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The assignment of a value to any economic system, especially in healthcare management, is the topic of this article. The assignment of a value to a clinical act is a very complex process, as it joins the complexity of estimating value in an economic system with the estimation of the value of well-being. An interdisciplinary approach joining disciplines such as Philosophy, Business, Psychology and Physics is used to analyse the assignment of a value; and it is obtained that it is necessary the integrated use of three concepts; viz., Truth, Good, and Beauty. It is also obtained that the concept of Beauty has the biggest difficulty in being computationally represented, and that to achieve such representation it is necessary the use of Statistical Philosophy, a here-proposed branch of the Philosophy of Information. Moreover, it is obtained that value is made of three types of value; viz., Truth-value, Good-value, and Beauty-value. Finally, it is made an assessment of the difficulty in choosing the appropriate necessary projection of the 3-vector value into a worthiness-scalar, a projection that is necessary because the choice of a best option, e.g. a best clinical act, always requires that the option is quantified by a scalar. (C) 2020 The Authors. Published by Elsevier B.V., NFL thanks Eduarda Sousa for support. Thanks to Sandra Lori for the drawings. All the funding was provided by FCT (Fundacao para a Ciencia e a Tecnologia): NFL was funded by a fellowship of project MEDPERSYST-POCI01-0145-FEDER-016428 and by the INESC-ID multiannual funding from the PIDDAC program (UID/CEC/50021/2020); and the work of both JN and VA has been supported within the project scope of UID/CEC/00319/2020.

The growing explosion of ideas such as Artificial Intelligence (AI), smart environments and ubiquitous computing has led to the creation of the Ambient Intelligence (AmI) paradigm. As AmI begins to take place, moving from a futuristic idea to a reality, we are gradually witnessing the creation of an omnipresent, responsive, and intelligent atmosphere in which thousands of tiny sensors and natural user interfaces will be embedded in our natural movements and in our social and physical interactions. Hence, a key challenge in this multi-disciplinary approach is to get machines to act according to ethical priorities that make sense to human beings. In this study, we improve the capacity for machine ethics to approach human ethics by assessing the computation of transaction values and we argue that it is possible to perform such a computation using recent work that describes the effects of human decision-making using an axiomatic framework. This paper clarifies the relationship between the brain’s 3-axes of neuroscience, the 3 Plato’s Transcendentals of philosophy and the biological evolution’s 3-components, as well as the top-down vs. bottom-up approaches to machine ethics.

An in-depth study of knowledge and technologies was made related to the various scientific, technical, and industrial domains necessary for the acquisition of skills and capabilities for the design and development of a multisensory fusion system for vehicle cockpits. After an extensive literature review, it was possible to determine the baselines of the solution to be developed and obtain a pipeline prototype.

Driverless vehicles are more and more becoming a reality. However, people still have some concerns in using them, the main concern is fear, hence the importance of creating a surveillance system inside those vehicles. For the detection and classification of human movements to be possible it is necessary to train the system with data representative enough for all kinds of possibilities. Although the production of large quantities of data becomes an expensive process and adds the problem of data protection, the use of synthetic data once they are artificially generated allows lower costs and eliminates the problem of data protection. A bibliographic study was carried out in this paper with articles from 2017 or later on the use of synthetic data. In these studies, it is noted that synthetic data is widely used with good results. As far as image capture is concerned, they show that 3D cameras have better results, but they are more expensive, so 2D cameras are more often used with later conversion to 3D images. The stitched puppet (SP) model is capable of adapting to the most difficult poses having obtained good results in its application in the FAUST dataset.

