113 research outputs found
MEG Decoding Across Subjects
Brain decoding is a data analysis paradigm for neuroimaging experiments that
is based on predicting the stimulus presented to the subject from the
concurrent brain activity. In order to make inference at the group level, a
straightforward but sometimes unsuccessful approach is to train a classifier on
the trials of a group of subjects and then to test it on unseen trials from new
subjects. The extreme difficulty is related to the structural and functional
variability across the subjects. We call this approach "decoding across
subjects". In this work, we address the problem of decoding across subjects for
magnetoencephalographic (MEG) experiments and we provide the following
contributions: first, we formally describe the problem and show that it belongs
to a machine learning sub-field called transductive transfer learning (TTL).
Second, we propose to use a simple TTL technique that accounts for the
differences between train data and test data. Third, we propose the use of
ensemble learning, and specifically of stacked generalization, to address the
variability across subjects within train data, with the aim of producing more
stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we
compare the standard approach of not modelling the differences across subjects,
to the proposed one of combining TTL and ensemble learning. We show that the
proposed approach is consistently more accurate than the standard one
Discovering Gender Differences in Facial Emotion Recognition via Implicit Behavioral Cues
We examine the utility of implicit behavioral cues in the form of EEG brain
signals and eye movements for gender recognition (GR) and emotion recognition
(ER). Specifically, the examined cues are acquired via low-cost, off-the-shelf
sensors. We asked 28 viewers (14 female) to recognize emotions from unoccluded
(no mask) as well as partially occluded (eye and mouth masked) emotive faces.
Obtained experimental results reveal that (a) reliable GR and ER is achievable
with EEG and eye features, (b) differential cognitive processing especially for
negative emotions is observed for males and females and (c) some of these
cognitive differences manifest under partial face occlusion, as typified by the
eye and mouth mask conditions.Comment: To be published in the Proceedings of Seventh International
Conference on Affective Computing and Intelligent Interaction.201
A Novel Scheme for Intelligent Recognition of Pornographic Images
Harmful contents are rising in internet day by day and this motivates the
essence of more research in fast and reliable obscene and immoral material
filtering. Pornographic image recognition is an important component in each
filtering system. In this paper, a new approach for detecting pornographic
images is introduced. In this approach, two new features are suggested. These
two features in combination with other simple traditional features provide
decent difference between porn and non-porn images. In addition, we applied
fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron)
and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of
system was evaluated over 18354 download images from internet. The attained
precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on
test dataset. Achieved results verify the performance of proposed system versus
other related works
Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism
Autism Spectrum Disorders (ASDs) are often associated with specific atypical
postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have
a specific visibility. While the identification and the quantification of SMM
patterns remain complex, its automation would provide support to accurate
tuning of the intervention in the therapy of autism. Therefore, it is essential
to develop automatic SMM detection systems in a real world setting, taking care
of strong inter-subject and intra-subject variability. Wireless accelerometer
sensing technology can provide a valid infrastructure for real-time SMM
detection, however such variability remains a problem also for machine learning
methods, in particular whenever handcrafted features extracted from
accelerometer signal are considered. Here, we propose to employ the deep
learning paradigm in order to learn discriminating features from multi-sensor
accelerometer signals. Our results provide preliminary evidence that feature
learning and transfer learning embedded in the deep architecture achieve higher
accurate SMM detectors in longitudinal scenarios.Comment: Presented at 5th NIPS Workshop on Machine Learning and Interpretation
in Neuroimaging (MLINI), 2015, (http://arxiv.org/html/1605.04435), Report-no:
MLINI/2015/1
Revealing Individual Neuroanatomical Heterogeneity in Alzheimer Disease Using Neuroanatomical Normative Modeling
BACKGROUND AND OBJECTIVES: Alzheimer's Disease (AD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology. To explore this, we employed neuroanatomical normative modelling to index regional patterns of variability in cortical thickness. We aimed to characterise individual differences and outliers in cortical thickness in patients with AD, people with mild cognitive impairment (MCI) and controls. Furthermore, we assessed the relationships between cortical thickness heterogeneity and cognitive function, amyloid-beta, phosphor-tau, ApoE genotype. Finally, we examined whether cortical thickness heterogeneity was predictive of conversion from MCI to AD. METHODS: Cortical thickness measurements across 148 brain regions were obtained from T1-weighted MRI scans from 62 sites of the Alzheimer's Disease Neuroimaging Initiative. AD was determined by clinical and neuropsychological examination with no comorbidities present. MCI participants had reported memory complaints, and controls were cognitively normal. A neuroanatomical normative model indexed cortical thickness distributions using a separate healthy reference dataset (n= 33,072), employing hierarchical Bayesian regression to predict cortical thickness per region using age and sex, whilst adjusting for site noise. Z-scores per region were calculated, resulting in a z-score 'brain map' per participant. Regions with z-scores <-1.96 were classified as outliers. RESULTS: Patients with AD (n=206) had a median of 12 outlier regions (out of a possible 148), with the highest proportion of outliers (47%) in the parahippocampal gyrus. For 62 regions, over 90% of these patients had cortical thicknesses within the normal range. Patients with AD had more outlier regions than people with MCI (n=662) or controls (n=159) [F(2, 1022) = 95.39), P = 2.0×10-16]. They were also more dissimilar to each other than people with MCI or controls [F(2, 1024) = 209.42, P = 2.2×10-16]. A greater number of outlier regions was associated with worse cognitive function, CSF protein concentrations and an increased risk of converting from MCI to AD within three years (HR = 1.028, 95% CI[1.016,1.039], P =1.8×10-16). DISCUSSION: Individualised normative maps of cortical thickness highlight the heterogeneous impact of AD on the brain. Regional outlier estimates have the potential to be a marker of disease and could be used to track an individual's disease progression or treatment response in clinical trials
Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study:a machine learning approach
Schizophrenia and related disorders have heterogeneous outcomes. Individualized prediction of long-term outcomes may be helpful in improving treatment decisions. Utilizing extensive baseline data of 523 patients with a psychotic disorder and variable illness duration, we predicted symptomatic and global outcomes at 3-year and 6-year follow-ups. We classified outcomes as (1) symptomatic: in remission or not in remission, and (2) global outcome, using the Global Assessment of Functioning (GAF) scale, divided into good (GAF >= 65) and poor (GAF < 65). Aiming for a robust and interpretable prediction model, we employed a linear support vector machine and recursive feature elimination within a nested cross-validation design to obtain a lean set of predictors. Generalization to out-of-study samples was estimated using leave-one-site-out cross-validation. Prediction accuracies were above chance and ranged from 62.2% to 64.7% (symptomatic outcome), and 63.5-67.6% (global outcome). Leave-one-site-out cross-validation demonstrated the robustness of our models, with a minor drop in predictive accuracies of 2.3% on average. Important predictors included GAF scores, psychotic symptoms, quality of life, antipsychotics use, psychosocial needs, and depressive symptoms. These robust, albeit modestly accurate, long-term prognostic predictions based on lean predictor sets indicate the potential of machine learning models complementing clinical judgment and decision-making. Future model development may benefit from studies scoping patient's and clinicians' needs in prognostication.</p
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