nils-herrmann/clean_scirepeval huggingface dataset:
Clustering-based framework for comparing fMRI analysis methods

In this paper, a cluster-based framework is introduced for comparing analysis methods of functional magnetic resonance images (fMRI). In the proposed framework, fMRI data is replaced with a feature space and each method considered as a clustering method in the new space. As a result, different methods can be compared by means of a cluster validity measure. The feature space is computed using a non-parametric method (principal component analysis-PCA). Four subjects have been analyzed with three methods and the proposed cluster-based framework has evaluated performance of the methods. The results are identical to those of the modified receiver operating characteristics (ROC). This validates the proposed approach.
Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms

A new rotation-invariant texture-analysis technique using Radon and wavelet transforms is proposed. This technique utilizes the Radon transform to convert the rotation to translation and then applies a translation-invariant wavelet transform to the result to extract texture features. A k-nearest neighbors classifier is employed to classify texture patterns. A method to find the optimal number of projections for the Radon transform is proposed. It is shown that the extracted features generate an efficient orthogonal feature space. It is also shown that the proposed features extract both of the local and directional information of the texture patterns. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. To test and evaluate the method, we employed several sets of textures along with different wavelet bases. Experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture-analysis methods.
Optimized Deep Learning for EEG Big Data and Seizure Prediction BCI via Internet of Things

A brain-computer interface (BCI) for seizure prediction provides a means of controlling epilepsy in medically refractory patients whose site of epileptogenicity cannot be resected but yet can be defined sufficiently to be selectively influenced by strategically implanted electrodes. Challenges remain in offering real-time solutions with such technology because of the immediacy of electrographic ictal behavior. The nonstationary nature of electroencephalographic (EEG) and electrocorticographic (ECoG) signals results in wide variation of both normal and ictal patterns among patients. The use of manually extracted features in a prediction task is impractical and the large amount of data generated even among a limited set of electrode contacts will create significant processing delays. Big data in such circumstances not only must allow for safe storage but provide high computational resources for recognition, capture and real-time processing of the preictal period in order to execute the timely abrogation of the ictal event. By leveraging the potential of cloud computing and deep learning, we develop and deploy BCI seizure prediction and localization from scalp EEG and ECoG big data. First, a new method for epileptic seizure prediction and localization of the seizure focus is presented. Second, an extended optimization approach on existing deep-learning structures, Stacked Auto-encoder and Convolutional Neural Network (CNN), is proposed based on principle component analysis (PCA), independent component analysis (ICA), and Differential Search Algorithm (DSA). Third, a cloud-computing solution (i.e., Internet of Things (IoT)), is developed to define the proposed structures for real-time processing, automatic computing and storage of big data. The ECoG clinical datasets on 11 patients illustrate the superiority of the proposed patient-specific BCI as an alternative to current methodology to offer support for patients with intractable focal epilepsy.
Connectivity abnormalities in autism spectrum disorder patients: A resting state fMRI study

Autism spectrum disorders (ASDs) are presented as social deficits that cause different behavior abnormalities. One of the most important characteristic of ASDs is repetitive behavior. Recent studies of functional connectivity show that these deficits may alter connections in the brain. Most of them show alternations in default mode network (DMN) only using a model based or model free method. In this study, we investigate roles of three resting state networks (default mode network (DMN), visual, and insular-temporal/ACC) in functional connectivity alternations of 24 ASDs. The study is done using independent component analysis (ICA) which is a data-driven method and can identify temporally relevant components. By obtaining correlations among time courses of these networks, functional connectivity among them is investigated as well. Therefore, this paper benefits from both methods of analysing fMRI data. Our experimental results show that in ASDs, within-network connections in DMN and visual network is decreased compared to the healthy controls. Also, functional connectivity between ACC and visual network, DMN and ACC is decreased, however, functional connectivity between DMN and visual network is increased.
Multi-modal data fusion using group-structured sparse canonical correlation analysis: A simulation study

Recent studies in the field of multi-modal data fusion demonstrate that considering the prior spatial structure and groupness information available from many years of intensive neuroimaging research can increase interpretability and detection accuracy of the hidden phenomena within multi-modal datasets. Although recent functional neuroimaging data analyses indicate that certain brain regions may participate in multiple groups (networks), the extent of group overlap is never considered in data fusion frameworks. To address this issue, here we propose a group-structured sparse canonical correlation analysis (gssCCA) method by employing groupness and sparsity constraints in a unified fusion framework. To investigate the performance of the proposed algorithm, we compare the effect of overlapping and disjoint gssCCA in a simulation study, to evaluate their ability in detecting multi-modal data associations. The results demonstrate that considering an overlapping group-structure constraint can increase the sensitivity and specificity of the true associations between multi-modal datasets.
Evaluation of functional connectivity in control and ADHD subjects by generalized Canonical Correlation and Probabilistic Independent Component Analysis

