Feature selection and classification of imbalanced datasets. Application to PET images of children with Autistic Spectrum Disorders

Learning with discriminative methods is generally based on minimizing themisclassification of training samples, which may be unsuitable for imbalanceddatasets where the recognition might be biased in favor of the most numerousclass. This problem can be addressed with a generative approach, which typicallyrequires more parameters to be determined leading to reduced performances inhigh dimension. In such situations, dimension reduction becomes a crucial issue.We propose a feature selection / classification algorithm based on generativemethods in order to predict the clinical status of a highly imbalanced datasetmade of PET scans of forty-five low-functioning children with autism spectrumdisorders (ASD) and thirteen non-ASD low-functioning children. ASDs aretypically characterized by impaired social interaction, narrow interests, andrepetitive behaviours, with a high variability in expression and severity. Thenumerous findings revealed by brain imaging studies suggest that ASD isassociated with a complex and distributed pattern of abnormalities that makesthe identification of a shared and common neuroimaging profile a difficult task.In this context, our goal is to identify the rest functional brain imagingabnormalities pattern associated with ASD and to validate its efficiency inindividual classification. The proposed feature selection algorithm detected acharacteristic pattern in the ASD group that included a hypoperfusion in theright Superior Temporal Sulcus (STS) and a hyperperfusion in the contralateralpostcentral area. Our algorithm allowed for a significantly accurate (88\%),sensitive (91\%) and specific (77\%) prediction of clinical category. For thisimbalanced dataset, with only 13 control scans, the proposed generativealgorithm outperformed other state-of-the-art discriminant methods. The highpredictive power of the characteristic pattern, which has been automaticallyidentified on whole brains without any priors, confirms previous findingsconcerning the role of STS in ASD. This work offers exciting possibilities forearly autism detection and/or the evaluation of treatment response in individualpatients

Pupil dilation reflects the dynamic integration of audiovisual emotional speech

Emotional speech perception is a multisensory process. When speaking with an individual we concurrently integrate the information from their voice and face to decode e.g., their feelings, moods, and emotions. However, the physiological reactions—such as the reflexive dilation of the pupil—associated to these processes remain mostly unknown. That is the aim of the current article, to investigate whether pupillary reactions can index the processes underlying the audiovisual integration of emotional signals. To investigate this question, we used an algorithm able to increase or decrease the smiles seen in a person’s face or heard in their voice, while preserving the temporal synchrony between visual and auditory channels. Using this algorithm, we created congruent and incongruent audiovisual smiles, and investigated participants’ gaze and pupillary reactions to manipulated stimuli. We found that pupil reactions can reflect emotional information mismatch in audiovisual speech. In our data, when participants were explicitly asked to extract emotional information from stimuli, the first fixation within emotionally mismatching areas (i.e., the mouth) triggered pupil dilation. These results reveal that pupil dilation can reflect the dynamic integration of audiovisual emotional speech and provide insights on how these reactions are triggered during stimulus perception

