scholarly journals Speech-evoked brain activity is more robust to competing speech when it is spoken by someone familiar

2020 ◽  
Author(s):  
Emma Holmes ◽  
Ingrid S. Johnsrude
Keyword(s):  
Brain Activity ◽  
Primary Auditory Cortex ◽  
Target Sentence ◽  
Complex Signal ◽  
Middle Temporal ◽  
Cortical Areas ◽  
Novel Approach ◽  
Familiar Voice ◽  
Competing Speech ◽  
The Brain

AbstractPeople are much better at understanding speech when it is spoken by a familiar talker—such as a friend or partner—than when the interlocutor is unfamiliar. This provides an opportunity to examine the substrates of intelligibility and familiarity, independent of acoustics. Is the familiarity effect evident as early as primary auditory cortex, or only at later processing stages? Here, we presented sentences spoken by naturally familiar talkers (the participant’s friend or partner) and unfamiliar talkers (the friends or partners of other participants). We compared multivariate activity in speech-sensitive regions of cortex between conditions in which target sentences were presented alone and conditions in which the same target sentences were presented at the same time as a competing sentence. Using representational similarity analysis (RSA), we demonstrate that the pattern of activity evoked by a spoken sentence is less degraded by the presence of a competing sentence when it is spoken by a friend or partner than by someone unfamiliar; the results cannot be explained by acoustic differences since familiar and unfamiliar talkers were nearly identical across the group. This familiar-voice advantage is most prominent in nonprimary auditory cortical areas, along the posterior superior and middle temporal gyri. Across participants, the magnitude of the familiar-unfamiliar RSA difference correlates with the familiar-voice benefit to intelligibility. Overall, our results demonstrate that experience-driven improvements in intelligibility are associated with enhanced patterns of neural activity in nonprimary auditory cortical areas.Significance statementSpeech is a complex signal, and we do not yet fully understand how the content of a spoken sentence is encoded in cortex. Here, we used a novel approach based on analysing multivariate activity: we compared activity evoked by highly intelligible sentences presented alone and by the same sentences presented with a competing masker. The distributed pattern of activity in speech-sensitive regions of the brain was more similar between the alone and masker conditions when the target sentence was spoken by someone familiar—the participant’s friend or partner—than someone unfamiliar. This metric correlated with the intelligibility of the familiar voice. These results imply that the spatial pattern of activity in speech-sensitive regions reflects the intelligibility of a spoken sentence.

Spatiotemporal Conversion of Auditory Information for Cochleotopic Mapping

2007 ◽  
Vol 19 (2) ◽  
pp. 351-370 ◽  
Author(s):  
Osamu Hoshino
Keyword(s):  
Action Potentials ◽  
Delay Line ◽  
Primary Auditory Cortex ◽  
Auditory Information ◽  
Latency Time ◽  
Delay Lines ◽  
Time Intervals ◽  
Communication Signals ◽  
Cortical Areas ◽  
The Brain

Auditory communication signals such as monkey calls are complex FM vocal sounds and in general induce action potentials in different timing in the primary auditory cortex. Delay line scheme is one of the effective ways for detecting such neuronal timing. However, the scheme is not straightforwardly applicable if the time intervals of signals are beyond the latency time of delay lines. In fact, monkey calls are often expressed in longer time intervals (hundreds of milliseconds to seconds) and are beyond the latency times observed in the brain (less than several hundreds of milliseconds). Here, we propose a cochleotopic map similar to that in vision known as a retinotopic map. We show that information about monkey calls could be mapped on a cochleotopic cortical network as spatiotemporal firing patterns of neurons, which can then be decomposed into simple (linearly sweeping) FM components and integrated into unified percepts by higher cortical networks. We suggest that the spatiotemporal conversion of auditory information may be essential for developing the cochleotopic map, which could serve as the foundation for later processing, or monkey call identification by higher cortical areas.

scholarly journals Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

2020 ◽  
Author(s):  
Z. Zavecz ◽  
K. Janacsek ◽  
P. Simor ◽  
M.X. Cohen ◽  
D. Nemeth
Keyword(s):  
Functional Connectivity ◽  
Memory Consolidation ◽  
Brain Activity ◽  
Serial Order ◽  
Order Information ◽  
Novel Approach ◽  
Continuous Eeg ◽  
Induced Changes ◽  
Probability Information ◽  
The Brain