Functional connectivity can be evaluated by temporal correlation between spatial neurophysiologic events or correlation between neural activities of brain regions. Unlike anatomical connectivity which represents physical connections between two brain regions, functional connectivity investigates interactions between brain regions. In this study, we implemented and evaluated Probabilistic Independent Component Analysis (PICA) and generalized Canonical Correlation Analysis (gCCA) on resting state data of healthy subjects and subjects with ADHD disorders from NITRC database. This was done to detect brain functional connectivity and compare the results in normal and diseased subjects. In addition, speed of the methods was compared. Our study shows that relative to PICA, gCCA has higher precision and accuracy and lower computational complexity and run time in addition to capability of extracting more reproducible statistical maps in both control and ADHD subjects.
Fixed and Random Effect Analysis of Multi-subject Spatial Activation Maps in Wavelet Domain

A method is proposed for modeling random and fixed effects in statistical maps of multisubject fMRI data. Using this model, we separate a fixed common activation map and a random effect for each subject. The proposed model shows a good multisubject activation detection for group analysis and also illustrate that the between subject variation lies mostly in activated areas. The method was implemented and tested on both simulated and experimental functional MRI data of 9 subjects.
Structured and Sparse Canonical Correlation Analysis as a Brain-Wide Multi-Modal Data Fusion Approach

Multi-modal data fusion has recently emerged as a comprehensive neuroimaging analysis approach, which usually uses canonical correlation analysis (CCA). However, the current CCA-based fusion approaches face problems like high-dimensionality, multi-collinearity, unimodal feature selection, asymmetry, and loss of spatial information in reshaping the imaging data into vectors. This paper proposes a structured and sparse CCA (ssCCA) technique as a novel CCA method to overcome the above problems. To investigate the performance of the proposed algorithm, we have compared three data fusion techniques: standard CCA, regularized CCA, and ssCCA, and evaluated their ability to detect multi-modal data associations. We have used simulations to compare the performance of these approaches and probe the effects of non-negativity constraint, the dimensionality of features, sample size, and noise power. The results demonstrate that ssCCA outperforms the existing standard and regularized CCA-based fusion approaches. We have also applied the methods to real functional magnetic resonance imaging (fMRI) and structural MRI data of Alzheimer’s disease (AD) patients (n = 34) and healthy control (HC) subjects (n = 42) from the ADNI database. The results illustrate that the proposed unsupervised technique differentiates the transition pattern between the subject-course of AD patients and HC subjects with a p-value of less than  $1\times 10^{\mathrm {\mathbf {-6}}}$ . Furthermore, we have depicted the brain mapping of functional areas that are most correlated with the anatomical changes in AD patients relative to HC subjects.
Pigment Melanin: Pattern for Iris Recognition

Recognition of iris based on visible light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, which is unavailable in near-infrared (NIR) imaging. This is due to the biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive the feature code for each subject. An important question is how the melanin patterns, which are extracted from VL, are independent of the iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost recognition performance. We have collected our own database (UTIRIS), consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of chromophores and improves the iris recognition rate.
Linear filter design for CNR enhancement of MR images with multiple interfering features

Maximizing the minimum absolute contrast-to-noise ratios (CNRs) between a desired feature and multiple interfering features, by linear combination of images in an MRI scene sequence, is attractive for medical image analysis and interpretation. Previously, we published a general formulation of the problem and a novel solution utilizing the simple and numerically stable method of Gram-Schmidt orthogonalization. Our approach derives explicit solutions for the general case of multiple interfering features in the scene. Here, we review the theoretical basis for the filter and present its applications to MRI scene sequences of the human brain with cerebral infarction, tumor, or multiple sclerosis. For these applications, a 20% to 100% improvement for the smallest absolute CNR is obtained.
A wavenumber-domain Kalman filtering approach to one-dimensional phase retrieval

The phase retrieval problem, i.e. computing a function known to have finite spatial support from knowledge of its Fourier magnitude, is considered. By writing the support constraint as a state equation in the Fourier domain (dual to the system used in state-space harmonic retrieval), an extended Kalman filter is used to compute the Fourier magnitudes as observations. The filter is initialized by solving a linear system of equations, followed by a nonlinear set of trigonometric equations.<<ETX>>
Neural network scoring of spots in X-Gal and -leu plates

