scholarly journals Speech tracking in auditory and motor regions reflects distinct linguistic features

2017 ◽  
Author(s):  
Anne Keitel ◽  
Joachim Gross ◽  
Christoph Kayser
Keyword(s):  
Time Scales ◽  
Speech Processing ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Temporal Cortex ◽  
Linguistic Features ◽  
Temporal Prediction ◽  
Beta Power ◽  
Statistical Regularities ◽  
Speech Tracking

AbstractDuring online speech processing, our brain tracks the acoustic fluctuations in speech at different time-scales. Previous research has focussed on generic time-scales (for example, delta or theta bands) that are assumed to map onto linguistic features such as prosody or syllables. However, given the high inter-subject variability in speaking patterns, such a generic association between the time-scales of brain activity and speech properties can be ambiguous. Here, we analyse speech tracking in source-localised magnetoencephalographic data by directly focusing on time-scales extracted from statistical regularities in the speech material. This revealed widespread tracking at the time-scales of phrases (0.6 – 1.3 Hz), words (1.8 – 3 Hz), syllables (2.8 – 4.8 Hz), and phonemes (8 – 12.4 Hz). Importantly, when examining the relevance for single-trial comprehension, we found stronger tracking for correctly comprehended trials in the left premotor cortex at the phrasal scale, and in left middle temporal cortex at the word scale. Control analyses using generic bands confirmed that these effects were specific to the stimulus-tailored speech regularities. Furthermore, we found that the phase at the phrasal time-scale coupled to beta-power in motor areas. This cross-frequency coupling likely mediates the comprehension effect in the motor system, and implies top-down temporal prediction in speech perception. Together, our results reveal specific functional and perceptually relevant roles of distinct entrainment processes along the auditory-motor pathway. These processes act concurrently at time-scales within the traditional delta band and highlight the role of neural tracking mechanisms that reflect the temporal characteristics of speech.

scholarly journals Increased connectivity among sensory and motor regions during visual and audiovisual speech perception

2020 ◽  
Author(s):  
Jonathan E Peelle ◽  
Brent Spehar ◽  
Michael S Jones ◽  
Sarah McConkey ◽  
Joel Myerson ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Speech Perception ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Temporal Cortex ◽  
Primary Auditory Cortex ◽  
Visual Speech ◽  
Auditory Signal ◽  
Audiovisual Speech ◽  
Audiovisual Speech Perception

In everyday conversation, we usually process the talker's face as well as the sound of their voice. Access to visual speech information is particularly useful when the auditory signal is degraded. Here we used fMRI to monitor brain activity while adults (n = 60) were presented with visual-only, auditory-only, and audiovisual words. As expected, audiovisual speech perception recruited both auditory and visual cortex, with a trend towards increased recruitment of premotor cortex in more difficult conditions (for example, in substantial background noise). We then investigated neural connectivity using psychophysiological interaction (PPI) analysis with seed regions in both primary auditory cortex and primary visual cortex. Connectivity between auditory and visual cortices was stronger in audiovisual conditions than in unimodal conditions, including a wide network of regions in posterior temporal cortex and prefrontal cortex. Taken together, our results suggest a prominent role for cross-region synchronization in understanding both visual-only and audiovisual speech.

scholarly journals Learning the statistics of pain: computational and neural mechanisms

2021 ◽  
Author(s):  
Flavia Mancini ◽  
Suyi Zhang ◽  
Ben Seymour
Keyword(s):  
Pain Intensity ◽  
Parietal Cortex ◽  
High Intensity ◽  
Posterior Parietal Cortex ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Left Hand ◽  
Statistical Inferences ◽  
Statistical Regularities ◽  
The Right

Abstract Pain invariably changes over time, and these temporal fluctuations are riddled with uncertainty about body safety. In theory, statistical regularities of pain through time contain useful information that can be learned, allowing the brain to generate expectations and inform behaviour. To investigate this, we exposed healthy participants to probabilistic sequences of low and high-intensity electrical stimuli to the left hand, containing sudden changes in stimulus frequencies. We demonstrate that humans can learn to extract these regularities, and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian models with dynamic update of beliefs. We studied brain activity using functional MRI whilst subjects performed the task, which allowed us to dissect the underlying neural correlates of these statistical inferences from their uncertainty and update. We found that the inferred frequency (posterior probability) of high intensity pain correlated with activity in bilateral sensorimotor cortex, secondary somatosensory cortex and right caudate. The uncertainty of statistical inferences of pain was encoded in the right superior parietal cortex. An intrinsic part of this hierarchical Bayesian model is the way that unexpected changes in frequency lead to shift beliefs and update the internal model. This is reflected by the KL divergence between consecutive posterior distributions and associated with brain responses in the premotor cortex, dorsolateral prefrontal cortex, and posterior parietal cortex. In conclusion, this study extends what is conventionally considered a sensory pain pathway dedicated to process pain intensity, to include the generation of Bayesian internal models of temporal statistics of pain intensity levels in sensorimotor regions, which are updated dynamically through the engagement of premotor, prefrontal and parietal regions.

