Studying electroencephalography (EEG) in response to transcranial magnetic stimulation (TMS) is gaining popularity for investigating the dynamics of complex neural architecture in the brain. For example, the primary motor cortex (M1) executes voluntary movements by complex connections with other associated subnetworks. To understand these connections better, we analyzed EEG signal response to TMS at left M1 from schizophrenia patients and healthy controls and in contrast with resting state EEG recording. After removing artifacts from EEG, we conducted 2D to 3D sLORETA conversion, a well-established source localization method, for estimating signal strength of 68 source dipoles or cortical regions inside the brain. Next, we studied dynamic connectivity by computing time-evolving spatial coherence of 2278 (=68*(68-1)/2) pairs of cortical regions, with sliding window technique of 200ms window size and 20ms shift over 1sec long data. Pairs with consistent coherence (coherence>0.8 during 200+ sliding windows of patients and controls combined) were chosen for identifying stable networks. For example, we found that during the resting state, precuneus was steadily coherent with middle and superior temporal gyrus in the left hemisphere in both patient and controls. Their connectivity pattern over the sliding windows significantly differed between patients and controls (pvalue<0.05). Whereas for M1, the same was true for two other coherent pairs namely, superamarginal gyrus with lateral occipital gyrus in right hemisphere and medial orbitofrontal gyrus with fusiform in left hemisphere. The TMS-EEG dynamic connectivity results can help to differentiate patient and normal subjects and also help to better understand the brain architecture and mechanisms.
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Neural Correlates of Audiovisual Temporal Binding Window in Individuals With Schizotypal and Autistic Traits: Evidence From Resting‐State Functional Connectivity
Temporal proximity is an important clue for multisensory integration. Previous evidence indicates that individuals with autism and schizophrenia are more likely to integrate multisensory inputs over a longer temporal binding window (TBW). However, whether such deficits in audiovisual temporal integration extend to subclinical populations with high schizotypal and autistic traits are unclear. Using audiovisual simultaneity judgment (SJ) tasks for nonspeech and speech stimuli, our results suggested that the width of the audiovisual TBW was not significantly correlated with self‐reported schizotypal and autistic traits in a group of young adults. Functional magnetic resonance imaging (fMRI) resting‐state activity was also acquired to explore the neural correlates underlying inter‐individual variability of TBW width. Across the entire sample, stronger resting‐state functional connectivity (rsFC) between the left superior temporal cortex and the left precuneus, and weaker rsFC between the left cerebellum and the right dorsal lateral prefrontal cortex were correlated with a narrower TBW for speech stimuli. Meanwhile, stronger rsFC between the left anterior superior temporal gyrus and the right inferior temporal gyrus was correlated with a wider audiovisual TBW for non‐speech stimuli. The TBW‐related rsFC was not affected by levels of subclinical traits. In conclusion, this study indicates that audiovisual temporal processing may not be affected by autistic and schizotypal traits and rsFC between brain regions responding to multisensory information and timing may account for the inter‐individual difference in TBW width.
Individuals with ASD and schizophrenia are more likely to perceive asynchronous auditory and visual events as occurring simultaneously even if they are well separated in time. We investigated whether similar difficulties in audiovisual temporal processing were present in subclinical populations with high autistic and schizotypal traits. We found that the ability to detect audiovisual asynchrony was not affected by different levels of autistic and schizotypal traits. We also found that connectivity of some brain regions engaging in multisensory and timing tasks might explain an individual's tendency to bind multisensory information within a wide or narrow time window.
