Separating 4D multi-task fMRI data of multiple subjects by independent component analysis with projection

Independent component analysis (ICA) is a widely accepted method to extract brain networks underlying cognitive processes from functional magnetic resonance imaging (fMRI) data. However, the application of ICA to multi-task fMRI data is limited due to the potential non-independency between task-related components. The ICA with projection (ICAp) method proposed by our group (Hum Brain Mapp 2009;30:417–31) is demonstrated to be able to solve the interactions among task-related components for single subject fMRI data. However, it still must be determined if ICAp is capable of processing multi-task fMRI data over a group of subjects. Moreover, it is unclear whether ICAp can be reliably applied to event-related (ER) fMRI data. In this study, we combined the projection method with the temporal concatenation method reported by Calhoun (Hum Brain Mapp 2008;29:828–38), referred to as group ICAp, to perform the group analysis of multi-task fMRI data. Both a human fMRI rest data-based simulation and real fMRI experiments, of block design and ER design, verified the feasibility and reliability of group ICAp, as well as demonstrated that ICAp had the strength to separate 4D multi-task fMRI data into multiple brain networks engaged in each cognitive task and to adequately find the commonalities and differences among multiple tasks.     

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation.     

Performance evaluation of nonnegative matrix factorization algorithms to estimate task-related neuronal activities from fMRI data

Nonnegative matrix factorization (NMF) is a blind source separation (BSS) algorithm which is based on the distinct constraint of nonnegativity of the estimated parameters as well as on the measured data. In this study, according to the potential feasibility of NMF for fMRI data, the four most popular NMF algorithms, corresponding to the following two types of (1) least-squares based update [i.e., alternating least-squares NMF (ALSNMF) and projected gradient descent NMF] and (2) multiplicative update (i.e., NMF based on Euclidean distance and NMF based on divergence cost function), were investigated by using them to estimate task-related neuronal activities. These algorithms were applied firstly to individual data from a single subject and, subsequently, to group data sets from multiple subjects. On the single-subject level, although all four algorithms detected task-related activation from simulated data, the performance of multiplicative update NMFs was significantly deteriorated when evaluated using visuomotor task fMRI data, for which they failed in estimating any task-related neuronal activities. In group-level analysis on both simulated data and real fMRI data, ALSNMF outperformed the other three algorithms. The presented findings may suggest that ALSNMF appears to be the most promising option among the tested NMF algorithms to extract task-related neuronal activities from fMRI data.     

Validating the performance of one-time decomposition for fMRI analysis using ICA with automatic target generation process

Independent component analysis (ICA) has been proven to be effective for functional magnetic resonance imaging (fMRI) data analysis. However, ICA decomposition requires to optimize the unmixing matrix iteratively whose initial values are generated randomly. Thus the randomness of the initialization leads to different ICA decomposition results. Therefore, just one-time decomposition for fMRI data analysis is not usually reliable. Under this circumstance, several methods about repeated decompositions with ICA (RDICA) were proposed to reveal the stability of ICA decomposition. Although utilizing RDICA has achieved satisfying results in validating the performance of ICA decomposition, RDICA cost much computing time. To mitigate the problem, in this paper, we propose a method, named ATGP-ICA, to do the fMRI data analysis. This method generates fixed initial values with automatic target generation process (ATGP) instead of being produced randomly. We performed experimental tests on both hybrid data and fMRI data to indicate the effectiveness of the new method and made a performance comparison of the traditional one-time decomposition with ICA (ODICA), RDICA and ATGP-ICA. The proposed method demonstrated that it not only could eliminate the randomness of ICA decomposition, but also could save much computing time compared to RDICA. Furthermore, the ROC (Receiver Operating Characteristic) power analysis also denoted the better signal reconstruction performance of ATGP-ICA than that of RDICA.     

Balanced steady state free precession for arterial spin labeling MRI: Initial experience for blood flow mapping in human brain,retina, and kidney

We implemented pseudo-continuous ASL (pCASL) with 2D and 3D balanced steady state free precession (bSSFP) readout for mapping blood flow in the human brain, retina, and kidney, free of distortion and signal dropout, which are typically observed in the most commonly used echo-planar imaging acquisition. High resolution functional brain imaging in the human visual cortex was feasible with 3D bSSFP pCASL. Blood flow of the human retina could be imaged with pCASL and bSSFP in conjunction with a phase cycling approach to suppress the banding artifacts associated with bSSFP. Furthermore, bSSFP based pCASL enabled us to map renal blood flow within a single breath hold. Control and test–retest experiments suggested that the measured blood flow values in retina and kidney were reliable. Because there is no specific imaging tool for mapping human retina blood flow and the standard contrast agent technique for mapping renal blood flow can cause problems for patients with kidney dysfunction, bSSFP based pCASL may provide a useful tool for the diagnosis of retinal and renal diseases and can complement existing imaging techniques.     

