Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.

Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In this work, we present a novel approach that uses group sparsity to model the whole brain multivariate pattern as a combination of regional patterns. More specifically, we use a sparse version of Multiple Kernel Learning (MKL) to simultaneously learn the contribution of each brain region, previously defined by an atlas, to the decision function. Our application of MKL provides two beneficial features: (1) it can lead to improved overall generalisation performance when the grouping structure imposed by the atlas is consistent with the data; (2) it can identify a subset of relevant brain regions for the predictive model. In order to investigate the effect of the grouping in the proposed MKL approach we compared the results of three different atlases using three different datasets. The method has been implemented in the new version of the open-source Pattern Recognition for Neuroimaging Toolbox (PRoNTo).

Auditory prediction errors as individual biomarkers of schizophrenia.

Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible imaging technologies such as electroencephalography (EEG). To achieve this, we used machine learning techniques and a combination of paradigms designed to elicit prediction errors or Mismatch Negativity (MMN) responses. MMN, an EEG component elicited by unpredictable changes in sequences of auditory stimuli, has previously been shown to be reduced in people with schizophrenia and this is arguably one of the most reproducible neurophysiological markers of schizophrenia. EEG data were acquired from 21 patients with schizophrenia and 22 healthy controls whilst they listened to three auditory oddball paradigms comprising sequences of tones which deviated in 10% of trials from regularly occurring standard tones. Deviant tones shared the same properties as standard tones, except for one physical aspect: 1) duration - the deviant stimulus was twice the duration of the standard; 2) monaural gap - deviants had a silent interval omitted from the standard, or 3) inter-aural timing difference, which caused the deviant location to be perceived as 90° away from the standards. We used multivariate pattern analysis, a machine learning technique implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo) to classify images generated through statistical parametric mapping (SPM) of spatiotemporal EEG data, i.e. event-related potentials measured on the two-dimensional surface of the scalp over time. Using support vector machine (SVM) and Gaussian processes classifiers (GPC), we were able classify individual patients and controls with balanced accuracies of up to 80.48% (p-values = 0.0326, FDR corrected) and an ROC analysis yielding an AUC of 0.87. Crucially, a GP regression revealed that MMN predicted global assessment of functioning (GAF) scores (correlation = 0.73, R2 = 0.53, p = 0.0006).

A comprehensive approach for diagnosing opportunities for improving the performance of a WWTP.

High quality services of wastewater treatment require a continuous assessment and improvement of the technical, environmental and economic performance. This paper demonstrates a comprehensive approach for benchmarking wastewater treatment plants (WWTPs), using performance indicators (PIs) and indices (PXs), in a 'plan-do-check-act' cycle routine driven by objectives. The performance objectives herein illustrated were to diagnose the effectiveness and energy performance of an oxidation ditch WWTP. The PI and PX results demonstrated an effective and reliable oxidation ditch (good-excellent performance), and a non-reliable UV disinfection (unsatisfactory-excellent performance) related with influent transmittance and total suspended solids. The energy performance increased with the treated wastewater volume and was unsatisfactory below 50% of plant capacity utilization. The oxidation ditch aeration performed unsatisfactorily and represented 38% of the plant energy consumption. The results allowed diagnosing opportunities for improving the energy and economic performance considering the influent flows, temperature and concentrations, and for levering the WWTP performance to acceptable-good effectiveness, reliability and energy efficiency. Regarding the plant reliability for fecal coliforms, improvement of UV lamp maintenance and optimization of the UV dose applied and microscreen recommissioning were suggested.

Performance indicators and indices of sludge management in urban wastewater treatment plants.

Sludge (or biosolids) management is highly complex and has a significant cost associated with the biosolids disposal, as well as with the energy and flocculant consumption in the sludge processing units. The sludge management performance indicators (PIs) and indices (PXs) are thus core measures of the performance assessment system developed for urban wastewater treatment plants (WWTPs). The key PIs proposed cover the sludge unit production and dry solids concentration (DS), disposal/beneficial use, quality compliance for agricultural use and costs, whereas the complementary PIs assess the plant reliability and the chemical reagents' use. A key PI was also developed for assessing the phosphorus reclamation, namely through the beneficial use of the biosolids and the reclaimed water in agriculture. The results of a field study with 17 Portuguese urban WWTPs in a 5-year period were used to derive the PI reference values which are neither inherent to the PI formulation nor literature-based. Clusters by sludge type (primary, activated, trickling filter and mixed sludge) and by digestion and dewatering processes were analysed and the reference values for sludge production and dry solids were proposed for two clusters: activated sludge or biofilter WWTPs with primary sedimentation, sludge anaerobic digestion and centrifuge dewatering; activated sludge WWTPs without primary sedimentation and anaerobic digestion and with centrifuge dewatering. The key PXs are computed for the DS after each processing unit and the complementary PXs for the energy consumption and the operating conditions DS-determining. The PX reference values are treatment specific and literature based. The PI and PX system was applied to a WWTP and the results demonstrate that it diagnosis the situation and indicates opportunities and measures for improving the WWTP performance in sludge management.

