ASHA 2007 Zemlin Memorial Award Lecture: The Neural Control of Speech

2008 ◽  
Vol 18 (1) ◽  
pp. 7-14
Author(s):  
Frank H. Guenther
Keyword(s):  
Neural Control ◽  
Brain Activity ◽  
Brain Regions ◽  
Apraxia Of Speech ◽  
Activation Patterns ◽  
Model Account ◽  
Wide Range ◽  
Ataxic Dysarthria ◽  
Memorial Award ◽  
Experimental Findings

Abstract Speech production involves coordinated processing in many regions of the brain. To better understand these processes, our research team has designed, tested, and refined a neural network model whose components correspond to brain regions involved in speech. Babbling and imitation phases are used to train neural mappings between phonological, articulatory, auditory, and somatosensory representations. After learning, the model can produce combinations of the sounds it has learned by commanding movements of an articulatory synthesizer. Computer simulations of the model account for a wide range of experimental findings, including data on acquisition of speaking skills, articulatory kinematics, and brain activity during speech. The model is also being used to investigate speech motor disorders, such as stuttering, apraxia of speech, and ataxic dysarthria. These projects compare the effects of damage to particular regions of the model to the kinematics, acoustics, or brain activation patterns of speakers with similar damage. Finally, insights from the model are being used to guide the design of a brain-computer interface for providing prosthetic speech to profoundly paralyzed individuals.

scholarly journals Dynamics of Functional Network Organization Through Graph Mixture Learning

2021 ◽  
Author(s):  
Ilaria Ricchi ◽  
Anjali Tarun ◽  
Hermina Petric Maretic ◽  
Pascal Frossard ◽  
Dimitri Van De Ville
Keyword(s):  
Structural Information ◽  
Brain Activity ◽  
Graph Laplacian ◽  
Brain Regions ◽  
Activation Patterns ◽  
Structural Connectome ◽  
Functional Brain Networks ◽  
Data Driven Approach ◽  
Time Courses ◽  
Network Patterns

Understanding the organizational principles of human brain activity at the systems level remains a major challenge in network neuroscience. Here, we introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patterns from regionally-averaged time-courses. We use the Graph Laplacian Mixture Model (GLMM), a generative model that treats functional data as a collection of signals expressed on multiple underlying graphs. By exploiting covariance between activity of brain regions, these graphs can be learned without resorting to structural information. To validate the proposed technique, we first apply it to task fMRI with a known experimental paradigm. The probability of each graph to occur at each time-point is found to be consistent with the task timing, while the spatial patterns associated to each epoch of the task are in line with previously established activation patterns using classical regression analysis. We further on apply the technique to resting state data, which leads to extracted graphs that correspond to well-known brain functional activation patterns. The GLMM allows to learn graphs entirely from the functional activity that, in practice, turn out to reveal high degrees of similarity to the structural connectome. We compared similarity of the default mode network estimated from different task data and comparing them to each other and to structure. Using different metrics, a similar distinction between high- and low-level cognitive tasks arises. Overall, this method allows us to infer relevant functional brain networks without the need of structural connectome information. Moreover, we find that these networks correspond better to structure compared to traditional methods.

scholarly journals Alcoholism gender differences in brain responsivity to emotional stimuli

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Kayle S Sawyer ◽  
Nasim Maleki ◽  
Trinity Urban ◽  
Ksenija Marinkovic ◽  
Steven Karson ◽  
...  
Keyword(s):  
Gender Differences ◽  
Brain Activity ◽  
Brain Regions ◽  
Gender Effects ◽  
Precentral Gyrus ◽  
Activation Patterns ◽  
Inferior Parietal Cortex ◽  
Underlying Basis ◽  
And Gender ◽  
Cortical Regions

