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This paper addresses the functional localization of intra-patient images of the brain. Functional images of the brain (fMRI and PET) provide information about brain function and metabolism whereas anatomical images (MRI and CT) supply the localization of structures with high spatial resolution. The goal is to find the geometric correspondence between functional and anatomical images in order to complement and fuse the information provided by each imaging modality. The proposed approach is based on a variational formulation of the image registration problem in the frequency domain. It has been implemented as a C/C$++$ library which is invoked from a GUI. This interface is routinely used in the clinical setting by physicians for research purposes (Inscanner, Alicante, Spain), and may be used as well for diagnosis and surgical planning. The registration of anatomic and functional intra-patient images of the brain makes it possible to obtain a geometric correspondence which allows for the localization of the functional processes that occur in the brain. Through 18 clinical experiments, it has been demonstrated how the proposed approach outperforms popular state-of-the-art registration methods in terms of efficiency, information theory-based measures (such as mutual information) and actual registration error (distance in space of corresponding landmarks).

Recent work presented a framework for space-time-resolved neurophysiological process imaging that augments existing electromagnetic source imaging techniques. In particular, a nonlinear Analytic Kalman filter (AKF) has been developed to efficiently infer the states and parameters of neural mass models believed to underlie the generation of electromagnetic source currents. Unfortunately, as the initialization determines the performance of the Kalman filter, and the ground truth is typically unavailable for initialization, this framework might produce suboptimal results unless significant effort is spent on tuning the initialization. Notably, the relation between the initialization and overall filter performance is only given implicitly and is expensive to evaluate; implying that conventional optimization techniques, e.g. gradient or sampling based, are inapplicable. To address this problem, a novel efficient framework based on blackbox optimization has been developed to find the optimal initialization by reducing the signal prediction error. Multiple state-of-the-art optimization methods were compared and distinctively, Gaussian process optimization decreased the objective function by 82.1% and parameter estimation error by 62.5% on average with the simulation data compared to no optimization applied. The framework took only 1.6h and reduced the objective function by an average of 13.2% on 3.75min 4714-source channel magnetoencephalography data. This yields an improved method of neurophysiological process imaging that can be used to uncover complex underpinnings of brain dynamics.

This paper offers a definition of precise, comprehensive, robust and practical neuroanatomical segmentation in magnetic resonance brain images with the goal of performing quantitative morphometric analyses. The main types of difficulties experienced with such problems are described, including those relating to the classification of MR signal intensities and the fact that there is insufficient information in the 2D image. To illustrate the details of obtaining a morphometric description, a case study of semi-automated methods is presented for segmenting the lateral ventricles and caudate nucleus in T1 coronal MR image data. The most significant remaining difficulties are summarized and are offered as objectives for further research.

Neuroscience and the Law.

Neurological Impairment and the Capacity to Consent to Participate in Research.

Ethical Responsibility to Manage Incidental Findings in Brain Imaging Research.

The Impact of Discrimination on Research into Depression.

Indian Pharma Industry: Trends, Predictions and Challenges.

RFID Smart Sensing.

Rational Design for the Next Generation of Vaccines.

Is Aging a Risk Factor for Cancer?

China's Synthetic Biology Research.

Ethical Responsibility to Manage Incidental Findings in Brain Imaging Research.

Translating Research at SingHealth: Cardiometabollic Diseases.

Universities: A Treasure Trove of Technologies.

The Increasing Need for Bioinformatics in Life Science and Biotechnology Research.

Site Mapping the Innovative Way.

New Antifungals or Old?

Tinnitus affects 10–15% of the population and tends to increase in frequency among older ages. The published reports have converged on that tinnitus is the sustained neuroplastic changes with aberrant activity residing in the auditory and nonauditory nervous systems that cause the sensation and problem of tinnitus. Clinically tinnitus remains as a subjective symptom and cannot be evaluated objectively. Our previous study has demonstrated the significantly different intensity dependence of auditory cortical evoked potential (ACEP) amplitudes to pure-tone stimuli recorded at some midline scalp electrodes between normal controls and tinnitus patients. Although ACEP test has been well known for its excellent temporal resolution of milliseconds and the latency and amplitude of ACEPs give ample information about the auditory processing of tinnitus, these measurements at each electrode deliver insufficient information about the spatial correlation among different cortical locations. According to the neurophysiological model of tinnitus, temporal and spatial information are both necessary and complementary to study the underlying mechanisms of problem tinnitus. Thus the purpose of this study was to demonstrate the spatial presentations of ACEP N1–P2 of normal controls and tinnitus patients. In this work, a program has been developed to translate the ACEPs recorded at 30 scalp electrodes to a two-dimensional presentation of isoelectric topography and current source density. The differences of intensity dependence about N1–P2 noted with previous study are reviewed on the two spatial presentations and compared between control subjects and tinnitus patients, while the patterns of correlation are demonstrated and compared with those recorded by functional brain imaging techniques like PET and fMRI.

In this paper we highlight a few features of the semantic gap problem in image interpretation. We show that semantic image interpretation can be seen as a symbol grounding problem. In this context, ontologies provide a powerful framework to represent domain knowledge, concepts and their relations, and to reason about them. They are likely to be more and more developed for image interpretation. A lot of image interpretation systems rely strongly on descriptions of objects through their characteristics such as shape, location, image intensities. However, spatial relations are very important too and provide a structural description of the imaged phenomenon, which is often more stable and less prone to variability than pure object descriptions. We show that spatial relations can be integrated in domain ontologies. Because of the intrinsic vagueness we have to cope with, at different levels (image objects, spatial relations, variability, questions to be answered, etc.), fuzzy representations are well adapted and provide a consistent formal framework to address this key issue, as well as the associated reasoning and decision making aspects. Our view is that ontology-based methods can be very useful for image interpretation if they are associated to operational models relating the ontology concepts to image information. In particular, we propose operational models of spatial relations, based on fuzzy representations.