Yiheng Tu

Alpha and gamma oscillation amplitudes synergistically predict the perception of forthcoming nociceptive stimuli

Ongoing fluctuations of intrinsic cortical networks determine the dynamic state of the brain, and influence the perception of forthcoming sensory inputs. The functional state of these networks is defined by the amplitude and phase of ongoing oscillations of neuronal populations at different frequencies. The contribution of functionally different cortical networks has yet to be elucidated, and only a clear dependence of sensory perception on prestimulus alpha oscillations has been clearly identified. Here, we combined electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) in a large sample of healthy participants to investigate how ongoing fluctuations in the activity of different cortical networks affect the perception of subsequent nociceptive stimuli. We observed that prestimulus EEG oscillations in the alpha (at bilateral central regions) and gamma (at parietal regions) bands negatively modulated the perception of subsequent stimuli. Combining information about alpha and gamma oscillations predicted subsequent perception significantly more accurately than either measure alone. In a parallel experiment, we found that prestimulus fMRI activity also modulated the perception of subsequent stimuli: perceptual ratings were higher when the BOLD signal was higher in nodes of the sensorimotor network and lower in nodes of the default mode network. Similar to what observed in the EEG data, prediction accuracy was improved when the amplitude of prestimulus BOLD signals in both networks was combined. These findings provide a comprehensive physiological basis to the idea that dynamic changes in brain state determine forthcoming behavioral outcomes. Hum Brain Mapp 37:501–514, 2016.

Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia

ABSTRACT The human brain is a highly dynamic system with non‐stationary neural activity and rapidly‐changing neural interaction. Resting‐state dynamic functional connectivity (dFC) has been widely studied during recent years, and the emerging aberrant dFC patterns have been identified as important features of many mental disorders such as schizophrenia (SZ). However, only focusing on the time‐varying patterns in FC is not enough, since the local neural activity itself (in contrast to the inter‐connectivity) is also found to be highly fluctuating from research using high‐temporal‐resolution imaging techniques. Exploring the time‐varying patterns in brain activity and their relationships with time‐varying brain connectivity is important for advancing our understanding of the co‐evolutionary property of brain network and the underlying mechanism of brain dynamics. In this study, we introduced a framework for characterizing time‐varying brain activity and exploring its associations with time‐varying brain connectivity, and applied this framework to a resting‐state fMRI dataset including 151 SZ patients and 163 age‐ and gender matched healthy controls (HCs). In this framework, 48 brain regions were first identified as intrinsic connectivity networks (ICNs) using group independent component analysis (GICA). A sliding window approach was then adopted for the estimation of dynamic amplitude of low‐frequency fluctuation (dALFF) and dFC, which were used to measure time‐varying brain activity and time‐varying brain connectivity respectively. The dALFF was further clustered into six reoccurring states by the k‐means clustering method and the group difference in occurrences of dALFF states was explored. Lastly, correlation coefficients between dALFF and dFC were calculated and the group difference in these dALFF‐dFC correlations was explored. Our results suggested that 1) ALFF of brain regions was highly fluctuating during the resting‐state and such dynamic patterns are altered in SZ, 2) dALFF and dFC were correlated in time and their correlations are altered in SZ. The overall results support and expand prior work on abnormalities of brain activity, static FC (sFC) and dFC in SZ, and provide new evidence on aberrant time‐varying brain activity and its associations with brain connectivity in SZ, which might underscore the disrupted brain cognitive functions in this mental disorder. HighlightsAmplitude of low‐frequency fluctuation (ALFF) is dynamic during the resting‐state.Reoccurring dynamic ALFF (dALFF) patterns are altered in SZ.dALFF and dynamic functional connectivity (dFC) are correlated in time.SZ patients show significantly different associations between dALFF and dFC.Associations between dALFF and dFC are correlated with cognitive scores.

An automated and fast approach to detect single-trial visual evoked potentials with application to brain–computer interface

OBJECTIVE This study aims (1) to develop an automated and fast approach for detecting visual evoked potentials (VEPs) in single trials and (2) to apply the single-trial VEP detection approach in designing a real-time and high-performance brain-computer interface (BCI) system. METHODS The single-trial VEP detection approach uses common spatial pattern (CSP) as a spatial filter and wavelet filtering (WF) a temporal-spectral filter to jointly enhance the signal-to-noise ratio (SNR) of single-trial VEPs. The performance of the joint spatial-temporal-spectral filtering approach was assessed in a four-command VEP-based BCI system. RESULTS The offline classification accuracy of the BCI system was significantly improved from 67.6±12.5% (raw data) to 97.3±2.1% (data filtered by CSP and WF). The proposed approach was successfully implemented in an online BCI system, where subjects could make 20 decisions in one minute with classification accuracy of 90%. CONCLUSIONS The proposed single-trial detection approach is able to obtain robust and reliable VEP waveform in an automatic and fast way and it is applicable in VEP based online BCI systems. SIGNIFICANCE This approach provides a real-time and automated solution for single-trial detection of evoked potentials or event-related potentials (EPs/ERPs) in various paradigms, which could benefit many applications such as BCI and intraoperative monitoring.