AbstractLong-term memory depends on memory consolidation that seems to rely on learning-induced changes in the brain activity. Here, we introduced a novel approach analyzing continuous EEG data to study learning-induced changes as well as trait-like characteristics in brain activity underlying consolidation. Thirty-one healthy young adults performed a learning task and their performance was retested after a short (~1h) delay, that enabled us to investigate the consolidation of serial-order and probability information simultaneously. EEG was recorded during a pre- and post-learning rest period and during learning. To investigate the brain activity associated with consolidation performance, we quantified similarities in EEG functional connectivity of learning and pre-learning rest (baseline similarity) as well as learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes in similarity from baseline to post-learning could indicate learning-induced changes, possibly spontaneous reactivation. Individuals with higher learning-induced changes in alpha frequency connectivity (8.5–9.5 Hz) showed better consolidation of serial-order information. This effect was stronger for more distant channels, highlighting the role of long-range centro-parietal networks underlying the consolidation of serial-order information. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5–3 Hz) and seemed to be dependent on more local, short-range connections. Beyond these associations with learning-induced changes, we also found substantial overlap between the baseline and post-learning similarity and their associations with consolidation performance, indicating that stable (trait-like) differences in functional connectivity networks may also be crucial for memory consolidation.Significance statementWe studied memory consolidation in humans by characterizing how similarity in neural oscillatory patterns during learning and rest periods supports consolidation. Previous studies on similarity focused on learning-induced changes (including reactivation) and neglected the stable individual characteristics that are present over resting periods and learning. Moreover, learning-induced changes are predominantly studied invasively in rodents or with neuroimaging or event-related electrophysiology techniques in humans. Here, we introduced a novel approach that enabled us 1) to reveal both learning-induced changes and trait-like individual differences in brain activity and 2) to study learning-induced changes in humans by analyzing continuous EEG. We investigated the consolidation of two types of information and revealed distinct learning-induced changes and trait-like characteristics underlying the different memory processes.

scholarly journals Novel Approach for EEG Signal Analysis in a Multifractal Paradigm of Motions. Epileptic and Eclamptic Seizures as Scale Transitions

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1024
Author(s):  
Stefan Andrei Irimiciuc ◽  
Andrei Zala ◽  
Dan Dimitriu ◽  
Loredana Maria Himiniuc ◽  
Maricel Agop ◽  
...  
Keyword(s):  
Brain Activity ◽  
Relativity Theory ◽  
Harmonic Mappings ◽  
Eeg Signal ◽  
Neuronal Dynamics ◽  
Novel Approach ◽  
Dynamic Methods ◽  
Time Variations ◽  
Scale Relativity ◽  
The Brain

Two different operational procedures are proposed for evaluating and predicting the onset of epileptic and eclamptic seizures. The first procedure analyzes the electrical activity of the brain (EEG signals) using nonlinear dynamic methods (the time variations of the standard deviation, the variance, the skewness and the kurtosis; the evolution in time of the spatial–temporal entropy; the variations of the Lyapunov coefficients, etc.). The second operational procedure reconstructs any type of EEG signal through harmonic mappings from the usual space to the hyperbolic one using the time homographic invariance of a multifractal-type Schrödinger equation in the framework of the scale relativity theory (i.e., in a multifractal paradigm of motions). More precisely, the explicit differential descriptions of the brain activity in the form of 2 × 2 matrices with real elements disclose, through the in-phase coherences at various scale resolutions (i.e., as scale transitions), the multitude of brain neuronal dynamics, especially sequences of epileptic and eclamptic seizures. These two operational procedures are not mutually exclusive, but rather become complementary, offering valuable information concerning epileptic and eclamptic seizures. In such context, the prediction of epileptic and eclamptic seizures becomes fundamental for patients not responding to medical treatment and also presenting an increased rate of seizure recurrence.

scholarly journals fMRI Dependent Components Analysis Reveals Dynamic Relations Between Functional Large Scale Cortical Networks

2016 ◽  
Author(s):  
Uri Hertz ◽  
Daniel Zoran ◽  
Yair Weiss ◽  
Amir Amedi
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Group Analysis ◽  
Mental States ◽  
Cortical Networks ◽  
Experimental Conditions ◽  
Dependency Structure ◽  
Cortical Areas ◽  
Novel Approach ◽  
Components Analysis

