Reciprocal Modulation of Cognitive and Emotional Aspects in Pianistic Performances

Background: High level piano performance requires complex integration of perceptual, motor, cognitive and emotive skills. Observations in psychology and neuroscience studies have suggested reciprocal inhibitory modulation of the cognition by emotion and emotion by cognition. However, it is still unclear how cognitive states may influence the pianistic performance. The aim of the present study is to verify the influence of cognitive and affective attention in the piano performances. Methods and Findings: Nine pianists were instructed to play the same piece of music, firstly focusing only on cognitive aspects of musical structure (cognitive performances), and secondly, paying attention solely on affective aspects (affective performances). Audio files from pianistic performances were examined using a computational model that retrieves nine specific musical features (descriptors) - loudness, articulation, brightness, harmonic complexity, event detection, key clarity, mode detection, pulse clarity and repetition. In addition, the number of volunteers' errors in the recording sessions was counted. Comments from pianists about their thoughts during performances were also evaluated. The analyses of audio files throughout musical descriptors indicated that the affective performances have more: agogics, legatos, pianos phrasing, and less perception of event density when compared to the cognitive ones. Error analysis demonstrated that volunteers misplayed more left hand notes in the cognitive performances than in the affective ones. Volunteers also played more wrong notes in affective than in cognitive performances. These results correspond to the volunteers' comments that in the affective performances, the cognitive aspects of piano execution are inhibited, whereas in the cognitive performances, the expressiveness is inhibited. Conclusions: Therefore, the present results indicate that attention to the emotional aspects of performance enhances expressiveness, but constrains cognitive and motor skills in the piano execution. In contrast, attention to the cognitive aspects may constrain the expressivity and automatism of piano performances.

Cognitive control associated with irritability induction: an autobiographical recall fMRI study

OBJECTIVE: Despite the relevance of irritability emotions to the treatment, prognosis and classification of psychiatric disorders, the neurobiological basis of this emotional state has been rarely investigated to date. We assessed the brain circuitry underlying personal script-driven irritability in healthy subjects (n = 11) using functional magnetic resonance imaging. METHOD: Blood oxygen level-dependent signal changes were recorded during auditory presentation of personal scripts of irritability in contrast to scripts of happiness or neutral emotional content. Self-rated emotional measurements and skin conductance recordings were also obtained. Images were acquired using a 1,5T magnetic resonance scanner. Brain activation maps were constructed from individual images, and between-condition differences in the mean power of experimental response were identified by using cluster-wise nonparametric tests. RESULTS: Compared to neutral scripts, increased blood oxygen level-dependent signal during irritability scripts was detected in the left subgenual anterior cingulate cortex, and in the left medial, anterolateral and posterolateral dorsal prefrontal cortex (cluster-wise p-value < 0.05). While the involvement of the subgenual cingulate and dorsal anterolateral prefrontal cortices was unique to the irritability state, increased blood oxygen level-dependent signal in dorsomedial and dorsal posterolateral prefrontal regions were also present during happiness induction. CONCLUSION: Irritability induction is associated with functional changes in a limited set of brain regions previously implicated in the mediation of emotional states. Changes in prefrontal and cingulate areas may be related to effortful cognitive control aspects that gain salience during the emergence of irritability.

Classification of breast masses using a committee machine of artificial neural networks

This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area A. under the receiver operating characteristics (ROC) curve. The A, result for the committee machine is compared with the A, results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier Tests are carried out using the student's t-distribution. The committee machine classifier outperforms the MLP SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the A, values of the four methods are, in order 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of A(z) (A(z) = 0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result. (c) 2008 SPIE and IS&T.

TESTING STATISTICAL HYPOTHESIS ON RANDOM TREES AND APPLICATIONS TO THE PROTEIN CLASSIFICATION PROBLEM

Efficient automatic protein classification is of central importance in genomic annotation. As an independent way to check the reliability of the classification, we propose a statistical approach to test if two sets of protein domain sequences coming from two families of the Pfam database are significantly different. We model protein sequences as realizations of Variable Length Markov Chains (VLMC) and we use the context trees as a signature of each protein family. Our approach is based on a Kolmogorov-Smirnov-type goodness-of-fit test proposed by Balding et at. [Limit theorems for sequences of random trees (2008), DOI: 10.1007/s11749-008-0092-z]. The test statistic is a supremum over the space of trees of a function of the two samples; its computation grows, in principle, exponentially fast with the maximal number of nodes of the potential trees. We show how to transform this problem into a max-flow over a related graph which can be solved using a Ford-Fulkerson algorithm in polynomial time on that number. We apply the test to 10 randomly chosen protein domain families from the seed of Pfam-A database (high quality, manually curated families). The test shows that the distributions of context trees coming from different families are significantly different. We emphasize that this is a novel mathematical approach to validate the automatic clustering of sequences in any context. We also study the performance of the test via simulations on Galton-Watson related processes.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Use of SVM Methods with Surface-Based Cortical and Volumetric Subcortical Measurements to Detect Alzheimer`s Disease

