Magnetic Resonance Imaging Time Series Research Articles

Clustering Functional Magnetic Resonance Imaging Time Series in Glioblastoma Characterization: A Review of the Evolution, Applications, and Potentials.

In this paper, we discuss how the clustering analysis technique can be applied to analyze functional magnetic resonance imaging (fMRI) time-series data in the context of glioblastoma (GBM), a highly heterogeneous brain tumor. The precise characterization of GBM is challenging and requires advanced analytical approaches. We have synthesized the existing literature to provide an overview of how clustering algorithms can help identify unique patterns within the dynamics of GBM. Our review shows that the clustering of fMRI time series has great potential for improving the differentiation between various subtypes of GBM, which is pivotal for developing personalized therapeutic strategies. Moreover, this method proves to be effective in capturing temporal changes occurring in GBM, enhancing the monitoring of disease progression and response to treatment. By thoroughly examining and consolidating the current research, this paper contributes to the understanding of how clustering techniques applied to fMRI data can refine the characterization of GBM. This article emphasizes the importance of incorporating cutting-edge data analysis techniques into neuroimaging and neuro-oncology research. By providing a detailed perspective, this approach may guide future investigations and boost the development of tailored therapeutic strategies for GBM.

Volumetric MRI with sparse sampling for MR-guided 3D motion tracking via sparse prior-augmented implicit neural representation learning.

Volumetric reconstruction of magnetic resonance imaging (MRI) from sparse samples is desirable for 3D motion tracking and promises to improve magnetic resonance (MR)-guided radiation treatment precision. Data-driven sparse MRI reconstruction, however, requires large-scale training datasets for prior learning, which is time-consuming and challenging to acquire in clinical settings. To investigate volumetric reconstruction of MRI from sparse samples of two orthogonal slices aided by sparse priors of two static 3D MRI through implicit neural representation (NeRP) learning, in support of 3D motion tracking during MR-guided radiotherapy. A multi-layer perceptron network was trained to parameterize the NeRP model of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first training the network to learn the NeRP of two static 3D MRI with different breathing motion states, prior information of patient breathing motion was embedded into network weights through optimization. The prior information was then augmented from two motion states to 31 motion states by querying the optimized network at interpolated and extrapolated motion state coordinates. Starting from the prior-augmented NeRP model as an initialization point, we further trained the network to fit sparse samples of two orthogonal MRI slices and the final volumetric reconstruction was obtained by querying the trained network at 3D spatial locations. We evaluated the proposed method using 5-min volumetric MRI time series with 340ms temporal resolution for seven abdominal patients with hepatocellular carcinoma, acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two volumetric MRI with inhale and exhale states respectively were selected from the first 30s of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to two orthogonal slices and compared model-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking. Across the seven patients evaluated, the peak signal-to-noise-ratio between model-reconstructed and ground truth MR images was 38.02±2.60dB and the structure similarity index measure was 0.98±0.01. Throughout the 4.5-min time period, gross tumor volume (GTV) motion estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 2.41±0.77mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1mm in all three orthogonal directions. A prior-augmented NeRP model has been developed to reconstruct volumetric MRI from sparse samples of orthogonal cine slices. Only one exhale and one inhale 3D MRI were needed to train the model to learn prior information of patient breathing motion for sparse image reconstruction. The proposed model has the potential of supporting 3D motion tracking during MR-guided radiotherapy for improved treatment precision and promises a major simplification of the workflow by eliminating the need for large-scale training datasets.

The effect of baffles on the hydrodynamics of a gas-solid fluidized bed studied using real-time magnetic resonance imaging

Understanding and predicting the hydrodynamics of gas bubbles and particle-laden phase in fluidized beds is essential for the successful design and efficient operation of this type of reactor. In this work, we used real-time magnetic resonance imaging (MRI) to investigate the effect of three commonly-used baffle geometries on gas bubble behavior and particle motion in a fluidized bed model with an inner diameter of 190 mm. MRI time series of the local particle density and velocity were acquired and used to study the size, number, and shape of gas bubbles as well as the motion of the particle phase. The superficial gas velocity was varied between 1 and 2 Umf. We found that baffles decreased the average equivalent bubble diameter with a simultaneous increase in the total number of bubbles. Moreover, baffles promoted bubble splitting and the formation of air cushions below the baffles and decreased the average particle velocity and acceleration in the bed. For two of the three baffle types investigated, the spatial variation of the particle velocity became larger compared to the bed without internal.

