Nonlinear dynamics underlying sensory processing dysfunction in schizophrenia

Decoding imagined speech with delay differential analysis

Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%) for strictly delimited classification tasks, but for more complex tasks non-invasive speech decoding typically yields a 20-50% classification accuracy. However, decoder generalization, or how well algorithms perform objectively across datasets, is complicated by the small size and heterogeneity of existing EEG datasets. Furthermore, the limited availability of open access code hampers a comparison between methods. This study explores the application of a novel non-linear method for signal processing, delay differential analysis (DDA), to speech decoding. We provide a systematic evaluation of its performance on two public imagined speech decoding datasets relative to all publicly available deep learning methods. The results support DDA as a compelling alternative or complementary approach to deep learning methods for speech decoding. DDA is a fast and efficient time-domain open-source method that fits data using only few strong features and does not require extensive preprocessing.

An advance in novel intelligent sensory technologies: From an implicit-tracking perspective of food perception

Food sensory evaluation mainly includes explicit and implicit measurement methods. Implicit measures of consumer perception are gaining significant attention in food sensory and consumer science as they provide effective, subconscious, objective analysis. A wide range of advanced technologies are now available for analyzing physiological and psychological responses, including facial analysis technology, neuroimaging technology, autonomic nervous system technology, and behavioral pattern measurement. However, researchers in the food field often lack systematic knowledge of these multidisciplinary technologies and struggle with interpreting their results. In order to bridge this gap, this review systematically describes the principles and highlights the applications in food sensory and consumer science of facial analysis technologies such as eye tracking, facial electromyography, and automatic facial expression analysis, as well as neuroimaging technologies like electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and functional near-infrared spectroscopy. Furthermore, we critically compare and discuss these advanced implicit techniques in the context of food sensory research and then accordingly propose prospects. Ultimately, we conclude that implicit measures should be complemented by traditional explicit measures to capture responses beyond preference. Facial analysis technologies offer a more objective reflection of sensory perception and attitudes toward food, whereas neuroimaging techniques provide valuable insight into the implicit physiological responses during food consumption. To enhance the interpretability and generalizability of implicit measurement results, further sensory studies are needed. Looking ahead, the combination of different methodological techniques in real-life situations holds promise for consumer sensory science in the field of food research.

Network-motif delay differential analysis of brain activity during seizures

Delay Differential Analysis (DDA) is a nonlinear method for analyzing time series based on principles from nonlinear dynamical systems. DDA is extended here to incorporate network aspects to improve the dynamical characterization of complex systems. To demonstrate its effectiveness, DDA with network capabilities was first applied to the well-known Rössler system under different parameter regimes and noise conditions. Network-motif DDA, based on cortical regions, was then applied to invasive intracranial electroencephalographic data from drug-resistant epilepsy patients undergoing presurgical monitoring. The directional network motifs between brain areas that emerge from this analysis change dramatically before, during, and after seizures. Neural systems provide a rich source of complex data, arising from varying internal states generated by network interactions.

Low-frequency repetitive transcranial magnetic stimulation alters the individual functional dynamical landscape

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive approach to modulate brain activity and behavior in humans. Still, how individual resting-state brain dynamics after rTMS evolves across different functional configurations is rarely studied. Here, using resting state fMRI data from healthy subjects, we aimed to examine the effects of rTMS to individual large-scale brain dynamics. Using Topological Data Analysis based Mapper approach, we construct the precise dynamic mapping (PDM) for each participant. To reveal the relationship between PDM and canonical functional representation of the resting brain, we annotated the graph using relative activation proportion of a set of large-scale resting-state networks (RSNs) and assigned the single brain volume to corresponding RSN-dominant or a hub state (not any RSN was dominant). Our results show that (i) low-frequency rTMS could induce changed temporal evolution of brain states; (ii) rTMS didn't alter the hub-periphery configurations underlined resting-state brain dynamics; and (iii) the rTMS effects on brain dynamics differ across the left frontal and occipital lobe. In conclusion, low-frequency rTMS significantly alters the individual temporo-spatial dynamics, and our finding further suggested a potential target-dependent alteration of brain dynamics. This work provides a new perspective to comprehend the heterogeneous effect of rTMS.

