Migraine is the Most Prevalent Primary Headache in Individuals with Temporomandibular Disorders

Aims: To assess the prevalence of primary headaches (HA) in adults with temporomandibular disorders (TMD) who were assessed in a specialty orofacial pain clinic, as well as in controls without TMD. Methods: The sample consisted of 158 individuals with TMD seen at a university-based specialty clinic, as well as 68 controls. The Research Diagnostic Criteria for TMD were used to diagnose the TMD patients. HAs were assessed using a structured interview and classified according to the Second Edition of the International Classification for Headache Disorders. Data were analyzed by chi-square tests with a significance level of 5% and odds ratio (OR) tests with a 95% confidence interval (CI). Results: HAs occurred in 45.6% of the control group (30.9% had migraine and 14.7% had tension-type headache [TTH]) and in 85.5% of individuals with TMD. Among individuals with TMD, migraine was the most prevalent primary HA (55.3%), followed by TTH (30.2%); 14.5% had no HA. In contrast to controls, the odds ratio (OR) for HA in those with TMD was 7.05 (95% confidence interval [CI] = 3.65-13.61; P = .000), for migraine, the OR was 2.76 (95% CI = 1.50-5.06; P = .001), and for TTH, the OR was 2.51 (95% CI = 1.18-5.35; P = .014). Myofascial pain/arthralgia was the most common TMD diagnosis (53.2%). The presence of HA or specific HAs was not associated with the time since the onset of TMD (P = .714). However, migraine frequency was positively associated with TMD pain severity (P = .000). Conclusion: TMD was associated with increased primary HA prevalence rates. Migraine was the most common primary HA diagnosis in individuals with TMD. J OROFAC PAIN 2010;24:287-292

Interactive Brazilian program to prevent eating disorders behaviors: A pilot study

During a four month scholarly leave in United States of America, researchers designed a culturally appropriate prevention program for eating disorders (ED) for Brazilian adolescent girls. The program ""Se Liga na Nutricao"" was modeled on other effective programs identified in a research literature review and was carried out over eleven interactive sessions. It was positively received by the adolescents who suggested that it be part of school curricula. The girls reported that it helped them to develop critical thinking skills with regards to sociocultural norms about body image, food and eating practices. (Eating Weight Disord. 15: e270-e274, 2010). (C)2010, Editrice Kurtis

Body image dissatisfaction and eating symptoms in mothers of adolescents with eating disorders

The purpose of the present study was to assess body dissatisfaction and eating symptoms in mothers of eating disorder (ED) female patients and to compare results with those of a control group. The case group consisted of 35 mothers of female adolescents (aged between 10 and 17 yrs) diagnosed with ED who attended the Interdisciplinary Project for Care, Teaching and Research on Eating Disorders in Childhood and Adolescence (PROTAD) at Clinicas Hospital Institute of Psychiatry of the Universidade de Sao Paulo Medical School. Demographic and socioeconomic data were collected. Eating symptoms were assessed using the Eating Attitudes Test (EAT-26) and body image was assessed by the Body Image Questionnaire (BSQ) and Stunkard Figure Rating Scale (FRS). The case group was compared to a control group consisting of 35 mothers of female adolescents (between 10 and 17 years) who attended a private school in the city of Sao Paulo, southeastern Brazil. With regard to EAT, BSQ and FRS scores, we found no statistically significant differences between the two groups. However, we found a positive correlation between BMI and BSQ scores in the control group (but not in the case group) and a positive correlation between EAT and FRS scores in the case group (but not in the control group). It appears to be advantageous to assess body image by combining more than one scale to evaluate additional components of the construct. (Eating Weight Disord. 15: e219-e225, 2010). (C)2010, Editrice Kurtis

HTR1B and HTR2C in autism spectrum disorders in Brazilian families

Autism spectrum disorders (ASD) is a group of behaviorally defined neuro developmental disabilities characterized by multiple genetic etiologies and a complex presentation. Several studies suggest the involvement of the serotonin system in the development of ASD, but only few have investigated serotonin receptors. We have performed a case-control and a family-based study with 9 polymorphisms mapped to two serotonin receptor genes (HTR1B and HTR2C) in 252 Brazilian male ASD patients of European ancestry. These analyses showed evidence of undertransmission of the HTR1B haplotypes containing alleles -161G and -261A at HTR1B gene to ASD (P=0.003), but no involvement of HTR2C to the predisposition to this disease. Considering the relatively low level of statistical significance and the power of our sample, further studies are required to confirm the association of these serotonin-related genes and ASD. (C) 2008 Elsevier B.V. All rights reserved.