Men and women may use alcohol to regulate emotions differently, with corresponding differences in neural responses. We explored how the viewing of different types of emotionally salient stimuli impacted brain activity observed through functional magnetic resonance imaging (fMRI) from 42 long-term abstinent alcoholic (25 women) and 46 nonalcoholic (24 women) participants. Analyses revealed blunted brain responsivity in alcoholic compared to nonalcoholic groups, as well as gender differences in those activation patterns. Brain activation in alcoholic men (ALCM) was significantly lower than in nonalcoholic men (NCM) in regions including rostral middle and superior frontal cortex, precentral gyrus, and inferior parietal cortex, whereas activation was higher in alcoholic women (ALCW) than in nonalcoholic women (NCW) in superior frontal and supramarginal cortical regions. The reduced brain reactivity of ALCM, and increases for ALCW, highlighted divergent brain regions and gender effects, suggesting possible differences in the underlying basis for development of alcohol use disorders.

scholarly journals The neural bases of program comprehension: a coordinate-based fMRI meta-analysis

2021 ◽  
Author(s):  
Yoshiharu Ikutani ◽  
Takeshi D. Itoh ◽  
Takatomi Kubo
Keyword(s):  
Brain Activity ◽  
Meta Analysis ◽  
Inferior Frontal Gyrus ◽  
Posterior Part ◽  
Program Comprehension ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Activation Patterns ◽  
Neural Bases ◽  
Kernel Density Analysis

AbstractThe understanding of brain activity during program comprehension have advanced thanks to noninvasive neuroimaging techniques, such as functional magnetic resonance imaging (fMRI). However, individual neuroimaging studies of program comprehension often provided inconsistent results and made it difficult to identify the neural bases. To identify the essential brain regions, this study performed a small meta-analysis on recent fMRI studies of program comprehension using multilevel kernel density analysis (MKDA). Our analysis identified a set of brain regions consistently activated in various program comprehension tasks. These regions consisted of three clusters, each of which centered at the left inferior frontal gyrus pars triangularis (IFG Tri), posterior part of middle temporal gyrus (pMTG), and right middle frontal gyrus (MFG). Additionally, subsequent analyses revealed relationships among the activation patterns in the previous studies and multiple cognitive functions. These findings suggest that program comprehension mainly recycles the language-related networks and partially employs other domain-general resources in the human brain.

scholarly journals Functional MRI of large scale activity in behaving mice

2020 ◽  
Author(s):  
Madalena S. Fonseca ◽  
Mattia G. Bergomi ◽  
Zachary F. Mainen ◽  
Noam Shemesh
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Spatial Scales ◽  
Distributed Processing ◽  
Brain Regions ◽  
Visually Guided ◽  
Dynamic Interactions ◽  
Complex Behaviour ◽  
Wide Range ◽  
Circuit Techniques

ABSTRACTBehaviour involves complex dynamic interactions across many brain regions. Detecting whole-brain activity in mice performing sophisticated behavioural tasks could facilitate insights into distributed processing underlying behaviour, guide local targeting, and help bridge the disparate spatial scales between rodent and human studies. Here, we present a comprehensive approach for recording brain-wide activity with functional magnetic resonance imaging (fMRI) compatible with a wide range of behavioural paradigms and neuroscience questions. We introduce hardware and procedural advances to allow multi-sensory, multi-action behavioural paradigms in the scanner. We identify signal artifacts arising from task-related body movements and propose novel strategies to suppress them. We validate and explore our approach in a 4-odour classical conditioning and a visually-guided operant task, illustrating how it can be used to extract information insofar intangible to rodent behaviour studies. Our work paves the way for future studies combining fMRI and local circuit techniques during complex behaviour to tackle multi-scale behavioural neuroscience questions.

scholarly journals Brain activity during real-time walking and with walking interventions after stroke: a systematic review

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Shannon B. Lim ◽  
Dennis R. Louie ◽  
Sue Peters ◽  
Teresa Liu-Ambrose ◽  
Lara A. Boyd ◽  
...  
Keyword(s):  
Systematic Review ◽  
Steady State ◽  
Real Time ◽  
Brain Activity ◽  
Brain Activation ◽  
Brain Regions ◽  
Activation Patterns ◽  
Post Stroke ◽  
Walking Performance ◽  
Walking Recovery

