scholarly journals Micro-state-based neural decoding of speech categorization using Bayesian non-parametrics

2021 ◽  
Author(s):  
Rakib Al-Fahad ◽  
Mohammed Yeasin ◽  
Kazi Ashraf Moinuddin ◽  
Gavin M Bidelman
Keyword(s):  
Speech Perception ◽  
Language Processing ◽  
Temporal Dynamics ◽  
Response Selection ◽  
Brain Connectivity ◽  
Brain Network ◽  
Rate Of Change ◽  
Computational Method ◽  
State Transitions ◽  
Speech Categorization

Understanding the many-to-many mapping between patterns of functional brain connectivity and discrete behavioral responses is critical for speech-language processing. We present a microstate-based analysis of EEG recordings to characterize spatio-temporal dynamics of neural activities that underly rapid speech categorization decisions. We implemented a data driven approach using Bayesian non-parametrics to capture the mapping between EEG and the speed of listeners phoneme identification [i.e., response time (RT)] during speech labeling tasks. Based on our empirical analyses, we show task-relevant events such as resting-state, stimulus coding, auditory-perceptual object (category) formation, and response selection can be explained using patterns of micro-state dwell-time and are decodable as unique time segments during speech perception. State-dependent activities localize to a fronto-temporo-parietal circuit (superior temporal, supramarginal, inferior frontal gyri) exposing a core decision brain network (DN) underlying rapid speech categorization. Furthermore, RTs were inversely proportional to the frequency of state transitions, such that the rate of change between brain microstates was higher for trials with slower compared to faster RTs. Our findings imply that during rapid speech perception, higher uncertainty producing prolonged RTs (slower decision-making) is associated with staying in the DN longer compared lower RTs (faster decisions). We also show that listeners perceptual RTs are highly sensitive to individual differences. Our computational method opens a new avenue in segmentation and dynamic brain connectivity for modeling neuroimaging data and understanding task-related cognitive events.

scholarly journals Cognitive Science Honors the Memory of Jeffrey Elman

Open Mind ◽  
2019 ◽  
Vol 3 ◽  
pp. 23-30
Author(s):  
Richard N. Aslin ◽  
Roger P. Levy
Keyword(s):  
Speech Perception ◽  
Cognitive Science ◽  
Language Processing ◽  
Collaborative Research ◽  
Scientific Community ◽  
Temporal Dynamics ◽  
Network Models ◽  
Recurrent Network ◽  
Simple Recurrent Network ◽  
Trace Model

Jeff Elman (1/22/1948–6/28/2018) was a major and much beloved figure in cognitive science, best known for his work on the TRACE model of speech perception, simple recurrent network models of the temporal dynamics of language processing, and his coauthored monograph, Rethinking Innateness. Beyond his individual and collaborative research, he is widely recognized for his lasting contributions to building our scientific community. Here we celebrate his contributions by briefly recounting his life’s work and sharing commentaries and reminiscences from a number of his closest colleagues over the years.

Influence of Primary Auditory Cortex in the Characterization of Autism Spectrum in Young Adults using Brain Connectivity Parameters and Deep Belief Networks: An fMRI Study

2020 ◽  
Vol 16 (9) ◽  
pp. 1059-1073
Author(s):  
Vidhusha Srinivasan ◽  
N. Udayakumar ◽  
Kavitha Anandan
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Language Processing ◽  
Brain Connectivity ◽  
Primary Auditory Cortex ◽  
Autism Spectrum ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
High Functioning ◽  
Low Functioning

Background: The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research. Objective: This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivity parameters and distinguishes the classes using deep belief networks. Methods: The task-based functional Magnetic Resonance Images (fMRI) of both high and low functioning autistic groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involved in a defined language processing task. The language processing regions of the brain, along with Default Mode Network (DMN) have been considered for the analysis. The functional connectivity maps have been plotted through graph theory procedures. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the individual groups. These parameters have been fed to Deep Belief Networks (DBN) to classify the subjects under consideration as either LFA or HFA. Results: Results showed increased functional connectivity in high functioning subjects. It was found that the additional interaction of the Primary Auditory Cortex lying in the temporal lobe, with other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for the low functioning group. Conclusion: Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been recognized. Therefore, this work that suggests the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating different groups in autism spectrum.