Here, we examine morphological changes in cortical thickness of patients with Alzheimer`s disease (AD) using image analysis algorithms for brain structure segmentation and study automatic classification of AD patients using cortical and volumetric data. Cortical thickness of AD patients (n = 14) was measured using MRI cortical surface-based analysis and compared with healthy subjects (n = 20). Data was analyzed using an automated algorithm for tissue segmentation and classification. A Support Vector Machine (SVM) was applied over the volumetric measurements of subcortical and cortical structures to separate AD patients from controls. The group analysis showed cortical thickness reduction in the superior temporal lobe, parahippocampal gyrus, and enthorhinal cortex in both hemispheres. We also found cortical thinning in the isthmus of cingulate gyrus and middle temporal gyrus at the right hemisphere, as well as a reduction of the cortical mantle in areas previously shown to be associated with AD. We also confirmed that automatic classification algorithms (SVM) could be helpful to distinguish AD patients from healthy controls. Moreover, the same areas implicated in the pathogenesis of AD were the main parameters driving the classification algorithm. While the patient sample used in this study was relatively small, we expect that using a database of regional volumes derived from MRI scans of a large number of subjects will increase the SVM power of AD patient identification.

Cognitive training increases platelet PLA(2) activity in healthy elderly subjects

Phospholipases A(2) (PLA(2)) are ubiquitous enzymes involved in membrane fatty acid metabolism and intracellular signalling. Recent studies have shown that PLA(2) subtypes are implicated in the modulation of pathways related to memory acquisition and retrieval. We investigated the effects of cognitive training on platelet PLA(2) activity in healthy elderly individuals. Twenty-three cognitively unimpaired older adults were randomly assigned to receive memory training or standard outpatient care only. Both groups were cognitively assessed by the same protocol, and the experimental group (EG) underwent a four-session memory training intervention. Pre- and post-test measures included prose and list recall, WAIS-III digit symbol, strategy use measures and platelet PLA(2) group activity. After cognitive training, patients in the EG group had significant increase in cytosolic, calcium-dependent PLA(2) (cPLA(2)), extracellular (or secreted), calcium-dependent PLA(2) (sPLA(2)), total platelet PLA(2) activity, and significant decrease in platelet calcium-independent PLA(2) (iPLA(2)) activity. Our results suggest that memory training may have a modulating effect in PLA(2)-mediated biological systems associated with cognitive functions and neurodegenerative diseases. (c) 2008 Elsevier Ltd. All rights reserved.

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Cognitive impairment and dementia in neurocysticercosis A cross-sectional controlled study

Objectives: Neurocysticercosis (NCYST) is the most frequent CNS parasitic disease worldwide, affecting more than 50 million people. However, some of its clinical findings, such as cognitive impairment and dementia, remain poorly characterized, with no controlled studies conducted so far. We investigated the frequency and the clinical profile of cognitive impairment and dementia in a sample of patients with NCYST in comparison with cognitively healthy controls (HC) and patients with cryptogenic epilepsy (CE). Methods: Forty treatment-naive patients with NCYST, aged 39.25 +/- 10.50 years and fulfilling absolute criteria for definitive active NCYST on MRI, were submitted to a comprehensive cognitive and functional evaluation and were compared with 49 HC and 28 patients with CE of similar age, educational level, and seizure frequency. Results: Patients with NCYST displayed significant impairment in executive functions, verbal and nonverbal memory, constructive praxis, and verbal fluency when compared with HC (p < 0.05). Dementia was diagnosed in 12.5% patients with NCYST according to DSM-IV criteria. When compared with patients with CE, patients with NCYST presented altered working and episodic verbal memory, executive functions, naming, verbal fluency, constructive praxis, and visual-spatial orientation. No correlation emerged between cognitive scores and number, localization, or type of NCYST lesions on MRI. Conclusions: Cognitive impairment was ubiquitous in this sample of patients with active neurocysticercosis (NCYST). Antiepileptic drug use and seizure frequency could not account for these features. Dementia was present in a significant proportion of patients. These data broaden our knowledge on the clinical presentations of NCYST and its impact in world public health. Neurology (R) 2010;74:1288-1295

Mild cognitive deficits associated to neocortical microgyria in mice with genetic deletion of cellular prion protein