AbstractInvestigations of real-time brain activations during walking have become increasingly important to aid in recovery of walking after a stroke. Individual brain activation patterns can be a valuable biomarker of neuroplasticity during the rehabilitation process and can result in improved personalized medicine for rehabilitation. The purpose of this systematic review is to explore the brain activation characteristics during walking post-stroke by determining: (1) if different components of gait (i.e., initiation/acceleration, steady-state, complex) result in different brain activations, (2) whether brain activations differ from healthy individuals. Six databases were searched resulting in 22 studies. Initiation/acceleration showed bilateral activation in frontal areas; steady-state and complex walking showed broad activations with the majority exploring and finding increases in frontal regions and some studies also showing increases in parietal activation. Asymmetrical activations were often related to performance asymmetry and were more common in studies with slower gait speed. Hyperactivations and asymmetrical activations commonly decreased with walking interventions and as walking performance improved. Hyperactivations often persisted in individuals who had experienced severe strokes. Only a third of the studies included comparisons to a healthy group: individuals post-stroke employed greater brain activation compared to young adults, while comparisons to older adults were less clear and limited. Current literature suggests some indicators of walking recovery however future studies investigating more brain regions and comparisons with healthy age-matched adults are needed to further understand the effect of stroke on walking-related brain activation.

Neural Networks for the Coordination of the Hands in Time

2003 ◽  
Vol 89 (2) ◽  
pp. 1126-1135 ◽  
Author(s):  
Fredrik Ullén ◽  
Hans Forssberg ◽  
H. Henrik Ehrsson
Keyword(s):  
Neural Control ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Serial Order ◽  
Brain Regions ◽  
Motor Area ◽  
Separate Experiment ◽  
Coordination Pattern ◽  
Finger Movements ◽  
Cingulate Motor Area

Without practice, bimanual movements can typically be performed either in phase or in antiphase. Complex temporal coordination, e.g., during movements at different frequencies with a noninteger ratio (polyrhythms), requires training. Here, we investigate the organization of the neural control systems for in-phase, antiphase, and polyrhythmic coordination using functional magnetic resonance imaging (fMRI). Brisk rhythmic tapping with the index fingers was used as a model behavior. We demonstrate different patterns of brain activity during in-phase and antiphase coordination. In-phase coordination was characterized by activation of the right anterior cerebellum and cingulate motor area (CMA). Antiphase coordination was accompanied by extensive fronto-parieto-temporal activations, including the supplementary motor area (SMA), the preSMA, and the bilateral inferior parietal gyri, premotor cortex, and superior temporal gyri. When contrasting polyrhythmic tapping with in-phase tapping, activity was seen in the same set of brain regions, and in the posterior cerebellum and the CMA. Antiphase and polyrhythmic coordination may thus to a large extent use common neural control circuitry. In a separate experiment, we analyzed the neural control of the rhythmic structure and the serial order of finger movements during polyrhythmic tapping. Polyrhythmic tapping was compared with an isochronous coordination pattern that retained the same serial order of finger movements as the polyrhythm. This experiment showed that the preSMA and the bilateral superior temporal gyri may be crucial for the rhythmic control of polyrhythmic tapping, while the cerebellum, the CMA, and the premotor cortices presumably are more involved in the ordinal control of the sequence of finger movements.

scholarly journals Revisiting Nonlinear Functional Brain Co-activations: Directed, Dynamic, and Delayed

2021 ◽  
Vol 15 ◽  
Author(s):  
Ignacio Cifre ◽  
Maria T. Miller Flores ◽  
Lucia Penalba ◽  
Jeremi K. Ochab ◽  
Dante R. Chialvo
Keyword(s):  
Functional Connectivity ◽  
Brain Regions ◽  
Significant Loss ◽  
Subsequent Work ◽  
Activation Patterns ◽  
Nonlinear Functional ◽  
Co Activation ◽  
Neuro Imaging ◽  
Experimental Findings ◽  
Correlation Properties

The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In the previous study, we first demonstrated that the relatively stronger blood oxygenated level dependent (BOLD) activations contain most of the information relevant to understand functional connectivity, and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this study, we revisit the correlation properties of these epochs to define a measure of nonlinear dynamic directed functional connectivity (nldFC) across regions of interest. We show that the proposed metric provides at once, without extensive numerical complications, directed information of the functional correlations, as well as a measure of temporal lags across regions, overall offering a different and complementary perspective in the analysis of brain co-activation patterns. In this study, we provide further details for the computations of these measures and for a proof of concept based on replicating existing results from an Autistic Syndrome database, and discuss the main features and advantages of the proposed strategy for the study of brain functional correlations.