AbstractOne of the major advantages of whole brain fMRI is the detection of large scale cortical networks. Dependent Components Analysis (DCA) is a novel approach designed to extract both cortical networks and their dependency structure. DCA is fundamentally different from prevalent data driven approaches, i.e. spatial ICA, in that instead of maximizing the independence of components it optimizes their dependency (in a tree graph structure, tDCA) depicting cortical areas as part of multiple cortical networks. Here tDCA was shown to reliably detect large scale functional networks in single subjects and in group analysis, by clustering non-noisy components on one branch of the tree structure. We used tDCA in three fMRI experiments in which identical auditory and visual stimuli were presented, but novelty information and task relevance were modified. tDCA components tended to include two anticorrelated networks, which were detected in two separate ICA components, or belonged in one component in seed functional connectivity. Although sensory components remained the same across experiments, other components changed as a function of the experimental conditions. These changes were either within component, where it encompassed other cortical areas, or between components, where the pattern of anticorrelated networks and their statistical dependency changed. Thus tDCA may prove to be a useful, robust tool that provides a rich description of the statistical structure underlying brain activity and its relationships to changes in experimental conditions. This tool may prove effective in detection and description of mental states, neural disorders and their dynamics.

scholarly journals The Track of Brain Activity during the Observation of TV Commercials with the High-Resolution EEG Technology

2009 ◽  
Vol 2009 ◽  
pp. 1-7 ◽  
Author(s):  
Laura Astolfi ◽  
Giovanni Vecchiato ◽  
Fabrizio De Vico Fallani ◽  
Serenella Salinari ◽  
Febo Cincotti ◽  
...  
Keyword(s):  
High Resolution ◽  
Brain Activity ◽  
Normal Subjects ◽  
Significant Information ◽  
Cortical Areas ◽  
Brodmann Areas ◽  
Tv Commercials ◽  
Tv Commercial ◽  
The Brain ◽  
High Resolution Eeg

We estimate cortical activity in normal subjects during the observation of TV commercials inserted within a movie by using high-resolution EEG techniques. The brain activity was evaluated in both time and frequency domains by solving the associate inverse problem of EEG with the use of realistic head models. In particular, we recover statistically significant information about cortical areas engaged by particular scenes inserted within the TV commercial proposed with respect to the brain activity estimated while watching a documentary. Results obtained in the population investigated suggest that the statistically significant brain activity during the observation of the TV commercial was mainly concentrated in frontoparietal cortical areas, roughly coincident with the Brodmann areas 8, 9, and 7, in the analyzed population.

scholarly journals BIOFEEDBACK IN THE MAGNETIC FIELD (ON STEREOTOPOGRAPHY OF COGNITIVE FUNCTIONS)

2013 ◽  
Vol 12 (2) ◽  
pp. 7-20 ◽  
Author(s):  
K. G. Mazhirina ◽  
M. A. Pokrovskiy ◽  
M. V. Rezakova ◽  
A. A. Savelov ◽  
A. A. Sokolov ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Self Regulation ◽  
Imitation Game ◽  
Middle Temporal ◽  
The Real ◽  
Cortical Areas ◽  
On Line ◽  
The Brain ◽  
The Magnetic Field ◽  
Qualitative Characteristics

The brain was mapped on-line using fMRI technology in the process of the development of self-regulation skills. We studied the dynamics of new neural networks being created in the real and simulated biofeedback models. It was shown that immersion in a virtual story brings about the large involvement of cortical areas, which are characterized by high values of voxels in the middle-temporal, occipital and frontal regions. We discuss the qualitative characteristics of the real and the imitation game periods.

scholarly journals The RIFF: an automated environment for studying the neural basis of auditory-guided complex behavior

2021 ◽  
Author(s):  
Maciej M Jankowski ◽  
Ana Polterovich ◽  
Alexander Kazakov ◽  
Johannes Niediek ◽  
Israel Nelken
Keyword(s):  
Brain Activity ◽  
Insular Cortex ◽  
Primary Auditory Cortex ◽  
Behavioral Observation ◽  
Learning Mechanisms ◽  
Neural Basis ◽  
Implanted Electrodes ◽  
Auditory Responses ◽  
Electrophysiological Recordings ◽  
The Brain

Behavior consists of the interaction between an organism and its environment, and is controlled by the brain. However, while brain activity varies at fast, sub-seconds time scales, behavioral measures tend to be temporally coarse, often limited just to the success or failure in a trial. The large gap between the temporal resolutions at which brain and behavior are observed likely impedes our understanding of the neural mechanisms underlying behavior. To overcome this problem, we developed the RIFF: an interactive arena for rats that has multiple feeding areas, multiple sound sources, and high-resolution tracking of behavior, with concomitant wireless electrophysiological recordings. The RIFF can be flexibly programmed to create arbitrarily complex tasks that the rats have to solve. It records unrestrained rat behavior together with neuronal data from chronically implanted electrodes. We present here a detailed description of the RIFF. We illustrate its power with results from two exemplary tasks. Rats learned within two days a complex task that required timed movement, and developed anticipatory behavior. Rats found solution strategies that differed between animals but were stable within each animal. We report auditory responses in and around primary auditory cortex as well as in the posterior insular cortex, but show that often the same neurons were also sensitive to non-auditory parameters such as rat location and body orientation. These parameters are crucial for state assessment and the selection of future actions. Our findings show that the complex, unrestrained behavior of rats can be studied in a controlled environment, enabling novel insights into the cognitive capabilities and learning mechanisms of rats. This combination of electrophysiology and detailed behavioral observation opens the way to a better understanding of how the brain controls behavior.