The frontal lobes are one of the most complex brain structures involved in both domain-general and specific functions. The goal of this work was to assess the anatomical and cognitive affectations from a unique case with massive bilateral frontal affectation. We report the case of GC, an eight-year old child with nearly complete affectation of bilateral frontal structures and spared temporal, parietal, occipital, and cerebellar regions. We performed behavioral, neuropsychological, and imaging (MRI, DTI, fMRI) evaluations. Neurological and neuropsychological examinations revealed a mixed pattern of affected (executive control/abstraction capacity) and considerably preserved (consciousness, language, memory, spatial orientation, and socio-emotional) functions. Both structural (DTI) and functional (fMRI) connectivity evidenced abnormal anterior connections of the amygdala and parietal networks. In addition, brain structural connectivity analysis revealed almost complete loss of frontal connections, with atypical temporo-posterior pathways. Similarly, functional connectivity showed an aberrant frontoparietal network and relative preservation of the posterior part of the default mode network and the visual network. We discuss this multilevel pattern of behavioral, structural, and functional connectivity results. With its unique pattern of compromised and preserved structures and functions, this exceptional case offers new constraints and challenges for neurocognitive theories. Fil: Ibanez Barassi, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina. Universidad Adolfo Ibañez; Chile. Australian Research Council; Australia Fil: Zimerman, Máximo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina Fil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina Fil: Lori, Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina Fil: Rapacioli, Melina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina Fil: Cardona Londoño, Juan Felipe. Universidad del Valle; Colombia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Suarez, Diana M. A.. Universidad del Valle; Colombia Fil: Herrera, Eduar. Universidad Icesi; Colombia Fil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina. Universidad Nacional de Cuyo; Argentina Fil: Manes, Facundo Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina

Thanks to Eduarda Sousa for support. NFL was supported by a fellowship of the project MEDPERSYST - POCI-01-0145-FEDER-016428, funded by Portugal’s FCT. This work was also supported by NORTE-01-0145-FEDER-000013, and NORTE 2020 under the Portugal 2020 Partnership Agreement through the FEDER, plus it was funded by the European Commission (FP7) “SwitchBox - Maintaining health in old age through homeostasis” (Contract HEALTH-F2-2010-259772), and co-financed by the Portuguese North Regional Operational Program (ON.2 – O Novo Norte), under the QREN through FEDER, and by the “Fundacao Calouste Gulbenkian” (Portugal) (Contract grant number: P-139977; project “TEMPO - Better mental health during ageing based on temporal prediction of individual brain ageing trajectories”). We gratefully acknowledge the support of the NVIDIA Corporation with their donation of a Quadro P6000 board used in this research. This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT within the Project Scope: UID/CEC/00319/2013.

Biomarkers represent a critical research area in neurodegeneration disease as they can contribute to studying potential disease-modifying agents, fostering timely therapeutic interventions, and alleviating associated financial costs. Functional connectivity (FC) analysis represents a promising approach to identify early biomarkers in specific diseases. Yet, virtually no study has tested whether potential FC biomarkers prove to be reliable and reproducible across different centers. As such, their implementation remains uncertain due to multiple sources of variability across studies: the numerous international centers capable conducting FC research vary in their scanning equipment and their samples’ socio-cultural background, and, more troublingly still, no gold-standard method exists to analyze FC. In this unprecedented study, we aim to address both issues by performing the first multicenter FC research in the behavioral-variant frontotemporal dementia (bvFTD), and by assessing multiple FC approaches to propose a gold-standard method for analysis. We enrolled 52 bvFTD patients and 60 controls from three international clinics (with different fMRI recording parameters), and three additional neurological patient groups. To evaluate FC, we focused on seed analysis, inter-regional connectivity, and several graph-theory approaches. Only graph-theory analysis, based on weighted-matrices, yielded consistent differences between bvFTD and controls across centers. Also, graph metrics robustly discriminated bvFTD from the other neurological conditions. The consistency of our findings across heterogeneous contexts highlights graph-theory as a potential gold-standard approach for brain network analysis in bvFTD. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

The main purpose of this article is to analyze the impact of the workers’ behavior in terms of their emotions and feelings in system’s performance, i.e., one is looking at issues concerned with Organizational Sustainability. Indeed, one’s aim is to define a process that motivates and inspires managers and personnel to act upon the limit, i.e., to achieve the organizational goals through an effective and efficient implementation of operational and behavioral strategies. The focus will be on the importance of specific psychosocial variables that may affect collective pro-organizational attitudes. Data that is increasing exponentially, and somehow being out of control, i.e., the question is to know the correct value of the information that may be behind these numbers., This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.