scholarly journals Neuronal Sequence Models for Bayesian Online Inference

2021 ◽  
Vol 4 ◽  
Author(s):  
Sascha Frölich ◽  
Dimitrije Marković ◽  
Stefan J. Kiebel
Keyword(s):  
Machine Learning ◽  
Sensory Input ◽  
Brain Activity ◽  
Brain Regions ◽  
Hierarchical Networks ◽  
Bayesian Brain ◽  
Fast Recognition ◽  
Wide Range ◽  
Human Ethology ◽  
The Brain

Various imaging and electrophysiological studies in a number of different species and brain regions have revealed that neuronal dynamics associated with diverse behavioral patterns and cognitive tasks take on a sequence-like structure, even when encoding stationary concepts. These neuronal sequences are characterized by robust and reproducible spatiotemporal activation patterns. This suggests that the role of neuronal sequences may be much more fundamental for brain function than is commonly believed. Furthermore, the idea that the brain is not simply a passive observer but an active predictor of its sensory input, is supported by an enormous amount of evidence in fields as diverse as human ethology and physiology, besides neuroscience. Hence, a central aspect of this review is to illustrate how neuronal sequences can be understood as critical for probabilistic predictive information processing, and what dynamical principles can be used as generators of neuronal sequences. Moreover, since different lines of evidence from neuroscience and computational modeling suggest that the brain is organized in a functional hierarchy of time scales, we will also review how models based on sequence-generating principles can be embedded in such a hierarchy, to form a generative model for recognition and prediction of sensory input. We shortly introduce the Bayesian brain hypothesis as a prominent mathematical description of how online, i.e., fast, recognition, and predictions may be computed by the brain. Finally, we briefly discuss some recent advances in machine learning, where spatiotemporally structured methods (akin to neuronal sequences) and hierarchical networks have independently been developed for a wide range of tasks. We conclude that the investigation of specific dynamical and structural principles of sequential brain activity not only helps us understand how the brain processes information and generates predictions, but also informs us about neuroscientific principles potentially useful for designing more efficient artificial neuronal networks for machine learning tasks.

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

2017 ◽  
Vol 97 (2) ◽  
pp. 767-837 ◽  
Author(s):  
Mikhail A. Lebedev ◽  
Miguel A. L. Nicolelis
Keyword(s):  
Neural Control ◽  
Large Scale ◽  
Brain Activity ◽  
Body Schema ◽  
Brain Machine Interfaces ◽  
Plastic Properties ◽  
Dynamic Circuits ◽  
Wide Range ◽  
Cord Injury ◽  
The Brain

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.

scholarly journals Brain Activity and Human Unilateral Chewing

2012 ◽  
Vol 92 (2) ◽  
pp. 136-142 ◽  
Author(s):  
A. Quintero ◽  
E. Ichesco ◽  
C. Myers ◽  
R. Schutt ◽  
G.E. Gerstner
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Initial Segment ◽  
Brain Activity ◽  
Brain Regions ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Activation Patterns ◽  
Gum Chewing

Brain mechanisms underlying mastication have been studied in non-human mammals but less so in humans. We used functional magnetic resonance imaging (fMRI) to evaluate brain activity in humans during gum chewing. Chewing was associated with activations in the cerebellum, motor cortex and caudate, cingulate, and brainstem. We also divided the 25-second chew-blocks into 5 segments of equal 5-second durations and evaluated activations within and between each of the 5 segments. This analysis revealed activation clusters unique to the initial segment, which may indicate brain regions involved with initiating chewing. Several clusters were uniquely activated during the last segment as well, which may represent brain regions involved with anticipatory or motor events associated with the end of the chew-block. In conclusion, this study provided evidence for specific brain areas associated with chewing in humans and demonstrated that brain activation patterns may dynamically change over the course of chewing sequences.

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