Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions.

State-space multivariate dynamical systems (MDS) (Ryali et al. 2011) and other causal estimation models are being increasingly used to identify directed functional interactions between brain regions. However, the validity and accuracy of such methods are poorly understood. Performance evaluation based on computer simulations of small artificial causal networks can address this problem to some extent, but they often involve simplifying assumptions that reduce biological validity of the resulting data. Here, we use a novel approach taking advantage of recently developed optogenetic fMRI (ofMRI) techniques to selectively stimulate brain regions while simultaneously recording high-resolution whole-brain fMRI data. ofMRI allows for a more direct investigation of causal influences from the stimulated site to brain regions activated downstream and is therefore ideal for evaluating causal estimation methods in vivo. We used ofMRI to investigate whether MDS models for fMRI can accurately estimate causal functional interactions between brain regions. Two cohorts of ofMRI data were acquired, one at Stanford University and the University of California Los Angeles (Cohort 1) and the other at the University of North Carolina Chapel Hill (Cohort 2). In each cohort, optical stimulation was delivered to the right primary motor cortex (M1). General linear model analysis revealed prominent downstream thalamic activation in Cohort 1, and caudate-putamen (CPu) activation in Cohort 2. MDS accurately estimated causal interactions from M1 to thalamus and from M1 to CPu in Cohort 1 and Cohort 2, respectively. As predicted, no causal influences were found in the reverse direction. Additional control analyses demonstrated the specificity of causal interactions between stimulated and target sites. Our findings suggest that MDS state-space models can accurately and reliably estimate causal interactions in ofMRI data and further validate their use for estimating causal interactions in fMRI. More generally, our study demonstrates that the combined use of optogenetics and fMRI provides a powerful new tool for evaluating computational methods designed to estimate causal interactions between distributed brain regions.

Role of the anterior insular cortex in integrative causal signaling during multisensory auditory-visual attention.

Coordinated attention to information from multiple senses is fundamental to our ability to respond to salient environmental events, yet little is known about brain network mechanisms that guide integration of information from multiple senses. Here we investigate dynamic causal mechanisms underlying multisensory auditory-visual attention, focusing on a network of right-hemisphere frontal-cingulate-parietal regions implicated in a wide range of tasks involving attention and cognitive control. Participants performed three 'oddball' attention tasks involving auditory, visual and multisensory auditory-visual stimuli during fMRI scanning. We found that the right anterior insula (rAI) demonstrated the most significant causal influences on all other frontal-cingulate-parietal regions, serving as a major causal control hub during multisensory attention. Crucially, we then tested two competing models of the role of the rAI in multisensory attention: an 'integrated' signaling model in which the rAI generates a common multisensory control signal associated with simultaneous attention to auditory and visual oddball stimuli versus a 'segregated' signaling model in which the rAI generates two segregated and independent signals in each sensory modality. We found strong support for the integrated, rather than the segregated, signaling model. Furthermore, the strength of the integrated control signal from the rAI was most pronounced on the dorsal anterior cingulate and posterior parietal cortices, two key nodes of saliency and central executive networks respectively. These results were preserved with the addition of a superior temporal sulcus region involved in multisensory processing. Our study provides new insights into the dynamic causal mechanisms by which the AI facilitates multisensory attention.

Inter-subject synchronization of brain responses during natural music listening.

Music is a cultural universal and a rich part of the human experience. However, little is known about common brain systems that support the processing and integration of extended, naturalistic 'real-world' music stimuli. We examined this question by presenting extended excerpts of symphonic music, and two pseudomusical stimuli in which the temporal and spectral structure of the Natural Music condition were disrupted, to non-musician participants undergoing functional brain imaging and analysing synchronized spatiotemporal activity patterns between listeners. We found that music synchronizes brain responses across listeners in bilateral auditory midbrain and thalamus, primary auditory and auditory association cortex, right-lateralized structures in frontal and parietal cortex, and motor planning regions of the brain. These effects were greater for natural music compared to the pseudo-musical control conditions. Remarkably, inter-subject synchronization in the inferior colliculus and medial geniculate nucleus was also greater for the natural music condition, indicating that synchronization at these early stages of auditory processing is not simply driven by spectro-temporal features of the stimulus. Increased synchronization during music listening was also evident in a right-hemisphere fronto-parietal attention network and bilateral cortical regions involved in motor planning. While these brain structures have previously been implicated in various aspects of musical processing, our results are the first to show that these regions track structural elements of a musical stimulus over extended time periods lasting minutes. Our results show that a hierarchical distributed network is synchronized between individuals during the processing of extended musical sequences, and provide new insight into the temporal integration of complex and biologically salient auditory sequences.