Latent brain state dynamics and cognitive flexibility in older adults

Cognitive impairment in older adults is a rapidly growing public health concern as the elderly population dramatically grows worldwide. While it is generally assumed that cognitive deficits in older adults are associated with reduced brain flexibility, quantitative evidence has been lacking. Here, we investigate brain flexibility in healthy older adults (ages 60-85) using a novel Bayesian switching dynamical system algorithm and ultrafast temporal resolution (TR = 490 ms) whole-brain fMRI data during performance of a Sternberg working memory task. We identify latent brain states and characterize their dynamic temporal properties, including state transitions, associated with encoding, maintenance, and retrieval. Crucially, we demonstrate that brain inflexibility is associated with slower and more fragmented transitions between latent brain states, and that brain inflexibility mediates the relation between age and cognitive inflexibility. Our study provides a novel neurocomputational framework for investigating latent dynamic circuit processes underlying brain flexibility and cognition in the context of aging.

Dynamical ergodicity DDA reveals causal structure in time series

Determining synchronization, causality, and dynamical similarity in highly complex nonlinear systems like brains is challenging. Although distinct, these measures are related by the unknown deterministic structure of the underlying dynamical system. For two systems that are not independent on each other, either because they result from a common process or they are already synchronized, causality measures typically fail. Here, we introduce dynamical ergodicity to assess dynamical similarity between time series and then combine this new measure with cross-dynamical delay differential analysis to estimate causal interactions between time series. We first tested this approach on simulated data from coupled Rössler systems where ground truth was known. We then applied it to intracranial electroencephalographic data from patients with epilepsy and found distinct dynamical states that were highly predictive of epileptic seizures.

Source decomposition of the frontocentral auditory steady-state gamma band response in schizophrenia patients and healthy subjects

AIM

Gamma-band auditory steady-state response (ASSR) is a neurophysiologic index that is increasingly used as a translational biomarker in the development of treatments of neuropsychiatric disorders. While gamma-band ASSR is generated by distributed networks of highly interactive temporal and frontal cortical sources, the majority of human gamma-band ASSR studies using electroencephalography (EEG) highlight activity from only a single frontocentral scalp site, Fz, where responses tend to be largest and reductions in schizophrenia patients are most evident. However, no previous study has characterized the relative source contributions to Fz, which is a necessary step to improve the concordance of preclinical and clinical EEG studies.

METHODS

A novel method to back-project the contributions of independent cortical source components was applied to assess the independent sources and their proportional contributions to Fz as well as source-resolved responses in 432 schizophrenia patients and 294 healthy subjects.

RESULTS

Independent contributions of gamma-band ASSR to Fz were detected from orbitofrontal, bilateral superior/middle/inferior temporal, bilateral middle frontal, and posterior cingulate gyri in both groups. In contrast to expectations, the groups showed comparable source contribution weight to gamma-band ASSR at Fz. While gamma-band ASSR reductions at Fz were present in schizophrenia patients consistent with previous studies, no group differences in individual source-level responses to Fz were detected.

CONCLUSION

Small differences in multiple independent sources summate to produce scalp-level differences at Fz. The identification of independent source contributions to a single scalp sensor represents a promising methodology for measuring dissociable and homologous biomarker targets in future translational studies.

Comparison and Analysis of EEG with Negative and Positive Symptoms of Schizophrenia

Schizophrenia is a type of major mental illness characterized by split personality in thinking and behaviour. Schizophrenia has become the fourth leading cause of disability among adolescents in developing countries. This article is based on the EEG signals of normal people, schizophrenia-positive patients, and schizophrenianegative patients. From the neuropsychological background and cognitive ability test results, the level of EEG amplitude, the frequency distribution of EEG in performing functional tasks, and the analysis of a comparative analysis of the degree of brain injury can help medical staff to accurately and effectively classify and diagnose the different symptoms of schizophrenia.

Sources of the frontocentral mismatch negativity and P3a responses in schizophrenia patients and healthy comparison subjects

BACKGROUND

Mismatch negativity (MMN) and P3a are event-related potential measures of early auditory information processing that are increasingly used as translational biomarkers in the development of treatments for neuropsychiatric disorders. These responses are reduced in schizophrenia patients over the frontocentral scalp electrodes and are associated with important domains of cognitive and psychosocial functioning. While MMN and P3a responses are generated by a dynamic network of cortical sources distributed across the temporal and frontal brain regions, it is not clear how these sources independently contribute to MMN and P3a at the primary frontocentral scalp electrode or to abnormalities observed in schizophrenia. This study aimed to determine the independent source contributions and characterize the magnitude of impairment in source-level MMN and P3a responses in schizophrenia patients.

