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About 3,439 papers for “fMRI classification”

Alzheimer progression classification using fMRI data

J. Noh, Hee-Deok Yang · Korean Institute of Smart Media · 2024

The development of functional magnetic resonance imaging (fMRI) has significantly contributed to mapping brain functions and understanding brain networks during rest. This paper proposes a CNN-LSTM-based classification model to classify the progression stages of Alzheimer's disease. Firstly, four preprocessing steps are performed to remove noise from the fMRI data before feature extraction. Secondly, the U-Net architecture is utilized to extract spatial features once preprocessing is completed. Thirdly, the extracted spatial features undergo LSTM processing to extract temporal features, ultimately leading to classification. Experiments were conducted by adjusting the temporal dimension of the data. Using 5-fold cross-validation, an average accuracy of 96.4% was achieved, indicating that the proposed method has high potential for identifying the progression of Alzheimer's disease by analyzing fMRI data.

Cited by 1

Improving fMRI Classification Through Network Deconvolution

Martinek, Jacob · Journal of International Crisis and Risk Communication Research · 2015

The structure of regional correlation graphs built from fMRI-derived data is frequently used in algorithms to automatically classify brain data. Transformation on the data is performed during pre-processing to remove irrelevant or inaccurate information to ensure that an accurate representation of the subject's resting-state connectivity is attained. Our research suggests and confirms that such pre-processed data still exhibits inherent transitivity, which is expected to obscure the true relationships between regions. This obfuscation prevents known solutions from developing an accurate understanding of a subject’s functional connectivity. By removing correlative transitivity, connectivity between regions is made more specific and automated classification is expected to improve. The task of utilizing fMRI to automatically diagnose Attention Deficit/Hyperactivity Disorder was posed by the ADHD-200 Consortium in a competition to draw in researchers and new ideas from outside of the neuroimaging discipline. Researchers have since worked with the competition dataset to produce ever-increasing detection rates. Our approach was empirically tested with a known solution to this problem to compare processing of treated and untreated data, and the detection rates were shown to improve in all cases with a weighted average increase of 5.88%.

Cited by 0Open access

fMRI Brain State Classification (Tier-0)

Research Jumpstart Community · Zenodo (CERN European Organization for Nuclear Research) · 2025

60-90 min tutorial on brain state classification from fMRI data

Cited by 0Open access

Classification of material properties in fMRI

Eric Baumgartner, Christiane B. Wiebel, Karl R. Gegenfurtner · Journal of Vision · 2013

The taxonomy of materials has been explored by means of machine classification (Liu et al., IEEE CVPR 2010) and also brain activation (Hiramatsu et al., NeuroImage 2011). Here we wanted to investigate whether information about material qualities like roughness or colorfulness can be found in the BOLD response to material images. We took photographs of 84 material samples and asked subjects to rate them with a seven-point Likert scale on different material qualities: colorfulness, roughness, texturedness, hardness, orderliness, and glossiness. We analyzed the material images according to the algorithm by Portilla and Simoncelli (IJCV 2000). In order to get parameters that grasped the color of our images, we also computed marginal statistics of their components in DKL space. A linear multivariate classifier was applied to the image statistics in order to discriminate between images with high and low ratings. This classification procedure achieved between 69% and 98% correct for the different properties. To see if the information contained in the image statistics would be reflected in brain activation patterns we scanned six subjects with fMRI while they were viewing the material images. A classifier was then applied to the voxel values of visually responsive voxels. We found classification accuracy to be significantly better than chance for three of six material qualities: colorfulness (63%, p<0.001), roughness (60%, p<0.05), and texturedness (64%, p<0.001). Our results demonstrate that information about the qualities of materials is present in the image statistics by Portilla and Simoncelli as well as fMRI activation patterns. In addition, we checked if the classification accuracy of the image statistics classifier would improve using the labels given by the fMRI classifier. At least for roughness, this was the case (69% vs 74%, p<0.05). This suggests that there might be a link between the observed brain activation and the image statistics. Meeting abstract presented at VSS 2013

Cited by 0Open access

fMRI Brain State Classification (Tier-0)

Research Jumpstart Community · Zenodo (CERN European Organization for Nuclear Research) · 2025

60-90 min tutorial on brain state classification from fMRI data

Cited by 0Open access

Random Subspace Ensembles for fMRI Classification

Ludmila I. Kuncheva, Juan J. Rodríguez, Catrin Plumpton et al. · IEEE Transactions on Medical Imaging · 2010

Classification of brain images obtained through functional magnetic resonance imaging (fMRI) poses a serious challenge to pattern recognition and machine learning due to the extremely large feature-to-instance ratio. This calls for revision and adaptation of the current state-of-the-art classification methods. We investigate the suitability of the random subspace (RS) ensemble method for fMRI classification. RS samples from the original feature set and builds one (base) classifier on each subset. The ensemble assigns a class label by either majority voting or averaging of output probabilities. Looking for guidelines for setting the two parameters of the method-ensemble size and feature sample size-we introduce three criteria calculated through these parameters: usability of the selected feature sets, coverage of the set of "important" features, and feature set diversity. Optimized together, these criteria work toward producing accurate and diverse individual classifiers. RS was tested on three fMRI datasets from single-subject experiments: the Haxby data (Haxby, 2001.) and two datasets collected in-house. We found that RS with support vector machines (SVM) as the base classifier outperformed single classifiers as well as some of the most widely used classifier ensembles such as bagging, AdaBoost, random forest, and rotation forest. The closest rivals were the single SVM and bagging of SVM classifiers. We use kappa-error diagrams to understand the success of RS.

