A Gaussian-mixed Fuzzy Clustering Model on Valence-Arousal-related fMRI Data-Set

Previous medical experiments illustrated that Valence and Arousal were high corresponded to brain response by amygdala and orbital frontal cortex through observation by functional magnetic resonance imaging (fMRI). In this paper, Valence-Arousal related fMRI data-set were acquired from the picture stimuli experiments, and finally the relative Valence -Arousal feature values for a given word that corresponding to a given picture stimuli were calculated. Gaussian bilateral filter and independent components analysis (ICA) based Gaussian component method were applied for image denosing and segmenting; to construct the timing signals of Valence and Arousal from fMRI data-set separately, expectation maximal of Gaussian mixed model was addressed to calculate the histogram, and furthermore, Otsu curve fitting algorithm was introduced to scale the computational complexity; time series based Valence -Arousal related curve were finally generated. In Valence-Arousal space, a fuzzy c-mean method was applied to get typical point that represented the word relative to the picture. Analyzed results showed the effectiveness of the proposed methods by comparing with other algorithms for feature extracting operations on fMRI data-set including power spectrum density (PSD), spline, shape-preserving and cubic fitting methods

Clasificación binaria, desbalanceada y contextual de voxels asociados a series temporales

En este artículo se presenta un método computacional para clasificación de regiones 3D en función de sus características dinámicas. La clasificación de voxels atípicos se implementa en función de las series temporales asociadas a los mismos. El método opera en clasificación binaria, clases desbalanceadas y correlación espacial de las series asociadas a cada clase. El método propuesto utiliza máquinas de soporte vectorial y difusión anisotrópica robusta para detectar la estructura subyacente en los datos y clasificar los voxels correspondientes en cada clase. Se presentan resultados experimentales del método propuesto para datos de resonancia magnética funcional e imágenes de rango.VI Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Clasificación binaria, desbalanceada y contextual de voxels asociados a series temporales

En este artículo se presenta un método computacional para clasificación de regiones 3D en función de sus características dinámicas. La clasificación de voxels atípicos se implementa en función de las series temporales asociadas a los mismos. El método opera en clasificación binaria, clases desbalanceadas y correlación espacial de las series asociadas a cada clase. El método propuesto utiliza máquinas de soporte vectorial y difusión anisotrópica robusta para detectar la estructura subyacente en los datos y clasificar los voxels correspondientes en cada clase. Se presentan resultados experimentales del método propuesto para datos de resonancia magnética funcional e imágenes de rango.VI Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Diffuse outlier time series detection technique for functional magnetic resonance imaging

We propose a new support vector machine (SVM) based method that improves the time series classi cation in magnetic resonance imaging (fMRI). We exploit the robust anisotropic di usion (RAD) technique to increase the classi cation performance of the one class support vector machine by taking into account the hypothesis of spatial relationship between active voxels. The proposed method was called Di use One Class Support Vector Machine (DOCSVM). DOCSVM method treats activated voxels as outliers and applies one class support vector machine to generate an activation map and RAD to include the neighborhood hypothesis, improving the classi cation and reducing the iteration steps with respect to RADSPM. We give a brief review of the main methods, present receiver operating characteristic (ROC) results and conclude suggesting further research alternatives.Presentado en el I Workshop Procesamiento de señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Clasificación binaria, desbalanceada y contextual de voxels asociados a series temporales

En este artículo se presenta un método computacional para clasificación de regiones 3D en función de sus características dinámicas. La clasificación de voxels atípicos se implementa en función de las series temporales asociadas a los mismos. El método opera en clasificación binaria, clases desbalanceadas y correlación espacial de las series asociadas a cada clase. El método propuesto utiliza máquinas de soporte vectorial y difusión anisotrópica robusta para detectar la estructura subyacente en los datos y clasificar los voxels correspondientes en cada clase. Se presentan resultados experimentales del método propuesto para datos de resonancia magnética funcional e imágenes de rango.VI Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Detección de áreas de interés bajo la hipótesis de relación espacial de voxels activados en fMRI

Las imágenes de resonancia magnética funcional (fMRI) utilizan una serie de imágenes de resonancia magnética para mapear de forma no invasiva las áreas de actividad neuronal aumentada del cerebro humano. La baja relación señal a ruido (SNR) de las imágenes funcionales, hace necesario el uso de técnicas de procesamiento de imágenes específicas, para la detección de regiones correlacionadas con la respuesta a un estímulo determinado. En este artículo se presenta un método alternativo para segmentar regiones activadas en imágenes de fMRI. Se propone abordar el problema en dos etapas de clasificación, una no supervisada y una segunda etapa supervisada. El método propuesto utiliza máquinas de soporte vectorial (SVM) y difusión anisotrópica (DA) para la generación de patrones de entrenamiento, y SVM para la clasificación de regiones activadas. La aplicación del método propuesto permite incluir valiosa información con respecto a la interrelación entre las series temporales correspondientes a cada elemento de volumen (v oxel) en un espacio 3-D.V Workshop Procesamiento de Señales y Sistemas de Tiempo RealRed de Universidades con Carreras de Informática (RedUNCI

Detección de áreas de interés bajo la hipótesis de relación espacial de voxels activados en fMRI