METHODS

A novel method was applied to back-project the contributions of 11 independent cortical source components to Fz, the primary scalp sensor that is used in clinical studies, in n = 589 schizophrenia patients and n = 449 healthy comparison subjects.

RESULTS

The groups showed comparable individual source contributions underlying both MMN and P3a responses at Fz. Source-level responses revealed an increasing magnitude of impairment in schizophrenia patients from the temporal to more frontal sources.

CONCLUSIONS

Schizophrenia patients have a normal architecture of source contributions that are accompanied by widespread abnormalities in source resolved mismatch and P3a responses, with more prominent deficits detected from the frontal sources. Quantification of source contributions and source-level responses accelerates clarification of the neural networks underlying MMN reduction at Fz in schizophrenia patients.

Precision multidimensional neural population code recovered from single intracellular recordings

Neurons in sensory cortices are more naturally and deeply integrated than any current neural population recording tools (e.g. electrode arrays, fluorescence imaging). Two concepts facilitate efforts to observe population neural code with single-cell recordings. First, even the highest quality single-cell recording studies find a fraction of the stimulus information in high-dimensional population recordings. Finding any of this missing information provides proof of principle. Second, neurons and neural populations are understood as coupled nonlinear differential equations. Therefore, fitted ordinary differential equations provide a basis for single-trial single-cell stimulus decoding. We obtained intracellular recordings of fluctuating transmembrane current and potential in mouse visual cortex during stimulation with drifting gratings. We use mean deflection from baseline when comparing to prior single-cell studies because action potentials are too sparse and the deflection response to drifting grating stimuli (e.g. tuning curves) are well studied. Equation-based decoders allowed more precise single-trial stimulus discrimination than tuning-curve-base decoders. Performance varied across recorded signal types in a manner consistent with population recording studies and both classification bases evinced distinct stimulus-evoked phases of population dynamics, providing further corroboration. Naturally and deeply integrated observations of population dynamics would be invaluable. We offer proof of principle and a versatile framework.

Abnormal Effective Connectivity Underlying Auditory Mismatch Negativity Impairments in Schizophrenia

BACKGROUND

Auditory mismatch negativity (MMN) is a translatable event-related potential biomarker, and its reduction in schizophrenia is associated with the severity of clinical symptoms. While MMN recorded at the scalp is generated by a distributed network of temporofrontal neural sources, the primary contributing sources and the dynamic interactions among sources underlying MMN impairments in schizophrenia have not been previously characterized.

METHODS

A novel data-driven analytic framework was applied to large cohorts of healthy comparison subjects (n = 449) and patients with schizophrenia (n = 589) to identify the independent contributing sources of MMN, characterize the patterns of effective connectivity underlying reduced MMN in patients, and explore the clinical significance of these abnormal source dynamics in schizophrenia.

RESULTS

A network of 11 independent contributing sources underlying MMN distributed across temporofrontal cortices was identified. Orderly shifts in peak source activity were detected in a steplike manner, starting at temporal structures and progressing across frontal brain regions. MMN reduction in patients was predominantly associated with reduced contributions from 3 frontal midline sources: orbitofrontal, anterior cingulate, and middle cingulate cortices. Patients showed increased connectivity from temporal to prefrontal regions in conjunction with decreased cross-hemispheric connectivity to prefrontal regions. The decreased connectivity strength of precentral to prefrontal regions in patients with schizophrenia was associated with greater severity of negative symptoms.

CONCLUSIONS

Alterations in the dynamic interactions among temporofrontal sources underlie MMN abnormalities in schizophrenia. These results advance our understanding of the neural substrates and temporal dynamics of normal and impaired information processing with novel applications for translatable biomarkers of neuropsychiatric disorders.