scholarly journals Brain rhythms shift and deploy attention

2019 ◽  
Author(s):  
Craig G. Richter ◽  
Conrado A. Bosman ◽  
Julien Vezoli ◽  
Jan-Mathijs Schoffelen ◽  
Pascal Fries
Keyword(s):  
Left Hemisphere ◽  
Striate Cortex ◽  
Brain Activity ◽  
Premotor Cortex ◽  
High Specificity ◽  
Beta Band ◽  
Brain Rhythms ◽  
Macaque Monkeys ◽  
Beta Power ◽  
High Beta

AbstractOne of the most central cognitive functions is attention. Its neuronal underpinnings have primarily been studied during conditions of sustained attention. Much less is known about the neuronal dynamics underlying the processes of shifting attention in space, as compared to maintaining it on one stimulus, and of deploying it to a particular stimulus. Here, we use ECoG to investigate four rhythms across large parts of the left hemisphere of two macaque monkeys during a task that allows investigation of deployment and shifting. Shifting involved a strong transient enhancement of power in a 2-7 Hz theta band in frontal, pre-motor and visual areas, and reductions of power in an 11-20 Hz beta band in a fronto-centro-parietal network and in a 29-36 Hz high-beta band in premotor cortex. Deployment of attention to the contralateral hemifield involved an enhancement of beta power in parietal areas, a concomitant reduction of high-beta power in pre-motor areas and an enhancement of power in a 60-76 Hz gamma band in extra-striate cortex. Effects due to shifting occurred earlier than effects due to deployment. These results demonstrate that the four investigated rhythms are involved in attentional allocation, with striking differences between shifting and deployment between different brain areas.SignificanceWe are often confronted by many visual stimuli, and attentional mechanisms select one stimulus for in-depth processing. This involves that attention is shifted between stimuli and deployed to one stimulus at a time. Prior studies have revealed that these processes are subserved by several brain rhythms. Therefore, we recorded brain activity in macaque monkeys with many electrodes distributed over large parts of their left hemisphere, while they performed a task that involved shifting and deploying attention. We found four dominant rhythms: theta (2-7 Hz), beta (11-20 Hz), high-beta (29-36 Hz) and gamma (60-76 Hz). Attentional shifting and deployment involved dynamic modulations in the strength of those rhythms with high specificity in space and time.

scholarly journals Auditory cortical micro-networks show differential connectivity during voice and speech processing in humans

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Florence Steiner ◽  
Marine Bobin ◽  
Sascha Frühholz
Keyword(s):  
Speech Processing ◽  
Neural Circuits ◽  
Brain Activity ◽  
Inhibitory Pathways ◽  
Ac Networks ◽  
Directional Connectivity ◽  
The Right ◽  
Voice Processing ◽  
Differential Connectivity ◽  
Voice And Speech

AbstractThe temporal voice areas (TVAs) in bilateral auditory cortex (AC) appear specialized for voice processing. Previous research assumed a uniform functional profile for the TVAs which are broadly spread along the bilateral AC. Alternatively, the TVAs might comprise separate AC nodes controlling differential neural functions for voice and speech decoding, organized as local micro-circuits. To investigate micro-circuits, we modeled the directional connectivity between TVA nodes during voice processing in humans while acquiring brain activity using neuroimaging. Results show several bilateral AC nodes for general voice decoding (speech and non-speech voices) and for speech decoding in particular. Furthermore, non-hierarchical and differential bilateral AC networks manifest distinct excitatory and inhibitory pathways for voice and speech processing. Finally, while voice and speech processing seem to have distinctive but integrated neural circuits in the left AC, the right AC reveals disintegrated neural circuits for both sounds. Altogether, we demonstrate a functional heterogeneity in the TVAs for voice decoding based on local micro-circuits.

scholarly journals The impact of body posture on intrinsic brain activity: The role of beta power at rest