Cited by 226

Visual object classification from fMRI data

Newaz, Syed Mishar, Taseeb, Taslim Ahmed, Haque, Abdullah Nurul · BRAC University Institutional Repository (BRAC University) · 2022

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Cited by 0

Classification of Alzheimer’s Progression Using fMRI Data

Ju-Hyeon Noh, Jun-Hyeok Kim, Hee-Deok Yang · Sensors · 2023

In the last three decades, the development of functional magnetic resonance imaging (fMRI) has significantly contributed to the understanding of the brain, functional brain mapping, and resting-state brain networks. Given the recent successes of deep learning in various fields, we propose a 3D-CNN-LSTM classification model to diagnose health conditions with the following classes: condition normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD). The proposed method employs spatial and temporal feature extractors, wherein the former utilizes a U-Net architecture to extract spatial features, and the latter utilizes long short-term memory (LSTM) to extract temporal features. Prior to feature extraction, we performed four-step pre-processing to remove noise from the fMRI data. In the comparative experiments, we trained each of the three models by adjusting the time dimension. The network exhibited an average accuracy of 96.4% when using five-fold cross-validation. These results show that the proposed method has high potential for identifying the progression of Alzheimer's by analyzing 4D fMRI data.

Cited by 27Open access

Cognitive state classification using transformed fMRI data

Hariharan Ramasangu, Neelam Sinha · 2014

One approach, for understanding human brain functioning, is to analyze the changes in the brain while performing cognitive tasks. Towards this, Functional Magnetic Resonance (fMR) images of subjects performing well-defined tasks are widely utilized for task-specific analyses. In this work, we propose a procedure to enable classification between two chosen cognitive tasks, using their respective fMR image sequences. The time series of expert-marked anatomically-mapped relevant voxels are processed and fed as input to the classical Naive Bayesian and SVM classifiers. The processing involves use of random sieve function, phase information in the data transformed using Fourier and Hilbert transformations. This processing results in improved classification, as against using the voxel intensities directly, as illustrated. The novelty of the proposed method lies in utilizing the phase information in the transformed domain, for classifying between the cognitive tasks along with random sieve function chosen with a particular probability distribution. The proposed classification procedure is applied on a publicly available dataset, StarPlus data, with 6 subjects performing the two distinct cognitive tasks of watching either a picture or a sentence. The classification accuracy stands at an average of 65.6%(using Naive Bayes classifier) and 76.4%(using SVM classifier) for raw data. The corresponding classification accuracy stands at 96.8% and 97.5% for Fourier transformed data. For Hilbert transformed data, it is 93.7% and 99%, for 6 subjects, on 2 cognitive tasks.

Cited by 7

ADHD fMRI classification through CBAM-Autoencoder framework

Shiwei Hong · Applied and Computational Engineering · 2024

This work presents a novel method for leveraging resting-state functional magnetic resonance imaging (fMRI) data to accurately detect Attention Deficit Hyperactivity Disorder (ADHD). The proposed method integrates the Convolutional Block Attention Module (CBAM) with a lightweight Autoencoder network to effectively extract and highlight salient features within fMRI scans. By leveraging attention mechanisms, the model focuses on important local details while filtering out irrelevant information, thereby enhancing diagnostic precision. Extensive experimentation on the ADHD-200 dataset showcases the efficacy of the proposed approach, demonstrating its ability to improve classification performance significantly. Specifically, the method achieved an average accuracy of 91.7% across the NYU, 93.8% across the KKI, 86.4% across the NI, 89.1% across the PU, and 83.5% across the PU_1 datasets. This research underscores the potential of attention-based deep learning techniques in advancing ADHD diagnosis using neuroimaging data.

Cited by 0Open access

Hand classification of fMRI ICA noise components

Ludovica Griffanti, Gwenaëlle Douaud, Janine Bijsterbosch et al. · NeuroImage · 2016

We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.

Cited by 606Open access

Cognitive state classification using transformed fMRI data

Hariharan Ramasangu, Neelam Sinha · arXiv (Cornell University) · 2016

One approach, for understanding human brain functioning, is to analyze the changes in the brain while performing cognitive tasks. Towards this, Functional Magnetic Resonance (fMR) images of subjects performing well-defined tasks are widely utilized for task-specific analyses. In this work, we propose a procedure to enable classification between two chosen cognitive tasks, using their respective fMR image sequences. The time series of expert-marked anatomically-mapped relevant voxels are processed and fed as input to the classical Naive Bayesian and SVM classifiers. The processing involves use of random sieve function, phase information in the data transformed using Fourier and Hilbert transformations. This processing results in improved classification, as against using the voxel intensities directly, as illustrated. The novelty of the proposed method lies in utilizing the phase information in the transformed domain, for classifying between the cognitive tasks along with random sieve function chosen with a particular probability distribution. The proposed classification procedure is applied on a publicly available dataset, StarPlus data, with 6 subjects performing the two distinct cognitive tasks of watching either a picture or a sentence. The classification accuracy stands at an average of 65.6%(using Naive Bayes classifier) and 76.4%(using SVM classifier) for raw data. The corresponding classification accuracy stands at 96.8% and 97.5% for Fourier transformed data. For Hilbert transformed data, it is 93.7% and 99%, for 6 subjects, on 2 cognitive tasks.

Cited by 0Open access