Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques

The aim of this study was to evaluate the contribution of diffusion and perfusion MR metrics in the discrimination of intracranial brain lesions at 3T MRI, and to investigate the potential diagnostic and predictive value that pattern recognition techniques may provide in tumor characterization using these metrics as classification features. Conventional MRI, diffusion weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic-susceptibility contrast imaging (DSCI) were performed on 115 patients with newly diagnosed intracranial tumors (low-and- high grade gliomas, meningiomas, solitary metastases). The Mann–Whitney U test was employed in order to identify statistical differences of the diffusion and perfusion parameters for different tumor comparisons in the intra-and peritumoral region. To assess the diagnostic contribution of these parameters, two different methods were used; the commonly used receiver operating characteristic (ROC) analysis and the more sophisticated SVM classification, and accuracy, sensitivity and specificity levels were obtained for both cases. The combination of all metrics provided the optimum diagnostic outcome. The highest predictive outcome was obtained using the SVM classification, although ROC analysis yielded high accuracies as well. It is evident that DWI/DTI and DSCI are useful techniques for tumor grading. Nevertheless, cellularity and vascularity are factors closely correlated in a non-linear way and thus difficult to evaluate and interpret through conventional methods of analysis. Hence, the combination of diffusion and perfusion metrics into a sophisticated classification scheme may provide the optimum diagnostic outcome. In conclusion, machine learning techniques may be used as an adjunctive diagnostic tool, which can be implemented into the clinical routine to optimize decision making.     

颅脑损伤后脑组织中水通道蛋白4表达的变化

目的研究颅脑损伤后水通道蛋白4（AQP-4）在脑组织中表达的变化及其临床意义。方法对27例颅脑损伤患者行开颅手术,将术中取得的挫伤水肿脑组织（损伤组）和颞极减压获得的相对正常脑组织（对照组）进行AQP-4的免疫组化和re-al- time PCR检测。结果损伤组各时点挫伤水肿脑组织AQP-4的表达量均明显高于对照组（均P＜0.05）;损伤24h后周围水肿脑组织中的AQP-4表达量显著升高。结论颅脑损伤后脑水肿的形成和发展与AQP-4的异常表达密切相关。     

神经激肽1受体拮抗剂对大鼠脑缺血再灌注的神经保护作用

目的观察大鼠局灶性脑缺血再灌注后神经激肽1受体拮抗剂对脑梗死体积、脑组织含水量及神经行为学变化的影响,探讨减轻神经源性炎症反应对脑缺血再灌注的保护作用。方法将54只大鼠分为药物组、假手术组、对照组,用线栓法建立右侧大脑中动脉缺血再灌注大鼠模型,药物组和对照组大鼠在再灌注时经尾静脉分别注入神经激肽1受体拮抗剂及生理盐水,假手术组只分离右侧颈总、颈内、颈外动脉。缺血再灌注后24h时采用免疫组化法检测P物质表达,氯化三苯基四氮唑染色法测定脑梗死体积,干-湿称重法检测脑组织含水量;分别于再灌注24h及72h时利用转棒试验和肢体不对称试验检测神经行为学变化。结果脑缺血再灌注后24h大鼠脑缺血区P物质的表达明显升高,脑组织含水量明显增加。药物组与对照组比较,脑组织含水量减少,神经功能改善。结论神经激肽1受体拮抗剂可通过抑制神经源性炎症反应而达到缺血再灌注损伤时的脑保护作用。     

Progress and problems in the application of focused ultrasound for blood-brain barrier disruption

Advances in neuroscience have resulted in the development of new diagnostic and therapeutic agents for potential use in the central nervous system (CNS). However, the ability to deliver the majority of these agents to the brain is limited by the blood-brain barrier (BBB), a specialized structure of the blood vessel wall that hampers transport and diffusion from the blood to the brain. Many CNS disorders could be treated with drugs, enzymes, genes, or large-molecule biotechnological products such as recombinant proteins, if they could cross the BBB. This article reviews the problems of the BBB presence in treating the vast majority of CNS diseases and the efforts to circumvent the BBB through the design of new drugs and the development of more sophisticated delivery methods. Recent advances in the development of noninvasive, targeted drug delivery by MRI-guided ultrasound-induced BBB disruption are also summarized.     

Diffusion-weighted imaging features of brain in obesity

PURPOSE: Obesity is characterized by an altered distribution of body fluid. However, distribution of fluid (extracellular/intracellular) in brain tissues has not been studied in obese subjects yet. The purpose of this study was to detect possible brain diffusion changes especially in satiety and hunger related centers in obese subjects by diffusion weighted imaging (DWI). METHODS: Conventional MRI and DWI of the brain was obtained from 81 obese patients (obese=68, morbid obese=13) and 29 age-matched, nonobese. The apparent diffusion coefficient (ADC) values were calculated in hypothalamus; amygdala; hippocampal gyrus; thalamus; insula; cingulate gyrus; orbitofrontal, dorsomedial and dorsolateral frontal, middle temporal and occipital cortex; cerebellum; midbrain and corpus striatum. RESULTS: The ADC values of hypothalamus, hippocampal gyrus, amygdala, insula, cerebellum and midbrain were significantly increased in patients (n:81) when compared to nonobese subjects. The ADC values of thalamus, hippocampal gyrus, amygdala, orbitofrontal, occipital, dorsolateral and middle temporal cortex, insula and midbrain were significantly increased in morbid obese when compared to nonobese subjects. The ADC values of orbitofrontal and occipital cortex were significantly higher in morbid obese than the values in the obese. The body mass index positively correlated with ADC values of amygdala, insula, orbitofrontal and middle temporal cortex. CONCLUSION: We observed increased ADC values of distinct locations related to satiety and hunger that suggest altered fluid distribution and/or vasogenic edema in obese subjects. Awareness of this abnormalities in brain tissue composition/function in obesity may contribute to better understanding of the underlying mechanisms.     

