基于时序区分子图的阿尔茨海默症辅助诊断方法

为解决现有区分子图方法在解决阿尔茨海默症辅助诊断上忽略脑网络动态连接变化的问题,提出一种基于时序区分子图的辅助诊断方法.将功能磁共振成像经过处理后形成二值矩阵并使同一测试者的多张动态脑网络形成时序差异图,之后进行频繁差异子图挖掘、频繁差异序列挖掘,进而筛选出保留脑网络时序特性的生物标记物——时序区分子图.获取ADNI公开数据集的一组数据进行实验,通过与现有的早期阿尔茨海默症辅助诊断方法进行大量的实验对比,证明本文方法的辅助诊断准确率在该数据集上提高了12.7%,进而证明所提方法的有效性.     

Activation of Visuomotor Systems during Visually Guided Movements: A Functional MRI Study

The dorsal stream is a dominant visuomotor pathway that connects the striate and extrastriate cortices to posterior parietal areas. In turn, the posterior parietal areas send projections to the frontal primary motor and premotor areas. This cortical pathway is hypothesized to be involved in the transformation of a visual input into the appropriate motor output. In this study we used functional magnetic resonance imaging (fMRI) of the entire brain to determine the patterns of activation that occurred while subjects performed a visually guided motor task. In nine human subjects, fMRI data were acquired on a 4-T whole-body MR system equipped with a head gradient coil and a birdcage RF coil using aT^*₂-weighted EPI sequence. Functional activation was determined for three different tasks: (1) a visuomotor task consisting of moving a cursor on a screen with a joystick in relation to various targets, (2) a hand movement task consisting of moving the joystick without visual input, and (3) a eye movement task consisting of moving the eyes alone without visual input. Blood oxygenation level-dependent (BOLD) contrast-based activation maps of each subject were generated using period cross-correlation statistics. Subsequently, each subject's brain was normalized to Talairach coordinates, and the individual maps were compared on a pixel by pixel basis. Significantly activated pixels common to at least four out of six subjects were retained to construct the final functional image. The pattern of activation during visually guided movements was consistent with the flow of information from striate and extrastriate visual areas, to the posterior parietal complex, and then to frontal motor areas. The extensive activation of this network and the reproducibility among subjects is consistent with a role for the dorsal stream in transforming visual information into motor behavior. Also extensively activated were the medial and lateral cerebellar structures, implicating the cortico–ponto–cerebellar pathway in visually guided movements. Thalamic activation, particularly of the pulvinar, suggests that this nucleus is an important subcortical target of the dorsal stream.     

Functional MRI and multivariate autoregressive models

Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI.     

Comparison of α-chloralose,medetomidine and isoflurane anesthesia for functional connectivity mapping in the rat

Functional connectivity measures based upon low-frequency blood-oxygenation-level-dependent functional magnetic resonance imaging (BOLD fMRI) signal fluctuations have become a widely used tool for investigating spontaneous brain activity in humans. Still unknown, however, is the precise relationship between neural activity, the hemodynamic response and fluctuations in the MRI signal. Recent work from several groups had shown that correlated low-frequency fluctuations in the BOLD signal can be detected in the anesthetized rat — a first step toward elucidating this relationship. Building on this preliminary work, through this study, we demonstrate that functional connectivity observed in the rat depends strongly on the type of anesthesia used. Power spectra of spontaneous fluctuations and the cross-correlation-based connectivity maps from rats anesthetized with α-chloralose, medetomidine or isoflurane are presented using a high-temporal-resolution imaging sequence that ensures minimal contamination from physiological noise. The results show less localized correlation in rats anesthetized with isoflurane as compared with rats anesthetized with α-chloralose or medetomidine. These experiments highlight the utility of using different types of anesthesia to explore the fundamental physiological relationships of the BOLD signal and suggest that the mechanisms contributing to functional connectivity involve a complicated relationship between changes in neural activity, neurovascular coupling and vascular reactivity.     

Respiratory noise correction using phase information

Respiratory noise is a confounding factor in functional magnetic resonance imaging (MRI) data analysis. A novel method called Respiratory noise Correction using Phase information is proposed to retrospectively correct for the respiratory noise in functional MRI (fMRI) time series. It is demonstrated that the respiratory movement and the phase of functional MRI images are highly correlated in time. The signal fluctuation due to respiratory movements can be effectively estimated from the phase variation and removed from the functional MRI time series using a Wiener filtering technique. In our experiments, this new method is compared with RETROICOR, which requires recording respiration signal simultaneously in an fMRI experiment. The two techniques show comparable performance with respect to the respiratory noise correction for fMRI time series. However, this technique is more advantageous because there is no need for monitoring the subjects’ respiration or changing functional MRI protocols. This technique is also potentially useful for correcting respiratory noise from abnormal breathing or when the respiration is not periodic.     

Changes in cerebral activity after decreased upper-limb hypertonus: an EMG-fMRI study

Objective

Whereas several studies have used functional magnetic resonance imaging (fMRI) to investigate motor recovery, whether therapy to decrease post-stroke hypertonus alters central motor patterns remains unclear. In this study, we used continuous electromyography (EMG)-fMRI to investigate possible changes in movement-related brain activation in patients receiving Botulinum toxin (BoNT-A) for hand-muscle hypertonus after chronic stroke.