Las imágenes de resonancia magnética funcional (fMRI) utilizan una serie de imágenes de resonancia magnética para mapear de forma no invasiva las áreas de actividad neuronal aumentada del cerebro humano. La baja relación señal a ruido (SNR) de las imágenes funcionales, hace necesario el uso de técnicas de procesamiento de imágenes específicas, para la detección de regiones correlacionadas con la respuesta a un estímulo determinado. En este artículo se presenta un método alternativo para segmentar regiones activadas en imágenes de fMRI. Se propone abordar el problema en dos etapas de clasificación, una no supervisada y una segunda etapa supervisada. El método propuesto utiliza máquinas de soporte vectorial (SVM) y difusión anisotrópica (DA) para la generación de patrones de entrenamiento, y SVM para la clasificación de regiones activadas. La aplicación del método propuesto permite incluir valiosa información con respecto a la interrelación entre las series temporales correspondientes a cada elemento de volumen (v oxel) en un espacio 3-D.V Workshop Procesamiento de Señales y Sistemas de Tiempo RealRed de Universidades con Carreras de Informática (RedUNCI

Abnormal Brain Connectivity Patterns in Adults with ADHD: A Coherence Study

Studies based on functional magnetic resonance imaging (fMRI) during the resting state have shown decreased functional connectivity between the dorsal anterior cingulate cortex (dACC) and regions of the Default Mode Network (DMN) in adult patients with Attention-Deficit/Hyperactivity Disorder (ADHD) relative to subjects with typical development (TD). Most studies used Pearson correlation coefficients among the BOLD signals from different brain regions to quantify functional connectivity. Since the Pearson correlation analysis only provides a limited description of functional connectivity, we investigated functional connectivity between the dACC and the posterior cingulate cortex (PCC) in three groups (adult patients with ADHD, n = 21; TD age-matched subjects, n = 21; young TD subjects, n = 21) using a more comprehensive analytical approach - unsupervised machine learning using a one-class support vector machine (OC-SVM) that quantifies an abnormality index for each individual. the median abnormality index for patients with ADHD was greater than for TD age-matched subjects (p = 0.014); the ADHD and young TD indices did not differ significantly (p = 0.480); the median abnormality index of young TD was greater than that of TD age-matched subjects (p = 0.016). Low frequencies below 0.05 Hz and around 0.20 Hz were the most relevant for discriminating between ADHD patients and TD age-matched controls and between the older and younger TD subjects. in addition, we validated our approach using the fMRI data of children publicly released by the ADHD-200 Competition, obtaining similar results. Our findings suggest that the abnormal coherence patterns observed in patients with ADHD in this study resemble the patterns observed in young typically developing subjects, which reinforces the hypothesis that ADHD is associated with brain maturation deficits.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)National Institute of Mental HealthNovartisJanssen-CilagAbbottEli-LillyShireBristol-Myers SquibbUniv Fed ABC, Ctr Math Computat & Cognit, Santo Andre, BrazilUniversidade Federal de São Paulo, Lab Interdisciplinar Neurociencias Clin, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychiat, São Paulo, BrazilHosp Clin Porto Alegre, Child & Adolescent Psychiat Div, ADHD Outpatient Program, Porto Alegre, RS, BrazilNYU, Ctr Child Study, Phyllis Green & Randolph Cowen Inst Pediat Neuros, New York, NY USAInst Nacl Psiquiatria Desenvolvimento, Porto Alegre, RS, BrazilUniversidade Federal de São Paulo, Lab Interdisciplinar Neurociencias Clin, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychiat, São Paulo, BrazilNational Institute of Mental Health: R01MH083246Web of Scienc

Abnormal brain connectivity patterns in adults with ADHD : a coherence study

Studies based on functional magnetic resonance imaging (fMRI) during the resting state have shown decreased functional connectivity between the dorsal anterior cingulate cortex (dACC) and regions of the Default Mode Network (DMN) in adult patients with Attention-Deficit/Hyperactivity Disorder (ADHD) relative to subjects with typical development (TD). Most studies used Pearson correlation coefficients among the BOLD signals from different brain regions to quantify functional connectivity. Since the Pearson correlation analysis only provides a limited description of functional connectivity, we investigated functional connectivity between the dACC and the posterior cingulate cortex (PCC) in three groups (adult patients with ADHD, n = 21; TD age-matched subjects, n = 21; young TD subjects, n = 21) using a more comprehensive analytical approach – unsupervised machine learning using a one-class support vector machine (OC-SVM) that quantifies an abnormality index for each individual. The median abnormality index for patients with ADHD was greater than for TD agematched subjects (p = 0.014); the ADHD and young TD indices did not differ significantly (p = 0.480); the median abnormality index of young TD was greater than that of TD age-matched subjects (p = 0.016). Low frequencies below 0.05 Hz and around 0.20 Hz were the most relevant for discriminating between ADHD patients and TD age-matched controls and between the older and younger TD subjects. In addition, we validated our approach using the fMRI data of children publicly released by the ADHD-200 Competition, obtaining similar results. Our findings suggest that the abnormal coherence patterns observed in patients with ADHD in this study resemble the patterns observed in young typically developing subjects, which reinforces the hypothesis that ADHD is associated with brain maturation deficits

Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines

Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer’s disease (AD) and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies) in a sample of healthy elderly individuals. We aim to answer the following questions: is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images) enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: (i) we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease). (ii) When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. (iii) We found important gender differences, and the possible causes of that finding are discussed