Anomalies de la connectivité cérébrale dans l autisme

PARIS7-Xavier Bichat (751182101) / SudocSudocFranceF

Multivariate methods for the joint analysis of neuroimaging and genetic data

L'imagerie cérébrale connaît un intérêt grandissant, en tant que phénotype intermédiaire, dans la compréhension du chemin complexe qui relie les gènes à un phénotype comportemental ou clinique. Dans ce contexte, un premier objectif est de proposer des méthodes capables d'identifier la part de variabilité génétique qui explique une certaine part de la variabilité observée en neuroimagerie. Les approches univariées classiques ignorent les effets conjoints qui peuvent exister entre plusieurs gènes ou les covariations potentielles entre régions cérébrales.Notre première contribution a été de chercher à améliorer la sensibilité de l'approche univariée en tirant avantage de la nature multivariée des données génétiques, au niveau local. En effet, nous adaptons l'inférence au niveau du cluster en neuroimagerie à des données de polymorphismes d'un seul nucléotide (SNP), en cherchant des clusters 1D de SNPs adjacents associés à un même phénotype d'imagerie. Ensuite, nous prolongeons cette idée et combinons les clusters de voxels avec les clusters de SNPs, en utilisant un test simple au niveau du "cluster 4D", qui détecte conjointement des régions cérébrale et génomique fortement associées. Nous obtenons des résultats préliminaires prometteurs, tant sur données simulées que sur données réelles.Notre deuxième contribution a été d'utiliser des méthodes multivariées exploratoires pour améliorer la puissance de détection des études d'imagerie génétique, en modélisant la nature multivariée potentielle des associations, à plus longue échelle, tant du point de vue de l'imagerie que de la génétique. La régression Partial Least Squares et l'analyse canonique ont été récemment proposées pour l'analyse de données génétiques et transcriptomiques. Nous proposons ici de transposer cette idée à l'analyse de données de génétique et d'imagerie. De plus, nous étudions différentes stratégies de régularisation et de réduction de dimension, combinées avec la PLS ou l'analyse canonique, afin de faire face au phénomène de sur-apprentissage dû aux très grandes dimensions des données. Nous proposons une étude comparative de ces différentes stratégies, sur des données simulées et des données réelles d'IRM fonctionnelle et de SNPs. Le filtrage univarié semble nécessaire. Cependant, c'est la combinaison du filtrage univarié et de la PLS régularisée L1 qui permet de détecter une association généralisable et significative sur les données réelles, ce qui suggère que la découverte d'associations en imagerie génétique nécessite une approche multivariée.Brain imaging is increasingly recognised as an interesting intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. Our first contribution is to improve the sensitivity of the univariate approach by taking advantage of the multivariate nature of the genetic data in a local way. Indeed, we adapt cluster-inference techniques from neuroimaging to Single Nucleotide Polymorphism (SNP) data, by looking for 1D clusters of adjacent SNPs associated with the same imaging phenotype. Then, we push further the concept of clusters and we combined voxel clusters and SNP clusters, by using a simple 4D cluster test that detects conjointly brain and genome regions with high associations. We obtain promising preliminary results on both simulated and real datasets .Our second contribution is to investigate exploratory multivariate methods to increase the detection power of imaging genetics studies, by accounting for the potential multivariate nature of the associations, at a longer range, on both the imaging and the genetics sides. Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse genetic and transcriptomic data. Here, we propose to transpose this idea to the genetics vs. imaging context. Moreover, we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA, to face the overfitting issues due to the very high dimensionality of the data. We propose a comparison study of the different strategies on both a simulated dataset and a real fMRI and SNP dataset. Univariate selection appears to be necessary to reduce the dimensionality. However, the generalisable and significant association uncovered on the real dataset by the two-step approach combining univariate filtering and L1-regularised PLS suggests that discovering meaningful imaging genetics associations calls for a multivariate approach.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Voxelwise multivariate statistics and brain-wide machine learning using the full diffusion tensor.

International audienceIn this paper, we propose to use the full diffusion tensor to perform brain-wide score prediction on diffusion tensor imaging (DTI) using the log-Euclidean framework., rather than the commonly used fractional anisotropy (FA). Indeed, scalar values such as the FA do not capture all the information contained in the diffusion tensor. Additionally, full tensor information is included in every step of the pre-processing pipeline: registration, smoothing and feature selection using voxelwise multivariate regression analysis. This approach was tested on data obtained from 30 children and adolescents with autism spectrum disorder and showed some improvement over the FA-only analysis

Cochlear implant benefits in deafness rehabilitation: PET study of temporal voice activations.

International audienceUNLABELLED: Cochlear implants may improve the medical and social prognosis of profound deafness. Nevertheless, some patients have experienced poor results without any clear explanations. One correlate may be an alteration in cortical voice processing. To test this hypothesis, we studied the activation of human temporal voice areas (TVA) using a well-standardized PET paradigm adapted from previous functional MRI (fMRI) studies. METHODS: A PET H(2)(15)O activation study was performed on 3 groups of adult volunteers: normal-hearing control subjects (n = 6) and cochlear-implanted postlingually deaf patients with >2 y of cochlear implant experience, with intelligibility scores in the "Lafon monosyllabic task" >80% (GOOD group; n = 6) or <20% (POOR group; n = 6). Relative cerebral blood flow was measured in 3 conditions: rest, passive listening to human voice, and nonvoice stimuli. RESULTS: Compared with silence, the activations induced by nonvoice stimuli were bilaterally located in the superior temporal regions in all groups. However these activations were significantly and similarly reduced in both cochlear implant groups, whereas control subjects showed supplementary activations. Compared with nonvoice, the voice stimuli induced bilateral activation of the TVA along the superior temporal sulcus (STS) in both the control and the GOOD groups. In contrast, these activations were not detected in the POOR group, which showed only left unilateral middle STS activation. CONCLUSION: These results suggest that PET is an adequate method to explore cochlear implant benefits and that this benefit could be linked to the activation of the TVA