Locally Low-Rank Denoising of Multi-Echo Functional MRI Data With Application in Resting-State Analysis.

Locally low-rank (LLR) denoising of functional magnetic resonance imaging (fMRI) time series image data is extended to multi-echo (ME) data. The proposed method extends the capabilities of non-physiologic noise suppression beyond single-echo applications with a dedicated ME algorithm. Following an institutional review board (IRB) approved protocol, resting-state fMRI data were acquired in 7 healthy subjects. A compact 3T scanner enabled whole-brain acquisition of multiband ME fMRI data at high spatial resolution (1.4 × 1.4 × 2.8 mm 3 ) with a 1810 ms repetition time (TR). Image data were denoised with ME-LLR preceding functional processing. The results of connectivity maps generated from denoised data were compared with maps generated with equivalent processing of non-denoised images. To assess ME-LLR as a method to reduce scan time, comparisons were made between maps computed from image data with full and retrospectively truncated durations. Assessments were completed with seed-based connectivity analyses using echo-combined image data. In a feasibility assessment, nondenoised and denoised full-duration echo-combined data were equivalently processed with independent component analysis (ICA) and compared. ME-LLR denoising yielded strengthened resting-state network connectivity maps after nuisance regression and seed-based connectivity analysis. In assessing ME-LLR as a scan reduction mechanism, maps generated from denoised data at half scan time showed comparable quality with maps generated from full-duration, non-denoised data, at both single subject and group levels. ME-LLR substantially increased temporal signal-to-noise ratio (tSNR) for image data respective to each individual echo and for image data after nuisance regression. Among echo-specific image volumes, increases in tSNR yielded by ME-LLR were most pronounced for image data with the longest echo time and thereby lowest SNR. ICA showed resting-state networks consistently identified between non-denoised and denoised data, with clearer demarcation of networks for ME-LLR. ME-LLR is demonstrated to suppress non-physiologic noise, enhance functional connectivity map quality, and could potentially facilitate scan time reduction in ME-fMRI.

Constructing high-order functional networks based on hypergraph for diagnosis of autism spectrum disorders.

High-order functional connectivity networks (FCNs) that reflect the connection relationships among multiple brain regions have become important tools for exploring the deep workings of the brain and revealing the mechanisms of brain diseases. The traditional high-order FCN constructed based on the "correlation of correlations" strategy, is a representative method for conducting whole-brain connectivity analysis and revealing global network characteristics. However, whole-brain connectivity analysis may be affected by noise carried by less important brain regions, resulting in redundant information and affecting the accuracy and reliability of the analysis. Moreover, this type of analysis has a high computational complexity. To address these issues, a new method for constructing high-order FCN based on hypergraphs is proposed in this article, which is used to accurately capture the real interaction relationships among brain regions. Specifically, first, a low-order FCN reflecting the connection relationships between pairs of brain regions based on resting-state functional Magnetic Resonance Imaging (rs-fMRI) time series is constructed, the method first constructs the low-order FCN that reflects the connection relationships between pairs of brain regions based on rs-fMRI time series, and then selects the "good friends" of each brain region from hypergraph perspective, which refers to the local friend circles with closer relationships. Then, the rs-fMRI time series corresponding to the "good friends" in each brain region's friend circle are averaged to obtain a sequence that reflects the intimacy between brain regions in each friend circle. Finally, hypergraph high-order FCN, which reflects the interaction relationships among multiple brain regions, is obtained by calculating the correlations based on the sequence of friend circles. The experimental results demonstrate that the proposed method outperforms traditional high-order FCN construction methods. Furthermore, integrating the high-order FCN constructed based on hypergraphs and the low-order FCN through feature fusion to achieve complementary information improves the accuracy of assisting in the diagnosis of brain diseases. In addition, the effectiveness of our method has only been validated in the diagnosis of ASD. For future work, we plan to extend this method to other brain connectivity patterns.