We have developed an image analysis system for scoring yeast growth and color development in images of 96-well plates, a common format for high throughput assays. We use a segmentation method to locate the plates and spots. Color histogram and wavelet features are extracted respectively from spots of X-Gal and -leu plates. Two artificial neural networks are separately employed to score spots on each plate. The performance of the system is evaluated using a data set of 50 images. The data set was divided into 25 training and 25 testing images. Accuracies of 99.7% and 95.2% have been achieved for scoring the X-Gal and -leu plates respectively.
Optimization of MRI protocols and pulse sequence parameters for eigenimage filtering

The eigenimage filter generates a composite image in which a desired feature is segmented. The signal-to-noise ratio (SNR) of the eigenimage is directly proportional to the dissimilarity between the desired and interfering features. Since image gray levels are analytical functions of magnetic resonance imaging (MRI) parameters, it is possible to maximize this dissimilarity by optimizing these parameters. For optimization, the authors consider four MRI pulse sequences: multiple spin-echo, spin-echo, inversion recovery, and gradient-echo. They use the mathematical expressions for MRI signals along with intrinsic tissue parameters to express the objective function in terms of MRI parameters. The objective function along with a set of diagnostic or instrumental constraints defines a multidimensional nonlinear constrained optimization problem, which is solved by the fixed point approach. The optimization technique is demonstrated through its application to phantom and brain images.<<ETX>>
A comparative analysis of several transformations for enhancement and segmentation of magnetic resonance images

The authors compare performance of several transformations, applied to magnetic resonance image (MRI) scene sequences for image enhancement and segmentation. Comparisons are made for principal component analysis, matched, modified-matched, maximum contrast, eigenimage, target point, ratio, log-ratio, and angle-image transformations. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), segmentation of a desired feature (SDF), and correction for partial volume averaging effects (CPV) are used as performance measures. For comparison, simplified analytical expressions for SNRs and CNRs of transformed images are derived, and CPV by a transformation is studied. Properties of transformations are investigated through their applications to simulation, phantom, and brain studies; advantages and weaknesses are discussed. The authors' conclusions are: (i) principal component analysis, matched, and angle-image are best for SNR. Improvement; (ii) maximum contrast is best for CNR improvement; (iii) eigenimage and target point are best for SDF; and (iv) eigenimage is best for simultaneous SNR and CNR improvements, SDF, and CPV.<<ETX>>
Extended Kalman filtering of point process observation

A temporal point process is a stochastic time series of binary events that occurs in continuous time. In computational neuroscience, the point process is used to model neuronal spiking activity; however, estimating the model parameters from spike train is a challenging problem. The state space point process filtering theory is a new technique for the estimation of the states and parameters. In order to use the stochastic filtering theory for the states of neuronal system with the Gaussian assumption, we apply the extended Kalman filter. In this regard, the extended Kalman filtering equations are derived for the point process observation. We illustrate the new filtering algorithm by estimating the effect of visual stimulus on the spiking activity of object selective neurons from the inferior temporal cortex of macaque monkey. Based on the goodness-offit assessment, the extended Kalman filter provides more accurate state estimate than the conventional methods.
Towards an inclusive computational model of visual cortex

Understanding the primatespsila visual system has been one of the challenging problems of different groups of scientists for years. Though many studies, from physiology and neuroscience to computer vision, are done on different aspects of visual processing in the cortex, a comprehensive computational model of visual cortex is still missing. We have implemented a computational model of object recognition in ventral visual pathway in our previous work. This hierarchical model covers visual areas V1/V2, V4/PIT, and AIT sending inputs to the Prefrontal Cortex (PFC) for categorization. To extend our model, in this work, we have added a simple model of motion detection in neurons of areas V1 and MT of the dorsal stream to our previous model. This has enabled the model to perform another principal function of the visual cortex, i.e., motion perception.
Sparse logistic regression for estimating time-varying functional connectivity networks: A simulation study