scholarly journals Effect of APOE ε4 on multimodal brain connectomic traits: a persistent homology study

2020 ◽  
Vol 21 (S21) ◽  
Author(s):  
Jin Li ◽  
◽  
Chenyuan Bian ◽  
Dandan Chen ◽  
Xianglian Meng ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Genetic Risk ◽  
Brain Connectivity ◽  
Persistent Homology ◽  
Brain Network ◽  
Resonance Imaging ◽  
Modeling Framework ◽  
Apoe Ε4 ◽  
Topological Features

Abstract Background Although genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer’s disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. This study aims to investigate APOE ε4 effects on brain connectivity from the perspective of multimodal connectome. Results Here, we propose a novel multimodal brain network modeling framework and a network quantification method based on persistent homology for identifying APOE ε4-related network differences. Specifically, we employ sparse representation to integrate multimodal brain network information derived from both the resting state functional magnetic resonance imaging (rs-fMRI) data and the diffusion-weighted magnetic resonance imaging (dw-MRI) data. Moreover, persistent homology is proposed to avoid the ad hoc selection of a specific regularization parameter and to capture valuable brain connectivity patterns from the topological perspective. The experimental results demonstrate that our method outperforms the competing methods, and reasonably yields connectomic patterns specific to APOE ε4 carriers and non-carriers. Conclusions We have proposed a multimodal framework that integrates structural and functional connectivity information for constructing a fused brain network with greater discriminative power. Using persistent homology to extract topological features from the fused brain network, our method can effectively identify APOE ε4-related brain connectomic biomarkers.

scholarly journals The Causal Role of Left and Right Superior Temporal Gyri in Speech Perception in Noise: A Transcranial Magnetic Stimulation Study

2020 ◽  
Vol 32 (6) ◽  
pp. 1092-1103 ◽  
Author(s):  
Dan Kennedy-Higgins ◽  
Joseph T. Devlin ◽  
Helen E. Nuttall ◽  
Patti Adank
Keyword(s):  
Speech Perception ◽  
Side Effects ◽  
Temporal Lobe ◽  
Language Processing ◽  
Background Noise ◽  
Superior Temporal Gyrus ◽  
Left Temporal Lobe ◽  
Neural Organization ◽  
Left And Right ◽  
Listening Strategies

Successful perception of speech in everyday listening conditions requires effective listening strategies to overcome common acoustic distortions, such as background noise. Convergent evidence from neuroimaging and clinical studies identify activation within the temporal lobes as key to successful speech perception. However, current neurobiological models disagree on whether the left temporal lobe is sufficient for successful speech perception or whether bilateral processing is required. We addressed this issue using TMS to selectively disrupt processing in either the left or right superior temporal gyrus (STG) of healthy participants to test whether the left temporal lobe is sufficient or whether both left and right STG are essential. Participants repeated keywords from sentences presented in background noise in a speech reception threshold task while receiving online repetitive TMS separately to the left STG, right STG, or vertex or while receiving no TMS. Results show an equal drop in performance following application of TMS to either left or right STG during the task. A separate group of participants performed a visual discrimination threshold task to control for the confounding side effects of TMS. Results show no effect of TMS on the control task, supporting the notion that the results of Experiment 1 can be attributed to modulation of cortical functioning in STG rather than to side effects associated with online TMS. These results indicate that successful speech perception in everyday listening conditions requires both left and right STG and thus have ramifications for our understanding of the neural organization of spoken language processing.

scholarly journals Sweet anticipation: Predictability of familiar music in autism

