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Multiple sclerosis (MS) is an inflammatory disease of the central nervous system (CNS) which affects nearly one million people worldwide, leading to a progressive decline of motor and sensory functions, and permanent disability. High b-value diffusion-weighted MR images (b of up to 14000 s/mm 2 ) were acquired from the brains of controls and MS patients. These diffusion MR images, in which signal decay is not monoexponential, were analyzed using the q-space approach that emphasizes the diffusion characteristics of the slow-diffusing component. From this analysis, displacement and probability maps were constructed. The computed q-space analyzed MR images that were compared with conventional T 1 , T 2 (fluid attenuated inversion recovery (FLAIR)), and diffusion tensor imaging (DTI) images were found to be sensitive to the pathophysiological state of white matter. The indices used to construct this qspace analyzed MR maps, provided a pronounced differentiation between normal tissue and tissues classified as MS plaques by the FLAIR images. More importantly, a pronounced differentiation was also observed between tissues classified by the FLAIR MR images as normal-appearing white matter (NAWM) in the MS brains, which are known to be abnormal, and the respective control tissues. The potential diagnostic capacity of high b-value diffusion q-space analyzed MR images is discussed, and experimental data that explains the consequences of using the q-space approach once the short pulse gradient approximation is violated are presented. Magn Reson Med 47:115-126, 2002.

How are objects represented in the human visual cortex? Two conflicting theories suggest either a holistic representation, in which objects are represented by a collection of object templates, or a part-based representation, in which objects are represented as collections of features or object parts. We studied this question using a gradual object-scrambling paradigm in which pictures of objects (faces and cars) were broken in a stepwise manner into an increasing number of blocks. Our results reveal a hierarchical axis oriented anterior--posteriorly in the organization of ventral object-areas. Along this axis, representations are arranged in bands of increasing sensitivity to image scrambling. The axis starts in early visual areas through retinotopic areas V4/V8 and continues into the lateral-occipital sulcus dorsally and the posterior fusiform girus ventrally, corresponding together to the previously described object-related lateral occipital complex (LOC). Regions showing the highest sensitivity to scrambling tended to be located at the most anterior-lateral regions of the complex. In these more anterior regions, breaking the images into 16 parts produced a significant reduction in activation. Interestingly, activation was not affected when images were cut in two halves, either horizontally or vertically. Car images generally produced a weaker activation compared to faces in the lateral occipital complex but showed the same tendency of increased scrambling sensitivity along the anterior--posterior axis. These results suggest the existence of a hierarchical axis along ventral occipito-temporal object-areas, in which the neuronal properties shift from sensitivity to local object features to a more global and holistic representation.

An important characteristic of visual perception is the fact that object recognition is largely immune to changes in viewing conditions. This invariance is obtained within a sequence of ventral stream visual areas beginning in area V1 and ending in high order occipito-temporal object areas (the lateral occipital complex, LOC). Here we studied whether this transformation could be observed in the contrast response of these areas. Subjects were presented with line drawings of common objects and faces in five different contrast levels (0, 4, 6, 10, and 100%). Our results show that indeed there was a gradual trend of increasing contrast invariance moving from area V1, which manifested high sensitivity to contrast changes, to the LOC, which showed a significantly higher degree of invariance at suprathreshold contrasts (from 10 to 100%). The trend toward increased invariance could be observed for both face and object images; however, it was more complete for the face images, while object images still manifested substantial sensitivity to contrast changes. Control experiments ruled out the involvement of attention effects or hemodynamic "ceiling" in producing the contrast invariance. The transition from V1 to LOC was gradual with areas along the ventral stream becoming increasingly contrast-invariant. These results further stress the hierarchical and gradual nature of the transition from early retinotopic areas to high order ones, in the build-up of abstract object representations.

The functional anatomy of syntactic transformations, a major computational operation invoked in sentence processing, was identified through a functional magnetic resonance imaging investigation. A grammaticality judgment task was used, presented through a novel hidden-blocks design. Subjects listened to transformational and nontransformational sentences in which a host of other complexity generators (number of words, prepositions, embeddings, etc.) were kept constant. A series of analyses revealed that the neural processing of transformations is localizable, evoking a highly lateralized and localized activation in the left inferior frontal gyrus (Broca's region) and bilateral activation in the posterior superior temporal sulcus. The pattern of activation associated with transformational analysis was distinct from the one observed in neighboring regions, and anatomically separable from the effects of verb complexity, which yielded significant activation in the left posterior superior temporal sulcus. Taken together with neuropsychological evidence, these results uncover the neural reality of syntactic transformations.

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Background Differentiation between glioblastoma and brain metastasis is highly important due to differing medical treatment strategies. While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between glioblastoma and solitary brain metastasis may be challenging due to their similar appearance on MRI. Purpose To differentiate between glioblastoma and brain metastasis subtypes using radiomics analysis based on conventional post‐contrast T1‐weighted (T1W) MRI. Study Type Retrospective. Subjects Data were acquired from 439 patients: 212 patients with glioblastoma and 227 patients with brain metastasis (breast, lung, and others). Field Strength/Sequence Post‐contrast 3D T1W gradient echo images, acquired with 1.5 and 3.0 T MR systems. Assessment Analysis included image preprocessing, segmentation of tumor area, and features extraction including: patients' clinical information, tumor location, first‐ and second‐order statistical, morphological, wavelet features, and bag‐of‐features. Following dimension reduction, classification was performed using various machine‐learning algorithms including support‐vector machine (SVM), k‐nearest neighbor, decision trees, and ensemble classifiers. Statistical Tests For classification, the data were divided into training (80%) and testing datasets (20%). Following optimization of the classifiers, mean sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. Results For the testing dataset, the best results for differentiation of glioblastoma from brain metastasis were obtained using the SVM classifier with mean accuracy = 0.85, sensitivity = 0.86, specificity = 0.85, and AUC = 0.96. The best classification results between glioblastoma and brain metastasis subtypes were obtained using SVM classifier with mean accuracy = 0.85, 0.89, 0.75, 0.90; sensitivity = 1.00, 0.60, 0.57, 0.11; specificity = 0.76, 0.92, 0.87, 0.99; and AUC = 0.98, 0.81, 0.83, 0.57 for the glioblastoma, breast, lung, and other brain metastases, respectively. Data Conclusion Differentiation between glioblastoma and brain metastasis showed a high success rate based on postcontrast T1W MRI. Classification between glioblastoma and brain metastasis subtypes may require additional MR sequences with other tissue contrasts. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:519–528.

Purpose: Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans.Methods: The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies. Since similarity is not guaranteed, sampling and reconstruction are adjusted during acquisition to match the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, via adaptive sampling, and in the reconstruction stage, with weighted reconstruction. In adaptive sampling, k -space sampling locations are optimized during acquisition. Weighted reconstruction uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. The approach was tested on 2D and 3D MRI scans of patients with brain tumors.Results: The longitudinal adaptive CS MRI (LACS-MRI) scheme provides reconstruction quality which outperforms other CS-based approaches for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial resolution. Compared with data sampled at Nyquist rate, LACS-MRI exhibits Signal-to-Error Ratio (SER) of 24.8dB with undersampling factor of 16.6 in 3D MRI.Conclusions reconstruction utilizing similarity of scans in longitudinal MRI studies, where possible. The proposed approach can play a major part and significantly reduce scanning time in many applications that consist of disease follow-up and monitoring of longitudinal changes in brain MRI.