Eigenvector centrality dynamics are related to Alzheimer's disease pathological changes in non-demented individuals.

Amyloid-β accumulation starts in highly connected brain regions and is associated with functional connectivity alterations in the early stages of Alzheimer's disease. This regional vulnerability is related to the high neuronal activity and strong fluctuations typical of these regions. Recently, dynamic functional connectivity was introduced to investigate changes in functional network organization over time. High dynamic functional connectivity variations indicate increased regional flexibility to participate in multiple subnetworks, promoting functional integration. Currently, only a limited number of studies have explored the temporal dynamics of functional connectivity in the pre-dementia stages of Alzheimer's disease. We study the associations between abnormal cerebrospinal fluid amyloid and both static and dynamic properties of functional hubs, using eigenvector centrality, and their relationship with cognitive performance, in 701 non-demented participants from the European Prevention of Alzheimer's Dementia cohort. Voxel-wise eigenvector centrality was computed for the whole functional magnetic resonance imaging time series (static), and within a sliding window (dynamic). Differences in static eigenvector centrality between amyloid positive (A+) and negative (A-) participants and amyloid-tau groups were found in a general linear model. Dynamic eigenvector centrality standard deviation and range were compared between groups within clusters of significant static eigenvector centrality differences, and within 10 canonical resting-state networks. The effect of the interaction between amyloid status and cognitive performance on dynamic eigenvector centrality variability was also evaluated with linear models. Models were corrected for age, sex, and education level. Lower static centrality was found in A+ participants in posterior brain areas including a parietal and an occipital cluster; higher static centrality was found in a medio-frontal cluster. Lower eigenvector centrality variability (standard deviation) occurred in A+ participants in the frontal cluster. The default mode network and the dorsal visual networks of A+ participants had lower dynamic eigenvector centrality variability. Centrality variability in the default mode network and dorsal visual networks were associated with cognitive performance in the A- and A+ groups, with lower variability being observed in A+ participants with good cognitive scores. Our results support the role and timing of eigenvector centrality alterations in very early stages of Alzheimer's disease and show that centrality variability over time adds relevant information on the dynamic patterns that cause static eigenvector centrality alterations. We propose that dynamic eigenvector centrality is an early biomarker of the interplay between early Alzheimer's disease pathology and cognitive decline.

Complexity analysis of heartbeat-related signals in brain MRI time series as a potential biomarker for ageing and cognitive performance

Getting older affects both the structure of the brain and some cognitive capabilities. Until now, magnetic resonance imaging (MRI) approaches have been unable to give a coherent reflection of the cognitive declines. It shows the limitation of the contrast mechanisms used in most MRI investigations, which are indirect measures of brain activities depending on multiple physiological and cognitive variables. However, MRI signals may contain information of brain activity beyond these commonly used signals caused by the neurovascular response. Here, we apply a zero-spin echo (ZSE) weighted MRI sequence, which can detect heartbeat-evoked signals (HES). Remarkably, these MRI signals have properties only known from electrophysiology. We investigated the complexity of the HES arising from this sequence in two age groups; young (18–29 years) and old (over 65 years). While comparing young and old participants, we show that the complexity of the HES decreases with age, where the stability and chaoticity of these HES are particularly sensitive to age. However, we also found individual differences which were independent of age. Complexity measures were related to scores from different cognitive batteries and showed that higher complexity may be related to better cognitive performance. These findings underpin the affinity of the HES to electrophysiological signals. The profound sensitivity of these changes in complexity shows the potential of HES for understanding brain dynamics that need to be tested in more extensive and diverse populations with clinical relevance for all neurovascular diseases.

