Functional connectivity across the human subcortical auditory system using a Gaussian copula graphical model approach with partial correlations DOI Creative Commons
Noirrit Kiran Chandra, Kevin R. Sitek, Bharath Chandrasekaran

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: Sept. 17, 2022

Abstract/Summary The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary pathway. Due to technical limitations of imaging small deep inside brain, most our knowledge is based on research in animal models using invasive methodologies. Advances ultra-high field functional magnetic resonance (fMRI) acquisition have enabled novel non-invasive investigations human subcortex, including fundamental features representation such as tonotopy periodotopy. However, connectivity across networks still underexplored humans, with ongoing development related methods. Traditionally, estimated from fMRI data full correlation matrices. partial correlations reveal relationship between two regions after removing effects all other regions, reflecting more direct connectivity. Partial analysis particularly promising ascending system, where sensory information passed an obligatory manner, nucleus up pathway, providing redundant but also increasingly abstract representations stimuli. While existing methods for learning conditional dependency assume independently identically Gaussian distributed data, exhibit significant deviations Gaussianity well high temporal autocorrelation. In this paper, we developed autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach estimate thereby infer patterns within while appropriately accounting autocorrelations successive scans. Our results show strong positive pathway each side (left right), midbrain thalamus, associative cortex. These are highly stable when splitting halves according schemes computing separately half cross-validation folds. contrast, correlation-based identified a rich network interconnectivity was not specific adjacent nodes Overall, demonstrate unique recoverable approaches reliable acquisitions.

Language: Английский

Advances in human intracranial electroencephalography research, guidelines and good practices DOI Creative Commons
Manuel Mercier, Anne‐Sophie Dubarry, François Tadel

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 260, P. 119438 - 119438

Published: July 2, 2022

Since the second half of twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into human brain. At interface between fundamental research clinic, iEEG provides high temporal resolution spatial specificity but comes with constraints, such as individual's tailored sparsity electrode sampling. Over years, researchers in neuroscience developed their practices to make most approach. Here we offer a critical review didactic framework for newcomers, well addressing issues encountered by proficient researchers. The scope is threefold: (i) common research, (ii) suggest potential guidelines working data answer frequently asked questions based on widespread practices, (iii) current neurophysiological knowledge methodologies, pave way good practice standards research. organization this paper follows steps processing. first section contextualizes collection. focuses localization electrodes. third highlights main pre-processing steps. fourth presents signal analysis methods. fifth discusses statistical approaches. sixth draws some unique perspectives Finally, ensure consistent nomenclature throughout manuscript align other guidelines, e.g., Brain Imaging Data Structure (BIDS) OHBM Committee Best Practices Analysis Sharing (COBIDAS), provide glossary disambiguate terms related

Language: Английский

Citations

129
The Temporal Voice Areas are not “just” Speech Areas DOI Creative Commons
Régis Trapeau, Etienne Thoret, Pascal Belin

et al.

Frontiers in Neuroscience, Journal Year: 2023, Volume and Issue: 16

Published: Jan. 4, 2023

The Temporal Voice Areas (TVAs) respond more strongly to speech sounds than non-speech vocal sounds, but does this make them "Speech" Areas? We provide a perspective on issue by combining univariate, multivariate, and representational similarity analyses of fMRI activations balanced set sounds. find that while activate the TVAs which is likely related their larger temporal modulations in syllabic rate, they do not appear additional areas nor are segregated from when higher activation controlled. It seems safe, then, continue calling these regions Areas.

Language: Английский

Citations

12
Cortical processing of discrete prosodic patterns in continuous speech DOI Creative Commons
G. Nike Gnanateja, Kyle Rupp,

Fernando Llanos

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 3, 2025

Language: Английский

Citations

0
Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations DOI Creative Commons
Noirrit Kiran Chandra, Kevin R. Sitek, Bharath Chandrasekaran

et al.

Imaging Neuroscience, Journal Year: 2024, Volume and Issue: 2, P. 1 - 23

Published: Jan. 1, 2024

The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary pathway. Due to technical limitations of imaging small deep inside brain, most our knowledge is based on research in animal models using invasive methodologies. Advances ultrahigh-field functional magnetic resonance (fMRI) acquisition have enabled novel noninvasive investigations human subcortex, including fundamental features representation such as tonotopy periodotopy. However, connectivity across networks still underexplored humans, with ongoing development related methods. Traditionally, estimated from fMRI data full correlation matrices. partial correlations reveal relationship between two regions after removing effects all other regions, reflecting more direct connectivity. Partial analysis particularly promising ascending system, where sensory information passed an obligatory manner, nucleus up pathway, providing redundant but also increasingly abstract representations stimuli. While existing methods for learning conditional dependency assume independently identically Gaussian distributed data, exhibit significant deviations Gaussianity well high-temporal autocorrelation. In this paper, we developed autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach estimate thereby infer patterns within while appropriately accounting autocorrelations successive scans. Our results show strong positive pathway each side (left right), midbrain thalamus, associative cortex. These are highly stable when splitting halves according schemes computing separately half cross-validation folds. contrast, correlation-based identified a rich network interconnectivity was not specific adjacent nodes Overall, demonstrate unique recoverable approaches reliable acquisitions.

Language: Английский

Citations

3
Cortical-striatal brain network distinguishes deepfake from real speaker identity DOI Creative Commons
Claudia Roswandowitz, Thayabaran Kathiresan, Elisa Pellegrino

et al.