Regional homogeneity of resting-state fMRI contributes to both neurovascular and task activation variations

The task induced blood oxygenation level dependent signal changes observed using functional magnetic resonance imaging (fMRI) are critically dependent on the relationship between neuronal activity and hemodynamic response. Therefore, understanding the nature of neurovascular coupling is important when interpreting fMRI signal changes evoked via task. In this study, we used regional homogeneity (ReHo), a measure of local synchronization of the BOLD time series, to investigate whether the similarities of one voxel with the surrounding voxels are a property of neurovascular coupling. FMRI scans were obtained from fourteen subjects during bilateral finger tapping (FTAP), digit–symbol substitution (DSST) and periodic breath holding (BH) paradigm. A resting-state scan was also obtained for each of the subjects for 4 min using identical imaging parameters. Inter-voxel correlation analyses were conducted between the resting-state ReHo, resting-state amplitude of low frequency fluctuations (ALFF), BH responses and task activations within the masks related to task activations. There was a reliable mean voxel-wise spatial correlation between ReHo and other neurovascular variables (BH responses and ALFF). We observed a moderate correlation between ReHo and task activations (FTAP: r = 0.32; DSST: r = 0.22) within the task positive network and a small yet reliable correlation within the default mode network (DSST: r = − 0.08). Subsequently, a linear regression was used to estimate the contribution of ReHo, ALFF and BH responses to the task activated voxels. The unique contribution of ReHo was minimal. The results suggest that regional synchrony of the BOLD activity is a property that can explain the variance of neurovascular coupling and task activations; but its contribution to task activations can be accounted for by other neurovascular factors such as the ALFF.     

Three Terpenoids and a Tocopherol‐Related Compound from Ricinus communis

Four new compounds named (3E,7Z,11E)‐19‐hydroxycasba‐3,7,11‐trien‐5‐one ( 1 ), 6α‐hydroxy‐10β‐methoxy‐7α,8α‐epoxy‐5‐oxocasbane‐20,10‐olide ( 2 ), 15α‐hydroxylup‐20(29)‐en‐3‐one ( 3 ), and (2R,4aR, 8aR)‐3,4,4a,8a‐tetrahydro‐4a‐hydroxy‐2,6,7,8a‐tetramethyl‐2‐(4,8,12‐trimethyltridecyl)‐2H‐chromene‐5,8‐dione ( 4 ) were isolated from the MeOH extracts of the aerial parts of Ricinus communis L. by chromatographic methods. Their structures were elucidated by extensive spectroscopic experiments.     

Reversible and Bijectively Related Posets

Call a poset reversible if every of its order-preserving self-bijections is an automorphism. Call two posets bijectively related if from each of the two posets exists an order-preserving bijection to the other. We present two examples of pairs of non-isomorphic, bijectively related posets and an example of a non-reversible poset that is bijectively related only to itself. Also, three classes of reversible posets are described and a sufficient condition for an order-preserving bijection to be an isomorphism is presented.     

Quantitative NumART2* mapping in functional MRI studies at 1.5 T

Quantitative mapping of the effective transverse relaxation time, T₂* and proton density was performed in a motor activation functional MRI (fMRI) study using multi-echo, echo planar imaging (EPI) and NumART₂* (Numerical Algorithm for Real time T₂*). Comparisons between NumART₂* and conventional single echo EPI with an echo time of 64 ms were performed for five healthy participants examined twice. Simulations were also performed to address specific issues associated with the two techniques, such as echo time-dependent signal variation. While the single echo contrast varied with the baseline T₂* value, relative changes in T₂* remained unaffected. Statistical analysis of the T₂* maps yielded fMRI activation patterns with an improved statistical detection relative to conventional EPI but with less activated voxels, suggesting that NumART₂* has superior spatial specificity. Two effects, inflow and dephasing, that may explain this finding were investigated. Particularly, a statistically significant increase in proton density was found in a brain area that was detected as activated by conventional EPI but not by NumART₂* while no such changes were observed in brain areas that showed stimulus correlated signal changes on T₂* maps.     

Exchange coupling in ultrathin epitaxial yttrium iron garnet films

Magnetic exchange coupling has been observed for ultrathin films of yttrium iron garnet (Y₃Fe₅O₁₂ or YIG). Single-crystalline YIG films were prepared on yttrium aluminium garnet (Y₃Al₅O₁₂ or YAG) substrates by pulsed laser deposition. (111) and (110) oriented substrates were used. Film thicknesses were varied from 180 ? to 4600 ?. Epitaxial growth of YIG on YAG was obtained in spite of the lattice mismatch of 3%. Magnetic hysteresis loops recorded for ultrathin YIG films have a “bee-waist” shape and show a coupling between two different magnetic phases. The first phase is magnetically soft YIG. A composition study by secondary ion mass spectroscopy shows the second phase to be Y₃Fe_5-xAl_xO₁₂ due to the interdiffusion of Fe and Al at the film/substrate interface. This compound is known to be magnetically harder and to have weaker magnetization than YIG. The coupling of the two phases leads to a hysteresis loop displacement at low temperatures. This displacement varies differently with film thickness for two substrate orientations. Assuming an interfacial coupling, the maximal interaction energy is estimated to be about 0.17 erg/cm² at 5 K for (111) oriented sample. Received 3 June 2002 / Received in final form 7 October 2002 Published online 27 January 2003 RID="a" ID="a"Presently at LPM, Université H. Poincaré, BP 239, 54506 Vandœuvre-lès-Nancy e-mail: popova@lpm.u-nancy.fr