scholarly journals Heritability of the human connectome: A connectotyping study

2018 ◽  
Vol 2 (2) ◽  
pp. 175-199 ◽  
Author(s):  
Oscar Miranda-Dominguez ◽  
Eric Feczko ◽  
David S. Grayson ◽  
Hasse Walum ◽  
Joel T. Nigg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Brain Function ◽  
Recent Progress ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Learning Framework ◽  
Novel Approach ◽  
The Brain ◽  
New Strategies

Recent progress in resting-state neuroimaging demonstrates that the brain exhibits highly individualized patterns of functional connectivity—a “connectotype.” How these individualized patterns may be constrained by environment and genetics is unknown. Here we ask whether the connectotype is familial and heritable. Using a novel approach to estimate familiality via a machine-learning framework, we analyzed resting-state fMRI scans from two well-characterized samples of child and adult siblings. First we show that individual connectotypes were reliably identified even several years after the initial scanning timepoint. Familial relationships between participants, such as siblings versus those who are unrelated, were also accurately characterized. The connectotype demonstrated substantial heritability driven by high-order systems including the fronto-parietal, dorsal attention, ventral attention, cingulo-opercular, and default systems. This work suggests that shared genetics and environment contribute toward producing complex, individualized patterns of distributed brain activity, rather than constraining local aspects of function. These insights offer new strategies for characterizing individual aberrations in brain function and evaluating heritability of brain networks.

scholarly journals Complementary topology of maintenance and manipulation brain networks in working memory

2018 ◽  
Author(s):  
S.W. Davis ◽  
C.A. Crowell ◽  
L. Beynel ◽  
L. Deng ◽  
D. Lakhlani ◽  
...  
Keyword(s):  
Working Memory ◽  
Parietal Cortex ◽  
Cognitive Functions ◽  
Short Term Memory ◽  
Brain Activity ◽  
Real Life ◽  
Protective Mechanism ◽  
Alphabetical Order ◽  
Novel Approach ◽  
The Brain

AbstractWorking memory (WM) is assumed to consist of a process that sustains memory representations in an active state (maintenance) and a process that operates on these activated representations (manipulation). Prior fMRI studies have examined maintenance and manipulation in separate task conditions, whereas in real life these processes operate simultaneously. In the current study, the neural mechanisms of maintenance and manipulation were disentangled during the same task by parametrically varying these processes. During fMRI, participants maintained consonant letters in WM while sorting them in alphabetical order. Maintenance was investigated by varying the number of letters held in WM and manipulation by varying the number of moves required to sort the list alphabetically. The study yielded three main findings. First, the degree of both maintenance and manipulation demand had significant effects on behavior that were associated with different cortical regions: maintenance was associated with bilateral prefrontal and left parietal cortex, and manipulation with right parietal activity, a link that is consistent with the role of parietal cortex in symbolic computations. Second, univariate fMRI and tractography based on diffusion-weighted imaging showed that maintenance and manipulation regions are supported by two dissociable structural networks. Finally, maintenance and manipulation functional networks became increasingly segregated with increasing demand, possibly reflecting the protection of information held in WM from interference generated by manipulation operations. These results represent a novel approach to study the brain as an adaptive system that coordinates multiple ongoing cognitive processes.Significance StatementDespite the importance of working memory (WM) in everyday life, little is known about how the brain is able to simultaneously maintain and manipulate information stored in short-term memory buffers. We examined evidence for two distinct, concurrent cognitive functions supporting maintenance and manipulation abilities by testing brain activity as participants performed a WM alphabetization task. We found behavioral and neural evidence in support of dissociable cognitive functions associated with these two operations. Furthermore, we found that connectivity between these networks was increasingly segregated as difficulty increased, and that this effect was positively related to individual WM ability. These results provide evidence that network segregation may act as a protective mechanism to enable successful performance under increasing WM demand.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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