A feature-selective independent component analysis method for functional MRI

Dynamic spectral signatures of mirror movements in the sensorimotor functional connectivity network of patients with Kallmann syndrome

In Kallmann syndrome (KS), the peculiar phenomenon of bimanual synkinesis or mirror movement (MM) has been associated with a spectral shift, from lower to higher frequencies, of the resting-state fMRI signal of the large-scale sensorimotor brain network (SMN). To possibly determine whether a similar frequency specificity exists across different functional connectivity SMN states, and to capture spontaneous transitions between them, we investigated the dynamic spectral changes of the SMN functional connectivity in KS patients with and without MM symptom. Brain MRI data were acquired at 3 Tesla in 39 KS patients (32 without MM, KSMM-, seven with MM, KSMM+) and 26 age- and sex-matched healthy control (HC) individuals. The imaging protocol included 20-min rs-fMRI scans enabling detailed spectro-temporal analyses of large-scale functional connectivity brain networks. Group independent component analysis was used to extract the SMN. A sliding window approach was used to extract the dynamic spectral power of the SMN functional connectivity within the canonical physiological frequency range of slow rs-fMRI signal fluctuations (0.01–0.25 Hz). K-means clustering was used to determine (and count) the most recurrent dynamic states of the SMN and detect the number of transitions between them. Two most recurrent states were identified, for which the spectral power peaked at a relatively lower (state 1) and higher (state 2) frequency. Compared to KS patients without MM and HC subjects, the SMN of KS patients with MM displayed significantly larger spectral power changes in the slow 3 canonical sub-band (0.073–0.198 Hz) and significantly fewer transitions between state 1 (less recurrent) and state 2 (more recurrent). These findings demonstrate that the presence of MM in KS patients is associated with reduced spontaneous transitions of the SMN between dynamic functional connectivity states and a higher recurrence and an increased spectral power change of the high-frequency state. These results provide novel information about the large-scale brain functional dynamics that could help to understand the pathologic mechanisms of bimanual synkinesis in KS syndrome and, potentially, other neurological disorders where MM may also occur.

Multimodal Imaging Analysis Reveals Frontal-Associated Networks in Relation to Individual Resilience Strength

Psychological resilience is regarded as a critical protective factor for preventing the development of mental illness from experienced adverse events. Personal strength is one key element of resilience that reflects an individual's reactions to negative life events and is crucial for successful adaptation. Previous studies have linked unimodal imaging measures with resilience. However, applying multimodal imaging measures could provide comprehensive organization information at the system level to examine whether an individual's resilience strength is reflected in the brain's structural and functional network. In this study, MRI was used to acquire multimodal imaging properties and subscales of personal strength in terms of resilience from 109 participants (48 females and 61 males). We employed a method of fusion independent component analysis to link the association between multimodal imaging components and personal strength of psychological resilience. The results reveal that a fusion component involving multimodal frontal networks in connecting with the parietal, occipital, and temporal regions is associated with the resilience score for personal strength. A multiple regression model further explains the predictive role of frontal-associated regions that cover a visual-related network regulating cognition and emotion to discern the perceived adverse experience. Overall, this study suggests that frontal-associated regions are related to individual resilience strength.

Temporally and spatially constrained ICA of fMRI data analysis

Constrained independent component analysis (CICA) is capable of eliminating the order ambiguity that is found in the standard ICA and extracting the desired independent components by incorporating prior information into the ICA contrast function. However, the current CICA method produces constraints that are based on only one type of prior information (temporal/spatial), which may increase the dependency of CICA on the accuracy of the prior information. To improve the robustness of CICA and to reduce the impact of the accuracy of prior information on CICA, we proposed a temporally and spatially constrained ICA (TSCICA) method that incorporated two types of prior information, both temporal and spatial, as constraints in the ICA. The proposed approach was tested using simulated fMRI data and was applied to a real fMRI experiment using 13 subjects who performed a movement task. Additionally, the performance of TSCICA was compared with the ICA method, the temporally CICA (TCICA) method and the spatially CICA (SCICA) method. The results from the simulation and from the real fMRI data demonstrated that TSCICA outperformed TCICA, SCICA and ICA in terms of robustness to noise. Moreover, the TSCICA method displayed better robustness to prior temporal/spatial information than the TCICA/SCICA method.

Transfer function analysis of respiratory and cardiac pulsations in human brain observed on dynamic magnetic resonance images

Magnetic resonance (MR) imaging provides a noninvasive, in vivo imaging technique for studying respiratory and cardiac pulsations in human brains, because these pulsations can be recorded as flow-related enhancement on dynamic MR images. By applying independent component analysis to dynamic MR images, respiratory and cardiac pulsations were observed. Using the signal-time curves of these pulsations as reference functions, the magnitude and phase of the transfer function were calculated on a pixel-by-pixel basis. The calculated magnitude and phase represented the amplitude change and temporal delay at each pixel as compared with the reference functions. In the transfer function analysis, near constant phases were found at the respiratory and cardiac frequency bands, indicating the existence of phase delay relative to the reference functions. In analyzing the dynamic MR images using the transfer function analysis, we found the following: (1) a good delineation of temporal delay of these pulsations can be achieved; (2) respiratory pulsation exists in the ventricular and cortical cerebrospinal fluid; (3) cardiac pulsation exists in the ventricular cerebrospinal fluid and intracranial vessels; and (4) a 180-degree phase delay or inverted amplitude is observed on phase images.

High classification accuracy for schizophrenia with rest and task FMRI data

We present a novel method to extract classification features from functional magnetic resonance imaging (fMRI) data collected at rest or during the performance of a task. By combining a two-level feature identification scheme with kernel principal component analysis (KPCA) and Fisher’s linear discriminant analysis (FLD), we achieve high classification rates in discriminating healthy controls from patients with schizophrenia. Experimental results using leave-one-out cross-validation show that features extracted from the default mode network (DMN) lead to a classification accuracy of over 90% in both data sets. Moreover, using a majority vote method that uses multiple features, we achieve a classification accuracy of 98% in auditory oddball (AOD) task and 93% in rest data. Several components, including DMN, temporal, and medial visual regions, are consistently present in the set of features that yield high classification accuracy. The features we have extracted thus show promise to be used as biomarkers for schizophrenia. Results also suggest that there may be different advantages to using resting fMRI data or task fMRI data.