PRoNTo: pattern recognition for neuroimaging toolbox.

In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these allow an increased sensitivity for the detection of spatially distributed effects compared to univariate techniques, they lack an established and accessible software framework. The goal of this work was to build a toolbox comprising all the necessary functionalities for multivariate analyses of neuroimaging data, based on machine learning models. The "Pattern Recognition for Neuroimaging Toolbox" (PRoNTo) is open-source, cross-platform, MATLAB-based and SPM compatible, therefore being suitable for both cognitive and clinical neuroscience research. In addition, it is designed to facilitate novel contributions from developers, aiming to improve the interaction between the neuroimaging and machine learning communities. Here, we introduce PRoNTo by presenting examples of possible research questions that can be addressed with the machine learning framework implemented in PRoNTo, and cannot be easily investigated with mass univariate statistical analysis.

Post-hoc selection of dynamic causal models.

Dynamic causal modelling (DCM) was originally proposed as a hypothesis driven procedure in which a small number of neurobiologically motivated models are compared. Model comparison in this context usually proceeds by individually fitting each model to data and then approximating the corresponding model evidence with a free energy bound. However, a recent trend has emerged for comparing very large numbers of models in a more exploratory manner. This led Friston and Penny (2011) to propose a post-hoc approximation to the model evidence, which is computed by optimising only the largest (full) model of a set of models. The evidence for any (reduced) submodel is then obtained using a generalisation of the Savage-Dickey density ratio (Dickey, 1971). The benefit of this post-hoc approach is a huge reduction in the computational time required for model fitting. This is because only a single model is fitted to data, allowing a potentially huge model space to be searched relatively quickly. In this paper, we explore the relationship between the free energy bound and post-hoc approximations to the model evidence in the context of deterministic (bilinear) dynamic causal models (DCMs) for functional magnetic resonance imaging data.

Bayesian model selection maps for group studies.

This technical note describes the construction of posterior probability maps (PPMs) for Bayesian model selection (BMS) at the group level. This technique allows neuroimagers to make inferences about regionally specific effects using imaging data from a group of subjects. These effects are characterised using Bayesian model comparisons that are analogous to the F-tests used in statistical parametric mapping, with the advantage that the models to be compared do not need to be nested. Additionally, an arbitrary number of models can be compared together. This note describes the integration of the Bayesian mapping approach with a random effects analysis model for BMS using group data. We illustrate the method using fMRI data from a group of subjects performing a target detection task.

EEG-fMRI integration: a critical review of biophysical modeling and data analysis approaches.

The diverse nature of cerebral activity, as measured using neuroimaging techniques, has been recognised long ago. It seems obvious that using single modality recordings can be limited when it comes to capturing its complex nature. Thus, it has been argued that moving to a multimodal approach will allow neuroscientists to better understand the dynamics and structure of this activity. This means that integrating information from different techniques, such as electroencephalography (EEG) and the blood oxygenated level dependent (BOLD) signal recorded with functional magnetic resonance imaging (fMRI), represents an important methodological challenge. In this work, we review the work that has been done thus far to derive EEG/fMRI integration approaches. This leads us to inspect the conditions under which such an integration approach could work or fail, and to disclose the types of scientific questions one could (and could not) hope to answer with it.

Estimating the transfer function from neuronal activity to BOLD using simultaneous EEG-fMRI.

Previous studies using combined electrical and hemodynamic measurements of brain activity, such as EEG and (BOLD) fMRI, have yielded discrepant results regarding the relationship between neuronal activity and the associated BOLD response. In particular, some studies suggest that this link, or transfer function, depends on the frequency content of neuronal activity, while others suggest that total neuronal power accounts for the changes in BOLD. Here we explored this dependency by comparing different frequency-dependent and -independent transfer functions, using simultaneous EEG-fMRI. Our results suggest that changes in BOLD are indeed associated with changes in the spectral profile of neuronal activity and that these changes do not arise from one specific spectral band. Instead they result from the dynamics of the various frequency components together, in particular, from the relative power between high and low frequencies. Understanding the nature of the link between neuronal activity and BOLD plays a crucial role in improving the interpretability of BOLD images as well as on the design of more robust and realistic models for the integration of EEG and fMRI.

The anthelmintic pyrantel acts as a low efficacious agonist and an open-channel blocker of mammalian acetylcholine receptors.

