Exploiting the Brain's Network Structure for Automatic Identification of ADHD Subjects.

Attention Deficit Hyperactive Disorder (ADHD) is a common behavioral problem affecting children. In this work, we investigate the automatic classification of ADHD subjects using the resting state Functional Magnetic Resonance Imaging (fMRI) sequences of the brain. We show that the brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We compute the pairwise correlation of brain voxels' activity over the time frame of the experimental protocol which helps to model the function of a brain as a network. Different network features are computed for each of the voxels constructing the network. The concatenation of the network features of all the voxels in a brain serves as the feature vector. Feature vectors from a set of subjects are then used to train a PCA-LDA (principal component analysis-linear discriminant analysis) based classifier. We hypothesized that ADHD-related differences lie in some specific regions of the brain and using features only from those regions is sufficient to discriminate ADHD and control subjects. We propose a method to create a brain mask that includes the useful regions only and demonstrate that using the feature from the masked regions improves classification accuracy on the test data set. We train our classifier with 776 subjects and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge. We demonstrate the utility of graph-motif features, specifically the maps that represent the frequency of participation of voxels in network cycles of length 3. The best classification performance (69.59%) is achieved using 3-cycle map features with masking. Our proposed approach holds promise in being able to diagnose and understand the disorder.

Rising Mental Health Incidence Among Adolescents in Westchester, NY.

CONTEXT: Many governments have publicly released healthcare data, which can be mined for insights about disease conditions, and their impact on society. METHODS: We present a big-data analytics approach to investigate data in the New York Statewide Planning and Research Cooperative System (SPARCS) consisting of 20 million patient records. FINDINGS: Whereas the age group 30-48 years exhibited an 18% decline in mental health (MH) disorders from 2009 to 2016, the age group 0-17 years showed a 5.4% increase. MH issues amongst the age group 0-17 years comprise a significant expenditure in New York State. Within this age group, we find a higher prevalence of MH disorders in females and minority populations. Westchester County has seen a 32% increase in incidences and a 41% increase in costs. CONCLUSIONS: Our approach is scalable to data from multiple government agencies and provides an independent perspective on health care issues, which can prove valuable to policy and decision-makers.

An oscillatory neural network model that demonstrates the benefits of multisensory learning.

Since the world consists of objects that stimulate multiple senses, it is advantageous for a vertebrate to integrate all the sensory information available. However, the precise mechanisms governing the temporal dynamics of multisensory processing are not well understood. We develop a computational modeling approach to investigate these mechanisms. We present an oscillatory neural network model for multisensory learning based on sparse spatio-temporal encoding. Recently published results in cognitive science show that multisensory integration produces greater and more efficient learning. We apply our computational model to qualitatively replicate these results. We vary learning protocols and system dynamics, and measure the rate at which our model learns to distinguish superposed presentations of multisensory objects. We show that the use of multiple channels accelerates learning and recall by up to 80%. When a sensory channel becomes disabled, the performance degradation is less than that experienced during the presentation of non-congruent stimuli. This research furthers our understanding of fundamental brain processes, paving the way for multiple advances including the building of machines with more human-like capabilities.

Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.

Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

Exploiting the brain's network structure in identifying ADHD subjects.

Attention Deficit Hyperactive Disorder (ADHD) is a common behavioral problem affecting children. In this work, we investigate the automatic classification of ADHD subjects using the resting state functional magnetic resonance imaging (fMRI) sequences of the brain. We show that brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We compute the pairwise correlation of brain voxels' activity over the time frame of the experimental protocol which helps to model the function of a brain as a network. Different network features are computed for each of the voxels constructing the network. The concatenation of the network features of all the voxels in a brain serves as the feature vector. Feature vectors from a set of subjects are then used to train a PCA-LDA (principal component analysis-linear discriminant analysis) based classifier. We hypothesized that ADHD related differences lie in some specific regions of brain and using features only from those regions are sufficient to discriminate ADHD and control subjects. We propose a method to create a brain mask which includes the useful regions only and demonstrate that using the feature from the masked regions improves classification accuracy on the test data set. We train our classifier with 776 subjects, and test on 171 subjects provided by the Neuro Bureau for the ADHD-200 challenge. We demonstrate the utility of graph-motif features, specifically the maps that represent the frequency of participation of voxels in network cycles of length 3. The best classification performance (69.59%) is achieved using 3-cycle map features with masking. Our proposed approach holds promise in being able to diagnose and understand the disorder.

Full-brain auto-regressive modeling (FARM) using fMRI.

In order to fully uncover the information potentially available in the fMRI signal, we model it as a multivariate auto-regressive process. To infer the model, we do not apply any form of clustering or dimensionality reduction, and solve the problem of under-determinacy using sparse regression. We find that only a few small clusters (with average size of 3-4 voxels) are useful in predicting the activity of other voxels, and demonstrate remarkable consistency within a subject as well as across multiple subjects. Moreover, we find that: (a) the areas that can predict activity of other voxels are consistent with previous results related to networks activated by the specific somatosensory task, as well as networks related to the default mode activity; (b) there is a global dynamical state dominated by two prominent (although not unique) streams, originating in the posterior parietal cortex and the posterior cingulate/precuneus cortex; (c) these streams span default mode and task-specific networks, and interact in several regions, notably the insula; and (d) the posterior cingulate is a central node of the default mode network, in terms of its ability to determine the future evolution of the rest of the nodes.

