rs-fMRI Dataset Research Articles

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

MTBI-DSANet: A deep self-attention model for diagnosing mild traumatic brain injury using multi-level functional connectivity networks

The main approach for analyzing resting-state functional magnetic resonance imaging (rs-fMRI) is the low-order functional connectivity network (LoFCN) based on the correlation between two brain regions. Based on LoFCN, researchers recently proposed the topographical high-order FCN (tHoFCN) and the associated high-order FCN (aHoFCN) to explore the high-order interactions among brain regions. In this work, we designed a Deep Self-Attention (DSA) framework called mTBI-DSANet to diagnose mild traumatic brain injury (mTBI) using multi-level FCNs, including LoFCN, tHoFCN, and aHoFCN. The multilayer perceptron and self-attention mechanism in mTBI-DSANet were designed to capture important features for the mTBI diagnosis. We evaluated the mTBI-DSANet’s performance on the real rs-fMRI dataset, which was collected by Third Xiangya Hospital of Central South University from April 2014 to February 2021. We compared the performance of mTBI-DSANet with distinct FCNs and their combinations under 10-fold cross-validation. Based on the LoFCN+aHoFCN combination, the average performance of mTBI-DSANet achieved the best accuracy of 0.834, which is significantly better than peer methods. The experiments demonstrated the potential of the mTBI-DSANet in assisting mTBI diagnosis.

Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths.

There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ιwill focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI(rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18months (84 scans in total), and the rs-fMRIdataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.

Multi-Perspective Feature Extraction and Fusion Based on Deep Latent Space for Diagnosis of Alzheimer's Diseases.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to construct functional connectivity (FC) in the brain for the diagnosis and analysis of brain disease. Current studies typically use the Pearson correlation coefficient to construct dynamic FC (dFC) networks, and then use this as a network metric to obtain the necessary features for brain disease diagnosis and analysis. This simple observational approach makes it difficult to extract potential high-level FC features from the representations, and also ignores the rich information on spatial and temporal variability in FC. In this paper, we construct the Latent Space Representation Network (LSRNet) and use two stages to train the network. In the first stage, an autoencoder is used to extract potential high-level features and inner connections in the dFC representations. In the second stage, high-level features are extracted using two perspective feature parses. Long Short-Term Memory (LSTM) networks are used to extract spatial and temporal features from the local perspective. Convolutional neural networks extract global high-level features from the global perspective. Finally, the fusion of spatial and temporal features with global high-level features is used to diagnose brain disease. In this paper, the proposed method is applied to the ANDI rs-fMRI dataset, and the classification accuracy reaches 84.6% for NC/eMCI, 95.1% for NC/AD, 80.6% for eMCI/lMCI, 84.2% for lMCI/AD and 57.3% for NC/eMCI/lMCI/AD. The experimental results show that the method has a good classification performance and provides a new approach to the diagnosis of other brain diseases.

Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder

The study of functional connectivity (FC) of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) has gained traction for uncovering FC patterns related to autism spectrum disorder (ASD). It is believed that the neurodynamic components of neuroimaging data enhance the measurement of the FC of brain nodes. Hence, methods based on linear correlations of rs-fMRI may not accurately represent the FC patterns of brain nodes in ASD patients. In this study, we proposed a new biomarker for ASD detection based on wavelet coherence and singular value decomposition. In essence, the proposed method provides a novel feature-vector based on extraction of the principal component of the neuronal dynamic FC patterns of rs-fMRI BOLD signals. The method, known as principal wavelet coherence (PWC), is implemented by applying singular value decomposition (SVD) on wavelet coherence (WC) and extracting the first principal component. ASD biomarkers are selected by analyzing the relationship between ASD severity scores and the amplitude of wavelet coherence fluctuation (WCF). The experimental rs-fMRI dataset is obtained from the publicly available Autism Brain Image Data Exchange (ABIDE), and includes 505 ASD patients and 530 normal control subjects. The data are randomly divided into 90% for training and cross-validation and the remaining 10% unseen data used for testing the performance of the trained network. With 95.2% accuracy on the ABIDE database, our ASD classification technique has better performance than previous methods. The results of this study illustrate the potential of PWC in representing FC dynamics between brain nodes and opens up possibilities for its clinical application in diagnosis of other neuropsychiatric disorders.

