Harmonization of multi-site functional MRI data with dual-projection based ICA model

Modern neuroimaging studies frequently merge magnetic resonance imaging (MRI) data from multiple sites. A larger and more diverse group of participants can increase the statistical power, enhance the reliability and reproducibility of neuroimaging research, and obtain findings more representative of the general population. However, measurement biases caused by site differences in scanners represent a barrier when pooling data collected from different sites. The existence of site effects can mask biological effects and lead to spurious findings. We recently proposed a powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove site-related effects from pooled data, demonstrating the method for simulated and in vivo structural MRI data. This study investigates the use of our DP-based ICA denoising method for harmonizing functional MRI (fMRI) data collected from the Autism Brain Imaging Data Exchange II. After frequency-domain and regional homogeneity analyses, two modalities, including amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), were used to validate our method. The results indicate that DP-based ICA denoising method removes unwanted site effects for both two fMRI modalities, with increases in the significance of the associations between non-imaging variables (age, sex, etc.) and fMRI measures. In conclusion, our DP method can be applied to fMRI data in multi-site studies, enabling more accurate and reliable neuroimaging research findings

Diffusion map for clustering fMRI spatial maps extracted by independent component analysis

Functional magnetic resonance imaging (fMRI) produces data about activity inside the brain, from which spatial maps can be extracted by independent component analysis (ICA). In datasets, there are n spatial maps that contain p voxels. The number of voxels is very high compared to the number of analyzed spatial maps. Clustering of the spatial maps is usually based on correlation matrices. This usually works well, although such a similarity matrix inherently can explain only a certain amount of the total variance contained in the high-dimensional data where n is relatively small but p is large. For high-dimensional space, it is reasonable to perform dimensionality reduction before clustering. In this research, we used the recently developed diffusion map for dimensionality reduction in conjunction with spectral clustering. This research revealed that the diffusion map based clustering worked as well as the more traditional methods, and produced more compact clusters when needed.Comment: 6 pages. 8 figures. Copyright (c) 2013 IEEE. Published at 2013 IEEE International Workshop on Machine Learning for Signal Processin

Fast and Effective Model Order Selection Method to Determine the Number of Sources

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201

Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression

To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscillatory brain patterns were determined by permutation correlation analysis between individual time courses of Fourier-ICA components and musical features. We found that (1) three components, including a beta sensorimotor network, a beta auditory network and an alpha medial visual network, were involved in music processing among most healthy subjects; and that (2) one alpha lateral component located in the left angular gyrus was engaged in music perception in most individuals with MDD. The proposed method allowed the statistical group comparison, and we found that: (1) the alpha lateral component was activated more strongly in healthy subjects than in the MDD individuals, and that (2) the derived frequency-dependent networks of musical feature processing seemed to be altered in MDD participants compared to healthy subjects. The proposed pipeline appears to be valuable for studying disrupted brain oscillations in psychiatric disorders during naturalistic paradigms.Peer reviewe

Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening

Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during freely listening to music. We used a data-driven method that combined music information retrieval with spatial Fourier Independent Components Analysis (spatial Fourier-ICA) to probe the interplay between the spatial profiles and the spectral patterns of the brain network emerging from music listening. Correlation analysis was performed between time courses of brain networks extracted from EEG data and musical feature time series extracted from music stimuli to derive the musical feature related oscillatory patterns in the listening brain. We found brain networks of musical feature processing were frequency-dependent. Musical feature time series, especially fluctuation centroid and key feature, were associated with an increased beta activation in the bilateral superior temporal gyrus. An increased alpha oscillation in the bilateral occipital cortex emerged during music listening, which was consistent with alpha functional suppression hypothesis in task-irrelevant regions. We also observed an increased delta-beta oscillatory activity in the prefrontal cortex associated with musical feature processing. In addition to these findings, the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.Peer reviewe

Hilbert-Huang versus Morlet wavelet transformation on mismatch negativity of children in uninterrupted sound paradigm

Background. Compared to the waveform or spectrum analysis of event-related potentials (ERPs), time-frequency representation (TFR) has the advantage of revealing the ERPs time and frequency domain information simultaneously. As the human brain could be modeled as a complicated nonlinear system, it is interesting from the view of psychological knowledge to study the performance of the nonlinear and linear time-frequency representation methods for ERP research. In this study Hilbert-Huang transformation (HHT) and Morlet wavelet transformation (MWT) were performed on mismatch negativity (MMN) of children. Participants were 102 children aged 8–16 years. MMN was elicited in a passive oddball paradigm with duration deviants. The stimuli consisted of an uninterrupted sound including two alternating 100 ms tones (600 and 800 Hz) with infrequent 50 ms or 30 ms 600 Hz deviant tones. In theory larger deviant should elicit larger MMN. This theoretical expectation is used as a criterion to test two TFR methods in this study. For statistical analysis MMN support to absence ratio (SAR) could be utilized to qualify TFR of MMN. Results. Compared to MWT, the TFR of MMN with HHT was much sharper, sparser, and clearer. Statistically, SAR showed significant difference between the MMNs elicited by two deviants with HHT but not with MWT, and the larger deviant elicited MMN with larger SAR. Conclusion. Support to absence ratio of Hilbert-Huang Transformation on mismatch negativity meets the theoretical expectations, i.e., the more deviant stimulus elicits larger MMN. However, Morlet wavelet transformation does not reveal that. Thus, HHT seems more appropriate in analyzing event-related potentials in the time-frequency domain. HHT appears to evaluate ERPs more accurately and provide theoretically valid information of the brain responses.peerReviewe