A Functional Neuroimaging Study of Expectancy Effects on Pain Response in Patients With Knee Osteoarthritis

Placebo treatments and healing rituals share much in common, such as the effects of expectancy, and have been used since the beginning of human history to treat pain. Previous mechanistic neuroimaging studies investigating the effects of expectancy on placebo analgesia have used young, healthy volunteers. Using functional magnetic resonance imaging (fMRI), we aimed to investigate the neural mechanisms by which expectancy evokes analgesia in older adults living with a chronic pain disorder and determine whether there are interactions with active treatment. In this fMRI study, we investigated the brain networks underlying expectancy in participants with chronic pain due to knee osteoarthritis (OA) after verum (genuine) and sham electroacupuncture treatment before and after experiencing calibrated experimental heat pain using a well tested expectancy manipulation model. We found that expectancy significantly and similarly modulates the pain experience in knee OA patients in both verum (n = 21, 11 female; mean ± SD age 57 ± 7 years) and sham (n = 22, 15 female; mean ± SD age 59 ± 7 years) acupuncture treatment groups. However, there were different patterns of changes in fMRI indices of brain activity associated with verum and sham treatment modalities specifically in the lateral prefrontal cortex. We also found that continuous electroacupuncture in knee OA patients can evoke significant regional coherence decreases in pain associated brain regions. Our results suggest that expectancy modulates the experience of pain in knee OA patients but may work through different pathways depending on the treatment modality and, we speculate, on pathophysiological states of the participants. PERSPECTIVE To investigate the neural mechanisms underlying pain modulation, we used an expectancy manipulation model and fMRI to study response to heat pain stimuli before and after verum or sham acupuncture treatment in chronic pain patients. Both relieve pain and each is each associated with a distinct pattern of brain activation.

Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subjects intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.

Decoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities

Pain is a highly subjective experience. Self-report is the gold standard for pain assessment in clinical practice, but it may not be available or reliable in some populations. Neuroimaging data, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have the potential to be used to provide physiology-based and quantitative nociceptive pain assessment tools that complements self-report. However, existing neuroimaging-based nociceptive pain assessments only rely on the information in pain-evoked brain activities, but neglect the fact that the perceived intensity of pain is also encoded by ongoing brain activities prior to painful stimulation. Here, we proposed to use machine learning algorithms to decode pain intensity from both pre-stimulus ongoing and post-stimulus evoked brain activities. Neural features that were correlated with intensity of laser-evoked nociceptive pain were extracted from high-dimensional pre- and post-stimulus EEG and fMRI activities using partial least-squares regression (PLSR). Further, we used support vector machine (SVM) to predict the intensity of pain from pain-related time-frequency EEG patterns and BOLD-fMRI patterns. Results showed that combining predictive information in pre- and post-stimulus brain activities can achieve significantly better performance in classifying high-pain and low-pain and in predicting the rating of perceived pain than only using post-stimulus brain activities. Therefore, the proposed pain prediction method holds great potential in basic research and clinical applications.

PCA-SIR: A new nonlinear supervised dimension reduction method with application to pain prediction from EEG

Dimension reduction is critical in identifying a small set of discriminative features that are predictive of behavior or cognition from high-dimensional neuroimaging data, such as EEG and fMRI. In the present study, we proposed a novel nonlinear supervised dimension reduction technique, named PCA-SIR (Principal Component Analysis and Sliced Inverse Regression), for analyzing high-dimensional EEG time-course data. Compared with conventional dimension reduction methods used for EEG, such as PCA and partial least-squares (PLS), the PCA-SIR method can make use of nonlinear relationship between class labels (i.e., behavioral or cognitive parameters) and predictors (i.e., EEG samples) to achieve the effective dimension reduction (e.d.r.) directions. We applied the new PCA-SIR method to predict the subjective pain perception (at a level ranging from 0 to 10) from single-trial laser-evoked EEG time courses. Experimental results on 96 subjects showed that reduced features by PCA-SIR can lead to significantly higher prediction accuracy than those by PCA and PLS. Therefore, PCA-SIR could be a promising supervised dimension reduction technique for multivariate pattern analysis of high-dimensional neuroimaging data.

Supervised nonlinear dimension reduction of functional magnetic resonance imaging data using Sliced Inverse Regression.