2020 ◽  
Author(s):  
Patricia Alves Da Mota ◽  
Eloise A Stark ◽  
Henrique M Fernandes ◽  
Christine Ahrends ◽  
Joana Cabral ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Group Analysis ◽  
Brain Network ◽  
Parahippocampal Gyrus ◽  
Dynamics Analysis ◽  
Typically Developing ◽  
Cognitive Profiles ◽  
Musical Excerpts ◽  
Temporal Pole ◽  
Music Track

AbstractAutism has been characterised by different behavioural and cognitive profiles compared to typically developing (TD) individuals, and increasingly these differences have been associated with differences in structural and functional brain connectivity. It is currently unknown as to whether autistic and TD listeners process music in the same way: emotionally, mnemonically, and perceptually. The present study explores the brain’s dynamical landscape linked to music familiarity in an fMRI dataset from autistic and TD individuals. Group analysis using leading eigenvector dynamics analysis (LEiDA) revealed significantly higher probability of occurrence of a brain network in TD compared to autistic individuals during listening to familiar music. This network includes limbic and paralimbic areas (amygdala, hippocampus, parahippocampal gyrus, and temporal pole). No significant differences were found between autistic and TD individuals while listening to a scrambled, i.e. unfamiliar and more unpredictable, version of the same music track. These findings provide novel neuroimaging insights into how autistic prediction monitoring may shape brain networks during listening to familiar musical excerpts.

scholarly journals Salience Network Disruption in US Army Soldiers with PTSD

2018 ◽  
Author(s):  
Chadi Abdallah ◽  
Christopher Averill ◽  
Amy Ramage ◽  
Lynnette Averill ◽  
Selin Goktas ◽  
...  
Keyword(s):  
Resting State ◽  
Right Hemisphere ◽  
Brain Connectivity ◽  
Brain Network ◽  
Data Driven ◽  
Salience Network ◽  
Symptom Provocation ◽  
Us Army ◽  
Data Driven Approach ◽  
Army Soldiers

BACKGROUND: Better understanding of the neurobiology of posttraumatic stress disorder (PTSD) may be critical to developing novel, effective therapeutics. Here, we conducted a data-driven investigation using a well-established, graph- based topological measure of nodal strength to determine the extent of functional dysconnectivity in a cohort of active duty US Army soldiers with PTSD compared to controls. METHODS: 102 participants with (n=50) or without PTSD (n=52) completed functional magnetic resonance imaging (fMRI) at rest and during symptom provocation using subject-specific script imagery. Vertex/voxel global brain connectivity with global signal regression (GBCr), a measure of nodal strength, was calculated as the average of its functional connectivity with all other vertices/voxels in the brain gray matter. RESULTS: In contrast to during resting-state, where there were no group differences, we found a significantly higher GBCr, in PTSD participants compared to controls, in areas within the right hemisphere, including anterior insula, caudal- ventrolateral prefrontal, and rostral-ventrolateral parietal cortices. Overall, these clusters overlapped with the ventral and dorsal salience networks. Post hoc analysis showed increased GBCr in these salience clusters during symptom provocation compared to resting-state. In addition, resting-state GBCr in the salience clusters predicted GBCr during symptom provocation in PTSD participants but not in controls. CONCLUSION: In PTSD, increased connectivity within the salience network has been previously hypothesized, based primarily on seed-based connectivity findings. The current results strongly support this hypothesis using whole-brain network measure in a fully data-driven approach. It remains to be seen in future studies whether these identified salience disturbances would normalize following treatment.

scholarly journals Percolation Analysis of Brain Structural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Shu Guo ◽  
Xiaoqi Chen ◽  
Yimeng Liu ◽  
Rui Kang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Failure Modes ◽  
Critical Infrastructure ◽  
Network Reliability ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Features ◽  
Brain Network ◽  
Structure Design ◽  
Reliability Design ◽  
The Brain