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0218977
Author(s):  
Brunella Donno ◽  
Daniele Migliorati ◽  
Filippo Zappasodi ◽  
Mauro Gianni Perrucci ◽  
Marcello Costantini
Keyword(s):  
Brain Activity ◽  
Body Posture ◽  
Beta Power ◽  
Intrinsic Brain Activity ◽  
The Impact

scholarly journals Brain Activation During Reading in Deep Dyslexia: An MEG Study

2000 ◽  
Vol 12 (4) ◽  
pp. 622-634 ◽  
Author(s):  
Matti Laine ◽  
Riitta Salmelin ◽  
Päivi Helenius ◽  
Reijo Marttila
Keyword(s):  
Semantic Processing ◽  
Semantic Analysis ◽  
Right Hemisphere ◽  
Word Reading ◽  
Brain Activity ◽  
Temporal Cortex ◽  
Single Word ◽  
Lexical Semantic ◽  
Deep Dyslexia ◽  
Single Word Reading

Magnetoencephalographic (MEG) changes in cortical activity were studied in a chronic Finnish-speaking deep dyslexic patient during single-word and sentence reading. It has been hypothesized that in deep dyslexia, written word recognition and its lexical-semantic analysis are subserved by the intact right hemisphere. However, in our patient, as well as in most nonimpaired readers, lexical-semantic processing as measured by sentence-final semantic-incongruency detection was related to the left superior-temporal cortex activation. Activations around this same cortical area could be identified in single-word reading as well. Another factor relevant to deep dyslexic reading, the morphological complexity of the presented words, was also studied. The effect of morphology was observed only during the preparation for oral output. By performing repeated recordings 1 year apart, we were able to document significant variability in both the spontaneous activity and the evoked responses in the lesioned left hemisphere even though at the behavioural level, the patient's performance was stable. The observed variability emphasizes the importance of estimating consistency of brain activity both within and between measurements in brain-damaged individuals.

Corticospinal Tract Microstructure Correlates With Beta Oscillatory Activity in the Primary Motor Cortex After Stroke

Stroke ◽  
2021 ◽  
Author(s):  
Robert Schulz ◽  
Marlene Bönstrup ◽  
Stephanie Guder ◽  
Jingchun Liu ◽  
Benedikt Frey ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Fractional Anisotropy ◽  
Primary Motor Cortex ◽  
Supplementary Motor Area ◽  
Premotor Cortex ◽  
Motor Impairment ◽  
Motor Area ◽  
Oscillatory Activity ◽  
Premotor Area ◽  
Beta Power

Background and Purpose: Cortical beta oscillations are reported to serve as robust measures of the integrity of the human motor system. Their alterations after stroke, such as reduced movement-related beta desynchronization in the primary motor cortex, have been repeatedly related to the level of impairment. However, there is only little data whether such measures of brain function might directly relate to structural brain changes after stroke. Methods: This multimodal study investigated 18 well-recovered patients with stroke (mean age 65 years, 12 males) by means of task-related EEG and diffusion-weighted structural MRI 3 months after stroke. Beta power at rest and movement-related beta desynchronization was assessed in 3 key motor areas of the ipsilesional hemisphere that are the primary motor cortex (M1), the ventral premotor area and the supplementary motor area. Template trajectories of corticospinal tracts (CST) originating from M1, premotor cortex, and supplementary motor area were used to quantify the microstructural state of CST subcomponents. Linear mixed-effects analyses were used to relate tract-related mean fractional anisotropy to EEG measures. Results: In the present cohort, we detected statistically significant reductions in ipsilesional CST fractional anisotropy but no alterations in EEG measures when compared with healthy controls. However, in patients with stroke, there was a significant association between both beta power at rest ( P =0.002) and movement-related beta desynchronization ( P =0.003) in M1 and fractional anisotropy of the CST specifically originating from M1. Similar structure-function relationships were neither evident for ventral premotor area and supplementary motor area, particularly with respect to their CST subcomponents originating from premotor cortex and supplementary motor area, in patients with stroke nor in controls. Conclusions: These data suggest there might be a link connecting microstructure of the CST originating from M1 pyramidal neurons and beta oscillatory activity, measures which have already been related to motor impairment in patients with stroke by previous reports.

scholarly journals Decoding Human Emotional Faces in the Dog’s Brain

2017 ◽  
Author(s):  
Raúl Hernández-Pérez ◽  
Luis Concha ◽  
Laura V. Cuaya
Keyword(s):  
Support Vector Machine Classifier ◽  
Brain Activity ◽  
Temporal Cortex ◽  
Support Vector ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Emotional Faces ◽  
Time Courses ◽  
Human Faces ◽  
Brain Fmri