The cellular prion protein (PrP(c)) has been implicated with the modulation of neuronal apoptosis, adhesion, neurite outgrowth and maintenance which are processes involved in the neocortical development. Malformations of cortical development (MCD) are frequently associated with neurological conditions including mental retardation, autism, and epilepsy. Here we investigated the behavioral performance of female adult PrP(c)-null mice (Prnp(%)) and their wild-type controls (Prnp(+/+)) presenting unilateral polymicrogyria, a MCD experimentally induced by neonatal freeze-lesion in the right hemisphere. injured mice from both genotypes presented similar locomotor activity but Prnp(%) mice showed a tendency to increase anxiety-related responses when compared to Prnp(+/+) animals. Additionally, injured Prnp(%) mice have a poorer performance in the social recognition task than sham-operated and Prnp(%) injured ones. Moreover the step-down inhibitory avoidance task was not affected by the procedure or the genotype of the animals. These data suggest that the genetic deletion of PrP(c) confers increased susceptibility to short-term social memory deficits induced by neonatal freezing model of polymicrogyria in mice. (C) 2008 Published by Elsevier B.V.

Nitric oxide modulation of methylphenidate-induced disruption of prepulse inhibition in Swiss mice

Drugs that facilitate dopaminergic neurotransmission induce cognitive and attentional deficits which include inability to filter sensory input measured by prepulse inhibition (PPI) Methylphenidate, an amphetamine analog is used in the treatment of attention deficit hyperactivity disorder Given that nitric oxide (NO) modulates dopamine effect our aim is to analyze the nitric oxide synthase (NOS) and soluble guanylate cyclase (sGC) inhibitors effect on PPI disruption induced by methylphenidate The inhibitors effects were compared to those produced by haloperidol and clozapine Male Swiss mice received a first I p. Injection (one hour before testing), of either saline, or N(G) nitro L-arginine (10, 40 or 90 mg/kg) or 7-Nitroindazole (3, 10, 30 or 60 mg/kg). or oxadiazolo-quinoxalin (5 or 10 mg/kg). or haloperidol (1 mg/kg), or clozapine (5 mg/kg) Thirty min later mice received the second injection of either saline or methylphenidate (20 or 30 mg/kg) or amphetamine (5 or 10 mg/kg). One group of mice received intracerebroventricular 7-Nitroindazole (50 or 100 nM) followed by systemic administration of saline or methylphenidate (30 mg/kg) The results revealed a methylphenidate dose-dependent disruption of PPI comparable to amphetamine. The effect was prevented by either nitric oxide synthase or guanilate cyclase inhibitors or clozapine or haloperidol In conclusion, methylphenidate induced a dose-dependent PPI disruption in Swiss mice modulated by dopamine and NO/sGC. The results corroborate the hypothesis of dopamine and NO interacting to modulate sensorimotor gating through central nervous system. It may be useful to understand methylphenidate and other psychostimulants effects (C) 2009 Elsevier B.V All rights reserved

Saving our science from ourselves: the plight of biological classification

Saving our science from ourselves: the plight of biological classification. Biological classification ( nomenclature, taxonomy, and systematics) is being sold short. The desire for new technologies, faster and cheaper taxonomic descriptions, identifications, and revisions is symptomatic of a lack of appreciation and understanding of classification. The problem of gadget-driven science, a lack of best practice and the inability to accept classification as a descriptive and empirical science are discussed. The worst cases scenario is a future in which classifications are purely artificial and uninformative.

Hemodynamic, morphometric and autonomic patterns in hypertensive rats - renin-angiotensin system modulation

BACKGROUND: Spontaneously hypertensive rats develop left ventricular hypertrophy, increased blood pressure and blood pressure variability, which are important determinants of heart damage, like the activation of renin-angiotensin system. AIMS: To investigate the effects of the time-course of hypertension over 1) hemodynamic and autonomic patterns (blood pressure; blood pressure variability; heart rate); 2) left ventricular hypertrophy; and 3) local and systemic Renin-angiotensin system of the spontaneously hypertensive rats. METHODS: Male spontaneously hypertensive rats were randomized into two groups: young (n=13) and adult (n=12). Hemodynamic signals (blood pressure, heart rate), blood pressure variability (BPV) and spectral analysis of the autonomic components of blood pressure were analyzed. LEFT ventricular hypertrophy was measured by the ratio of LV mass to body weight (mg/g), by myocyte diameter (μm) and by relative fibrosis area (RFA, %). ACE and ACE2 activities were measured by fluorometry (UF/min), and plasma renin activity (PRA) was assessed by a radioimmunoassay (ng/mL/h). Cardiac gene expressions of Agt, Ace and Ace2 were quantified by RT-PCR (AU). RESULTS: The time-course of hypertension in spontaneously hypertensive rats increased BPV and reduced the alpha index in adult spontaneously hypertensive rats. Adult rats showed increases in left ventricular hypertrophy and in RFA. Compared to young spontaneously hypertensive rats, adult spontaneously hypertensive rats had lower cardiac ACE and ACE2 activities, and high levels of PRA. No change was observed in gene expression of Renin-angiotensin system components. CONCLUSIONS: The observed autonomic dysfunction and modulation of Renin-angiotensin system activity are contributing factors to end-organ damage in hypertension and could be interacting. Our findings suggest that the management of hypertensive disease must start before blood pressure reaches the highest stable levels and the consequent established end-organ damage is reached.

