapter, the authors measured the brain activity of architecture and psychology students while they viewed images of buildings of different architectural styles as well as general scenes. From functional magnetic resonance imaging (fMRI) patterns, they were able to decode which architectural style participants viewed. Despite finding a strong behavioral expertise effect for architectural styles between the two groups of participants, the authors could not find any differences in brain activity. Surprisingly, they found that the fusiform face area, which is typically not involved in scene perception, was tightly linked with scene-selective brain regions for the decoding of architectural styles but not for entry-level scenes categories.

Brain kernel: a new spatial covariance function for fMRI data

2021 ◽  
Author(s):  
Anqi Wu ◽  
Samuel A. Nastase ◽  
Christopher A Baldassano ◽  
Nicholas B Turk-Browne ◽  
Kenneth A. Norman ◽  
...  
Keyword(s):  
Covariance Function ◽  
Brain Activity ◽  
Activity Patterns ◽  
Bilateral Symmetry ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fmri Data ◽  
Spatial Covariance ◽  
Spatial Correlation Structure ◽  
The Brain

A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore the fact that correlations in neural activity may fall off at different rates in different brain areas, or exhibit discontinuities across anatomical or functional boundaries. Moreover, such methods do not exploit the fact that widely separated brain regions may exhibit strong correlations due to bilateral symmetry or the network organization of brain regions. To capture this non-stationary spatial correlation structure, we introduce the brain kernel, a continuous covariance function for whole-brain activity patterns. We define the brain kernel in terms of a continuous nonlinear mapping from 3D brain coordinates to a latent embedding space, parametrized with a Gaussian process (GP). The brain kernel specifies the prior covariance between voxels as a function of the distance between their locations in embedding space. The GP mapping warps the brain nonlinearly so that highly correlated voxels are close together in latent space, and uncorrelated voxels are far apart. We estimate the brain kernel using resting-state fMRI data, and we develop an exact, scalable inference method based on block coordinate descent to overcome the challenges of high dimensionality (10-100K voxels). Finally, we illustrate the brain kernel's usefulness with applications to brain decoding and factor analysis with multiple task-based fMRI datasets.

scholarly journals Microsleep is associated with brain activity patterns unperturbed by auditory inputs

2019 ◽  
Vol 122 (6) ◽  
pp. 2568-2575
Author(s):  
Zixin Yong ◽  
Joo Huang Tan ◽  
Po-Jang Hsieh
Keyword(s):  
Auditory Cortex ◽  
Brain Activity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Auditory Stimulation ◽  
Simple Task ◽  
Highly Active ◽  
Early Afternoon ◽  
Mri Scans ◽  
The Brain

Microsleeps are brief episodes of arousal level decrease manifested through behavioral signs. Brain activity during microsleep in the presence of external stimulus remains poorly understood. In this study, we sought to understand neural responses to auditory stimulation during microsleep. We gave participants the simple task of listening to audios of different pitches and amplitude modulation frequencies during early afternoon functional MRI scans. We found the following: 1) microsleep was associated with cortical activations in broad motor and sensory regions and deactivations in thalamus, irrespective of auditory stimulation; 2) high and low pitch audios elicited different activity patterns in the auditory cortex during awake but not microsleep state; and 3) during microsleep, spatial activity patterns in broad brain regions were similar regardless of the presence or types of auditory stimulus (i.e., stimulus invariant). These findings show that the brain is highly active during microsleep but the activity patterns across broad regions are unperturbed by auditory inputs. NEW & NOTEWORTHY During deep drowsy states, auditory inputs could induce activations in the auditory cortex, but the activation patterns lose differentiation to high/low pitch stimuli. Instead of random activations, activity patterns across the brain during microsleep appear to be structured and may reflect underlying neurophysiological processes that remain unclear.

scholarly journals Faces and voices in the brain: a modality-general person-identity representation in superior temporal sulcus

2018 ◽  
Author(s):  
Maria Tsantani ◽  
Nikolaus Kriegeskorte ◽  
Carolyn McGettigan ◽  
Lúcia Garrido
Keyword(s):  
Auditory Processing ◽  
Brain Activity ◽  
Activity Patterns ◽  
Superior Temporal Sulcus ◽  
Brain Regions ◽  
Similarity Analysis ◽  
Representational Similarity Analysis ◽  
Person Identity ◽  
Identity Representation ◽  
The Brain

AbstractFace-selective and voice-selective brain regions have been shown to represent face-identity and voice-identity, respectively. Here we investigated whether there are modality-general person-identity representations in the brain that can be driven by either a face or a voice, and that invariantly represent naturalistically varying face and voice tokens of the same identity. According to two distinct models, such representations could exist either in multimodal brain regions (Campanella and Belin, 2007) or in face-selective brain regions via direct coupling between face- and voice-selective regions (von Kriegstein et al., 2005). To test the predictions of these two models, we used fMRI to measure brain activity patterns elicited by the faces and voices of familiar people in multimodal, face-selective and voice-selective brain regions. We used representational similarity analysis (RSA) to compare the representational geometries of face- and voice-elicited person-identities, and to investigate the degree to which pattern discriminants for pairs of identities generalise from one modality to the other. We found no matching geometries for faces and voices in any brain regions. However, we showed crossmodal generalisation of the pattern discriminants in the multimodal right posterior superior temporal sulcus (rpSTS), suggesting a modality-general person-identity representation in this region. Importantly, the rpSTS showed invariant representations of face- and voice-identities, in that discriminants were trained and tested on independent face videos (different viewpoint, lighting, background) and voice recordings (different vocalizations). Our findings support the Multimodal Processing Model, which proposes that face and voice information is integrated in multimodal brain regions.Significance statementIt is possible to identify a familiar person either by looking at their face or by listening to their voice. Using fMRI and representational similarity analysis (RSA) we show that the right posterior superior sulcus (rpSTS), a multimodal brain region that responds to both faces and voices, contains representations that can distinguish between familiar people independently of whether we are looking at their face or listening to their voice. Crucially, these representations generalised across different particular face videos and voice recordings. Our findings suggest that identity information from visual and auditory processing systems is combined and integrated in the multimodal rpSTS region.