AbstractDogs can interpret emotional human faces (especially the ones expressing happiness), yet the cerebral correlates of this process are unknown. Using functional magnetic resonance imaging (fMRI) we studied eight awake and unrestrained dogs. In Experiment 1 dogs observed happy and neutral human faces, and found increased brain activity when viewing happy human faces in temporal cortex and caudate. In Experiment 2 the dogs were presented with human faces expressing happiness, anger, fear, or sadness. Using the resulting cluster from Experiment 1 we trained a linear support vector machine classifier to discriminate between pairs of emotions and found that it could only discriminate between happiness and the other emotions. Finally, evaluation of the whole-brain fMRI time courses through a similar classifier allowed us to predict the emotion being observed by the dogs. Our results show that human emotions are specifically represented in dogs’ brains, highlighting their importance for inter-species communication.

scholarly journals Using experience to improve: how errors shape behavior and brain activity in monkeys

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5395
Author(s):  
Jose L. Pardo-Vazquez ◽  
Carlos Acuña
Keyword(s):  
Neuronal Activity ◽  
Decision Process ◽  
Brain Activity ◽  
Single Cells ◽  
Premotor Cortex ◽  
Strong Support ◽  
Behavioral Performance ◽  
Previous Trial ◽  
Current Trial ◽  
Future Behavior

Previous works have shown that neurons from the ventral premotor cortex (PMv) represent several elements of perceptual decisions. One of the most striking findings was that, after the outcome of the choice is known, neurons from PMv encode all the information necessary for evaluating the decision process. These results prompted us to suggest that this cortical area could be involved in shaping future behavior. In this work, we have characterized neuronal activity and behavioral performance as a function of the outcome of the previous trial. We found that the outcome of the immediately previous trial (n−1) significantly changes, in the current trial (n), the activity of single cells and behavioral performance. The outcome of trial n−2, however, does not affect either behavior or neuronal activity. Moreover, the outcome of difficult trials had a greater impact on performance and recruited more PMv neurons than the outcome of easy trials. These results give strong support to our suggestion that PMv neurons evaluate the decision process and use this information to modify future behavior.

scholarly journals Altered effective connectivity in migraine patients during emotional stimuli: a multi-frequency magnetoencephalography study

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Jing Ren ◽  
Qun Yao ◽  
Minjie Tian ◽  
Feng Li ◽  
Yueqiu Chen ◽  
...  
Keyword(s):  
Brain Activity ◽  
Effective Connectivity ◽  
Temporal Cortex ◽  
Primary Headache ◽  
Clustering Coefficient ◽  
Frequency Range ◽  
Emotional Stimuli ◽  
Emotional Modulation ◽  
Gamma Frequency ◽  
Wide Range

Abstract Background Migraine is a common and disabling primary headache, which is associated with a wide range of psychiatric comorbidities. However, the mechanisms of emotion processing in migraine are not fully understood yet. The present study aimed to investigate the neural network during neutral, positive, and negative emotional stimuli in the migraine patients. Methods A total of 24 migraine patients and 24 age- and sex-matching healthy controls were enrolled in this study. Neuromagnetic brain activity was recorded using a whole-head magnetoencephalography (MEG) system upon exposure to human facial expression stimuli. MEG data were analyzed in multi-frequency ranges from 1 to 100 Hz. Results The migraine patients exhibited a significant enhancement in the effective connectivity from the prefrontal lobe to the temporal cortex during the negative emotional stimuli in the gamma frequency (30–90 Hz). Graph theory analysis revealed that the migraine patients had an increased degree and clustering coefficient of connectivity in the delta frequency range (1–4 Hz) upon exposure to positive emotional stimuli and an increased degree of connectivity in the delta frequency range (1–4 Hz) upon exposure to negative emotional stimuli. Clinical correlation analysis showed that the history, attack frequency, duration, and neuropsychological scales of the migraine patients had a negative correlation with the network parameters in certain frequency ranges. Conclusions The results suggested that the individuals with migraine showed deviant effective connectivity in viewing the human facial expressions in multi-frequencies. The prefrontal-temporal pathway might be related to the altered negative emotional modulation in migraine. These findings suggested that migraine might be characterized by more universal altered cerebral processing of negative stimuli. Since the significant result in this study was frequency-specific, more independent replicative studies are needed to confirm these results, and to elucidate the neurocircuitry underlying the association between migraine and emotional conditions.

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