Contributions of local speech encoding and functional connectivity to audio-visual speech perception

Seeing a speaker’s face enhances speech intelligibility in adverse environments. We investigated the underlying network mechanisms by quantifying local speech representations and directed connectivity in MEG data obtained while human participants listened to speech of varying acoustic SNR and visual context. During high acoustic SNR speech encoding by temporally entrained brain activity was strong in temporal and inferior frontal cortex, while during low SNR strong entrainment emerged in premotor and superior frontal cortex. These changes in local encoding were accompanied by changes in directed connectivity along the ventral stream and the auditory-premotor axis. Importantly, the behavioral benefit arising from seeing the speaker’s face was not predicted by changes in local encoding but rather by enhanced functional connectivity between temporal and inferior frontal cortex. Our results demonstrate a role of auditory-frontal interactions in visual speech representations and suggest that functional connectivity along the ventral pathway facilitates speech comprehension in multisensory environments

Should I stay or should I go? How local-global implicit temporal expectancy shapes proactive motor control: An hdEEG study

In this study, we investigated the effect of global temporal prediction on the brain capability to implicitly adjust proactive motor control. We used the Dynamic Temporal Prediction (DTP), in which local and global predictions of an imperative stimulus were manipulated by using different stimulus-onset asynchronies (SOAs), presented with several distribution probabilities. At a behavioural level, the results show a performance adjustment (reaction time decrease) depending on the implicit use of global prediction. At a neurophysiological level, three separate computational steps underlying motor control were investigated. First, the expectancy implementation was associated with global probability-dependent contingent negative variation (CNV) modulation supported by the recruitment of a frontoparietal network involving the anterior cingulate, the left intraparietal sulcus, the occipital, and the premotor areas. Second, the response implementation was modulated by the global prediction fostering stimulus processing (P3 increase) at the motor response level, as suggested by both oscillatory (beta desynchronization), as well as source analysis (frontal cortical network). Third, the expectancy violation lead to a negativity increase (omission-detection potential) time locked to the global rule violation and additionally, to delta and theta power increase interpreted as inhibitory control and rule violation detection, respectively. The expectancy violation further engaged a left lateralized network including the temporal parietal junction (TPJ) and the motor cortex, suggesting involvement of attentional reorienting and a motor adjustment. Finally, these findings provide new insights on the neurocognitive mechanisms underlying proactive motor control, suggesting an overlapping between implicit and explicit processes

Layer-Specific fMRI Reflects Different Neuronal Computations at Different Depths in Human V1

Recent work has established that cerebral blood flow is regulated at a spatial scale that can be resolved by high field fMRI to show cortical columns in humans. While cortical columns represent a cluster of neurons with similar response properties (spanning from the pial surface to the white matter), important information regarding neuronal interactions and computational processes is also contained within a single column, distributed across the six cortical lamina. A basic understanding of underlying neuronal circuitry or computations may be revealed through investigations of the distribution of neural responses at different cortical depths. In this study, we used T2-weighted imaging with 0.7 mm (isotropic) resolution to measure fMRI responses at different depths in the gray matter while human subjects observed images with either recognizable or scrambled (physically impossible) objects. Intact and scrambled images were partially occluded, resulting in clusters of activity distributed across primary visual cortex. A subset of the identified clusters of voxels showed a preference for scrambled objects over intact; in these clusters, the fMRI response in middle layers was stronger during the presentation of scrambled objects than during the presentation of intact objects. A second experiment, using stimuli targeted at either the magnocellular or the parvocellular visual pathway, shows that laminar profiles in response to parvocellular-targeted stimuli peak in more superficial layers. These findings provide new evidence for the differential sensitivity of high-field fMRI to modulations of the neural responses at different cortical depths

Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

Functional magnetic resonance imaging (fMRI) is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD) in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities

Spectral pattern similarity analysis: Tutorial and application in developmental cognitive neuroscience

The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a number of representational properties have been identified that are linked to cognitive performance, in particular the stability, distinctiveness, and specificity of neural patterns. However, although growing cognitive abilities across childhood suggest advancements in representational quality, developmental studies still rarely utilize information-based pattern similarity approaches, especially in electroencephalography (EEG) research. Here, we provide a comprehensive methodological introduction and step-by-step tutorial for pattern similarity analysis of spectral (frequency-resolved) EEG data including a publicly available pipeline and sample dataset with data from children and adults. We discuss computation of single-subject pattern similarities and their statistical comparison at the within-person to the between-group level as well as the illustration and interpretation of the results. This tutorial targets both novice and more experienced EEG researchers and aims to facilitate the usage of spectral pattern similarity analyses, making these methodologies more readily accessible for (developmental) cognitive neuroscientists

Entrainment enhances theta oscillations and improves episodic memory.

Neural oscillations in the theta band have been linked to episodic memory, but it is unclear whether activity patterns that give rise to theta play a causal role in episodic retrieval. Here, we used rhythmic auditory and visual stimulation to entrain neural oscillations to assess whether theta activity contributes to successful memory retrieval. In two separate experiments, human subjects studied words and were subsequently tested on memory for the words ('item recognition') and the context in which each had been previously studied ('source memory'). Between study and test, subjects in the entrainment groups were exposed to audiovisual stimuli designed to enhance activity at 5.5 Hz, whereas subjects in the control groups were exposed to white noise (Expt. 1) or 14 Hz entrainment (Expt. 2). Theta entrainment selectively increased source memory performance in both studies. Electroencephalography (EEG) data in Expt. 2 revealed that theta entrainment resulted in band-specific enhancement of theta power during the entrainment period and during post-entrainment memory retrieval. These results demonstrate a direct link between theta activity and episodic memory retrieval. Targeted manipulation of theta activity could be a promising new approach to enhance theta activity and memory performance in healthy individuals and in patients with memory disorders

Uncovering contextual biases in human decision-making. A multivariate analysis approach for patterns of functional magnetic resonance imaging data and event-related potentials

Decision-making is a fundamental aspect of human cognition and behaviour. Every day, we make a multitude of decisions, ranging from rather simple perceptual choices to complex financial decisions. The underlying cognitive and neural mechanisms appear to directly deploy external information, gathered by our senses, as well as internal information, such as preferences and beliefs. Ideally, this results in well-informed decisions and successful goal-directed behaviour. In reality, however, we are often faced with decision situations in which we do not have clear preferences, or access to all information. In these situations, contextual factors appear to have a strong influence on decision-makers. This work highlights recent research supporting the hypothesis that contextual information can exert significant biases on a variety of decision processes outside decision-makers’ awareness. These studies further exemplify a content-based cognitive neuroscience approach to human decision-making research, building on multivariate analysis techniques for brain imaging data to directly predict the content of decision outcomes and other decision-related variables from brain activity