50 research outputs found
Feature selection technique for time-series fMRI data of schizophrenia patients
No full textRelated Research Article: Mean deviation based identification of activated voxels from time-series fMRI data of schizophrenia patients, F1000Research.
Cite the article as "Chatterjee I. Mean deviation based identification of activated voxels from time-series fMRI data of schizophrenia patients. F1000Research 2018, 7:1615 (doi: 10.12688/f1000research.16405.1)"
Cite the software as " CHATTERJEE, INDRANATH. (2018, September 29). Feature selection technique for time-series fMRI data of schizophrenia patients (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.1438539 "
By: Indranath Chattterjee
Department of Computer Science, University of Delhi, Delhi-110007,
E-mail: [email protected]
Run the code in the following order:
Step 1. Run the 'script_dataload.m' locating the proper directory containing all the runs of the preprocessed time-series fMRI data.
Step 2. Run the 'intersection_union_script.m'
Step 3. If want to classify with SVM, run 'classification_SVM.m', and if with ELM then run 'classification_ELM.m'
The MAT-file named 'Contrast_AudOdd_Dev_Std_15T_2D.mat' contains the data for 34 healthy subjects (first 34) and 34 schizophrenia patients (next 34). These are the contrast maps obtained from GLM analysis of time-series fMRI data for 4 runs of Auditory-oddball task. Each row contains the 153594 voxels covering the whole brain for each subject. The 4 runs of each subject's data are averaged.
This program will fetch the time-series fMRI data of each of the 4 runs of the Auditory-oddball (AUD) task for each of the 34 healthy subjects and 34 schizophrenia patients.
This program will try to find the changes in the activation pattern within a particular voxel along its time-series with the help of mean-deviation based analysis.
Using the simple set operations, the index of the relevant brain voxels will be obtained.
This index file can be fed as input to the classifier as a feature set during building the classification model.
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GICA supported region-based feature selection technique for fMRI data.
No full textG-ICA supported region-based feature selection technique for fMRI data.
By: Indranath Chattterjee
Department of computer science, University of Delhi, Delhi-110007,
E-mail: [email protected]
Run the code in the following order:
1. Run GICA on 3 runs of SIRP task fMRI data
2. Taking mean of all the runs for each subject for each component.
3. Now we have 13 components for each of the subjects.
4. Select 116 brain regions from AAL atlas.
5. For each component,
a. Mapped the connected brain regions identified by ICA with the marked regions in the atlas.
b. From each component, take those regions of the AAL atlas map which is also present in that particular component.
c. For each region, we measured five statistical features (say a1 to a5) on the basis of voxel values of that particular region for all the subjects individually.
d. Find the FDR score for each of the five measures for each region for all the subjects.
e. Sort and arrange the FDR scores in descending order. Higher the FDR score, more important the particular measure is.
f. All the statistical measures for every region are arranged in descending order of FDR score. (5 measure*116 region = 580 features)
g. Runs the scheme in LOOCV leaving one subject for testing.
i. Iteratively add each of the features (a1 to a5) for all one by one according to descending FDR score. (Sequential forward selection)
ii. Training set and test set are built with the selected feature subset.
iii. Feature subsets are selected, if it results the high accuracy in classification task. This way the best set of features can be obtained.
iv. Check to see which region has more contribution in selection of feature subset, i.e. the more number of statistical features in a particular region.
v. Count and save those regions.
vi. Repeat the same for all 68 subjects, as LOOCV runs.
h. Check the average classification accuracy for each component and note the identified brain regions after all rounds of LOOCV classification.
6. Now check and note the brain regions occurring most often in each of the components and also check the frequency of its occurrence during 68 times LOOCV classification.
