170 research outputs found
TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy
We introduce TempoCave, a novel visualization application for analyzing
dynamic brain networks, or connectomes. TempoCave provides a range of
functionality to explore metrics related to the activity patterns and modular
affiliations of different regions in the brain. These patterns are calculated
by processing raw data retrieved functional magnetic resonance imaging (fMRI)
scans, which creates a network of weighted edges between each brain region,
where the weight indicates how likely these regions are to activate
synchronously. In particular, we support the analysis needs of clinical
psychologists, who examine these modular affiliations and weighted edges and
their temporal dynamics, utilizing them to understand relationships between
neurological disorders and brain activity, which could have a significant
impact on the way in which patients are diagnosed and treated. We summarize the
core functionality of TempoCave, which supports a range of comparative tasks,
and runs both in a desktop mode and in an immersive mode. Furthermore, we
present a real-world use case that analyzes pre- and post-treatment connectome
datasets from 27 subjects in a clinical study investigating the use of
cognitive behavior therapy to treat major depression disorder, indicating that
TempoCave can provide new insight into the dynamic behavior of the human brain
Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning
With the rapid growth in smartphone usage, more organizations begin to focus
on providing better services for mobile users. User identification can help
these organizations to identify their customers and then cater services that
have been customized for them. Currently, the use of cookies is the most common
form to identify users. However, cookies are not easily transportable (e.g.,
when a user uses a different login account, cookies do not follow the user).
This limitation motivates the need to use behavior biometric for user
identification. In this paper, we propose DEEPSERVICE, a new technique that can
identify mobile users based on user's keystroke information captured by a
special keyboard or web browser. Our evaluation results indicate that
DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy).
The technique is also efficient and only takes less than 1 ms to perform
identification.Comment: 2017 Joint European Conference on Machine Learning and Knowledge
Discovery in Database
EEG Classification based on Image Configuration in Social Anxiety Disorder
The problem of detecting the presence of Social Anxiety Disorder (SAD) using
Electroencephalography (EEG) for classification has seen limited study and is
addressed with a new approach that seeks to exploit the knowledge of EEG sensor
spatial configuration. Two classification models, one which ignores the
configuration (model 1) and one that exploits it with different interpolation
methods (model 2), are studied. Performance of these two models is examined for
analyzing 34 EEG data channels each consisting of five frequency bands and
further decomposed with a filter bank. The data are collected from 64 subjects
consisting of healthy controls and patients with SAD. Validity of our
hypothesis that model 2 will significantly outperform model 1 is borne out in
the results, with accuracy -- higher for model 2 for each machine
learning algorithm we investigated. Convolutional Neural Networks (CNN) were
found to provide much better performance than SVM and kNNs
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
The increasing use of electronic forms of communication presents new
opportunities in the study of mental health, including the ability to
investigate the manifestations of psychiatric diseases unobtrusively and in the
setting of patients' daily lives. A pilot study to explore the possible
connections between bipolar affective disorder and mobile phone usage was
conducted. In this study, participants were provided a mobile phone to use as
their primary phone. This phone was loaded with a custom keyboard that
collected metadata consisting of keypress entry time and accelerometer
movement. Individual character data with the exceptions of the backspace key
and space bar were not collected due to privacy concerns. We propose an
end-to-end deep architecture based on late fusion, named DeepMood, to model the
multi-view metadata for the prediction of mood scores. Experimental results
show that 90.31% prediction accuracy on the depression score can be achieved
based on session-level mobile phone typing dynamics which is typically less
than one minute. It demonstrates the feasibility of using mobile phone metadata
to infer mood disturbance and severity.Comment: KDD 201
TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy
We introduce TempoCave, a novel visualization application for analyzing
dynamic brain networks, or connectomes. TempoCave provides a range of
functionality to explore metrics related to the activity patterns and modular
affiliations of different regions in the brain. These patterns are calculated
by processing raw data retrieved functional magnetic resonance imaging (fMRI)
scans, which creates a network of weighted edges between each brain region,
where the weight indicates how likely these regions are to activate
synchronously. In particular, we support the analysis needs of clinical
psychologists, who examine these modular affiliations and weighted edges and
their temporal dynamics, utilizing them to understand relationships between
neurological disorders and brain activity, which could have a significant
impact on the way in which patients are diagnosed and treated. We summarize the
core functionality of TempoCave, which supports a range of comparative tasks,
and runs both in a desktop mode and in an immersive mode. Furthermore, we
present a real-world use case that analyzes pre- and post-treatment connectome
datasets from 27 subjects in a clinical study investigating the use of
cognitive behavior therapy to treat major depression disorder, indicating that
TempoCave can provide new insight into the dynamic behavior of the human brain
Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes
The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activities between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, which was first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC aligns well with the observed FC when compared with that simulated traditional structural connectome
Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis
Network analysis of human brain connectivity is critically important for
understanding brain function and disease states. Embedding a brain network as a
whole graph instance into a meaningful low-dimensional representation can be
used to investigate disease mechanisms and inform therapeutic interventions.
Moreover, by exploiting information from multiple neuroimaging modalities or
views, we are able to obtain an embedding that is more useful than the
embedding learned from an individual view. Therefore, multi-view multi-graph
embedding becomes a crucial task. Currently, only a few studies have been
devoted to this topic, and most of them focus on the vector-based strategy
which will cause structural information contained in the original graphs lost.
As a novel attempt to tackle this problem, we propose Multi-view Multi-graph
Embedding (M2E) by stacking multi-graphs into multiple partially-symmetric
tensors and using tensor techniques to simultaneously leverage the dependencies
and correlations among multi-view and multi-graph brain networks. Extensive
experiments on real HIV and bipolar disorder brain network datasets demonstrate
the superior performance of M2E on clustering brain networks by leveraging the
multi-view multi-graph interactions
EEG Classification based on Image Configuration in Social Anxiety Disorder
The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting of five frequency bands and further decomposed with a filter bank. The data are collected from 64 subjects consisting of healthy controls and patients with SAD. Validity of our hypothesis that model 2 will significantly outperform model 1 is borne out in the results, with accuracy 6– 7% higher for model 2 for each machine learning algorithm we investigated. Convolutional Neural Networks (CNN) were found to provide much better performance than SVM and kNNs. Index Terms— EEG, deep learning, classification
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