WHOCARES: WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions

Objective. To spatio-temporally resolve cardiac signals in functional magnetic resonance imaging (fMRI) time-series of the human brain using neither external physiological measurements nor ad hoc modelling assumptions. Approach. Cardiac pulsation is a physiological confound of fMRI time-series that introduces spurious signal fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast to resolve cardiac pulsation. Depending on the ratio between the instantaneous heart-rate and the acquisition sampling frequency (, with TR being the repetition time), the cardiac signal may alias into the frequency band of neural activation so that its removal through spectral filtering techniques is generally not possible. In this paper, we show that it is feasible to temporally and spatially resolve cardiac signals throughout the brain even when cardiac aliasing occurs by combining fMRI hyper-sampling with simultaneous multislice (SMS) imaging. The technique, which we name WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), was developed on 695 healthy subjects selected from the Human Connectome Project and its performance validated against the RETROICOR, HAPPY and the pulse oxymeter signal regression methods. Main results. WHOCARES is capable of retrieving voxel-wise cardiac signal regressors. This is achieved without employing external physiological recordings nor through ad hoc modelling assumptions. The performance of WHOCARES was, on average, superior to RETROICOR, HAPPY and the pulse oxymeter regression methods. Significance. WHOCARES holds basis for the reliable mapping of cardiac activity in fMRI time-series. WHOCARES can be employed for the retrospective removal of cardiac noise in publicly available fMRI datasets where physiological recordings are not available. WHOCARES is freely available at https://github.com/gferrazzi/WHOCARES.

An ICA Investigation into the Effect of Physiological Noise Correction on Dimensionality and Spatial Maps of Intrinsic Connectivity Networks.

Physiological processes such as cardiac pulsations and respiration can induce signal modulations in functional magnetic resonance imaging (fMRI) time series, and confound inferences made about neural processing from analyses of the blood oxygenation level-dependent (BOLD) signals. Retrospective image space correction of physiological noise (RETROICOR) is a widely used approach to reduce physiological signals in data. Independent component analysis (ICA) is a valuable blind source separation method for analyzing brain networks, referred to as intrinsic connectivity networks (ICNs). Previously, we showed that temporal properties of the ICA-derived networks such as spectral power and functional network connectivity could be impacted by RETROICOR corrections. The goal of this study is to investigate the effect of retrospective correction of physiological artifacts on the ICA dimensionality (model order) and intensities of ICN spatial maps. To this aim, brain BOLD fMRI, heartbeat, and respiration were measured in 22 healthy subjects during resting state. ICA dimensionality was estimated using minimum description length (MDL) based on i.i.d. data samples and smoothness FWHM kernel, and entropy-rate based order selection by finite memory length model (ER-FM) and autoregressive model (ER-AR). Differences in spatial maps between the raw and denoised data were compared using the paired t-test and false discovery rate (FDR) thresholding was used to correct for multiple comparisons. Results showed that ICA dimensionality was greater in the raw data compared to the denoised data. Significant differences were found in the intensities of spatial maps for three ICNs: basal ganglia, precuneus, and frontal network. These preliminary results indicate that the retrospective physiological noise correction can induce change in the resting state spatial map intensity related to the within-network connectivity. More research is needed to understand this effect.

Blood Oxygen Level-Dependent Lag Patterns Differ Between Rest and Task Conditions, but Are Largely Typical in Autism.