Statistics of natural scenes and cortical color processing.

We investigate the spatial correlations of orientation and color information in natural images. We find that the correlation of orientation information falls off rapidly with increasing distance, while color information is more highly correlated over longer distances. We show that orientation and color information are statistically independent in natural images and that the spatial correlation of jointly encoded orientation and color information decays faster than that of color alone. Our findings suggest that: (a) orientation and color information should be processed in separate channels and (b) the organization of cortical color and orientation selectivity at low spatial frequencies is a reflection of the cortical adaptation to the statistical structure of the visual world. These findings are in agreement with biological observations, as form and color are thought to be represented by different classes of neurons in the primary visual cortex, and the receptive fields of color-selective neurons are larger than those of orientation-selective neurons. The agreement between our findings and biological observations supports the ecological theory of perception.

Prediction and interpretation of distributed neural activity with sparse models.

We explore to what extent the combination of predictive and interpretable modeling can provide new insights for functional brain imaging. For this, we apply a recently introduced regularized regression technique, the Elastic Net, to the analysis of the PBAIC 2007 competition data. Elastic Net regression controls via one parameter the number of voxels in the resulting model, and via another the degree to which correlated voxels are included. We find that this method produces highly predictive models of fMRI data that provide evidence for the distributed nature of neural function. We also use the flexibility of Elastic Net to demonstrate that model robustness can be improved without compromising predictability, in turn revealing the importance of localized clusters of activity. Our findings highlight the functional significance of patterns of distributed clusters of localized activity, and underscore the importance of models that are both predictive and interpretable.

A cluster overlap measure for comparison of activations in fMRI studies.

Most fMRI studies use voxel-wise statistics to carry out intrasubject as well as inter-subject analysis. We show that statistics derived from voxel-wise comparisons are likely to be noisy and error prone, especially for inter-subject comparisons. In this paper we propose a novel metric called weighted cluster coverage to compare two activation maps. This metric is based on the intersection of spatially contiguous clusters of activations. It is found to be more robust than voxel-wise comparisons and could potentially lead to more statistical power in fMRI-based group studies.

Unsupervised segmentation with dynamical units.

In this paper, we present a novel network to separate mixtures of inputs that have been previously learned. A significant capability of the network is that it segments the components of each input object that most contribute to its classification. The network consists of amplitude-phase units that can synchronize their dynamics, so that separation is determined by the amplitude of units in an output layer, and segmentation by phase similarity between input and output layer units. Learning is unsupervised and based on a Hebbian update, and the architecture is very simple. Moreover, efficient segmentation can be achieved even when there is considerable superposition of the inputs. The network dynamics are derived from an objective function that rewards sparse coding in the generalized amplitude-phase variables. We argue that this objective function can provide a possible formal interpretation of the binding problem and that the implementation of the network architecture and dynamics is biologically plausible.

Identifying directed links in large scale functional networks: application to brain fMRI.

BACKGROUND: Biological experiments increasingly yield data representing large ensembles of interacting variables, making the application of advanced analytical tools a forbidding task. We present a method to extract networks of correlated activity, specifically from functional MRI data, such that: (a) network nodes represent voxels, and (b) the network links can be directed or undirected, representing temporal relationships between the nodes. The method provides a snapshot of the ongoing dynamics of the brain without sacrificing resolution, as the analysis is tractable even for very large numbers of voxels. RESULTS: We find that, based on topological properties of the networks, the method provides enough information about the dynamics to discriminate between subtly different brain states. Moreover, the statistical regularities previously reported are qualitatively preserved, i.e. the resulting networks display scale-free and small-world topologies. CONCLUSION: Our method expands previous approaches to render large scale functional networks, and creates the basis for an extensive and -due to the presence of mixtures of directed and undirected links- richer motif analysis of functional relationships.

High performance computing environment for multidimensional image analysis.

BACKGROUND: The processing of images acquired through microscopy is a challenging task due to the large size of datasets (several gigabytes) and the fast turnaround time required. If the throughput of the image processing stage is significantly increased, it can have a major impact in microscopy applications. RESULTS: We present a high performance computing (HPC) solution to this problem. This involves decomposing the spatial 3D image into segments that are assigned to unique processors, and matched to the 3D torus architecture of the IBM Blue Gene/L machine. Communication between segments is restricted to the nearest neighbors. When running on a 2 Ghz Intel CPU, the task of 3D median filtering on a typical 256 megabyte dataset takes two and a half hours, whereas by using 1024 nodes of Blue Gene, this task can be performed in 18.8 seconds, a 478x speedup. CONCLUSION: Our parallel solution dramatically improves the performance of image processing, feature extraction and 3D reconstruction tasks. This increased throughput permits biologists to conduct unprecedented large scale experiments with massive datasets.