Interoception, the sensing of visceral body signals, involves an interplay between neural and autonomic mechanisms. Clinical studies into this domain have focused on patients with neurological and psychiatric disorders, showing that damage to relevant brain mechanisms can variously alter interoceptive functions. However, the association between peripheral cardiac-system alterations and neurocognitive markers of interoception remains poorly understood. To bridge this gap, we examined multidimensional neural markers of interoception in patients with early stage of hypertensive disease (HTD) and healthy controls. Strategically, we recruited only HTD patients without cognitive impairment (as shown by neuropsychological tests), brain atrophy (as assessed with voxel-based morphometry), or white matter abnormalities (as evidenced by diffusion tensor imaging analysis). Interoceptive domains were assessed through (a) a behavioral heartbeat detection task; (b) measures of the heart-evoked potential (HEP), an electrophysiological cortical signature of attention to cardiac signals; and (c) neuroimaging recordings (MRI and fMRI) to evaluate anatomical and functional connectivity properties of key interoceptive regions (namely, the insula and the anterior cingulate cortex). Relative to controls, patients exhibited poorer interoceptive performance and reduced HEP modulations, alongside an abnormal association between interoceptive performance and both the volume and functional connectivity of the above regions. Such results suggest that peripheral cardiac-system impairments can be associated with abnormal behavioral and neurocognitive signatures of interoception. More generally, our findings indicate that interoceptive processes entail bidirectional influences between the cardiovascular and the central nervous systems., Work has been partially supported by the CONICET, the INECO Foundation and regular projects of CONICYT/FONDECYT (1170010); FONCyT‐PICT (2012‐0412 and 2012‐1309), and FONDAP (15150012), info:eu-repo/semantics/publishedVersion

Um errata deste artigo encontra-se disponível em: http://hdl.handle.net/1822/52993, High Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings. In such settings, the computation time is critical, and finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI), a form of HARDI data, is very relevant to increase data-processing speed. Here we analyze a method for reducing the computation time of the dMRI-based axonal orientation distribution function h by using Monte Carlo sampling-based methods for voxel selection. Results evidenced a robust reduction in required data sampling of about 50 % without losing signal’s quality. Moreover, we show that the convergence to the correct value in this type of Monte Carlo HARDI/DSI data-processing has a linear improvement in data-processing speed of the ODF determination. Although further improvements are needed, our results represent a promissory step for future processing time reduction in big data., We thank the financial support by QREN, FEDER, COMPETE, Investigador FCT, FCT Ciencia 2007, FCT PTDC/SAU-BEB/100147/2008, FCT Project Scope UID/CEC/00319/2013, and the ERASMUS projects (FCT stands for "Fundacao para a Ciencia e Tecnologia"). We are thankful the relevant scientific conversations with Alard Roebroeck, Rainer Goebel, Van Wedeen, and Gina Caetano. Data collection for this work was in part from "Human Connectome Project" (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California., info:eu-repo/semantics/publishedVersion

The topic of Wormholes in distributed computing is about creating two different realms with different characteristics, the synchronous Wormholes and the asynchronous payload with the goal of using the wormholes to control the synchronism of the payload processes. We describe the characteristics of Wormholes in distributed computing, and relate them to issues in Physics, specifically, wormholes in general relativity and entanglement in quantum mechanics. The entanglement in quantum mechanics is about the existence of fixed relations between different physical systems as if they were still the same system. The entanglement is made evident by the occurrence of decoherence, which transform the multiple outcome possibilities of quantum systems into a single outcome “classical physics”-like objective reality. It is here presented the similarity between the decoherence process in quantum physics and the consensus problem in distributed computing. The approach to quantum mechanics used is quantum Darwinism, a Darwinian approach to decoherence where the environment controls the outcome of a measurement. It is here proposed that wormhole systems can be used to implement environment-based control of distributed computing systems.

We propose an approach to analyze and synthesize a set of human facial and vocal expressions, and then use the classified expressions to decide the robot’s response in a human-robot-interaction. During a human-to-human conversation, a person senses the interlocutor’s face and voice, perceives her/his emotional expressions, and processes this information in order to decide which response to give. Moreover, observed emotions are taken into account and the response may be aggressive, funny (henceforth meaning humorous) or just neutral according to not only the observed emotions, but also the personality of the person. The purpose of our proposed structure is to endow robots with the capability to model human emotions, and thus several subproblems need to be solved: feature extraction, classification, decision and synthesis. In the proposed approach we integrate two classifiers for emotion recognition from audio and video, and then use a new method for fusion with the social behavior profile. To keep the person engaged in the interaction, after each iterance of analysis, the robot synthesizes human voice with both lips synchronization and facial expressions. The social behavior profile conducts the personality of the robot. The structure and work flow of the synthesis and decision are addressed, and the Bayesian networks are discussed. We also studied how to analyze and synthesize the emotion from the facial expression and vocal expression. A new probabilistic structure that enables a higher level of interaction between a human and a robot is proposed.