Improved voice activity detection algorithm using wavelet and support vector machine

This paper proposes an improved voice activity detection (VAD) algorithm using wavelet and support vector machine (SVM) for European Telecommunication Standards Institution (ETS1) adaptive multi-rate (AMR) narrow-band (NB) and wide-band (WB) speech codecs. First, based on the wavelet transform, the original IIR filter bank and pitch/tone detector are implemented, respectively, via the wavelet filter bank and the wavelet-based pitch/tone detection algorithm. The wavelet filter bank can divide input speech signal into several frequency bands so that the signal power level at each sub-band can be calculated. In addition, the background noise level can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then the proposed algorithm can apply SVM to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database with different noise conditions show that the proposed algorithm gives considerable VAD performances superior to the AMR-NB VAD Options 1 and 2, and AMR-WB VAD. (C) 2009 Elsevier Ltd. All rights reserved.

Relative entropy measures applied to healthy and pathological voice characterization

Nowadays, noninvasive methods of diagnosis have increased due to demands of the population that requires fast, simple and painless exams. These methods have become possible because of the growth of technology that provides the necessary means of collecting and processing signals. New methods of analysis have been developed to understand the complexity of voice signals, such as nonlinear dynamics aiming at the exploration of voice signals dynamic nature. The purpose of this paper is to characterize healthy and pathological voice signals with the aid of relative entropy measures. Phase space reconstruction technique is also used as a way to select interesting regions of the signals. Three groups of samples were used, one from healthy individuals and the other two from people with nodule in the vocal fold and Reinke`s edema. All of them are recordings of sustained vowel /a/ from Brazilian Portuguese. The paper shows that nonlinear dynamical methods seem to be a suitable technique for voice signal analysis, due to the chaotic component of the human voice. Relative entropy is well suited due to its sensibility to uncertainties, since the pathologies are characterized by an increase in the signal complexity and unpredictability. The results showed that the pathological groups had higher entropy values in accordance with other vocal acoustic parameters presented. This suggests that these techniques may improve and complement the recent voice analysis methods available for clinicians. (C) 2008 Elsevier Inc. All rights reserved.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Assessment of PD Speech Anomalies @ Home

Background: Voice processing in real-time is challenging. A drawback of previous work for Hypokinetic Dysarthria (HKD) recognition is the requirement of controlled settings in a laboratory environment. A personal digital assistant (PDA) has been developed for home assessment of PD patients. The PDA offers sound processing capabilities, which allow for developing a module for recognition and quantification HKD. Objective: To compose an algorithm for assessment of PD speech severity in the home environment based on a review synthesis. Methods: A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that are robust to medication changes in Levodopa-responsive patients are investigated for HKD recognition. Keywords such as Hypokinetic Dysarthria , and Speech recognition in real time were used in the search engines. IEEE explorer produced the most useful search hits as compared to Google Scholar, ELIN, EBRARY, PubMed and LIBRIS. Results: Vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since relevant speech segments (consonants and vowels) contains minority of speech energy, intelligibility can be improved by amplifying the voice signal using amplitude compression. Pause detection and peak to average power rate calculations for voice segmentation produce rich voice features in real time. Enhancements in voice segmentation can be done by inducing Zero-Crossing rate (ZCR). Consonants have high ZCR whereas vowels have low ZCR. Wavelet transform is found promising for voice analysis since it quantizes non-stationary voice signals over time-series using scale and translation parameters. In this way voice intelligibility in the waveforms can be analyzed in each time frame. Conclusions: This review evaluated HKD recognition algorithms to develop a tool for PD speech home-assessment using modern mobile technology. An algorithm that tackles realtime constraints in HKD recognition based on the review synthesis is proposed. We suggest that speech features may be further processed using wavelet transforms and used with a neural network for detection and quantification of speech anomalies related to PD. Based on this model, patients' speech can be automatically categorized according to UPDRS speech ratings.

A Meeting of East and West: Can Eastern-Influenced Therapies be Effective in the Treatment of Stress and Mood Disorders?

Given that the human brain is plastic and that structural alterations have been seen in monks who meditate on a regular basis, the question arises of whether these two facts are actually related. Furthermore, if this is in fact the case, would it be possible to apply these findings to the public? In this paper I will present the different conditions that induce neuroplasticity as well as give an overview of meditation and the ways that it is practiced nowadays. To this end I will argue that if monks are able to alter the structure of their brains and the brain is naturally inclined to heal itself then incorporating eastern practices, such as mindfulness and imagery, into western therapies could benefit patients suffering from mood disorders and, in particular, stress.