Estimation of static and dynamic functional connectivity in resting-state fMRI using zero-frequency resonator.

Resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level-dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs-fMRI is spontaneous event-related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low-valued inflection points. Here, we extract the event-related temporal onsets from the rs-fMRI time courses using a zero-frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal-to-noise ratio (SNR) segments are also obtained in the rs-fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR-based CR distinguishes the coactivation and non-coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR-driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points.

Network-wise analysis of movie-specific information in dynamic functional connectivity using COBE.

Movie watching during functional magnetic resonance imaging has emerged as a promising tool to measure the complex behavior of the brain in response to a stimulus similar to real-life situations. It has been observed that presenting a movie (sequence of events) as a stimulus will lead to a unique time course of dynamic functional connectivity related to movie stimuli that can be compared across the participants. We assume that the observed dynamic functional connectivity across subjects can be divided into following 2 components: (i) specific to a movie stimulus (depicting group-level behavior in functional connectivity) and (ii) individual-specific behavior (not necessarily common across the subjects). In this work, using the dynamic time warping distance measure, we have shown the extent of similarity between the temporal sequences of functional connectivity while the underlying movie stimuli were same and different. Further, the temporal sequence of functional connectivity patterns related to a movie is enhanced by suppressing the subject-specific components of dynamic functional connectivity using common and orthogonal basis extraction. Quantitative analysis using the F-ratio measure reveals significant differences in dynamic functional connectivity within the somatomotor network and default mode network, as well as between the occipital network and somatomotor networks.

Impact of Amplitude and Phase of fMRI time series for Functional Connectivity Analysis.

PURPOSE: Several studies in the field of fMRI have reported the synchrony between the brain regions using instantaneous phase (IP) representation (derived from analytic representation of BOLD time series). We hypothesized that instantaneous amplitude (IA) representation from different brain regions might give additional information to the functional brain networks. To validate this, we explored this representation of resting state BOLD fMRI signal for deriving resting state networks (RSNs) and compared it with the IP representation based RSNs. METHOD: Resting state fMRI data of 100 healthy adults (age=20-35 years, 54 females) from the population of 500 Subjects of HCP dataset were studied. Data was acquired using a 3T scanner in four runs (15-min each) with the phase encoding directions: Left to Right (LR), Right to Left (RL). These four runs were acquired in two sessions, and subjects were asked to keep their eyes open with a fixation on a white cross. The IA and IP representations were derived from a narrow-band filtered BOLD time series using Hilbert transforms and a seed-based approach is used to compute the RSNs in the brain. RESULTS: The experimental results demonstrate that within the frequency range 0.01-0.1 Hz, IA representation based RSNs have the highest similarity score between the two sessions for the motor network. Whereas for fronto-parietal network, IP based activation maps have the highest similarity score for all the frequency bands. For higher frequency band (0.198-0.25 Hz) consistency of the obtained RSNs across two sessions reduced for both IA and IP representations. Fusion of IA and IP representations based RSNs in comparison to those of IP based representation, leads to 3-10% improvement in the similarity scores between the default mode network obtained for the two sessions. In addition, the same comparison demonstrates 15-20% improvement for the motor network in the frequency bands: 0.01-0.04 Hz, 0.04-0.07 Hz, slow5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz). It is also observed that the similarity score between two sessions using instantaneous frequency (IF) (derivative of unwrapped IP) representation in exploring functional connectivity (FC) networks is comparable with those obtained using IP representation. CONCLUSION: Our findings suggest that IA-representation based measures can estimate RSNs with the reproducibility between the sessions comparable to that of the IP representation-based methods. This study demonstrates that IA and IP representations contain the complementary information of BOLD signal, and their fusion improves the results of FC.

Enhancing the network specific individual characteristics in rs-fMRI functional connectivity by dictionary learning.

Most fMRI inferences are based on analyzing the scans of a cohort. Thus, the individual variability of a subject is often overlooked in these studies. Recently, there has been a growing interest in individual differences in brain connectivity also known as individual connectome. Various studies have demonstrated the individual specific component of functional connectivity (FC), which has enormous potential to identify participants across consecutive testing sessions. Many machine learning and dictionary learning-based approaches have been used to extract these subject-specific components either from the blood oxygen level dependent (BOLD) signal or from the FC. In addition, several studies have reported that some resting-state networks have more individual-specific information than others. This study compares four different dictionary-learning algorithms that compute the individual variability from the network-specific FC computed from resting-state functional Magnetic Resonance Imaging (rs-fMRI) data having 10 scans per subject. The study also compares the effect of two FC normalization techniques, namely, Fisher Z normalization and degree normalization on the extracted subject-specific components. To quantitatively evaluate the extracted subject-specific component, a metric named Overlap is proposed, and it is used in combination with the existing differential identifiability I diff metric. It is based on the hypothesis that the subject-specific FC vectors should be similar within the same subject and different across different subjects. Results indicate that Fisher Z transformed subject-specific fronto-parietal and default mode network extracted using Common Orthogonal Basis Extraction (COBE) dictionary learning have the best features to identify a participant.

Estimation of neuronal task information in fMRI using zero frequency resonator.