During the refereeing procedure of Anthropomorphic Quantum Darwinism by Thomas Durt, it became apparent in the dialogue between him and me that the definition of information in Physics is something about which not all authors agreed. This text aims at describing the concepts associated to information that are accepted as the standard in the Physics world community.

Godel’s incompleteness applies to any system with recursively enumerable axioms and rules of inference. Chaitin’s approach to Godel’s incompleteness relates the incompleteness to the amount of information contained in the axioms. Zurek’s quantum Darwinism attempts the physical description of the universe using information as one of its major components. The capacity of quantum Darwinism to describe quantum measurement in great detail without requiring ad-hoc non-unitary evolution makes it a good candidate for describing the transition from quantum to classical. A baby-universe diffusion model of cosmic inflation is analyzed using quantum Darwinism. In this model cosmic inflation can be approximated as Brownian motion of a quantum field, and quantum Darwinism implies that molecular interaction during Brownian motion will make the quantum field decohere. The quantum Darwinism approach to decoherence in the baby-universe cosmic-inflation model yields the decoherence times of the baby-universes. The result is the equation relating the baby-universe’s decoherence time with the Hubble parameter, and that the decoherence time is considerably shorter than the cosmic inflation period.

In q-space diffusion NMR, the probability P(r,td) of a molecule having a displacement r in a diffusion time td is obtained under the assumption that the diffusion-encoding gradient g has an infinitesimal duration. However, this assumption may not always hold, particularly in human MRI where the diffusion-encoding gradient duration delta is typically of the same order of magnitude as the time offset Delta between encoding gradients. In this case, finite-delta effects complicate the interpretation of displacement probabilities measured in q-space MRI, and the form by which the signal intensity relates to them. By considering the displacement-specific dephasing,r/eiphi, of a set of spins accumulating a constant displacement vector r in the total time Delta+delta during which diffusion is encoded, the probability recovered by a finite-delta q-space experiment can be interpreted. It is shown theoretically that a data analysis using a modified q-space index q=gammadeltaetag, with gamma the gyromagnetic ratio and eta=square root (Delta-delta/3)/(Delta+delta), recovers the correct displacement probability distribution if diffusion is multi-Gaussian free diffusion. With this analysis, we show that the displacement distribution P(r,texp) is measured at the experimental diffusion-encoding time texp=Delta+delta, and not at the reduced diffusion time tr=Delta-delta/3 as is generally assumed in the NMR and MRI literature. It is also shown that, by defining a probability P(y,Delta) that a time tdelta exists such that a displacement y occurs from time t to t+Delta, it is possible to describe the physical significance of the result obtained when we use the q-space formalism valid for infinitesimal delta when delta is not infinitesimal. These deductions were confirmed by simulations for homogeneous Gaussian diffusion and for heterogeneous diffusion in permeable microscopic Gaussian domains that are homogeneous on the microm scale. The results also hold for diffusion inside restricted spherical reflecting domains, but only if the radius of the domain is larger than a critical size. The simulations of the displacement-specific dephasing obtain that if deltadeltac then eta is not equal to square root (Delta-delta/3)/(Delta+delta) which implies that we can no longer obtain the correct displacement probability from the displacement distribution. In the case that /g/=18 mT/m and Delta-delta=5 ms, the parameter deltac in ms is given by "deltac=0.49a2+0.24" where a is the sphere's radius expressed in microm. Simulation of q-space restricted diffusion MRI experiments indicate that if eta=square root (Delta-delta/3)/(Delta+delta), the recovered displacement probability is always better than the Gaussian approximation, and the measured diffusion coefficient matches the diffusion coefficient at time texp=Delta+delta better than it matches the diffusion coefficient at time tr=Delta-delta/3. These results indicate that q-space MRI measurements of displacement probability distributions are theoretically possible in biological tissues using finite-duration diffusion-encoding gradients provided certain compartment size and diffusion encoding gradient duration constraints are met.

