Frontal top-down signals increase coupling of auditory low-frequency oscillations to continuous speech in human listeners

Humans show a remarkable ability to understand continuous speech even under adverse listening conditions. This ability critically relies on dynamically updated predictions of incoming sensory information, but exactly how top-down predictions improve speech processing is still unclear. Brain oscillations are a likely mechanism for these top-down predictions [1 and 2]. Quasi-rhythmic components in speech are known to entrain low-frequency oscillations in auditory areas [3 and 4], and this entrainment increases with intelligibility [5]. We hypothesize that top-down signals from frontal brain areas causally modulate the phase of brain oscillations in auditory cortex. We use magnetoencephalography (MEG) to monitor brain oscillations in 22 participants during continuous speech perception. We characterize prominent spectral components of speech-brain coupling in auditory cortex and use causal connectivity analysis (transfer entropy) to identify the top-down signals driving this coupling more strongly during intelligible speech than during unintelligible speech. We report three main findings. First, frontal and motor cortices significantly modulate the phase of speech-coupled low-frequency oscillations in auditory cortex, and this effect depends on intelligibility of speech. Second, top-down signals are significantly stronger for left auditory cortex than for right auditory cortex. Third, speech-auditory cortex coupling is enhanced as a function of stronger top-down signals. Together, our results suggest that low-frequency brain oscillations play a role in implementing predictive top-down control during continuous speech perception and that top-down control is largely directed at left auditory cortex. This suggests a close relationship between (left-lateralized) speech production areas and the implementation of top-down control in continuous speech perception

Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures

Mechanisms of motor learning: by humans, for robots

Whenever we perform a movement and interact with objects in our environment, our central nervous system (CNS) adapts and controls the redundant system of muscles actuating our limbs to produce suitable forces and impedance for the interaction. As modern robots are increasingly used to interact with objects, humans and other robots, they too require to continuously adapt the interaction forces and impedance to the situation. This thesis investigated the motor mechanisms in humans through a series of technical developments and experiments, and utilized the result to implement biomimetic motor behaviours on a robot. Original tools were first developed, which enabled two novel motor imaging experiments using functional magnetic resonance imaging (fMRI). The first experiment investigated the neural correlates of force and impedance control to understand the control structure employed by the human brain. The second experiment developed a regressor free technique to detect dynamic changes in brain activations during learning, and applied this technique to investigate changes in neural activity during adaptation to force fields and visuomotor rotations. In parallel, a psychophysical experiment investigated motor optimization in humans in a task characterized by multiple error-effort optima. Finally a computational model derived from some of these results was implemented to exhibit human like control and adaptation of force, impedance and movement trajectory in a robot

Cerebral activations related to ballistic, stepwise interrupted and gradually modulated movements in parkinson patients

Patients with Parkinson's disease (PD) experience impaired initiation and inhibition of movements such as difficulty to start/stop walking. At single-joint level this is accompanied by reduced inhibition of antagonist muscle activity. While normal basal ganglia (BG) contributions to motor control include selecting appropriate muscles by inhibiting others, it is unclear how PD-related changes in BG function cause impaired movement initiation and inhibition at single-joint level. To further elucidate these changes we studied 4 right-hand movement tasks with fMRI, by dissociating activations related to abrupt movement initiation, inhibition and gradual movement modulation. Initiation and inhibition were inferred from ballistic and stepwise interrupted movement, respectively, while smooth wrist circumduction enabled the assessment of gradually modulated movement. Task-related activations were compared between PD patients (N = 12) and healthy subjects (N = 18). In healthy subjects, movement initiation was characterized by antero-ventral striatum, substantia nigra (SN) and premotor activations while inhibition was dominated by subthalamic nucleus (STN) and pallidal activations, in line with the known role of these areas in simple movement. Gradual movement mainly involved antero-dorsal putamen and pallidum. Compared to healthy subjects, patients showed reduced striatal/SN and increased pallidal activation for initiation, whereas for inhibition STN activation was reduced and striatal-thalamo-cortical activation increased. For gradual movement patients showed reduced pallidal and increased thalamo-cortical activation. We conclude that PD-related changes during movement initiation fit the (rather static) model of alterations in direct and indirect BG pathways. Reduced STN activation and regional cortical increased activation in PD during inhibition and gradual movement modulation are better explained by a dynamic model that also takes into account enhanced responsiveness to external stimuli in this disease and the effects of hyper-fluctuating cortical inputs to the striatum and STN in particular

Action Intention Modulates the Activity Pattern in Early Visual Areas

The activity pattern in the early visual cortex (EVC) can be used to predict upcoming actions as it is functionally connected to higher-order motor areas. However, the mechanism by which the EVC enhances action-relevant features is unclear. We explored this using fMRI. Participants performed Align or Open Hand movements to two oriented objects. We localized the calcarine sulcus, corresponding to the periphery, and the occipital pole, corresponding to the fovea. During planning, univariate analysis did not reveal significant results so we used multi-voxel pattern analysis (MVPA) to decode action type and object orientation. Though objects were located in the periphery, we found a significant decoding accuracy for orientation in an action-dependent manner in the occipital pole and action network areas. We established the functional connectivity between the EVC and somatomotor areas during planning using psychophysiological interaction (PPI) analysis. Taken together, our results show object orientation is modulated by action preparation

Effective connectivity reveals right-hemisphere dominance in audiospatial perception: implications for models of spatial neglect