Interpretation of Social Interactions: Functional Imaging of Cognitive-Semiotic Categories During Naturalistic Viewing

Social interactions arise from patterns of communicative signs, whose perception and interpretation require a multitude of cognitive functions. The semiotic framework of Peirce’s Universal Categories (UCs) laid ground for a novel cognitive-semiotic typology of social interactions. During functional magnetic resonance imaging (fMRI), 16 volunteers watched a movie narrative encompassing verbal and non-verbal social interactions. Three types of non-verbal interactions were coded (“unresolved,” “non-habitual,” and “habitual”) based on a typology reflecting Peirce’s UCs. As expected, the auditory cortex responded to verbal interactions, but non-verbal interactions modulated temporal areas as well. Conceivably, when speech was lacking, ambiguous visual information (unresolved interactions) primed auditory processing in contrast to learned behavioral patterns (habitual interactions). The latter recruited a parahippocampal-occipital network supporting conceptual processing and associative memory retrieval. Requesting semiotic contextualization, non-habitual interactions activated visuo-spatial and contextual rule-learning areas such as the temporo-parietal junction and right lateral prefrontal cortex. In summary, the cognitive-semiotic typology reflected distinct sensory and association networks underlying the interpretation of observed non-verbal social interactions

Childhood Sexual Abuse and the Development of Recurrent Major Depression in Chinese Women

Background Our prior study in Han Chinese women has shown that women with a history of childhood sexual abuse (CSA) are at increased risk for developing major depression (MD). Would this relationship be found in our whole data set? Method Three levels of CSA (non-genital, genital, and intercourse) were assessed by self-report in two groups of Han Chinese women: 6017 clinically ascertained with recurrent MD and 5983 matched controls. Diagnostic and other risk factor information was assessed at personal interview. Odds ratios (ORs) were calculated by logistic regression. Results We confirmed earlier results by replicating prior analyses in 3,950 new recurrent MD cases. There were no significant differences between the two data sets. Any form of CSA was significantly associated with recurrent MD (OR 4.06, 95% confidence interval (CI) [3.19–5.24]). This association strengthened with increasing CSA severity: non-genital (OR 2.21, 95% CI 1.58–3.15), genital (OR 5.24, 95% CI 3.52–8.15) and intercourse (OR 10.65, 95% CI 5.56–23.71). Among the depressed women, those with CSA had an earlier age of onset, longer depressive episodes. Recurrent MD patients those with CSA had an increased risk for dysthymia (OR 1.60, 95%CI 1.11–2.27) and phobia (OR 1.41, 95%CI 1.09–1.80). Any form of CSA was significantly associated with suicidal ideation or attempt (OR 1.50, 95% CI 1.20–1.89) and feelings of worthlessness or guilt (OR 1.41, 95% CI 1.02–2.02). Intercourse (OR 3.47, 95%CI 1.66–8.22), use of force and threats (OR 1.95, 95%CI 1.05–3.82) and how strongly the victims were affected at the time (OR 1.39, 95%CI 1.20–1.64) were significantly associated with recurrent MD

Memory-Based Mismatch Response to Frequency Changes in Rats

Any occasional changes in the acoustic environment are of potential importance for survival. In humans, the preattentive detection of such changes generates the mismatch negativity (MMN) component of event-related brain potentials. MMN is elicited to rare changes (‘deviants’) in a series of otherwise regularly repeating stimuli (‘standards’). Deviant stimuli are detected on the basis of a neural comparison process between the input from the current stimulus and the sensory memory trace of the standard stimuli. It is, however, unclear to what extent animals show a similar comparison process in response to auditory changes. To resolve this issue, epidural potentials were recorded above the primary auditory cortex of urethane-anesthetized rats. In an oddball condition, tone frequency was used to differentiate deviants interspersed randomly among a standard tone. Mismatch responses were observed at 60–100 ms after stimulus onset for frequency increases of 5% and 12.5% but not for similarly descending deviants. The response diminished when the silent inter-stimulus interval was increased from 375 ms to 600 ms for +5% deviants and from 600 ms to 1000 ms for +12.5% deviants. In comparison to the oddball condition the response also diminished in a control condition in which no repetitive standards were presented (equiprobable condition). These findings suggest that the rat mismatch response is similar to the human MMN and indicate that anesthetized rats provide a valuable model for studies of central auditory processing