We present a description, biological results and a reliability analysis for the method of diffusion tensor tracking (DTT) of white matter fiber pathways. In DTT, diffusion-tensor MRI (DT-MRI) data are collected and processed to visualize the line trajectories of fiber bundles within white matter (WM) pathways of living humans. A detailed description of the data acquisition is given. Technical aspects and experimental results are illustrated for the geniculo-calcarine tract with broad projections to visual cortex, occipital and parietal U-fibers, and the temporo-calcarine ventral pathway. To better understand sources of error and to optimize the method, accuracy and precision were analyzed by computer simulations. In the simulations, noisy DT-MRI data were computed that would be obtained for a WM pathway having a helical trajectory passing through gray matter. The error vector between the real and ideal track was computed, and random errors accumulated with the square root of track length consistent with a random-walk process. Random error was most dependent on signal-to-noise ratio, followed by number of averages, pathway anisotropy and voxel size, in decreasing order. Systematic error only occurred for a few conditions, and was most dependent on the stepping algorithm, anisotropy of the surrounding tissue, and non-equal voxel dimensions. Both random and systematic errors were typically below the voxel dimension. Other effects such as track rebound and track recovery also depended on experimental conditions. The methods, biological results and error analysis herein may improve the understanding and optimization of DTT for use in various applications in neuroscience and medicine.

The purpose of this work is to develop improvements on the most modern forms of white matter fiber tracking in the human brain. This development requires the use of computer simulations to assess the limits of the fiber tracking technology, and then the results of that simulation are used to make a thorough assessment of the experimental results in living human brains. The used fiber tracking techniques are q-ball imaging and diffusion spectrum imaging.

The recent advances in Magnetic Resonance Imaging gradient, regarding strength and computation speed, led to the development of Echo-Planar Imaging pulse-sequences with faster acquisition times. This kind of sequence is used in functional MRI and diffusion-weighted Magnetic Resonance Imaging and it presents more distortions than slower sequences. This work aims to compare different spatial distortion correction methods for Echo-Planar Imaging sequences with a new proposed pipeline which consists in performing a Field Map correction after a registration process.

Diffusion MRI allows the obtaining of an approximation of the water displacement's probability density function (PDF) and orientation distribution function (ODF). Examples of techniques used in obtaining these distributions being q-space imaging (QSI), and q-ball imaging (QBI), respectively. Shannon information quantifies the discriminative power of a symbol based on its probability. We quantified the information amount of a white matter fiber bundle being used to discriminate those fibers using specific diffusion MRI data treatment techniques. The equations developed are new and it is also described how they will help in future experimental calculations. An example of experimental ODF surfaces and ODF based white matter fiber tracking in living humans is also shown to highlight possible future advantages of Shannon information usage in describing crossing white matter fiber bundles.

OCD has been hypothesized to involve the failures in both cognitive and behavioral inhibitory processes. There is evidence that the hyperactivation of cortical–subcortical pathways may be involved in the failure of these inhibitory systems associated with OCD. Despite this consensus on the role of frontal–subcortical pathways in OCD, recent studies have been showing that brain regions other than the frontal–subcortical loops may be needed to understand the different cognitive and emotional deficits in OCD. Some studies have been finding evidence for decreased metabolic activity in areas such as left inferior parietal and parieto- occipital junction suggesting the possible existence of visual processing deficits. While there has been inconsistent data regarding visual processing in OCD, recent studies have been claiming that these patients have abnormal patterns of visual processing social rich stimuli, particularly emotional arousing stimuli. Thus, in this article, we hypothesize that the fronto-subcortical activation consistently found in OCD may be due to a deactivation of occipital/parietal regions associated with visual-perceptual processing of incoming social rich stimuli. Additionally, this dissociation may be more evident as the emotional intensity of the social stimulus increases.

Matter and selfhood in Kant’s Physics: A contemporary reappraisal. Capítulo do livro de E. Pires, B. Nonnenmacher & S. Von Stupnagel (Eds.), Relations of the Self. Bezüge des Selbst. Selbstreferentielle Prozesse in philosophischen Perspektiven.

Purpose To use MRI diffusion-tensor tracking (DTT) to test for the presence of unknown neuronal fiber pathways interconnecting the mid-fusiform cortex and anteromedial temporal lobe in humans. Such pathways are hypothesized to exist because these regions coactivate in functional MRI (fMRI) studies of emotion-valued faces and words, suggesting a functional link that could be mediated by neuronal connections. Materials and Methods A total of 15 normal human subjects were studied using unbiased DTT approaches designed for probing unknown pathways, including whole-brain seeding and large pathway-selection volumes. Several quality-control steps verified the results. Results Parallel amygdalo-fusiform and hippocampo-fusiform pathways were found in all subjects. The pathways begin/end at the mid-fusiform gyrus above the lateral occipitotemporal sulcus bilaterally. The superior pathway ends/begins at the superolateral amygdala. The inferior pathway crosses medially and ends/begins at the hippocampal head. The pathways are left-lateralized, with consistently larger cross-sectional area, higher anisotropy, and lower minimum eigenvalue (D-min) on the left, where D-min assesses intrinsic cross-fiber diffusivity independent of curvature. Conclusion A previously-undescribed pathway system interconnecting the mid-fusiform region with the amygdala/hippocampus has been revealed. This pathway system may be important for recognition, memory consolidation, and emotional modulation of face, object, and lexical information, which may be disrupted in conditions such as Alzheimer's disease. J. Magn. Reson. Imaging 2009. © 2009 Wiley-Liss, Inc.