The brain network is one specific type of critical infrastructure networks, which supports the cognitive function of biological systems. With the importance of network reliability in system design, evaluation, operation, and maintenance, we use the percolation methods of network reliability on brain networks and study the network resistance to disturbances and relevant failure modes. In this paper, we compare the brain networks of different species, including cat, fly, human, mouse, and macaque. The differences in structural features reflect the requirements for varying levels of functional specialization and integration, which determine the reliability of brain networks. In the percolation process, we apply different forms of disturbances to the brain networks based on metrics that characterize the network structure. Our findings suggest that the brain networks are mostly reliable against random or k-core-based percolation with their structure design, yet becomes vulnerable under betweenness or degree-based percolation. Our results might be useful to identify and distinguish brain connectivity failures that have been shown to be related to brain disorders, as well as the reliability design of other technological networks.

scholarly journals Brain connectivity during Alzheimer’s disease progression and its cognitive impact in a transgenic rat model

2019 ◽  
Author(s):  
Emma Muñoz-Moreno ◽  
Raúl Tudela ◽  
Xavier López-Gil ◽  
Guadalupe Soria
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Transgenic Animals ◽  
Brain Network ◽  
Cognitive Outcome ◽  
Structural Network ◽  
New Treatments

ABSTRACTThe research of Alzheimer’s disease (AD) in their early stages and its progression till symptomatic onset is essential to understand the pathology and investigate new treatments. Animal models provide a helpful approach to this research, since they allow for controlled follow-up during the disease evolution. In this work, transgenic TgF344-AD rats were longitudinally evaluated starting at 6 months of age. Every 3 months, cognitive abilities were assessed by a memory-related task and magnetic resonance imaging (MRI) was acquired. Structural and functional brain networks were estimated and characterized by graph metrics to identify differences between the groups in connectivity, its evolution with age, and its influence on cognition. Structural networks of transgenic animals were altered since the earliest stage. Likewise, aging significantly affected network metrics in TgF344-AD, but not in the control group. In addition, while the structural brain network influenced cognitive outcome in transgenic animals, functional network impacted how control subjects performed. TgF344-AD brain network alterations were present from very early stages, difficult to identify in clinical research. Likewise, the characterization of aging in these animals, involving structural network reorganization and its effects on cognition, opens a window to evaluate new treatments for the disease.AUTHOR SUMMARYWe have applied magnetic resonance image based connectomics to characterize TgF344-AD rats, a transgenic model of Alzheimer’s disease (AD). This represents a highly translational approach, what is essential to investigate potential treatments. TgF344-AD animals were evaluated from early to advanced ages to describe alterations in brain connectivity and how brain networks are affected by age. Results showed that aging had a bigger impact in the structural connectivity of the TgF344-AD than in control animals, and that changes in the structural network, already observed at early ages, significantly influenced cognitive outcome of transgenic animals. Alterations in connectivity were similar to the described in AD human studies, and complement them providing insights into earlier stages and a plot of AD effects throughout the whole life span.

scholarly journals From static to temporal network theory: Applications to functional brain connectivity

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

Phonotactics and Syllable Structure in Infant Speech Perception

2016 ◽  
Author(s):  
Tania S. Zamuner ◽  
Viktor Kharlamov
Keyword(s):  
Speech Perception ◽  
Language Acquisition ◽  
Language Processing ◽  
Syllable Structure ◽  
Structure Form ◽  
Modeling Studies ◽  
Wide Range ◽  
Infant Speech Perception ◽  
Sound Patterns ◽  
Linguistic Structures

Phonotactics and syllable structure form an integral part of phonological competence and may be used to discover other aspects of language. Given the importance of such knowledge to the process of language acquisition, numerous studies have investigated the development of phonotactic and syllabic knowledge in order to determine when infants become sensitive to these sound patterns and how they may use this knowledge in language processing. Considering that infants’ first exposure to linguistic structures comes from speech perception, we provide an overview of the perception-related issues that have been investigated experimentally and point out issues that have not yet been addressed in the literature. We begin with phonotactic development, examining a wide range of sound patterns, followed by a discussion of the acquisition of syllable structure and a brief summary of various outstanding issues that may be of interest to the reader, including production-related investigations and phonological modeling studies.

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