Understanding dynamics of the brain networks using functional Magnetic Resonance Imaging (fMRI) is an interesting neuroimaging topic, and sliding window based methods are widely employed to explore the time-varying functional connectivity networks. This paper presents a novel method for estimating dynamic functional connectivity networks using a Sparse Logistic Regression (SLR) model. This method uses a new protocol to optimize the problem of estimating time-varying networks based on a neighborhood selection procedure. To investigate the performance of the proposed approach, we have compared three time varying functional connectivity estimation techniques: combination of Sliding Window and Graphical Lasso (SWGL); Smooth Incremental Graphical Lasso Estimation (SINGLE), and SLR, in terms of their ability to recover true dynamics of functional connectivity networks. Simulation results demonstrate superiority of the SLR approach relative to the other two methods.
Deep Learning with Edge Computing for Localization of Epileptogenicity Using Multimodal rs-fMRI and EEG Big Data

Epilepsy is a chronic brain disorder characterized by the occurrence of spontaneous seizures of which about 30 percent of patients remain medically intractable and may undergo surgical intervention; despite the latter, some may still fail to attain a seizure-free outcome. Functional changes may precede structural ones in the epileptic brain and may be detectable using existing noninvasive modalities. Functional connectivity analysis through electroencephalography (EEG) and resting state-functional magnetic resonance imaging (rs-fMRI), complemented by diffusion tensor imaging (DTI), has provided such meaningful input in cases of temporal lobe epilepsy (TLE). Recently, the emergence of edge computing has provided competent solutions enabling context-aware and real-time response services for users. By leveraging the potential of autonomic edge computing in epilepsy, we develop and deploy both noninvasive and invasive methods for the monitoring, evaluation and regulation of the epileptic brain, with responsive neurostimulation (RNS; Neuropace). First, an autonomic edge computing framework is proposed for processing of big data as part of a decision support system for surgical candidacy. Second, an optimized model for estimation of the epileptogenic network using independently acquired EEG and rs-fMRI is presented. Third, an unsupervised feature extraction model is developed based on a convolutional deep learning structure for distinguishing interictal epileptic discharge (IED) periods from nonIED periods using electrographic signals from electrocorticography (ECoG). Experimental and simulation results from actual patient data validate the effectiveness of the proposed methods.
A new non-segmentation shape-based image indexing method

This paper proposes a new feature vector for non-segmentation shape-based image indexing and retrieval. This feature considers correlation between neighboring edges. Hence it includes information of continuous edges and lines of images and describes major shape properties of images. This scheme is effective and robustly tolerates translation, scaling, color, illumination, and small viewing position variations. Experimental results show superiority of proposed scheme over several other indexing methods. Averages of precision and recall rates of this new indexing scheme for retrieval as compared with edge direction histogram are 1.26 and 1.04 times, respectively.
Model-independent method for fMRI analysis

This paper presents a fast method for delineation of activated areas of the brain from functional magnetic resonance imaging (fMRI) time series data. The steps of the work accomplished are as follows. 1) It is shown that the detection performance evaluated by the area under the receiver operating characteristic curve is directly related to the signal-to-noise ratio (SNR) of the composite image generated in the detection process. 2) Detection and segmentation of activated areas are formulated in a vector space framework. In this formulation, a linear transformation (image combination method) is shown to be desirable to maximize the SNR of the activated areas subject to the constraint of removing inactive areas. 3) An analytical solution for the problem is found. 4) Image pixel vectors and expected time series pattern (signature) for inactive pixels are used to calculate weighting vector and identify activated regions. 5) Signatures of the activated regions are used to segment different activities. 6) Segmented images by the proposed method are compared with those generated by the conventional methods (correlation, t-statistic, and z statistic). Detection performance and SNRs of the images are compared. The proposed approach outperforms the conventional methods of fMRI analysis. In addition, it is model-independent and does not require a priori knowledge of the fMRI response to the paradigm. Since the method is linear and most of the work is done analytically, numerical implementation and execution of the method are much faster than the conventional methods.
Rule-based decision-making framework for knowledge-based anatomical landmark localization (K-BALL)

K-BALL is a general method for localization of anatomical phenomena of the same origin with natural discrepancies distributed over a reference space, e.g., human brain anatomical structures. In this paper, we focus on information analysis step (2nd step) of K-BALL during which landmarks extracted in its first step are evaluated. We provide a framework in which rules are automatically generated based on estimated and derived models. We show that the rules based on the derived models can improve the overall success rate of K-BALL. Each rule evaluates the extracted points by producing an intermediate confidence factor (ICNF). A total confidence factor is calculated using ICNF's to facilitate the acceptance or rejection of a set of points as landmarks of interest. Using the rules merely based on the estimated models, simulation study produced an overall success rate of 91.8%. Using the rules based on both of the estimated and derived models, this rate increased to 92.5%