Neural basis of interindividual variability in social perception in typically developing children and adolescents using diffusion tensor imaging

International audienceHumans show great interindividual variability in the degree they engage in social relationship. The neural basis of this variability is still poorly understood, particularly in children. In this study, we aimed to investigate the neural basis of interindividual variability in the first step of social behavior, that is social perception, in typically developing children. For that purpose, we first used eye-tracking to objectively measure eye-gaze processing during passive visualization of social movie clips in 24 children and adolescents (10.5 ± 2.9 y). Secondly, we correlated eye-tracking data with measures of fractional anisotropy, an index of white matter microstructure, obtained using diffusion tensor imaging MRI. The results showed a large interindividual variability in the number of fixations to the eyes of characters during visualization of social scenes. In addition, whole-brain analysis showed a significant positive correlation between FA and number of fixations to the eyes,mainly in the temporal part of the superior longitudinal fasciculi bilaterally, adjacent to the posterior superior temporal cortex. Our results indicate the existence of a neural signature associated with the interindividual variability in social perception in children, contributing for better understanding the neural basis of typical and atypical development of a broader social expertise

Brain voice processing with bilateral cochlear implants: a positron emission tomography study.

International audienceMost cochlear implantations are unilateral. To explore the benefits of a binaural cochlear implant, we used water-labelled oxygen-15 positron emission tomography. Relative cerebral blood flow was measured in a binaural implant group (n = 11), while the subjects were passively listening to human voice sounds, environmental sounds non-voice or silence. Binaural auditory stimulation in the cochlear implant group bilaterally activated the temporal voice areas, whereas monaural cochlear implant stimulation only activated the left temporal voice area. Direct comparison of the binaural and the monaural cochlear implant stimulation condition revealed an additional right temporal activation during voice processing in the binaural condition and the activation of a right fronto-parietal cortical network during sound processing that has been implicated in attention. These findings provide evidence that a bilateral cochlear implant stimulation enhanced the spectral cues associated with sound perception and improved brain processing of voice stimuli in the right temporal region when compared to a monaural cochlear implant stimulation. Moreover, the recruitment of sensory attention resources in a right fronto-parietal network allowed patients with bilateral cochlear implant stimulation to enhance their sound discrimination, whereas the same patients with only one cochlear implant stimulation had more auditory perception difficulties

Processing of voices in deafness rehabilitation by auditory brainstem implant.

International audienceThe superior temporal sulcus (STS) is specifically involved in processing the human voice. Profound acquired deafness by post-meningitis ossified cochlea and by bilateral vestibular schwannoma in neurofibromatosis type 2 patients are two indications for auditory brainstem implantation (ABI). In order to objectively measure the cortical voice processing of a group of ABI patients, we studied the activation of the human temporal voice areas (TVA) by PET H(2)(15)O, performed in a group of implanted deaf adults (n=7) with more than two years of auditory brainstem implant experience, with an intelligibility score average of 17%+/-17 [mean+/-SD]. Relative cerebral blood flow (rCBF) was measured in the three following conditions: during silence, while passive listening to human voice, and to non-voice stimuli. Compared to silence, the activations induced by voice and non-voice stimuli were bilaterally located in the superior temporal regions. However, compared to non-voice stimuli, the voice stimuli did not induce specific supplementary activation of the TVA along the STS. The comparison of ABI group with a normal-hearing controls group (n=7) showed that TVA activations were significantly enhanced among controls group. ABI allowed the transmission of sound stimuli to temporal brain regions but lacked transmitting the specific cues of the human voice to the TVA. Moreover, among groups, during silent condition, brain visual regions showed higher rCBF in ABI group, although temporal brain regions had higher rCBF in the controls group. ABI patients had consequently developed enhanced visual strategies to keep interacting with their environment