Background/Introduction: Autism spectrum disorder (ASD) is characterized by atypical functional connectivity (FC) within and between distributed brain networks. However, FC findings have often been inconsistent, possibly due to a focus on static FC rather than brain dynamics. Lagged connectivity analyses aim at evaluating temporal latency, and presumably neural propagation, between regions. This approach may, therefore, reveal a more detailed picture of network organization in ASD than traditional FC methods. Methods: The current study evaluated whole-brain lag patterns in adolescents with ASD (n = 28) and their typically developing peers (n = 22). Functional magnetic resonance imaging data were collected during rest and during a lexico-semantic decision task. Optimal lag was calculated for each pair of regions of interest by using cross-covariance, and mean latency projections were calculated for each region. Results: Latency projections did not regionally differ between groups, with the same regions emerging among the "earliest" and "latest." Although many of the longest absolute latencies were preserved across resting-state and task conditions, lag patterns overall were affected by condition, as many regions shifted toward zero-lag during task performance. Lag structure was also strongly associated with literature-derived estimates of arterial transit time. Discussion: Results suggest that lag patterns are broadly typical in ASD but undergo changes during task performance. Moreover, lag patterns appear to reflect a combination of neural and vascular sources, which should be carefully considered when interpreting lagged FC. Impact statement Altered brain dynamics have been proposed in autism spectrum disorder (ASD). Lagged functional connectivity analysis uses cross-correlation between functional magnetic resonance imaging (fMRI) time series to determine regional latency. Few studies have examined blood oxygen level-dependent (BOLD) lag in ASD, and findings have been inconsistent. Using multi-echo fMRI data with improved artifact detection and removal, we find differences in lag structure between task and rest states, but not between adolescents with ASD and typically developing peers. Additional analyses exploring links with arterial transit time, however, highlight the impact of vascular organization on BOLD lag patterns and its potential to confound measures of neural dynamics.

Interpreting null models of resting-state functional MRI dynamics: not throwing the model out with the hypothesis

Null models are useful for assessing whether a dataset exhibits a non-trivial property of interest. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fMRI) time series. Interpretation of null-model testing in this context may not be straightforward because (i) null hypotheses associated to different null models are sometimes unclear and (ii) fMRI metrics might be ‘trivial’, i.e. preserved under the null hypothesis, and still be useful in neuroimaging applications. In this commentary, we review several commonly used null models of fMRI time series and discuss the interpretation of the corresponding tests. We argue that, while null-model testing allows for a better characterization of the statistical properties of fMRI time series and associated metrics, it should not be considered as a mandatory validation step to assess their relevance in representing brain functional dynamics.

Estimating Effective Connectivity by Recurrent Generative Adversarial Networks.

Estimating effective connectivity from functional magnetic resonance imaging (fMRI) time series data has become a very hot topic in neuroinformatics and brain informatics. However, it is hard for the current methods to accurately estimate the effective connectivity due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for estimating effective connectivity based on recurrent generative adversarial networks, called EC-RGAN. The proposed framework employs the generator that consists of a set of effective connectivity generators based on recurrent neural networks to generate the fMRI time series of each brain region, and uses the discriminator to distinguish between the joint distributions of the real and generated fMRI time series. When the model is well-trained and generated fMRI data is similar to real fMRI data, EC-RGAN outputs the effective connectivity by means of the causal parameters of the effective connectivity generators. Experimental results on both simulated and real-world fMRI time series data demonstrate the efficacy of our proposed framework.

Topological data analysis of task-based fMRI data from experiments on schizophrenia

We use methods from computational algebraic topology to study functional brain networks in which nodes represent brain regions and weighted edges encode the similarity of functional magnetic resonance imaging (fMRI) time series from each region. With these tools, which allow one to characterize topological invariants such as loops in high-dimensional data, we are able to gain understanding of low-dimensional structures in networks in a way that complements traditional approaches that are based on pairwise interactions. In the present paper, we use persistent homology to analyze networks that we construct from task-based fMRI data from schizophrenia patients, healthy controls, and healthy siblings of schizophrenia patients. We thereby explore the persistence of topological structures such as loops at different scales in these networks. We use persistence landscapes and persistence images to represent the output of our persistent-homology calculations, and we study the persistence landscapes and persistence images using k-means clustering and community detection. Based on our analysis of persistence landscapes, we find that the members of the sibling cohort have topological features (specifically, their one-dimensional loops) that are distinct from the other two cohorts. From the persistence images, we are able to distinguish all three subject groups and to determine the brain regions in the loops (with four or more edges) that allow us to make these distinctions.

Single-index models with functional connectivity network predictors.