Identification and quantitation of auxins in plants by liquid chromatography/electrospray ionization ion trap mass spectrometry.

Auxin is an important phylohormone, which regulates specific physiological responses such as division, elongation and differentiation of cells. A new method using liquid chromatography/electrospray ionization ion trap mass spectrometry (LC/ESI-ITMS) has been developed for identification and quantitation of four auxins. Under the optimum conditions, four auxins (indole-3-acetic acid, indole-3-propionic acid, indole-3-butyric acid and 1-naphthylacetic acid) were completely separated and quantitated within 7 min with a minimum detection limit of 8.0 ng mL(-1) with relative standard deviations lower than 5.0%. This method also has been applied to analysis of auxins in Chinese cabbage where, even with a complicated serious background perturbation due to the natural biological matrix, the mean recoveries ranged from 77.5% to 99.8%. Finally, we discuss the MS-relevant properties of the identified auxins in detail.

Identification of beta-sitosterol, stigmasterol and ergosterin in A. roxburghii using supercritical fluid extraction followed by liquid chromatography/atmospheric pressure chemical ionization ion trap mass spectrometry.

beta-Sitosterol and stigmasterol are the most common phytosterols in traditional Chinese medicine. They have been proved to have many important bioactivities. To the best of our knowledge, this is the first report that beta-sitosterol, stigmasterol and ergosterol coexisting in A. roxburghii herbs can be simultaneously extracted by a supercritical fluid extraction (SFE) procedure; then a simple high-performance liquid chromatography/atmospheric pressure chemical ionization ion trap mass spectrometry (HPLC/APCI/MS) method was developed for simultaneous identification and determination of these three compounds. The ion trap MS/MS detector was equipped with an atmospheric pressure chemical ionization source operating in the positive ion mode, APCI(+). The linear responses were obtained in the concentration range of 0.50-150 microg/mL (r = 0.9999) for ergosterol, 5-400 microg/mL (r = 0.9999) for stigmasterol, and 10-2000 microg/mL (r = 0.9998) for beta-sitosterol. An orthogonal L(9) (3(3)) test design was employed for optimization of the SFE process. Under the optimized conditions, i.e. pressure of 25 mPa, temperature of 45 degrees C and ethanol as modifier, the concentrations of sterols in the extract were found to be 2.89% (g/g) for beta-sitosterol, 3.56% (g/g) for stigmasterol and 2.96% (g/g) for ergosterin. The SFE method was also compared with a previously developed Soxhlet extraction. The SFE method produced higher yields of sterols than that of the Soxhlet extraction. The proposed method has been successfully used for identification and quantitation of beta-sitosterol, stigmasterol and ergosterin in a real A. roxburghii sample.

Use of liquid chromatography-atmospheric pressure chemical ionization-ion trap mass spectrometry for identification of oleanolic acid and ursolic acid in Anoectochilus roxburghii (wall.) Lindl.

Oleanolic acid (OA) and ursolic acid (UA) are the two important bioactive compounds in Anoectochilus roxburghii (wall) Lindl (A. roxburghii), which has been used as a traditional Chinese medicine. So far, there has been no report to indicate that A. roxburghii contains these two bioactive compounds. It is necessary to develop an effective method to extract and analyze OA and UA in A. roxburghii. In this paper, a quantitative method, consisting of supercritical fluid extraction (SFE) followed by liquid chromatography-atmospheric pressure chemical ionization-ion trap mass spectrometry (LC-APCI-IT-MS) analysis, was developed for identification of OA and UA in A. roxburghii. The extraction was carried out by using CO(2) as the supercritical fluid and ethanol as the modifier before LC separation. The mobile phase used for LC separation consisted of acetic acid (1%, v/v), water (15%, v/v) and methanol (84%, v/v), and the elution was performed at a flow rate of 0.8 ml/min. The mass spectrometer was operated in APCI(+) mode with selected ion monitoring (SIM) to quantify OA and UA at m/z 439.4. Under optimum conditions, the linear responses of OA and UA were obtained in the concentration range of 0.5-80 (r = 0.9992) and 0.5-50 microg/ml (r = 0.9989) with the detection limits of 0.125 and 0.085 microg/ml, respectively. The proposed method has been used for the identification and quantitation of OA and UA in a real A. roxburghii sample.