Impaired Brain Information Transmission Efficiency and Flexibility in Parkinson's Disease and Rapid Eye Movement Sleep Behavior Disorder: Evidence from Functional Connectivity and Functional Dynamics.

Parkinson's disease (PD) is a common neurodegenerative disorder. Rapid eye movement sleep behavior disorder (RBD) is one of the prodromal symptoms of PD. Studies have shown that brain information transmission is affected in PD patients. Consequently, we hypothesized that brain information transmission is impaired in RBD and PD. To prove our hypothesis, we performed functional connectivity (FC) and functional dynamics analysis of three aspects—based on the whole brain, within the resting-state network (RSN), and the interaction between RSNs—using normal control (NC) (n = 21), RBD (n = 24), and PD (n = 45) resting-state functional magnetic resonance imaging (rs-fMRI) data sets. Furthermore, we tested the explanatory power of FC and functional dynamics for the clinical features. Our results found that the global functional dynamics and FC of RBD and PD were impaired. Within RSN, the impairment concentrated in the visual network (VIS) and sensorimotor network (SMN), and the impaired degree of SMN in RBD was higher than that in PD. On the interaction between RSNs, RBD showed a widespread decrease, and PD showed a focal decrease which concentrated in SMN and VIS. Finally, we proved FC and functional dynamics were related to clinical features. These differences confirmed that brain information transmission efficiency and flexibility are impaired in RBD and PD, and these impairments are associated with the clinical features of patients.

Functional connectivity of cortical resting-state networks is differentially affected by rest conditions

Optimal conditions for resting-state functional magnetic resonance imaging (rs-fMRI) are still highly debated. Here, we comprehensively assessed the effects of various rest conditions on all cortical resting-state networks (RSNs) defined by an established atlas. Twenty-two healthy college students (22 ± 4 years old, 12 females) were scanned on a GE 3T MRI scanner. Rs-fMRI datasets were collected under four different conditions for each subject: (1) eyes open in dim light (Eyes-Open), (2) eyes closed and awake (Eyes-Closed), (3) eyes closed while remembering four numbers through the scan session (Eyes-Closed-Number) and (4) asked to watch a movie (Movie). We completed a thorough examination of the 17 functional RSNs defined by Yeo and colleagues. Importantly, the movie led to changes in global connectivity and should be avoided as a rest condition. Conversely, there were no significant connectivity differences between conditions within the frontoparietal control and limbic networks and the following subnetworks as defined by Yeo et al.: default-B, dorsal-attention-B and salience/ventral-attention-B. These were not even significant when compared to the highly stimulative Movie condition. A significant difference was not found between Eyes-Closed and Eyes-Closed-Number conditions in whole-brain, within-network and between-network comparisons. When considering other rest conditions, however, we observed connectivity changes in some subnetworks, including those of the default-mode network. Overall, we found conditions with more external stimulation led to more changes in functional connectivity during rs-fMRI. In conclusion, the comprehensive results of our study can aid in choosing rest conditions for the study of overall and specific functional networks.

The alterations of brain functional connectivity networks in major depressive disorder detected by machine learning through multisite rs-fMRI data

BackgroundThe current diagnosis of major depressive disorder (MDD) is mainly based on the patient's self-report and clinical symptoms. Machine learning methods are used to identify MDD using resting-state functional magnetic resonance imaging (rs-fMRI) data. However, due to large site differences in multisite rs-fMRI data and the difficulty of sample collection, most of the current machine learning studies use small sample sizes of rs-fMRI datasets to detect the alterations of functional connectivity (FC) or network attribute (NA), which may affect the reliability of the experimental results. MethodsMultisite rs-fMRI data were used to increase the size of the sample, and then we extracted the functional connectivity (FC) and network attribute (NA) features from 1611 rs-fMRI data (832 patients with MDD (MDDs) and 779 healthy controls (HCs)). ComBat algorithm was used to harmonize the data variances caused by the multisite effect, and multivariate linear regression was used to remove age and sex covariates. Two-sample t-test and wrapper-based feature selection methods (support vector machine recursive feature elimination with cross-validation (SVM-RFECV) and LightGBM's "feature_importances_" function) were used to select important features. The Shapley additive explanations (SHAP) method was used to assign the contribution of features to the best classification effect model. ResultsThe best result was obtained from the LinearSVM model trained with the 136 important features selected by SVMRFE-CV. In the nested five-fold cross-validation (consisting of an outer and an inner loop of five-fold cross-validation) of 1611 data, the model achieved the accuracy, sensitivity, and specificity of 68.90 %, 71.75 %, and 65.84 %, respectively. The 136 important features were tested in a small dataset and obtained excellent classification results after balancing the ratio between patients with depression and HCs. ConclusionsThe combined use of FC and NA features is effective for classifying MDDs and HCs. The important FC and NA features extracted from the large sample dataset have some generalization performance and may be used as a reference for the altered brain functional connectivity networks in MDD.