Functional connectivity is defined as the undirected association between two or more functional magnetic resonance imaging (fMRI) time series. Increasingly, subject-level functional connectivity data have been used to predict and classify clinical outcomes and subject attributes. We propose a single-index model wherein response variables and sparse functional connectivity network valued predictors are linked by an unspecified smooth function in order to accommodate potentially nonlinear relationships. We exploit the network structure of functional connectivity by imposing meaningful sparsity constraints, which lead not only to the identification of association of interactions between regions with the response but also the assessment of whether or not the functional connectivity associated with a brain region is related to the response variable. We demonstrate the effectiveness of the proposed model in simulation studies and in an application to a resting-state fMRI data set from the Human Connectome Project to model fluid intelligence and sex and to identify predictive links between brain regions.

Interindividual Signatures of fMRI Temporal Fluctuations.

The complexity and variability of human brain activity, such as quantified from Functional Magnetic Resonance Imaging (fMRI) time series, have been widely studied as potential markers of healthy and pathological states. However, the extent to which fMRI temporal features exhibit stable markers of inter-individual differences in brain function across healthy young adults is currently an open question. In this study, we draw upon two widely used time-series measures-a nonlinear complexity measure (sample entropy; SampEn) and a spectral measure of low-frequency content (fALFF)-to capture dynamic properties of resting-state fMRI in a large sample of young adults from the Human Connectome Project. We observe that these two measures are closely related, and that both generate reproducible patterns across brain regions over four different fMRI runs, with intra-class correlations of up to 0.8. Moreover, we find that both metrics can uniquely differentiate subjects with high identification rates (ca. 89%). Canonical correlation analysis revealed a significant relationship between multivariate brain temporal features and behavioral measures. Overall, these findings suggest that regional profiles of fMRI temporal characteristics may provide stable markers of individual differences, and motivate future studies to further probe relationships between fMRI time series metrics and behavior.

Divergent Alterations of Structural–Functional Connectivity Couplings in First-episode and Chronic Schizophrenia Patients

Emerging evidence suggests that the coupling relating the structural connectivity (SC) of the brain to its functional connectivity (FC) exhibits remarkable changes during development, normal aging, and diseases. Although altered structural–functional connectivity couplings (SC–FC couplings) have been previously reported in schizophrenia patients, the alterations in SC–FC couplings of different illness stages of schizophrenia (SZ) remain largely unknown. In this study, we collected structural and resting-state functional MRI data from 73 normal controls (NCs), 61 first-episode (FeSZ) and 78 chronic (CSZ) schizophrenia patients. Positive and negative syndrome scale (PANSS) scores were assessed for all patients. Structural and functional brain networks were constructed using gray matter volume (GMV) and resting-state magnetic resonance imaging (rs-fMRI) time series measurements. At the connectivity level, the CSZ patients showed significantly increased SC–FC coupling strength compared with the FeSZ patients. At the node strength level, significant decreased SC–FC coupling strength was observed in the FeSZ patients compared to that of the NCs, and the coupling strength was positively correlated with negative PANSS scores. These results demonstrated divergent alterations of SC–FC couplings in FeSZ and CSZ patients. Our findings provide new insight into the neuropathological mechanisms underlying the developmental course of SZ.

The Coupled Brains of Captivated Audiences

Abstract. Suspense not only creates a strong psychological tension within individuals, but it does so reliably across viewers who become collectively engaged with the story. Despite its prevalence in media psychology, limited work has examined suspense from a media neuroscience perspective, and thus the biological underpinnings of suspense remain unknown. Here we examine continuous brain responses of 494 viewers watching a suspenseful movie. To create a time-resolved measure of the degree to which a movie aligns audience-wide brain responses, we computed dynamic inter-subject correlations of functional magnetic resonance imaging (fMRI) time series among all viewers using sliding-window analysis. In parallel, we captured in-the-moment reports of suspense in an independent sample via continuous response measurement (CRM). We found that dynamic inter-subject correlations over the course of the movie tracked well with the reported suspense in the CRM sample, particularly in regions associated with emotional salience and higher cognitive processes. These results are compatible with theoretical views on motivated attention and psychological tension. The finding that fMRI-based audience response measurement relates to audience reports of suspense creates new opportunities for research on the mechanisms of suspense and other entertainment phenomena and has applied potential for measuring audience responses in a nonreactive and objective fashion.