Detecting the location of salient sounds in the environment rests on the brain's ability to use differences in sounds arriving at both ears. Functional neuroimaging studies in humans indicate that the left and right auditory hemispaces are coded asymmetrically, with a rightward attentional bias that reflects spatial attention in vision. Neuropsychological observations in patients with spatial neglect have led to the formulation of two competing models: the orientation bias and right-hemisphere dominance models. The orientation bias model posits a symmetrical mapping between one side of the sensorium and the contralateral hemisphere, with mutual inhibition of the ipsilateral hemisphere. The right-hemisphere dominance model introduces a functional asymmetry in the brain's coding of space: the left hemisphere represents the right side, whereas the right hemisphere represents both sides of the sensorium. We used Dynamic Causal Modeling of effective connectivity and Bayesian model comparison to adjudicate between these alternative network architectures, based on human electroencephalographic data acquired during an auditory location oddball paradigm. Our results support a hemispheric asymmetry in a frontoparietal network that conforms to the right-hemisphere dominance model.Weshow that, within this frontoparietal network, forward connectivity increases selectively in the hemisphere contralateral to the side of sensory stimulation. We interpret this finding in light of hierarchical predictive coding as a selective increase in attentional gain, which is mediated by feedforward connections that carry precision-weighted prediction errors during perceptual inference. This finding supports the disconnection hypothesis of unilateral neglect and has implications for theories of its etiology

Functional characterization of correct and incorrect feature integration

Advance access publication date 30 April 2022Our sensory system constantly receives information from the environment and our own body. Despite our impression to the contrary, we remain largely unaware of this information and often cannot report it correctly. Although perceptual processing does not require conscious effort on the part of the observer, it is often complex, giving rise to errors such as incorrect integration of features (illusory conjunctions). In the present study, we use functional magnetic resonance imaging to study the neural bases of feature integration in a dual task that produced ~30% illusions. A distributed set of regions demonstrated increased activity for correct compared to incorrect (illusory) feature integration, with increased functional coupling between occipital and parietal regions. In contrast, incorrect feature integration (illusions) was associated with increased occipital (V1–V2) responses at early stages, reduced functional connectivity between right occipital regions and the frontal eye field at later stages, and an overall decrease in coactivation between occipital and parietal regions. These results underscore the role of parietal regions in feature integration and highlight the relevance of functional occipito-frontal interactions in perceptual processing.This work was supported by the Spanish Ministry of Science and Innovation research projects PSI2017-88136 and PID2020-119033GB-I00 and the local government of Andalusia (Proyectos de I+D+i en el marco del Programa Operativo FEDER, B-SEJ-570-UGR20) to Ana B. Chica. Pedro M. Paz- Alonso was supported by grants from the Spanish Ministry of Science and Innovation [RYC-2014- 15440 and PGC2018-093408-B-I00], Neuroscience projects from the Fundación Tatiana Pérez de Guzmán el Bueno, Basque Government [PIBA-2021-1-0003], and a grant from “la Caixa” Banking Foundation under the project code LCF/PR/HR19/52160002. BCBL acknowledges support by the Basque Government through the BERC 2022-2025 program and by the Spanish State Research Agency through BCBL Severo Ochoa excellence accreditation CEX2020-001010-S

Feedforward and feedback control in apraxia of speech: effects of noise masking on vowel production

PURPOSE: This study was designed to test two hypotheses about apraxia of speech (AOS) derived from the Directions Into Velocities of Articulators (DIVA) model (Guenther et al., 2006): the feedforward system deficit hypothesis and the feedback system deficit hypothesis. METHOD: The authors used noise masking to minimize auditory feedback during speech. Six speakers with AOS and aphasia, 4 with aphasia without AOS, and 2 groups of speakers without impairment (younger and older adults) participated. Acoustic measures of vowel contrast, variability, and duration were analyzed. RESULTS: Younger, but not older, speakers without impairment showed significantly reduced vowel contrast with noise masking. Relative to older controls, the AOS group showed longer vowel durations overall (regardless of masking condition) and a greater reduction in vowel contrast under masking conditions. There were no significant differences in variability. Three of the 6 speakers with AOS demonstrated the group pattern. Speakers with aphasia without AOS did not differ from controls in contrast, duration, or variability. CONCLUSION: The greater reduction in vowel contrast with masking noise for the AOS group is consistent with the feedforward system deficit hypothesis but not with the feedback system deficit hypothesis; however, effects were small and not present in all individual speakers with AOS. Theoretical implications and alternative interpretations of these findings are discussed.R01 DC002852 - NIDCD NIH HHS; R01 DC007683 - NIDCD NIH HH

Early Category-Specific Cortical Activation Revealed by Visual Stimulus Inversion

Visual categorization may already start within the first 100-ms after stimulus onset, in contrast with the long-held view that during this early stage all complex stimuli are processed equally and that category-specific cortical activation occurs only at later stages. The neural basis of this proposed early stage of high-level analysis is however poorly understood. To address this question we used magnetoencephalography and anatomically-constrained distributed source modeling to monitor brain activity with millisecond-resolution while subjects performed an orientation task on the upright and upside-down presented images of three different stimulus categories: faces, houses and bodies. Significant inversion effects were found for all three stimulus categories between 70–100-ms after picture onset with a highly category-specific cortical distribution. Differential responses between upright and inverted faces were found in well-established face-selective areas of the inferior occipital cortex and right fusiform gyrus. In addition, early category-specific inversion effects were found well beyond visual areas. Our results provide the first direct evidence that category-specific processing in high-level category-sensitive cortical areas already takes place within the first 100-ms of visual processing, significantly earlier than previously thought, and suggests the existence of fast category-specific neocortical routes in the human brain