Dimension reduction is essential for identifying a small set of discriminative features that are predictive of behavior or cognition from high-dimensional functional magnetic resonance imaging (fMRI) data. However, conventional linear dimension reduction techniques cannot reduce the dimension effectively if the relationship between imaging data and behavioral parameters are nonlinear. In the paper, we proposed a novel supervised dimension reduction technique, named PC-SIR (Principal Component - Sliced Inverse Regression), for analyzing high-dimensional fMRI data. The PC-SIR method is an important extension of the renowned SIR method, which can achieve the effective dimension reduction (e.d.r.) directions even the relationship between class labels and predictors is nonlinear but is unable to handle high-dimensional data. By using PCA prior to SIR to orthogonalize and reduce the predictors, PC-SIR can overcome the limitation of SIR and thus can be used for fMRI data. Simulation showed that PC-SIR can result in a more accurate identification of brain activation as well as better prediction than support vector regression (SVR) and partial least square regression (PLSR). Then, we applied PC-SIR on real fMRI data recorded in a pain stimulation experiment to identify pain-related brain regions and predict the pain perception. Results on 32 subjects showed that PC-SIR can lead to significantly higher prediction accuracy than SVR and PLSR. Therefore, PC-SIR could be a promising dimension reduction technique for multivariate pattern analysis of fMRI.Dimension reduction is essential for identifying a small set of discriminative features that are predictive of behavior or cognition from high-dimensional functional magnetic resonance imaging (fMRI) data. However, conventional linear dimension reduction techniques cannot reduce the dimension effectively if the relationship between imaging data and behavioral parameters are nonlinear. In the paper, we proposed a novel supervised dimension reduction technique, named PC-SIR (Principal Component - Sliced Inverse Regression), for analyzing high-dimensional fMRI data. The PC-SIR method is an important extension of the renowned SIR method, which can achieve the effective dimension reduction (e.d.r.) directions even the relationship between class labels and predictors is nonlinear but is unable to handle high-dimensional data. By using PCA prior to SIR to orthogonalize and reduce the predictors, PC-SIR can overcome the limitation of SIR and thus can be used for fMRI data. Simulation showed that PC-SIR can result in a more accurate identification of brain activation as well as better prediction than support vector regression (SVR) and partial least square regression (PLSR). Then, we applied PC-SIR on real fMRI data recorded in a pain stimulation experiment to identify pain-related brain regions and predict the pain perception. Results on 32 subjects showed that PC-SIR can lead to significantly higher prediction accuracy than SVR and PLSR. Therefore, PC-SIR could be a promising dimension reduction technique for multivariate pattern analysis of fMRI.

A novel and effective fMRI decoding approach based on sliced inverse regression and its application to pain prediction

Abstract Dimension reduction is essential in fMRI decoding, but the complex relationship between fMRI data and class labels is often unknown or not well modeled so that the most effective dimension reduction (e.d.r.) directions can hardly be identified. In the present study, we introduce a novel fMRI decoding approach based on an effective and general dimension reduction method, namely sliced inverse regression (SIR), which can exploit class information for estimating e.d.r. directions even when the relationship between fMRI data and class labels is not explicitly known. We incorporate singular value decomposition (SVD) into SIR to overcome SIRs limitation in dealing with ultra-high-dimensional data, and integrate SVD-SIR into a pattern classifier to enable quantification of the contributions of fMRI voxels to class labels. The resultant new SIR decoding analysis (SIR-DA) approach is capable of decoding behavioral responses and identifying predictive fMRI patterns. Simulation results showed that SIR-DA can more accurately detect e.d.r. directions and achieve higher classification accuracy than decoding approaches based on conventional dimension reduction methods. Further, we applied SIR-DA on real-world pain-evoked fMRI data to decode the level of pain perception and showed that SIR-DA can achieve higher accuracy in pain prediction than conventional methods. These results suggest that SIR-DA is an effective data-driven technique to decode behavioral or cognitive states from fMRI data and to uncover unknown brain patterns associated with behavior or cognitive responses.

Decreased structural connectivity and resting-state brain activity in the lateral occipital cortex is associated with social communication deficits in boys with autism spectrum disorder

ABSTRACT Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder characterized by atypical social communication and repetitive behaviors. In this study, we applied a multimodal approach to investigate brain structural connectivity, resting state activity, and surface area, as well as their associations with the core symptoms of ASD. Data from forty boys with ASD (mean age, 11.5 years; age range, 5.5–19.5) and forty boys with typical development (TD) (mean age, 12.3; age range, 5.8–19.7) were extracted from the Autism Brain Imaging Data Exchange II (ABIDE II) for data analysis. We found significantly decreased structural connectivity, resting state brain activity, and surface area at the occipital cortex in boys with ASD compared to boys with TD. In addition, we found that resting state brain activity and surface area in the lateral occipital cortex was negatively correlated with communication scores in boys with ASD. Our results suggest that decreased structural connectivity and resting‐state brain activity in the occipital cortex may impair the integration of verbal and non‐verbal communication cues in boys with ASD, thereby impacting their social development. HighlightsWe applied a multimodal approach to investigate the neuropathology of ASD.ASD showed decreased fALFF and surface area at the occipital cortex.ASD is associated with decreased FA and track length in left CCG and right UNC.Functional and structural changes were associated with ASD communication scores.