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Mean deviation based identification of activated voxels from time-series fMRI data of schizophrenia patients [version 2; referees: 2 approved]
Get PDFBackground: Schizophrenia is a serious mental illness affecting different regions of the brain, which causes symptoms such as hallucinations and delusions. Functional magnetic resonance imaging (fMRI) is the most popular technique to study the functional activation patterns of the brain. The fMRI data is four-dimensional, composed of 3D brain images over time. Each voxel of the 3D brain volume is associated with a time series of signal intensity values. This study aimed to identify the distinct voxels from time-series fMRI data that show high functional activation during a task. Methods: In this study, a novel mean-deviation based approach was applied to time-series fMRI data of 34 schizophrenia patients and 34 healthy subjects. The statistical measures such as mean and median were used to find the functional changes in each voxel over time. The voxels that show significant changes for each subject were selected and thus used as the feature set during the classification of schizophrenia patients and healthy controls. Results: The proposed approach identifies a set of relevant voxels that are used to distinguish between healthy and schizophrenia subjects with high classification accuracy. The study shows functional changes in brain regions such as superior frontal gyrus, cuneus, medial frontal gyrus, middle occipital gyrus, and superior temporal gyrus. Conclusions: This work describes a simple yet novel feature selection algorithm for time-series fMRI data to identify the activated brain voxels that are generally affected in schizophrenia. The brain regions identified in this study may further help clinicians to understand the illness for better medical intervention. It may be possible to explore the approach to fMRI data of other psychological disorders
Anomaly detection from time-series fMRI data of schizophrenia patients.
No full textThis is the abstract of the project was performed at the OHBM Brainhack 2018, National University of Singapore (NUS), Singapore during June 14-16, 2018
Rajalaksmi-adarsana
Get PDFBengali fiction. The author is unknown. This monograph is from the private collection of Indranath Majumder, Kolkata, W.B., India
Sunitira prema
Get PDFA Bengali novel. The author is unknown. This monograph is from the private collection of Indranath Majumder, Kolkata, W.B., India
Unveiling Functional Biomarkers in Schizophrenia: Insights from Region of Interest Analysis Using Machine Learning
Get PDFBackground: Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. There were many attempts to understand these basic mechanisms behind the disorder, yet we know very little. By employing machine learning techniques with age-matched samples from the auditory oddball task using multi-site functional magnetic resonance imaging (fMRI) data, this study aims to address these challenges. Methods: The study employed a three-stage model to gain a better understanding of the neurobiology underlying schizophrenia and techniques that could be applied for diagnosis. At first, we constructed four-level hierarchical sets from each fMRI volume of 34 schizophrenia patients (SZ) and healthy controls (HC) individually in terms of hemisphere, gyrus, lobes, and Brodmann areas. Second, we employed statistical methods, namely, t-tests and Pearson's correlation, to assess the group differences in cortical activation. Finally, we assessed the predictive power of the brain regions for machine learning algorithms using K-nearest Neighbor (KNN), Naive Bayes, Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), and Extreme Learning Machine (ELM). Results: Our investigation depicts promising results, obtaining an accuracy of up to 84% when applying Pearson's correlation-selected features at lobes and Brodmann region level (81% for Gyrus), as well as Hemispheres involving different stages. Thus, the results of our study were consistent with previous studies that have revealed some functional abnormalities in several brain regions. We also discovered the involvement of other brain regions which were never sufficiently studied in previous literature, such as the posterior lobe (posterior cerebellum), Pyramis, and Brodmann Area 34. Conclusions: We present a unique and comprehensive approach to investigating the neurological basis of schizophrenia in this study. By bridging the gap between neuroimaging and computable analysis, we aim to improve diagnostic accuracy in patients with schizophrenia and identify potential prognostic markers for disease progression
Tarabati
Get PDFA Bengali novel. According to the title page not for public circulation [prakasarthe mudrita nahe]. The author is unkown This monograph is from the private collection of Indranath Majumder, Kolkata, W.B., India
LRE-MMF: a novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population
Get PDFDevelopment of a Machine Learning-Based Framework for Predicting Vessel Size Based on Container Capacity
No full textPorts are important hubs in logistics and supply chain systems, where the majority of the available data is still not being fully exploited. Container throughput is the amount of work done by the TEU and the ability to handle containers at a minimal cost. This capacity of container throughput is the most important part of the scale of services, which is a crucial factor in selecting port terminals. At the port container terminal, it is necessary to allocate an appropriate number of available quay cranes to the berth before container ships arrive at the port container terminal. Predicting the size of a ship is especially important for calculating the number of quay cranes that should be allocated to ships that will eventually dock at the port terminal. Machine learning techniques are flexible tools for utilizing and unlocking the value of the data. In this paper, we used neighborhood component analysis as a tool for feature selection and state-of-the-art machine learning algorithms for multiclass classification. The paper proposes a novel two-stage approach for estimating and predicting vessel size based on container capacity. Our proposed approach revealed seven unique features of port data, which are the essential parameters for the identification of the vessel size. We obtained the highest average classification accuracy of 97.6% with the linear support vector machine classifier. This study paves a new direction for research in port logistics incorporating machine learning