Pyrantel is an anthelmintic which acts as an agonist of nicotinic receptors (AChRs) of nematodes and exerts its therapeutic effects by depolarizing their muscle membranes. Here we explore at the single-channel level the action of pyrantel at mammalian muscle AChR. AChR currents are elicited by pyrantel. However, openings do not appear in clearly identifiable clusters over a range of pyrantel concentrations (1-300 microM). The mean open time decreases as a function of concentration, indicating an additional open-channel block. Single-channel recordings in the presence of high ACh concentrations and pyrantel demonstrate that the anthelmintic acts as a high-affinity open-channel blocker. When analyzed in terms of a sequential blocking scheme, the calculated forward rate constant for the blocking process is 8x10(7) M(-1) x s(-1), the apparent dissociation constant is 8 microM at a membrane potential of -70 mV and the process is voltage dependent. Pyrantel displaces alpha-bungarotoxin binding but the concentration dependence of equilibrium binding is shifted towards higher concentrations with respect to that of ACh binding. Thus, by acting at the binding site pyrantel activates mammalian AChRs with low efficacy, and by sterical blockade of the pore, the activated channels are then rapidly inhibited.

The U-box protein family in plants.

The U-box is a highly conserved domain recently identified at the C terminus of yeast UFD2, an E4 ubiquitination factor. In yeast, UFD2 is the only U-box-containing protein, but there are two UFD2 homologs and several other proteins containing a U-box domain in humans. Intriguingly, a database search revealed 37 predicted proteins containing a U-box in Arabidopsis. The plant U-box (PUB) proteins form five distinct subclasses, suggesting that they play diverse roles. The ARC1 gene from Brassica, required for self-incompatibility, is currently the only PUB gene functionally characterized. Here, we discuss the characteristics and possible functions of the PUB gene family.

Storage proteins from Lathyrus sativus seeds.

The proteins from Lathyrus sativus Linn. (chickling vetch or grass pea) seeds were investigated. Protein constitutes approximately 20% of the seed dry weight, >60% of which is composed by globulins and 30% by albumins. A single, 24 kDa polypeptide comprises more than half of the protein present in the albumin fraction. The globulins may be fractionated into three main components, which were named alpha-lathyrin (the major globulin), beta-lathyrin, and gamma-lathyrin. alpha-Lathyrin, with a sedimentation coefficient of approximately 18S, is composed of three main types of unglycosylated subunits (50-66 kDa), each of which produce, upon reduction, a heavy and a light polypeptide chain, by analogy with 11S. beta-Lathyrin, with a sedimentation coefficient of 13S, is composed by a relatively large number of subunits (8-66 kDa). Two major polypeptides are glycosylated and exhibit structural similarity with beta-conglutin from Lupinus albus. One of these possesses an internal disulfide bond. gamma-Lathyrin, with a sedimentation coefficient of approximately 5S, contains two interacting, unglycosylated polypeptides, with no disulfide bonds: the major 24 kDa albumin and the heavier (20 kDa) polypeptide chain of La. sativus lectin.

Tuberous sclerosis in a 20-week gestation fetus: immunohistochemical study.

We report an autopsy case of tuberous sclerosis complex (TSC) in a 20-week gestational age female fetus. The brain showed lesions suggestive of early cortical tubers and subependymal hamartomatous nodules. The large cells within these nodular clusters were variably immunoreactive for glial fibrillary acidic protein (GFAP) and vimentin and negative for synaptophysin and neurofilament. Subependymal radial glia expressed both vimentin and GFAP, but subpial radial glia either did not express these markers (in contrast to an age-matched control) or were absent. Tuberin expression was noted in heterotopic neurons in the white matter and brain cells consistent with Cajal Retzius cells in the neocortical molecular layer, very weakly in superficial cortical neurons, neurons in the basal ganglia, Purkinje cells and external granular cells of cerebellum, cranial nerve nuclei neurons, occasional germinal matrix cells, ependymal cells, choroid plexus epithelium, and pituitary gland neuroendocrine cells; it was not seen within the cells of subependymal nodules. The pattern of tuberin immunoreactivity was similar to that which we have observed in older TSC patients. Proliferating cell labeling indexes were comparable in the germinal matrix of the TSC patient and an age-matched control. Abnormal subpial radial glia may be responsible for some of the neuronal migration abnormalities that appear to result in neocortical tubers.

[Demographic aging: a proposal to consider the course of events]. / Envelhecimento demografico: proposta de reflexao sobre o curso dos factos.

PIP: The author discusses methods of developing ideas about how aging societies should react to demographic aging. "Some ideas are introduced that the author believes to be fundamental to a consideration of the subject. One of these is the concept of demographic ageing as a natural development that does not necessarily mean that societies where it happens are condemned." (EXCERPT)^ieng

Neuronal cytoskeletal abnormalities in human cerebral cortical dysplasia.