Reproducibility of functional connectivity metrics estimated from resting-state functional MRI with differences in days, coils, and global signal regression.

In multisite studies, differences in imaging acquisition systems could affect the reproducibility of the results when examining changes in brain function using resting-state functional magnetic resonance imaging (rs-fMRI). This is also important for longitudinal studies, in which changes in equipment settings can occur. This study examined the reproducibility of functional connectivity (FC) metrics estimated from rs-fMRI data acquired using scanner receiver coils with different numbers of channels. This study involved 80 rs-fMRI datasets from 20 healthy volunteers scanned in two independent imaging sessions using both 12- and 32-channel coils for each session. We used independent component analysis (ICA) to evaluate the FC of canonical resting-state networks (RSNs) and graph theory to calculate several whole-brain network metrics. The effect of global signal regression (GSR) as a preprocessing step was also considered. Comparisons within and between receiver coils were performed. Irrespective of the GSR, RSNs derived from rs-fMRI data acquired using the same receiver coil were reproducible, but not from different receiver coils. However, both the GSR and the channel count of the receiver coil have discernible effects on the reproducibility of network metrics estimated using whole-brain network analysis. The data acquired using the 32-channel coil tended to have better reproducibility than those acquired using the 12-channel coil. Our findings suggest that the reproducibility of FC metrics estimated from rs-fMRI data acquired using different receiver coils showed some level of dependence on the preprocessing method and the type of analysis performed.

Resting State Analysis: Simulation and validation of RS-fMRI dataset for ADHD Subjects

Resting State Analysis: Simulation and validation of RS-fMRI dataset for ADHD Subjects

Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study

The past decade has seen an increasing number of applications of deep learning (DL) techniques to biomedical fields, especially in neuroimaging-based analysis. Such DL-based methods are generally data-intensive and require a large number of training instances, which might be infeasible to acquire from a single acquisition site, especially for data, such as fMRI scans, due to the time and costs that they demand. We can attempt to address this issue by combining fMRI data from various sites, thereby creating a bigger heterogeneous dataset. Unfortunately, the inherent differences in the combined data, known as batch effects, often hamper learning a model. To mitigate this issue, techniques such as multi-source domain adaptation [Multi-source Domain Adversarial Networks (MSDA)] aim at learning an effective classification function that uses (learned) domain-invariant latent features. This article analyzes and compares the performance of various popular MSDA methods [MDAN, Domain AggRegation Networks (DARN), Multi-Domain Matching Networks (MDMN), and Moment Matching for MSDA (M3SDA)] at predicting different labels (illness, age, and sex) of images from two public rs-fMRI datasets: ABIDE 1and ADHD-200. It also evaluates the impact of various conditions such as class imbalance, the number of sites along with a comparison of the degree of adaptation of each of the methods, thereby presenting the effectiveness of MSDA models in neuroimaging-based applications.

The Neuro-Endo-Microbio-Ome Study: A Pilot Study of Neurobiological Alterations Pre- Versus Post-Bariatric Surgery.

