Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study

Allen, Elena A.; Erhardt, Erik B.; Wei, Yonghua; Eichele, Tom; Calhoun, Vince D.

doi:10.1016/j.neuroimage.2011.10.010

Cited by 212 publications

(237 citation statements)

References 59 publications

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“…In a first analysis step requiring minimal prior assumptions on data characteristics, we applied a multivariate group independent component analysis (ICA) (16)(17)(18)(19) to the data of 26 healthy subjects (13 pairs) in step A. This data-driven approach identified 16 maximally independent sources (components) that, together, account for the observed fMRI data of all subjects (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, subject-specific time courses of each component are available via back reconstruction, for which the partitioned data reduction matrices are projected to aggregate component matrix [components in rows; used back reconstruction algorithm GICA I (16,17,18)]. This is called PCA-based back reconstruction and was shown to be highly accurate compared with other approaches to back reconstruction (17).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Information flow between interacting human brains: Identification, validation, and relationship to social expertise

Bilek¹,

Ruf

Schäfer³

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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131

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Social interactions are fundamental for human behavior, but the quantification of their neural underpinnings remains challenging. Here, we used hyperscanning functional MRI (fMRI) to study information flow between brains of human dyads during real-time social interaction in a joint attention paradigm. In a hardware setup enabling immersive audiovisual interaction of subjects in linked fMRI scanners, we characterize cross-brain connectivity components that are unique to interacting individuals, identifying information flow between the sender's and receiver's temporoparietal junction. We replicate these findings in an independent sample and validate our methods by demonstrating that cross-brain connectivity relates to a key real-world measure of social behavior. Together, our findings support a central role of human-specific cortical areas in the brain dynamics of dyadic interactions and provide an approach for the noninvasive examination of the neural basis of healthy and disturbed human social behavior with minimal a priori assumptions.fMRI | hyperscanning | joint attention H uman social interactions have likely shaped brain evolution and are critical for development, health, and society. Defining their neural underpinnings is a key goal of social neuroscience. Interacting dyads, the simplest and fundamental form of human interaction, have been examined with behavioral setups that used real movement interactions during communication in real time as a proxy (1-4), providing mathematical models representing human interaction, goal sharing, mutual engagement, and coordination. To identify the neural systems supporting these behaviors, neuroimaging would be the tool of choice, but studying dyadic interactions with this method is both experimentally and analytically challenging. Consequently, the neural processes underlying human social interactions remain incompletely understood.Experimentally, studying dyads with neuroimaging technology that allows only one participant per scanner provides challenges that have been addressed in the literature in one of two ways. First, the audiovisual experiences of human social contact have been simulated using stimuli such as photographs, recorded videos, or computerized avatars in the absence of human interaction (5-7), or, recently, immersive audiovisual linkups have been used with one of the two participants being scanned (8, 9). Secondly, pioneering neuroimaging experiments have coupled two scanner sites over the Internet, a setup called hyperscanning, enabling subjects to observe higher-level behavioral responses such as choices made to accept or reject an offer in real time while in the scanners (10, 11). In the current study, we aimed to combine the advantages of these experimental approaches by enabling two humans to see (and possibly hear) each other in a hyperscanning framework, enabling an immersive social interaction while both participant's brains are imaged. To do so, we implemented a setup with delay-free data transmission and precisely synchronized data acquisitio...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Information flow between interacting human brains: Identification, validation, and relationship to social expertise

Bilek¹,

Ruf

Schäfer³

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

144

131

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“…During the scan, subjects were exposed to visual stimuli on an LCD-Monitor eyeglass (Resonance Technology, Inc. Northridge, USA). Conditions alternated between a rest (R) and a VF condition, each lasting 30 s. The task started with the R-condition 7 (gray screen with a fixation cross). In the VF condition, a letter was displayed and subjects were instructed to silently produce as many verbs starting with the shown letter.…”

Section: Fmri Task and Analysismentioning

confidence: 99%

“…In both cases, the region of interest (ROI) is chosen using anatomic landmarks. However, from functional brain studies it is well known that besides substantial individuality of cortical morphology [2,3], there is considerable interindividual variability in terms of the exact location of cortical activation for specific brain functions [4,5,6,7]. Hence, if metabolic disturbances are hypothesized to be restricted to or most expressed in limited areas representing specific brain function, the ROIs for MRS investigations have to be defined functionally in each subject.…”

Section: Introductionmentioning

confidence: 99%

Magnetic resonance spectroscopy investigations of functionally defined language areas in schizophrenia patients with and without auditory hallucinations

et al. 2014

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Background: Cerebral dysfunction occurring in mental disorders can show metabolic disturbances which are limited to circumscribed brain areas. Auditory hallucinations have been shown to be related to defined cortical areas linked to specific language functions. Here, we investigated if the study of metabolic changes in auditory hallucinations requires a functional rather than an anatomical definition of their location and size to allow a reliable investigation by magnetic resonance spectroscopy (MRS). Methods: Schizophrenia patients with (AH; n=12) and without hallucinations (NH; n=8) and healthy controls (HC; n=11) underwent a verbal fluency task in functional MRI (fMRI) to functionally define Broca's and Wernicke's areas. Left and right Heschl's gyrus were defined anatomically. Results: The mean distances in native space between the fMRI-defined regions and a corresponding anatomically defined area were 12.4±6.1 mm (range: 2.7-36.1 mm) for Broca's area and 16.8±6.2 mm (range:4.5-26.4 mm) for Wernicke's area, respectively. Hence, the spatial variance was of similar extent as the size of the investigated regions. Splitting the investigations into a single voxel examination in the frontal brain and a spectroscopic imaging part for the more homogeneous field areas led to good spectral quality for almost all spectra. In Broca's area, there was a significant group effect (p = 0.03) with lower levels of N-acetyl-aspartate (NAA) in NH compared to HC (p = 0.02). There were positive associations of NAA levels in the left Heschl's gyrus with total (p = 0.03) and negative (p = 0.006) PANSS scores. In Broca's area, there was a negative association of myo-inositol levels with total PANSS scores (p = 0.008). Conclusion: This study supports the neurodegenerative hypothesis of schizophrenia only in a frontal region whereas the results obtained from temporal regions are in contrast to the majority of previous studies. Future research should test the hypothesis raised by this study that a functional definition of language regions is needed if neurochemical imbalances are expected to be restricted to functional foci. Highlights

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“…The data processing method simplifies the computational expense and benefits the improvement of the generation performance. Some typical feature extraction methods, such as wavelet packet transform (WPT) [7][8][9][10], empirical mode decomposition (EMD) [11], time-domain statistical features (TDSF) [12,13] and independent component analysis (ICA) [14][15][16][17] have been proved to be equivalent to a large-scale matrix factorization problem (i.e., there may be still some irrelevant or redundant noise in the extracted features) [18]. In order to resolve this problem, a feature selection method could be employed to wipe off irrelevant and redundant information so that the dimension of extracted feature is reduced.…”

Section: Introductionmentioning

confidence: 99%

Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

2016

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Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some "insignificant' feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL), the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.

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Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study

Cited by 212 publications

References 59 publications

Information flow between interacting human brains: Identification, validation, and relationship to social expertise

Information flow between interacting human brains: Identification, validation, and relationship to social expertise

Magnetic resonance spectroscopy investigations of functionally defined language areas in schizophrenia patients with and without auditory hallucinations

Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

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