- NSF-PAR ID:
- 10452354
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Autism Research
- Volume:
- 14
- Issue:
- 4
- ISSN:
- 1939-3792
- Page Range / eLocation ID:
- p. 668-680
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Binding across sensory modalities yields substantial perceptual benefits, including enhanced speech intelligibility. The coincidence of sensory inputs across time is a fundamental cue for this integration process. Recent work has suggested that individuals with diagnoses of schizophrenia (
SZ ) and autism spectrum disorder (ASD ) will characterize auditory and visual events as synchronous over larger temporal disparities than their neurotypical counterparts. Namely, these clinical populations possess an enlarged temporal binding window (TBW ). Although patients withSZ andASD share aspects of their symptomatology, phenotypic similarities may result from distinct etiologies. To examine similarities and variances in audiovisual temporal function in these two populations, individuals diagnosed withASD (n = 46; controlsn = 40) andSZ (n = 16, controls = 16) completed an audiovisual simultaneity judgment task. In addition to standard psychometric analyses, synchrony judgments were assessed using Bayesian causal inference modeling. This approach permits distinguishing between distinct causes of an enlargedTBW : ana priori bias to bind sensory information and poor fidelity in the sensory representation. Findings indicate that bothASD andSZ populations show deficits in multisensory temporal acuity. Importantly, results suggest that while the widerTBW s inASD most prominently results from atypical priors, the widerTBW s inSZ results from a trend toward changes in prior and weaknesses in the sensory representations. Results are discussed in light of currentASD andSZ theories and highlight that different perceptual training paradigms focused on improving multisensory integration may be most effective in these two clinical populations and emphasize that similar phenotypes may emanate from distinct mechanistic causes. -
null (Ed.)Abstract Neurobiological models of speech perception posit that both left and right posterior temporal brain regions are involved in the early auditory analysis of speech sounds. However, frank deficits in speech perception are not readily observed in individuals with right hemisphere damage. Instead, damage to the right hemisphere is often associated with impairments in vocal identity processing. Herein lies an apparent paradox: The mapping between acoustics and speech sound categories can vary substantially across talkers, so why might right hemisphere damage selectively impair vocal identity processing without obvious effects on speech perception? In this review, I attempt to clarify the role of the right hemisphere in speech perception through a careful consideration of its role in processing vocal identity. I review evidence showing that right posterior superior temporal, right anterior superior temporal, and right inferior / middle frontal regions all play distinct roles in vocal identity processing. In considering the implications of these findings for neurobiological accounts of speech perception, I argue that the recruitment of right posterior superior temporal cortex during speech perception may specifically reflect the process of conditioning phonetic identity on talker information. I suggest that the relative lack of involvement of other right hemisphere regions in speech perception may be because speech perception does not necessarily place a high burden on talker processing systems, and I argue that the extant literature hints at potential subclinical impairments in the speech perception abilities of individuals with right hemisphere damage.more » « less
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Background Schizophrenia is a brain disorder characterized by diffuse, diverse, and wide-spread changes in gray matter volume (GM) and white matter structure (fractional anisotropy, FA), as well as cognitive impairments that greatly impact an individual’s quality of life. While the relationship of each of these image modalities and their links to schizophrenia status and cognitive impairment has been investigated separately, a multimodal fusion via parallel independent component analysis (pICA) affords the opportunity to explore the relationships between the changes in GM and FA, and the implications these network changes have on cognitive performance. Methods Images from 73 subjects with schizophrenia (SZ) and 82 healthy controls (HC) were drawn from an existing dataset. We investigated 12 components from each feature (FA and GM). Loading coefficients from the images were used to identify pairs of features that were significantly correlated and showed significant group differences between HC and SZ. MANCOVA analysis uncovered the relationships the identified spatial maps had with age, gender, and a global cognitive performance score. Results Three component pairs showed significant group differences (HC > SZ) in both gray and white matter measurements. Two of the component pairs identified networks of gray matter that drove significant relationships with cognition (HC > SZ) after accounting for age and gender. The gray and white matter structural networks identified in these three component pairs pull broadly from many regions, including the right and left thalamus, lateral occipital cortex, multiple regions of the middle temporal gyrus, precuneus cortex, postcentral gyrus, cingulate gyrus/cingulum, lingual gyrus, and brain stem. Conclusion The results of this multimodal analysis adds to our understanding of how the relationship between GM, FA, and cognition differs between HC and SZ by highlighting the correlated intermodal covariance of these structural networks and their differential relationships with cognitive performance. Previous unimodal research has found similar areas of GM and FA differences between these groups, and the cognitive deficits associated with SZ have been well documented. This study allowed us to evaluate the intercorrelated covariance of these structural networks and how these networks are involved the differences in cognitive performance between HC and SZ.more » « less
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Autism spectrum disorder is increasingly understood to be based on atypical signal transfer among multiple interconnected networks in the brain. Relative temporal patterns of neural activity have been shown to underlie both the altered neurophysiology and the altered behaviors in a variety of neurogenic disorders. We assessed brain network dynamics variability in autism spectrum disorders (ASD) using measures of synchronization (phase‐locking) strength, and timing of synchronization and desynchronization of neural activity (desynchronization ratio) across frequency bands of resting‐state electroencephalography (EEG). Our analysis indicated that frontoparietal synchronization is higher in ASD but with more short periods of desynchronization. It also indicates that the relationship between the properties of neural synchronization and behavior is different in ASD and typically developing populations. Recent theoretical studies suggest that neural networks with a high desynchronization ratio have increased sensitivity to inputs. Our results point to the potential significance of this phenomenon to the autistic brain. This sensitivity may disrupt the production of an appropriate neural and behavioral responses to external stimuli. Cognitive processes dependent on the integration of activity from multiple networks maybe, as a result, particularly vulnerable to disruption.
Autism Res 2020, 13: 24–31Lay Summary Parts of the brain can work together by synchronizing the activity of the neurons. We recorded the electrical activity of the brain in adolescents with autism spectrum disorder and then compared the recording to that of their peers without the diagnosis. We found that in participants with autism, there were a lot of very short time periods of non‐synchronized activity between frontal and parietal parts of the brain. Mathematical models show that the brain system with this kind of activity is very sensitive to external events.