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.     

A study on small-world brain functional networks altered by postherpetic neuralgia

Understanding the effect of postherpetic neuralgia (PHN) pain on brain activity is important for clinical strategies. This is the first study, to our knowledge, to relate PHN pain to small-world properties of brain functional networks. Functional magnetic resonance imaging (fMRI) was used to construct functional brain networks of the subjects during the resting state. Sixteen patients with PHN pain and 16 (8 males, 8 females for both groups) age-matched controls were studied. The PHN patients exhibited decreased local efficiency along with non-significant changes of global efficiency in comparison with the healthy controls. Moreover, regional nodal efficiency was found to be significantly affected by PHN pain in the areas related to sense (postcentral gyrus, inferior parietal gyrus and thalamus), memory/affective processes (parahippocampal gyrus) and emotional activities (putamen). Significant correlation (p < 0.05) was also found between the nodal efficiency of putamen and pain intensity in PHN patients. Our results suggest that PHN modulates the local efficiency, and the small-world properties of brain networks may have potentials to objectively evaluate pain information in clinic.     

Assessing and improving the stability of chemometric models in small sample size situations

Small sample sizes are very common in multivariate analysis. Sample sizes of 10–100 statistically independent objects (rejects from processes or loading dock analysis, or patients with a rare disease), each with hundreds of data points, cause unstable models with poor predictive quality. Model stability is assessed by comparing models that were built using slightly varying training data. Iterated k-fold cross-validation is used for this purpose. Aggregation stabilizes models. It is possible to assess the quality of the aggregated model without calculating further models. The validation and aggregation methods investigated in this study apply to regression as well as to classification. These techniques are useful for analyzing data with large numbers of variates, e.g., any spectral data like FT-IR, Raman, UV/VIS, fluorescence, AAS, and MS. FT-IR images of tumor tissue were used in this study. Some tissue types occur frequently, while some are very rare. They are classified using LDA. Initial models were severely unstable. Aggregation stabilizes the predictions. The hit rate increased from 67% to 82%.     

Classification of malignant gliomas by infrared spectroscopic imaging and linear discriminant analysis

Infrared (IR) spectroscopy provides a sensitive molecular fingerprint for tissue without external markers. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. Infrared imaging spectrometers equipped with multi-channel detectors combine the spectral and spatial information. Tissue areas of 4 x 4 mm(2) can be analyzed within a few minutes in the macroscopic imaging mode. An approach is described to apply this methodology to human astrocytic gliomas, which are graded according to their malignancy from one to four. Multiple IR images of three tissue sections from one patient with a malignant glioma are acquired and assigned to the six classes normal brain tissue, astrocytoma grade II, astrocytoma grade III, glioblastoma multiforme grade IV, hemorrhage, and other tissue by a linear discriminant analysis model which was trained by data from a single-channel detector. Before the model is applied here, the spectra are shown to be virtually identical. The first specimen contained approximately 95% malignant glioma regions, that means astrocytoma grade III or glioblastoma. The smaller percentage of 12-34% malignant glioma in the second specimen is consistent with its location at the tumor periphery. The detection of less than 0.2% malignant glioma in the third specimen points to a location outside the tumor. The results were correlated with the cellularity of the tissue which was obtained from the histopathologic gold standard. Potential applications of IR spectroscopic imaging as a rapid tool to complement established diagnostic methods are discussed.     

The accuracy of whole brain N-acetylaspartate quantification

A non-localizing pulse sequence to quantify the total amount of N-acetylaspartate (NAA) in the whole brain (WBNAA) was introduced recently [Magn. Reson. Med. 40, 684–689 (1998)]. However, it is known that regional magnetic field inhomogeneities, ΔB₀s, arising from susceptibility differences at tissue interfaces, shift and broaden local resonances to outside the integration window, leading to an underestimation of the true amount of NAA in the entire brain. To quantify the upper limit of this loss, the whole-head proton MR spectrum (¹H-MRS) of the water was integrated over the same frequency width as the NAA. The ratio of this area/total-water-line was 75 ± 5% in 5 volunteers. The procedure was repeated with the brain-only water peak, obtained by summing signals only from voxels within that organ from a three-dimensional chemical-shift-imaging (3D CSI) set. It indicated that <10% of the water signal loss occurred in the brain. Therefore, by analogy, WBNAA accounts for >90% of that metabolite.