88 research outputs found
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
Construction of embedded fMRI resting-state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling, Isometric Feature Mapping, Diffusion Maps, Locally Linear Embedding and kernel PCA. Furthermore, based on key global graph-theoretic properties of the embedded FCN, we compare their classification potential using machine learning. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the cross correlation metric. We show that diffusion maps with the cross correlation metric outperform the other combinations
ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions fromresting state fMRI data of patients with Schizophrenia
We construct Functional Connectivity Networks (FCN) from resting state fMRI (rsfMRI) recordings towards the classification of brain activity between healthy and schizophrenic subjects using a publicly available dataset (the COBRE dataset) of 145 subjects (74 healthy controls and 71 schizophrenic subjects). First, we match the anatomy of the brain of each individual to the Desikan- Killiany brain atlas. Then, we use the conventional approach of correlating the parcellated time series to construct FCN and ISOMAP, a nonlinear manifold learning algorithm to produce low-dimensional embeddings of the correlation matrices. For the classification analysis, we computed five key local graph-theoretic measures of the FCN and used the LASSO and Random Forest (RF) algorithms for feature selection. For the classification we used standard linear Support Vector Machines. The classification performance is tested by a double cross-validation scheme [consisting of an outer and an inner loop of “Leave one out” cross-validation (LOOCV)]. The standard cross-correlation methodology produced a classification rate of 73.1%, while ISOMAP resulted in 79.3%, thus providing a simpler model with a smaller number of features as chosen from LASSO and RF, namely the participation coefficient of the right thalamus and the strength of the right lingual gyrus
Novel Biomarker Identification Approaches for Schizophrenia using fMRI and Retinal Electrophysiology
University of Minnesota Ph.D. dissertation. November 2017. Major: Biomedical Engineering. Advisors: Kelvin Lim, Theoden Netoff. 1 computer file (PDF); vi, 109 pages.Schizophrenia is a chronic mental illness. The exact cause if schizophrenia is not yet known. Extensive research has been done to identify robust biomarkers for the disease using non-invasive brain imaging techniques. A robust biomarker can be informative about pathophysiology of the disease and can guide clinicians into developing more effective interventions. The aim of this dissertation is two folds. First, we seek to identify robust biomarkers using resting state fMRI activity from a cohort of schizophrenic and healthy subjects in a purely data driven approach. We will calculate multivariate network measures and use them as features for classification of the subjects into healthy and diseased. The network measures will be calculated using nodes defined by the AAL anatomical atlas as well as a functional atlas constructed from the fMRI activity. Network measures with high classification rate may be used as potential biomarkers. We will employ double cross-validation to estimate generalizability of our results to a new population of subjects that were not used in biomarker identification. Second, we seek to identify biomarkers using electroretinogram (ERG). We will use a data driven approach to classify individuals based on the pattern of retinal activity they exhibit in response to visual stimulation. Characteristics of the ERG result in high classification rate are presented as potential biomarkers of schizophrenia
Data Driven Classification Using fMRI Network Measures: Application to Schizophrenia
Using classification to identify biomarkers for various brain disorders has become a common practice among the functional MR imaging community. Typical classification pipeline includes taking the time series, extracting features from them, and using them to classify a set of patients and healthy controls. The most informative features are then presented as novel biomarkers. In this paper, we compared the results of single and double cross validation schemes on a cohort of 170 subjects with schizophrenia and healthy control subjects. We used graph theoretic measures as our features, comparing the use of functional and anatomical atlases to define nodes and the effect of prewhitening to remove autocorrelation trends. We found that double cross validation resulted in a 20% decrease in classification performance compared to single cross validation. The anatomical atlas resulted in higher classification results. Prewhitening resulted in a 10% boost in classification performance. Overall, a classification performance of 80% was obtained with a double-cross validation scheme using prewhitened time series and an anatomical brain atlas. However, reproducibility of classification within subjects across scans was surprisingly low and comparable to across subject classification rates, indicating that subject state during the short scan significantly influences the estimated features and classification performance
Element-centric clustering comparison unifies overlaps and hierarchy
Clustering is one of the most universal approaches for understanding complex
data. A pivotal aspect of clustering analysis is quantitatively comparing
clusterings; clustering comparison is the basis for many tasks such as
clustering evaluation, consensus clustering, and tracking the temporal
evolution of clusters. In particular, the extrinsic evaluation of clustering
methods requires comparing the uncovered clusterings to planted clusterings or
known metadata. Yet, as we demonstrate, existing clustering comparison measures
have critical biases which undermine their usefulness, and no measure
accommodates both overlapping and hierarchical clusterings. Here we unify the
comparison of disjoint, overlapping, and hierarchically structured clusterings
by proposing a new element-centric framework: elements are compared based on
the relationships induced by the cluster structure, as opposed to the
traditional cluster-centric philosophy. We demonstrate that, in contrast to
standard clustering similarity measures, our framework does not suffer from
critical biases and naturally provides unique insights into how the clusterings
differ. We illustrate the strengths of our framework by revealing new insights
into the organization of clusters in two applications: the improved
classification of schizophrenia based on the overlapping and hierarchical
community structure of fMRI brain networks, and the disentanglement of various
social homophily factors in Facebook social networks. The universality of
clustering suggests far-reaching impact of our framework throughout all areas
of science
An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works
Schizophrenia (SZ) is a mental disorder that typically emerges in late
adolescence or early adulthood. It reduces the life expectancy of patients by
15 years. Abnormal behavior, perception of emotions, social relationships, and
reality perception are among its most significant symptoms. Past studies have
revealed the temporal and anterior lobes of hippocampus regions of brain get
affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and
decreased volume of white and gray matter can be observed due to this disease.
The magnetic resonance imaging (MRI) is the popular neuroimaging technique used
to explore structural/functional brain abnormalities in SZ disorder owing to
its high spatial resolution. Various artificial intelligence (AI) techniques
have been employed with advanced image/signal processing methods to obtain
accurate diagnosis of SZ. This paper presents a comprehensive overview of
studies conducted on automated diagnosis of SZ using MRI modalities. Main
findings, various challenges, and future works in developing the automated SZ
detection are described in this paper
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence
or early adulthood. It reduces the life expectancy of patients by 15 years.
Abnormal behavior, perception of emotions, social relationships, and reality
perception are among its most significant symptoms. Past studies have revealed
that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased
volume of white and gray matter can be observed due to this disease. Magnetic
resonance imaging (MRI) is the popular neuroimaging technique used to
explore structural/functional brain abnormalities in SZ disorder, owing to its
high spatial resolution. Various artificial intelligence (AI) techniques have been
employed with advanced image/signal processing methods to accurately diagnose
SZ. This paper presents a comprehensive overview of studies conducted on
the automated diagnosis of SZ using MRI modalities. First, an AI-based computer
aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections
are presented. Then, this section introduces the most important conventional
machine learning (ML) and deep learning (DL) techniques in the diagnosis of
diagnosing SZ. A comprehensive comparison is also made between ML and DL
studies in the discussion section. In the following, the most important challenges
in diagnosing SZ are addressed. Future works in diagnosing SZ using AI
techniques and MRI modalities are recommended in another section. Results,
conclusion, and research findings are also presented at the end.Ministerio de Ciencia e Innovación
(España)/ FEDER under the RTI2018-098913-B100 projectConsejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and
FEDER under CV20-45250 and A-TIC-080-UGR18 project
- …