The cytoskeletal abnormalities of cortical neurons in human cerebral cortical dysplasia were compared by immunohistochemical methods to the neurofibrillary tangles of Alzheimer's disease (AD). Surgical specimens from cortical resections performed for the treatment of intractable childhood seizures as well as autopsied samples from AD patients were analyzed with different antibodies directed against high- or medium-molecular mass neurofilament epitopes, phosphorylated or non-phosphorylated forms of neurofilaments, ubiquitin, the microtubule-associated protein tau, and paired helical filaments (PHF), a defining feature of AD tangles. A strong abnormal increase in immunoreactivity to the high and medium molecular mass neurofilament epitopes was seen in hypertrophic neurons of cortical dysplasia. These neurofilamentous accumulations of cortical dysplasia as well as AD tangles also displayed immunoreactivity with antibodies against phosphorylated and non-phosphorylated neuro-filament epitopes, tau and ubiquitin. Only the AD tangles, however, were immunoreactive to the antiserum to PHF. These results replicate and extend our previous findings that the neurofibrillary accumulations in cerebral cortical dysplasia share some common antigens with the neurofibrillary tangles of AD but do not demonstrate immunoreactivity to PHF antiserum. The results also suggest that the cytoskeletal abnormalities observed in neurons of cortical dysplasia may result in part from alterations in the level of expression, in phosphorylation state or in transport of cytoskeletal components.

Neuropathologic study of resected cerebral tissue from patients with infantile spasms.

Studies to date on the neuropathologic substrates of infantile spasms have largely utilized autopsy material of children who die after a long and complicated seizure history. This makes the interpretation of primary versus secondary changes in the cerebral tissue difficult if not impossible. We have recently had the opportunity to review the neuropathologic changes in cortical tissue resected from infants and children with a history of infantile spasms. The major identifiable abnormalities were destructive lesions, sometimes classifiable as cystic-gliotic encephalomalacia, and dysplastic changes of varying degree. The cortical dysplasias had some similarity to cerebral changes described in tuberous sclerosis, including the presence of bizarre gemistocytic "balloon" cells, and secondary cytoskeletal changes within neuronal cell bodies. Such material provides an opportunity to apply immunohistochemical and molecular techniques to epileptic tissue in an attempt to understand the morphologic substrates of infantile spasms and other types of generalized epilepsy.

An assessment of the proliferative potential of 'balloon cells' in focal cortical resections performed for childhood epilepsy.

Cerebral cortical dysplasia is an uncommon pathological substrate of severe intractable childhood epilepsy, sometimes treated by hemispherectomy. Neuropathological findings include abnormal gyrus formation, loss of cortical lamination, unusual giant neurons and 'balloon cells' of indeterminate histogenesis similar in appearance to neoplastic gemistocytic astrocytes. In order to investigate the proliferative potential of 'balloon cells', we used Crocker's silver impregnation technique to demonstrate nucleolar organizer regions (AgNORs) involved in cellular proliferation, together with immunohistochemical evaluation of proliferating cell nuclear antigen (PCNA) expression. Balloon cells (5.56 +/- 0.24) had significantly (P < 0.001) greater AgNOR numbers than reactive astrocytes (3.89 +/- 0.15), neurons (2.30 +/- 0.13) or giant neurons (4.26 +/- 0.20). However, when corrected for nuclear size, results showed that 'balloon cells' (0.093 +/- 0.006) had significantly (P < 0.001) fewer AgNORs/square micrometre of nuclear area than reactive astrocytes (0.225 +/- 0.016) and had significantly (P < 0.001) more AgNORs/square micrometre of nuclear area than normal (0.048 +/- 0.003) or giant neurons (0.054 +/- 0.003). On the assumption that astrocytes are typical interphase cells and that normal neurons are post-mitotic, the results suggest that 'balloon cells' are unlikely to be undergoing proliferative activity and, when adjusted for nuclear size, the number of AgNORs/unit of nuclear area is more reflective of cellular ploidy than of proliferative activity in non-neoplastic neural tissues. The virtual absence of PCNA expression by 'balloon cell' nuclei supports such an interpretation of the AgNOR results.

Diabetic retinopathy in a population of 1,302 insulin dependent diabetics (IDDM) diagnosed before 30 years of age.

The authors analyse the data resulting from the first ophthalmological observation of 1,302 insulin dependent diabetics whose age at diagnosis is less than 30 years and who have been observed regularly by the Portuguese Diabetic Association. The prevalence of retinopathy is 41, 6%; 34.3% is non-proliferative and 7.3% is proliferative. Retinopathy is more frequent in males (P < 0.001). The prevalence of retinopathy increases with the duration of diabetes and it is equal to or greater than 80% in people who have had diabetes for 10 years or more. 'Poor' glucose control, the coexistence of other late complications and arterial hypertension increase the risk of retinopathy (P < 0.001).