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: June 11, 2024

Abstract Deepfakes are viral ingredients of digital environments, and they can trick human cognition into misperceiving the fake as real. Here, we test neurocognitive sensitivity 25 participants to accept or reject person identities recreated in audio deepfakes. We generate high-quality voice identity clones from natural speakers by using advanced deepfake technologies. During an matching task, show intermediate performance with voices, indicating levels deception resistance spoofing. On brain level, univariate multivariate analyses consistently reveal a central cortico-striatal network that decoded vocal acoustic pattern deepfake-level (auditory cortex), well speaker (nucleus accumbens), which valued for their social relevance. This is embedded broader neural object recognition network. Humans thus be partly tricked deepfakes, but mechanisms identified during processing open windows strengthening resilience information.

Language: Английский

Citations

2
Reconstructing Voice Identity from Noninvasive Auditory Cortex Recordings DOI Open Access
Charly Lamothe, Etienne Thoret, Régis Trapeau

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 28, 2024

Abstract The cerebral processing of voice information is known to engage, in human as well non-human primates, “temporal areas” (TVAs) that respond preferentially conspecific vocalizations. However, how represented by neuronal populations these areas, particularly speaker identity information, remains poorly understood. Here, we used a deep neural network (DNN) generate high-level, small-dimension representational space for identity—the ‘voice latent space’ (VLS)—and examined its linear relation with activity via encoding, similarity, and decoding analyses. We find the VLS maps onto fMRI measures response tens thousands stimuli from hundreds different identities better accounts geometry TVAs than A1. Moreover, allowed TVA-based reconstructions preserved essential aspects assessed both machine classifiers listeners. These results indicate DNN-derived provides high-level representations TVAs.

Language: Английский

Citations

1
Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls DOI Creative Commons

Spencer Kinsey,

Katarzyna Kazimierczak,

Pablo Andrés-Camazón

et al.

Nature Mental Health, Journal Year: 2024, Volume and Issue: 2(12), P. 1464 - 1475

Published: Nov. 21, 2024

Abstract Schizophrenia is a chronic brain disorder associated with widespread alterations in functional connectivity. Although data-driven approaches such as independent component analysis are often used to study how schizophrenia impacts linearly connected networks, within the underlying nonlinear connectivity structure remain largely unknown. Here we report of networks from explicitly magnetic resonance imaging case–control dataset. We found systematic spatial variation, higher weight core regions, suggesting that linear analyses underestimate network centers. also unique incorporating default-mode, cingulo-opercular and central executive regions exhibits hypoconnectivity schizophrenia, indicating typically hidden patterns may reflect inefficient integration psychosis. Moreover, including those previously implicated auditory, linguistic self-referential cognition exhibit heightened statistical sensitivity diagnosis, collectively underscoring potential our methodology resolve complex phenomena transform clinical analysis.

Language: Английский

Citations

1
The effects of attention in auditory–visual integration revealed by time-varying networks DOI Creative Commons
Yuhao Jiang,

Rui Qiao,

Yupan Shi

et al.

Frontiers in Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: Aug. 2, 2023

Attention and audiovisual integration are crucial subjects in the field of brain information processing. A large number previous studies have sought to determine relationship between them through specific experiments, but failed reach a unified conclusion. The reported explored frameworks early, late, parallel integration, though network analysis has been employed sparingly. In this study, we time-varying analysis, which offers comprehensive dynamic insight into cognitive processing, explore attention auditory-visual integration. combination high spatial resolution functional magnetic resonance imaging (fMRI) temporal electroencephalography (EEG) was used. Firstly, generalized linear model (GLM) find task-related fMRI activations, selected as regions interesting (ROIs) for nodes network. Then electrical activity cortex estimated via normalized minimum norm estimation (MNE) source localization method. Finally, constructed using adaptive directed transfer function (ADTF) technology. Notably, Task-related activations were mainly observed bilateral temporoparietal junction (TPJ), superior gyrus (STG), primary visual auditory areas. And revealed that V1/A1↔STG occurred before TPJ↔STG. Therefore, results supported theory attention, aligning with early framework.

Language: Английский

Citations

2
The path of voices in our brain DOI Creative Commons
Benjamin Morillon, Luc H. Arnal, Pascal Belin

et al.

PLoS Biology, Journal Year: 2022, Volume and Issue: 20(7), P. e3001742 - e3001742

Published: July 29, 2022

Categorising voices is crucial for auditory-based social interactions. A recent study by Rupp and colleagues in PLOS Biology capitalises on human intracranial recordings to describe the spatiotemporal pattern of neural activity leading voice-selective responses associative auditory cortex.

Language: Английский

Citations

3
Electrocorticography reveals the dynamics of famous voice responses in human fusiform gyrus DOI
Ariane E. Rhone, Kyle Rupp, Jasmine L. Hect

et al.

Journal of Neurophysiology, Journal Year: 2022, Volume and Issue: 129(2), P. 342 - 346

Published: Dec. 28, 2022

Interactions between auditory and visual cortices play an important role in person identification, but the dynamics of these interactions remain poorly understood. We performed direct brain recordings fusiform face cortex human epilepsy patients performing a famous voice naming task, revealing processing cortex. The findings support model top-down from to facilitate recognition.

Language: Английский

Citations

3