Methods

We studied eight stroke patients all of whom had hemiparesis and associated upper-limb hypertonus. All patients underwent an fMRI-EMG recording and clinical-neurological assessment before BoNT-A and 5 weeks thereafter. The handgrip motor task during imaging was fixed across both patients and controls. The movements were metronome paced, movement amplitude and force were controlled with a plastic orthosis, dynamometer and EMG recording. An age-matched control group was recruited from among healthy volunteers underwent the same fMRI-EMG recording.

Results

Before BoNT-A, while patients moved the paretic hand, fMRI detected wide bilateral activation in the sensorymotor areas (SM1), in the supplementary motor area (SMA) and cerebellum. After BoNT-A blood oxygenation level-dependent (BOLD) activation decreased in ipsilateral and contralateral motor areas and became more lateralized. BOLD activation decreased also in ipsilateral cerebellar regions and in the SMA.

Conclusion

Changes in peripheral upper-limb hypertonus after BoNT-A were associated to an improvement in active movements and more lateralized and focalized activation of motor areas. The clinical and EMG-fMRI coregistration technique we used to study hand-muscle hypertonus in patients receiving BoNT-A after chronic stroke should be useful in future studies seeking improved strategies for post-stroke neurorehabilitation.     

Functional exploration of the human spinal cord during voluntary movement and somatosensory stimulation

Demonstrations of the possibility of obtaining functional information from the spinal cord in humans using functional magnetic resonance imaging (fMRI) have been growing in number and sophistication, but the technique and the results that it provides are still perceived by the scientific community with a greater degree of scepticism than fMRI investigations of brain function. Here we review the literature on spinal fMRI in humans during voluntary movements and somatosensory stimulation. Particular attention is given to study design, acquisition and statistical analysis of the images, and to the agreement between the obtained results and existing knowledge regarding spinal cord anatomy and physiology.     

Decoding neural events from fMRI BOLD signal: A comparison of existing approaches and development of a new algorithm

Neuroimaging methodology predominantly relies on the blood oxygenation level dependent (BOLD) signal. While the BOLD signal is a valid measure of neuronal activity, variances in fluctuations of the BOLD signal are not only due to fluctuations in neural activity. Thus, a remaining problem in neuroimaging analyses is developing methods that ensure specific inferences about neural activity that are not confounded by unrelated sources of noise in the BOLD signal. Here, we develop and test a new algorithm for performing semiblind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that treats the neural event as an observable, but intermediate, probabilistic representation of the system's state. We test and compare this new algorithm against three other recent deconvolution algorithms under varied levels of autocorrelated and Gaussian noise, hemodynamic response function (HRF) misspecification and observation sampling rate. Further, we compare the algorithms' performance using two models to simulate BOLD data: a convolution of neural events with a known (or misspecified) HRF versus a biophysically accurate balloon model of hemodynamics. We also examine the algorithms' performance on real task data. The results demonstrated good performance of all algorithms, though the new algorithm generally outperformed the others (3.0% improvement) under simulated resting-state experimental conditions exhibiting multiple, realistic confounding factors (as well as 10.3% improvement on a real Stroop task). The simulations also demonstrate that the greatest negative influence on deconvolution accuracy is observation sampling rate. Practical and theoretical implications of these results for improving inferences about neural activity from fMRI BOLD signal are discussed.     

Multi-shot turbo spin-echo for 3D vascular space occupancy imaging

Vascular space occupancy (VASO) is a magnetic resonance imaging technique sensitive to cerebral blood volume, and is a potential alternative to the blood oxygenation level dependent (BOLD) sensitive technique as a basis for functional mapping of the neurovascular response to a task. Many implementations of VASO have made use of echo-planar imaging strategies that allow rapid acquisition, but risk introducing potentially confounding BOLD effects. Recently, multi-slice and 3D VASO techniques have been implemented to increase the imaging volume beyond the single slice of early reports. These techniques usually rely, however, on advanced scanner software or hardware not yet available in many centers. In the present study, we have implemented a short-echo time, multi-shot 3D Turbo Spin-Echo (TSE) VASO sequence that provided 8-slice coverage on a routine clinical scanner. The proposed VASO sequence was tested in assessing the response of the human motor cortex during a block design finger tapping task in 10 healthy subjects. Significant VASO responses, inversely correlated with the task, were found at both individual and group level. The location and extent of VASO responses were in close correspondence to those observed using a conventional BOLD acquisition in the same subjects. Although the spatial coverage and temporal resolution achieved were limited, robust and consistent VASO responses were observed. The use of a susceptibility insensitive volumetric TSE VASO sequence may have advantages in locations where conventional BOLD and echo-planar based VASO imaging is compromised.     

Community structure and modularity in networks of correlated brain activity

Functional connectivity patterns derived from neuroimaging data may be represented as graphs or networks, with individual image voxels or anatomically-defined structures representing the nodes, and a measure of correlation between the responses in each pair of nodes determining the edges. This explicit network representation allows network-analysis approaches to be applied to the characterization of functional connections within the brain. Much recent research in complex networks has focused on methods to identify community structure, i.e. cohesive clusters of strongly interconnected nodes. One class of such algorithms determines a partition of a network into 'sub-networks' based on the optimization of a modularity parameter, thus also providing a measure of the degree of segregation versus integration in the full network. Here, we demonstrate that a community structure algorithm based on the maximization of modularity, applied to a functional connectivity network calculated from the responses to acute fluoxetine challenge in the rat, can identify communities whose distributions correspond to anatomically meaningful structures and include compelling functional subdivisions in the brain. We also discuss the biological interpretation of the modularity parameter in terms of segregation and integration of brain function.