Impaired Sensory Processing During Low-Oxygen Exposure: A Noninvasive Approach to Detecting Changes in Cognitive States

The ability to detect novelty in our environment is a critical sensory function. A reliable set of event-related potentials (ERP), known as the auditory deviance response (ADR), are elicited in the absence of directed attention and indexes functionally relevant networks. The ADR consists of three peaks: mismatch negativity (MMN), P3a, and reorienting negativity (RON) that are sequentially evoked in response to unattended changes in repetitive background stimulation. While previous studies have established the ADR's sensitivity to a range of pharmacologic and nonpharmacologic interventions and are leading candidate biomarkers of perturbations of the central nervous system (CNS), here we sought to determine if ADR peaks are sensitive to decreases in breathable oxygen. Participants performed a visuomotor tracking task while EEG was recorded during two 27-min sessions. The two sessions differed in the amount of environmental oxygen available: 10.6% O₂ (hypoxia) versus 20.4% O₂ (normoxia). ERPs were measured while a series of identical, or "standard," tones combined with occasional "oddball," tones, were presented. MMN, P3a, and RON were assessed in response to the oddball compared to the standard stimuli. Behavioral impairment during hypoxia was demonstrated by a deficit in tracking performance compared to the normoxia condition. Whereas no changes were detected in the MMN or RON, the amplitude of the P3a component was significantly reduced during hypoxia compared to normoxia, within the first 9 min of exposure. To our knowledge, this is the first study to demonstrate the effect of low oxygen exposure on passively elicited neural measures of early sensory processing. This study demonstrates that passively elicited EEG measures, reflecting preattentive auditory processing, are disrupted by acute hypoxia. Results have implications for the development of biomarkers for the noninvasive assessment of CNS perturbations.

Cortical chimera states predict epileptic seizures

A chimera state is a spatiotemporal pattern of broken symmetry, where synchrony (coherent state) and asynchrony (incoherent state) coexist. Here, we report chimera states in electrocorticography recordings preceding, by several hours, each of seven seizures in one patient with epilepsy. Before the seizures, the onset channels are not synchronized, while the remaining channels are synchronized. During the seizures, this pattern of behavior flips and the nononset channels show a more asynchronous behavior. At a seizure offset, synchrony can be observed that might facilitate termination.

Decomposing the constituent oscillatory dynamics underlying mismatch negativity generation in schizophrenia: Distinct relationships to clinical and cognitive functioning

Abnormalities in early auditory information processing (EAIP) contribute to higher-order deficits in cognition and psychosocial functioning in schizophrenia. A passive auditory oddball paradigm is commonly used to evoke event-related potential (ERP) measures of EAIP reflecting auditory sensory registration and deviance detection, including mismatch negativity (MMN) and P3a responses. MMN and P3a have been extensively studied in healthy subjects and neuropsychiatric patient populations and are increasingly used as translational biomarkers in the development of novel therapeutics. Despite widespread use, relatively few studies have examined the constituent oscillatory elements and the extent to which sensory registration and deviance detection represent distinct or intercorrelated processes. This study aimed to determine the factor structure and clinical correlates of these oscillatory measures in schizophrenia patients (n = 706) and healthy comparison subjects (n = 615) who underwent clinical, cognitive, and functional characterization and EEG testing via their participation in the Consortium of Genomics in Schizophrenia (COGS-2) study. Results revealed significant deficits in theta-band (4-7 Hz) evoked power and phase locking in patients. Exploratory factor analyses of both ERP and oscillatory measures revealed two dissociable factors reflecting sensory registration and deviance detection. While each factor shared a significant correlation with social cognition, the deviance detection factor had a unique relationship to multiple cognitive and clinical domains. Results support the continued advancement of functionally relevant oscillatory measures underlying EAIP in the development of precognitive therapeutics.

Exploration of time-frequency reassignment and homologous inter-hemispheric asymmetry analysis of MCI-AD brain activity

BACKGROUND

In this study, nonlinear based time-frequency (TF) and time domain investigations are employed for the analysis of electroencephalogram (EEG) signals of mild cognitive impairment-Alzheimer's disease (MCI-AD) patients and healthy controls. This study attempts to comprehend the cognitive decline of MCI-AD under both resting and cognitive task conditions.

RESULTS

Wavelet-based synchrosqueezing transform (SST) alleviates the smearing of energy observed in the spectrogram around the central frequencies in short-time Fourier transform (STFT), and continuous wavelet transform (CWT). A precise TF representation is assured due to the reassignment of scale variable to the frequency variable. It is discerned from the studies of time domain measures encompassing fractal dimension (FD) and approximate entropy (ApEn), that the parietal lobe is the most affected in MCI-AD under both resting and cognitive states. Alterations in asymmetry in the brain hemispheres are analysed using the homologous areas inter-hemispheric symmetry (HArS).

CONCLUSION

Time and time-frequency domain analysis of EEG signals have been used for distinguishing various brain states. Therefore, EEG analysis is highly useful for the screening of AD in its prodromal phase.