In functional magnetic resonance imaging (fMRI), temporal onsets of BOLD events contain crucial information on activity-inducing signals and make a significant impact in the analysis of functional connectivity (FC). In literature, the estimation of the onsets of the BOLD events from the acquired blood oxygen level-dependent (BOLD) signal using fMRI is mostly performed by choosing locations with a high value of the BOLD signal. This approach may give false onset points because it can incorporate redundant onsets which could be due to non-neuronal activity or can exclude true low-valued BOLD signals. In this study, we present a novel approach to estimating the temporal onsets of the BOLD events using a zero frequency resonator (ZFR) without necessitating information regarding the experimental paradigm (EP). The proposed approach exploits the impulse-like characteristic of activity-inducing signal to estimate the temporal onset points of BOLD events using ZFR which has been widely studied in the area of speech signal processing to estimate the glottal closure instances. The idea behind the approach is that an ideal neuronal impulse has, in principle, equal energy at all frequencies, including around the zero frequency, and will preserve the information of the temporal onsets of the BOLD events at its output. The ZFR-based approach estimates two important features, namely: 1) task-induced temporal onsets of the BOLD events in the fMRI time course and 2) high SNR (HSNR) regions around the estimated BOLD events. Both the estimated features are used to obtain the FC. Results are demonstrated using both the synthetic and experimental (event-related finger tapping and block design working memory) data. We show that a small number of plausible time points, estimated by ZFR, can convey sufficient information indicating the associated activation pattern. The method also illustrates its significance over the conventional correlation and threshold-based conditional rate analysis to estimate FC. The study demonstrates that ZFR-estimated BOLD events and HSNR regions can produce sufficient functionality of the brain in the task paradigm.

Detection of mitotic HEp-2 cell images: role of feature representation and classification framework under class skew.

We propose and analyze a framework to detect and identify the mitotic type staining patterns among different non-mitotic (interphase) patterns on HEp-2 cell substrate specimen images. This is considered as a principal task in computer-aided diagnosis (CAD) of the autoimmune disorders. Due to the rare appearance of mitotic patterns in whole slide/specimen images, the sample skew between mitotic and non-mitotic patterns is an important consideration.We suggest to apply some effective samples skew balancing strategies for the task of classification between mitotic v/s interphase patterns. Another aspect of this study is to consider the morphology and texture-based differences between both the classes that can be incorporated through effective morphology and texture-based descriptors, including the Gabor and LM (Leung-Malik) filter banks and also through some contemporary filter banks derived from convolutional neural networks (CNN).The proposed framework is evaluated on a public dataset and we demonstrate good performance (0.99 or 1 Matthews correlation coefficient (MCC) in many cases), across various experiments. The study also presents a comparison between hand-engineered and CNN-based feature representation, along with the comparisons with state-of-the-art approaches. Hence, the framework proves to be a good solution for the mentioned skewed classification problem.

Analyzing the Effect of Resolution of Network Nodes on the Resting State Functional Connectivity Maps of Schizophrenic Human Brains.

Functional connectivity (FC) mapping from resting-state functional magnetic resonance imaging (rsfMRI) data is a widely used technique to characterize the brain abnormalities in mental health disorders. Using atlases for brain parcellation is an important intermediate step in calculation of FC maps. Atlases with varying resolution (number of nodes in an atlas) have been deployed by researchers to study the abnormal brain functions in Schizophrenia. In this work, we compared the variations in FC maps of Schizophrenic brains obtained from three different atlases: AAL atlas (2002), Dosenbach atlas (2010), and the Brainnetome atlas (2016). To evaluate the atlas-dependent variations in FC maps, we relied on the capability of the features of FC maps in accurately classifying a given data into healthy or Schizophrenia group. Our results indicate that the high-resolution Dosenbach and Brainnetome atlases perform better than AAL atlas in terms of the accuracy, sensitivity and specificity of the SVM classifier.

Effectiveness of GAN-based Synthetic Samples Generation of Minority Patterns in HEp-2 Cell Images.

In this paper, we present a framework to address the augmentation of images for the rare and minor appearance of mitotic type staining patterns, for Human Epithelium Type2 (HEp-2) cell images. The identification of mitotic patterns among non-mitotic/interphase patterns is important in the process of diagnosis of various autoimmune disorders. This task leads to a pattern classification problem between mitotic v/s interphase. However, among the two classes, typically, the number of mitotic cells are relatively very less. Thus, in this work, we propose to generate synthetic mitotic samples, which can be used to augment the number of mitotic samples and balance the samples of mitotic and interphase patterns in classification paradigm. An effective feature representation is used, to validate the usefulness of the synthetic samples in classification task, along with a subjective validation done by a medical expert. The results demonstrate that the approach of generating and mingling synthetic samples with existing training data works well and yields good performance, with 0.98 balanced class accuracy (BcA) in one case, over a public dataset, i.e., UQ-SNP I3A Task-3 mitotic cell identification dataset.

Detecting mitotic cells in HEp-2 images as anomalies via one class classifier.

We propose a novel framework for classification of mitotic v/s non-mitotic cells in a Computer Aided Diagnosis (CAD) system for Anti-Nuclear Antibodies (ANA) detection. In the proposed work, due to unique characteristics (the rare occurrence) of the mitotic cells, their identification is posed as an anomaly detection approach. This will resolve the issue of data imbalance, which can arise in the traditional binary classification paradigm for mitotic v/s non-mitotic cell image classification. Here, the characteristics of only non-mitotic/interphase cells are captured using a well-defined feature representation to characterize the non-mitotic class distribution well, and the mitotic class is posed as an anomalous class. This framework requires training data only for the majority (non-mitotic) class, to build the classification model. The feature representation of the non-mitotic class includes morphology, texture, and Convolutional Neural Network (CNN) based feature representations, coupled with Bag-of-Words (BoW) and Spatial Pyramid Pooling (SPP) based summarization techniques. For classification, in this work, we employ the One-Class Support Vector Machines (OC-SVM). The proposed classification framework is validated on a publicly available dataset, and across various experiments, we demonstrate comparable or better performance over binary classification, attaining 0.99 (max.) F-Score in one case. The proposed framework proves to be an effective way to solve the mentioned problem statement, where there are less number of samples in one of the classes.