5-HT1A autoreceptor modulation of locomotor activity induced by nitric oxide in the rat dorsal raphe nucleus

The dorsal raphe nucleus (DRN) is the origin of ascending serotonergic projections and is considered to be an important component of the brain circuit that mediates anxiety- and depression-related behaviors. A large fraction of DRN serotonin-positive neurons contain nitric oxide (NO). Disruption of NO-mediated neurotransmission in the DRN by NO synthase inhibitors produces anxiolytic- and antidepressant-like effects in rats and also induces nonspecific interference with locomotor activity. We investigated the involvement of the 5-HT1A autoreceptor in the locomotor effects induced by NO in the DRN of male Wistar rats (280-310 g, N = 9-10 per group). The NO donor 3-morpholinosylnomine hydrochloride (SIN-1, 150, and 300 nmol) and the NO scavenger S-3-carboxy-4-hydroxyphenylglycine (carboxy-PTIO, 0.1-3.0 nmol) were injected into the DRN of rats immediately before they were exposed to the open field for 10 min. To evaluate the involvement of the 5-HT1A receptor and the N-methyl-D-aspartate (NMDA) glutamate receptor in the locomotor effects of NO, animals were pretreated with the 5-HT1A receptor agonist 8-hydroxy-2-(di-n-propylamino)tetralin (8-OH-DPAT, 8 nmol), the 5-HT1A receptor antagonist N-(2-[4-(2-methoxyphenyl)-1-piperazinyl]ethyl)-N-2-pyridinyl-cyclohexanecarboxamide maleate (WAY-100635, 0.37 nmol), and the NMDA receptor antagonist DL-2-amino-7-phosphonoheptanoic acid (AP7, 1 nmol), followed by microinjection of SIN-1 into the DRN. SIN-1 increased the distance traveled (mean ± SEM) in the open-field test (4431 ± 306.1 cm; F7,63 = 2.44, P = 0.028) and this effect was blocked by previous 8-OH-DPAT (2885 ± 490.4 cm) or AP7 (3335 ± 283.5 cm) administration (P < 0.05, Duncan test). These results indicate that 5-HT1A receptor activation and/or facilitation of glutamate neurotransmission can modulate the locomotor effects induced by NO in the DRN.

Modulation of peritoneal macrophage activity by the saturation state of the fatty acid moiety of phosphatidylcholine

To determine the effects of saturated and unsaturated fatty acids in phosphatidylcholine (PC) on macrophage activity, peritoneal lavage cells were cultured in the presence of phosphatidylcholine rich in saturated or unsaturated fatty acids (sat PC and unsat PC, respectively), both used at concentrations of 32 and 64 µM. The treatment of peritoneal macrophages with 64 µM unsat PC increased the production of hydrogen peroxide by 48.3% compared to control (148.3 ± 16.3 vs 100.0 ± 1.8%, N = 15), and both doses of unsat PC increased adhesion capacity by nearly 50%. Moreover, 64 µM unsat PC decreased neutral red uptake by lysosomes by 32.5% compared to the untreated group (67.5 ± 6.8 vs 100.0 ± 5.5%, N = 15), while both 32 and 64 µM unsat PC decreased the production of lipopolysaccharide-elicited nitric oxide by 30.4% (13.5 ± 2.6 vs 19.4 ± 2.5 µM) and 46.4% (10.4 ± 3.1 vs 19.4 ± 2.5 µM), respectively. Unsat PC did not affect anion production in non-stimulated cells or phagocytosis of unopsonized zymosan particles. A different result pattern was obtained for macrophages treated with sat PC. Phorbol 12-miristate 13-acetate-elicited superoxide production and neutral red uptake were decreased by nearly 25% by 32 and 64 µM sat PC, respectively. Sat PC did not affect nitric oxide or hydrogen peroxide production, adhesion capacity or zymosan phagocytosis. Thus, PC modifies macrophage activity, but this effect depends on cell activation state, fatty acid saturation and esterification to PC molecule and PC concentration. Taken together, these results indicate that the fatty acid moiety of PC modulates macrophage activity and, consequently, is likely to affect immune system regulation in vivo.