To obtain normative human cerebral data and evaluate the anatomic information in quantitative diffusion anisotropy magnetic resonance (MR) imaging.Quantitative diffusion anisotropy MR images were obtained in 13 healthy adults by using single-shot echo-planar MR imaging and a combination of tetrahedral and orthogonal gradient encoding (whole-brain coverage in about 1 minute). White matter (WM) anatomy was assessed at visual inspection, and values were measured in various brain regions. Different anisotropy measures, including total anisotropy (A sigma), were compared on the basis of information content, rotational invariance, and susceptibility to noise. Partial volume and noise effects were simulated.Anisotropy MR images depicted WM features not typically seen on conventional MR images (e.g., external capsule, thalamic substructures, basal ganglia, occipital WM, thickness of the internal capsule). Statistically significant anisotropy differences occurred across brain regions, which were reproducible within and across subjects. A sigma was highest in commissural WM and progressively lower in projection and association WM. This order paralleled that of known resistance to spread of vasogenic edema, which suggested that anisotropy may be sensitive to WM histologic structure. Gray matter (GM) A sigma data were consistent with zero anisotropy, and partial volume WM-GM effects were approximately linear. A sigma image quality could be effectively improved by means of averaging.Quantitative diffusion anisotropy images can be obtained rapidly and demonstrate subtle WM anatomy. Different histologic types of WM have significant and reproducible anisotropy differences.

Functional imaging with positron emission tomography and functional MRI has revolutionized studies of the human brain. Understanding the organization of brain systems, especially those used for cognition, remains limited, however, because no methods currently exist for noninvasive tracking of neuronal connections between functional regions [Crick, F. & Jones, E. (1993) Nature (London) 361, 109–110]. Detailed connectivities have been studied in animals through invasive tracer techniques, but these invasive studies cannot be done in humans, and animal results cannot always be extrapolated to human systems. We have developed noninvasive neuronal fiber tracking for use in living humans, utilizing the unique ability of MRI to characterize water diffusion. We reconstructed fiber trajectories throughout the brain by tracking the direction of fastest diffusion (the fiber direction) from a grid of seed points, and then selected tracks that join anatomically or functionally (functional MRI) defined regions. We demonstrate diffusion tracking of fiber bundles in a variety of white matter classes with examples in the corpus callosum, geniculo-calcarine, and subcortical association pathways. Tracks covered long distances, navigated through divergences and tight curves, and manifested topological separations in the geniculo-calcarine tract consistent with tracer studies in animals and retinotopy studies in humans. Additionally, previously undescribed topologies were revealed in the other pathways. This approach enhances the power of modern imaging by enabling study of fiber connections among anatomically and functionally defined brain regions in individual human subjects.

The main problem in design fuzzy models is to identify their structure. This means recognise the variables that better characterise the system dynamics, the number of membership functions partitioning each variable, as well as their distribution and fuzziness degree. This work presents two pre-processing methods for structure identification of fuzzy models. The first approach uses the statistical method of Principal Component Analysis (PCA). The second one uses a clustering technique called autonomous mountain-clustering method. The statistical method of Principal Component Analysis helps to select the variables that dominate the system dynamics. Besides, this method contributes to design fuzzy models with better performance. The second approach identifies the fuzzy model order. That is, the method identifies the number of membership functions attributed to each variable, as well as their position and width. So, the autonomous mountain-clustering eliminates the usual “trial-and-error” mechanism. The pre-processing methods can be used to initialize the neuro-fuzzy techniques and therefore accelerate their learning process. We test these methods using a simple learning process applied to extract the fuzzy model of an experimental electrohydraulic system. The results show that a good modeling capability is achieved without employ any complicated optimisation procedure to structure identification.