scholarly journals Meaningful patterns of information in the brain revealed through analysis of errors

2019 ◽  
Author(s):  
Alexandra Woolgar ◽  
Nadene Dermody ◽  
Soheil Afshar ◽  
Mark A. Williams ◽  
Anina N. Rich
Keyword(s):  
Visual Cortex ◽  
Brain Activity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Stimulus Response ◽  
Correct Rule ◽  
Frontoparietal Cortex ◽  
Response Task ◽  
Incorrect Information ◽  
The Brain

SummaryGreat excitement has surrounded our ability to decode task information from human brain activity patterns, reinforcing the dominant view of the brain as an information processor. We tested a fundamental but overlooked assumption: that such decodable information is actually used by the brain to generate cognition and behaviour. Participants performed a challenging stimulus-response task during fMRI. Our novel analyses trained a pattern classifier on data from correct trials, and used it to examine stimulus and rule coding on error trials. There was a striking interaction in which frontoparietal cortex systematically represented incorrect rule but correct stimulus information when participants used the wrong rule, and incorrect stimulus but correct rule information on other types of errors. Visual cortex, by contrast, did not code correct or incorrect information on error. Thus behaviour was tightly linked to coding in frontoparietal cortex and only weakly linked to coding in visual cortex. Human behaviour may indeed result from information-like patterns of activity in the brain, but this relationship is stronger in some brain regions than in others. Testing for information coding on error can help establish which patterns constitute behaviourally-meaningful information.

Cannabidiol Has a Unique Effect on Global Brain Activity: A Pharmacological, Functional MRI Study in Awake Mice 

2021 ◽  
Author(s):  
Aymen Sadaka ◽  
Ana Ozuna ◽  
Richard Ortiz ◽  
Praveen Kulkarni ◽  
Clare Johnson ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Brain Activity ◽  
Stress Disorders ◽  
Functional Coupling ◽  
Specific Information ◽  
Imaging Data ◽  
Post Traumatic Stress ◽  
Brain Areas ◽  
Dose Dependent ◽  
The Brain

Abstract Background: The phytocannabinoid cannabidiol (CBD) is a potential treatment for post-traumatic stress disorders. How does CBD interact with the brain to alter behavior? We hypothesized that CBD would produce a dose-dependent reduction in brain activity and functional coupling in neural circuitry associated with fear and defense. Methods: During the scanning session awake mice were given vehicle or CBD (3, 10, or 30 mg/kg I.P.) and imaged for 10 min post treatment. Mice were also treated with the 10 mg/kg dose of CBD and imaged one hr later for resting state BOLD functional connectivity (rsFC). Imaging data were registered to a 3D MRI mouse atlas providing site-specific information on 138 different brain areas. Blood samples were collected for CBD measurements.Results: CBD produced a dose-dependent polarization of activation along the rostral-caudal axis of the brain. The olfactory bulb and prefrontal cortex showed an increase in positive BOLD whereas the brainstem and cerebellum showed a decrease in BOLD signal. This negative BOLD affected many areas connected to the ascending reticular activating system (ARAS). The ARAS was decoupled to much of the brain but was hyperconnected to the olfactory system and prefrontal cortex. The pattern of ARAS connectivity closely overlapped with brain areas showing high levels N-acyl-phosphatidylethanolamines-specific phospholipase D (NAPE-PLD) messenger RNA.Conclusion: The CBD-induced decrease in ARAS activity is consistent with an emerging literature suggesting that CBD reduces autonomic arousal under conditions of emotional and physical stress. The putative target and mechanism of action is NAPE-PLD the enzyme responsible for the biosynthesis of lipid signaling molecules like anandamide.

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 The Localization of Activity Sources in a Three-Dimensional Model of the Human Brain Using the Joint Analysis of EEG / sMRI Data

2019 ◽  
Vol 17 (3) ◽  
pp. 18-28
Author(s):  
E. Bykova ◽  
A. Savostyanov
Keyword(s):  
Inverse Problem ◽  
Brain Activity ◽  
Three Dimensional ◽  
Human Head ◽  
Joint Analysis ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
Use Of Data ◽  
The Individual ◽  
The Brain

Despite the large number of existing methods of the diagnosis of the brain, brain remains the least studied part of the human body. Electroencephalography (EEG) is one of the most popular methods of studying of brain activity due to its relative cheapness, harmless, and mobility of equipment. While analyzing the EEG data of the brain, the problem of solving of the inverse problem of electroencephalography, the localization of the sources of electrical activity of the brain, arises. This problem can be formulated as follows: according to the signals recorded on the surface of the head, it is necessary to determine the location of sources of these signals in the brain. The purpose of my research is to develop a software system for localization of brain activity sources based on the joint analysis of EEG and sMRI data. There are various approaches to solving of the inverse problem of EEG. To obtain the most exact results, some of them involve the use of data on the individual anatomy of the human head – structural magnetic resonance imaging (sMRI data). In this paper, one of these approaches is supposed to be used – Electromagnetic Spatiotemporal Independent Component Analysis (EMSICA) proposed by A. Tsai. The article describes the main stages of the system, such as preprocessing of the initial data; the calculation of the special matrix of the EMSICA approach, the values of which show the level of activity of a certain part of the brain; visualization of brain activity sources on its three-dimensional model.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

Sign in / Sign up

Export Citation Format

Share Document