Multiple functional connectivity networks fusion for schizophrenia diagnosis.

Accurate diagnosis of schizophrenia is of great importance to patients and clinicians. Recent studies have found that different frequency bands contain complementary information for diagnosis and prognosis. However, conventional multiple frequency functional connectivity (FC) networks using Pearson's correlation coefficient (PCC) are usually based on pairwise correlations among different brain regions on single frequency band, while ignoring the interactions between regions in different frequency bands, the relationship among different networks, and the nonlinear properties of blood-oxygen-level-dependent (BOLD) signal. To take into account these relationships, we propose in this study a multiple networks fusion method for schizophrenia diagnosis. Specifically, we first construct FC networks within the same and across frequency from the resting-state functional magnetic resonance imaging (rs-fMRI) time series by using extended maximal information coefficient (eMIC) based on four frequency bands: slow-5 (0.01-0.027Hz), slow-4 (0.027-0.073Hz), slow-3 (0.073-0.198Hz), and slow-2 (0.198-0.25Hz). Then, these networks are combined nonlinearly through network fusion, which generates a unified network for each subject. Features extracted from the unified network are used for final classification. Experimental results demonstrated that the interaction between distinct brain regions across different frequency bands can significantly improve the classification performance, comparing with conventional FC analysis based on specific or entire low-frequency band. The promising results suggest that our proposed framework would be a useful tool in computer-aided diagnosis of schizophrenia. Graphical abstract The flowchart of proposed classification framework.

Sphenoid Sinus Fat Packing in Transsphenoidal Surgery: Long-Term Fate Assessment Using Magnetic Resonance Imaging

Background and Objectives: Following the transsphenoidal approach (TSA), appropriate sphenoid sinus fat packing has been preferred to prevent postoperative cerebrospinal fluid leakage; however, studies on the behavior of fat tissue transplanted in the sphenoid sinus are lacking. This study aimed to determine the long-term fate of these fat grafts using magnetic resonance imaging (MRI).Subjects and Method: A total of 139 postoperative MRI scans of 41 patients who underwent sphenoid sinus fat packing using the standard TSA were evaluated. Additionally, MRI time series indicating the vital fat volumes were assessed postoperatively.Results: In 82.9% of cases, the fat volumes measured in the final MRI scans declined to <20% of the initial volumes; only 4.9% of cases exhibited declines to >60% of the initial volume. The fat tissue volume decreased significantly with time, with a median half-life of 18 months. Typically, the sphenoid sinus was eventually almost filled with air rather than transplanted fat. In the subgroup analysis, the fat clearance rate was significantly lower in patients with residual tumors than in those without such remnants (p=0.013).Conclusion: Long-term MRI surveillance of fat grafts in the sphenoid sinus revealed that the transplanted fat graft had degraded and was gradually eliminated.

Individual Topological Analysis of Synchronization-Based Brain Connectivity

Functional connectivity analysis aims at assessing the strength of functional coupling between the signal responses in distinct brain areas. Usually, functional magnetic resonance imaging (fMRI) time series connections are estimated through zero-lag correlation metrics that quantify the statistical similarity between pairs of regions or spectral measures that assess synchronization at a frequency band of interest. Here, we explored the application of a new metric to assess the functional synchronization in phase space between fMRI time series in a resting state. We applied a complete topological analysis to the resulting connectivity matrix to uncover both the macro-scale organization of the brain and detect the most important nodes. The synchronization metric is also compared with Pearson’s correlation coefficient and spectral coherence to highlight similarities and differences between the topologies of the three functional networks. We found that the individual topological organization of the resulting synchronization-based connectivity networks shows a finer modular organization than that identified with the other two metrics and a low overlap with the modular partitions of the other two networks suggesting that the derived topological information is not redundant and could be potentially integrated to provide a multi-scale description of functional connectivity.