Plausible phenotype mechanisms following bariatric surgery include changes in neural and gastrointestinal physiology. This pilot study aims to investigate individual and combined neurologic, gut microbiome, and plasma hormone changes pre- versus post-vertical sleeve gastrectomy (VSG), Roux-en-Y gastric bypass (RYGB), and medical weight loss (MWL). We hypothesized post-weight loss phenotype would be associated with changes in central reward system brain connectivity, differences in postprandial gut hormone responses, and increased gut microbiome diversity. Subjects included participants undergoing VSG, n = 7; RYGB, n = 9; and MWL, n = 6. Ghrelin, glucagon-like peptide-1, peptide-YY, gut microbiome, and resting state functional magnetic resonance imaging (rsfMRI; using fractional amplitude of low-frequency fluctuations [fALFF]) were measured pre- and post-intervention in fasting and fed states. We explored phenotype characterization using clustering on gut hormone, microbiome, and rsfMRI datasets and a combined analysis. We observed more widespread fALFF differences post-bariatric surgery versus post-MWL. Decreased post-prandial fALFF was seen in food reward regions post-RYGB. The highest number of microbial taxa that increased post-intervention occurred in the RYGB group, followed by VSG and MWL. The combined hormone, microbiome, and MRI dataset most accurately clustered samples into pre- versus post-VSG phenotypes followed by RYGB subjects. The data suggest surgical weight loss (VSG and RYGB) has a bigger impact on brain and gut function versus MWL and leads to lesser post-prandial activation of food-related neural circuits. VSG subjects had the greatest phenotype differences in interactions of microbiome, rsfMRI, and gut hormone features, followed by RYGB and MWL. These results will inform future prospective research studying gut-brain changes post-bariatric surgery.

Functional Connectivity Underlying Symptoms in Preschool Boys With Autism: A Resting-State Functional Magnetic Resonance Imaging Study

BackgroundSingle-sex children have been regarded as one of the best subjects to understand the abnormal development patterns of autism spectrum disorders (ASDs). However, the functional connectivity (FC) behind their symptoms is still unknown.MethodsBased on FC analysis, the acquired resting-state functional magnetic resonance imaging (rs-fMRI) data sets, including 86 boys with ASD and 54 normal controls (NC), were used to detect the neural synchronous activity between brain regions. Pearson correlation analysis was used to evaluate the relationship between the abnormal FC value and clinical features.ResultsIndividuals with ASD showed enhanced FC between the right calcarine and the right lingual gyrus (LG). The right medial orbital frontal cortex also showed increased FC with bilateral inferior temporal gyrus (ITG) [two-tailed, voxel-level p < 0.001, gaussian random field (GRF) correction, cluster-level p < 0.05]. We did not find a correlation between the abnormal FC value and clinical scales.ConclusionOur study reveals a possible relationship between atypical visual attention and poor learning ability in subjects with ASD, and delayed social language development may be a secondary symptom to ASD.

Alzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging.

Alzheimer’s disease (AD) affects the quality of life as it causes; memory loss, difficulty in thinking, learning, and performing familiar tasks. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate and analyze different brain regions for AD identification. This study investigates the effectiveness of using correlated transfer function (CorrTF) as a new biomarker to extract the essential features from rs-fMRI, along with support vector machine (SVM) ordered hierarchically, in order to distinguish between the different AD stages. Additionally, we explored the regions, showing significant changes based on the CorrTF extracted features’ strength among different AD stages. First, the process was initialized by applying the preprocessing on rs-fMRI data samples to reduce noise and retain the essential information. Then, the automated anatomical labeling (AAL) atlas was employed to divide the brain into 116 regions, where the intensity time series was calculated, and the CorrTF features were extracted for each region. The proposed framework employed the SVM classifier in two different methodologies, hierarchical and flat multi-classification schemes, to differentiate between the different AD stages for early detection purposes. The ADNI rs-fMRI dataset, employed in this study, consists of 167, 102, 129, and 114 normal, early, late mild cognitive impairment (MCI), and AD subjects, respectively. The proposed schemes achieved an average accuracy of 98.2% and 95.5% for hierarchical and flat multi-classification tasks, respectively, calculated using ten folds cross-validation. Therefore, CorrTF is considered a promising biomarker for AD early-stage identification. Moreover, the significant changes in the strengths of CorrTF connections among the different AD stages can help us identify and explore the affected brain regions and their latent associations during the progression of AD.

Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive functional neuroimaging modality that has been widely used to investigate functional connectomes in the brain. Since noise and artifacts generated by non-neuronal physiological activities are predominant in raw rs-fMRI data, effective noise removal is one of the most important preprocessing steps prior to any subsequent analysis. For rs-fMRI denoising, a common trend is to decompose rs-fMRI data into multiple components and then regress out noise-related components. Therefore, various machine learning techniques have been used in such analyses with predefined procedures and manually engineered features. However, the lack of a universal definition of a noise-related source or artifact complicates manual feature engineering. Manual feature selection can result in the failure to capture unknown types of noise. Furthermore, the possibility that the hand-crafted features will only work for the broader population (e.g., healthy adults) but not for “outliers” (e.g., infants or subjects that belong to a disease cohort) is quite high. In practice, we have limited knowledge of which features should be extracted; thus, multi-classifier assembly must be implemented to improve performance, although this process is quite time-consuming. However, in real rs-fMRI applications, fast and accurate automatic identification of noise-related components on different datasets is critical. To solve this problem, we propose a novel, automatic, and end-to-end deep learning framework dedicated to noise-related component identification via a faster and more effective multi-layer feature extraction strategy that learns deeply embedded spatio-temporal features of the components. In this study, we achieved remarkable performance on various rs-fMRI datasets, including multiple adult rs-fMRI datasets from different rs-fMRI studies and an infant rs-fMRI dataset, which is quite heterogeneous and differs from that of adults. Our proposed framework also dramatically increases the noise detection speed owing to its inherent ability for deep learning (< 1s for single-component classification). It can be easily integrated into any preprocessing pipeline, even those that do not use standard procedures but depend on alternative toolboxes.

P035 An examination of resting state functional connectivity in patients with active Crohn’s disease

Abstract Background Crohn’s disease (CD) and its comorbidities may be associated with a specific intrinsic neural phenotype that can be measured using resting state functional connectivity (RSFC). RSFC alterations have been reported in CD in remission but have not been investigated in the active phase of the disease. Here, the primary aim was to study the effects of active CD on resting state networks (RSNs), hence identifying RSFC alterations specific to active CD status. A second aim was to investigate associations between disease duration and abdominal pain with RSFC in active CD. Methods 25 active CD patients and 25 age-, BMI- and gender-matched healthy controls (HC) were recruited. Active disease was defined as CRP &amp;gt; 5 mg/dl, or faecal calprotectin (FCP) &amp;gt;250 μg/g or through ileocolonoscopy or MRE. RsfMRI datasets were acquired on a 3T Philips Achieva scanner, with data corrected for physiological noise and motion. ICA analysis was carried out using MELODIC (FSL software). A multi-session temporal concatenation was used to generate 30 independent component (IC) maps of RSNs. A dual regression analysis with variance normalization, using group IC maps from HC group as the network template, was performed. Differences in RSN between HCs and CD patients and associations with covariates of interest were assessed. Results RSNs comprising the visual, cerebellar, frontal parietal and frontal RSN, default mode network (DMN), salience, temporal and dorsal attention RSN were identified. Enhancement of activity and increase connectivity in the medial visual RSN (orbital frontal cortex), in the frontal parietal network (occipital fusiform gyrus, thalamus, caudate, posterior cingulate cortex, postcentral gyrus) were observed in CD relative to HC. Further, decreased activity was seen in the salience network (cerebellum, postcentral gyrus), cerebellar network (occipital fusiform gyrus, cerebellum) and the DMN (parahippocampal gyrus, cerebellum) in CD relative to HC. Greater abdominal pain scores were associated with lower connectivity in the precuneus in the medial visual RSN, in the parietal operculum in the salience RSN, as well as reduced connectivity in the cerebellum in the frontal RSN. Finally, greater disease duration was associated with greater connectivity in the visual RSN in the middle temporal gyrus and planum temporale. Conclusion Alterations in RSFC between active CD and HC in RSNs implicated in cognition, attention, emotion and pain may reflect neuroplasticity in response to chronic systemic inflammation, abdominal pain, disease duration and severity. Identification of a neural phenotype specific to active CD status, may help guide intervention constituting a neurobehavioural precision medicine approach to treatment.

Medial temporal lobe contributions to resting-state networks

The medial temporal lobe (MTL) is a set of interconnected brain regions that have been shown to play a central role in behavior as well as in neurological disease. Recent studies using resting-state functional magnetic resonance imaging (rsfMRI) have attempted to understand the MTL in terms of its functional connectivity with the rest of the brain. However, the exact characterization of the whole-brain networks that co-activate with the MTL as well as how the various sub-regions of the MTL are associated with these networks remains poorly understood. Here, we attempted to advance these issues by exploiting the high spatial resolution 7T rsfMRI dataset from the Human Connectome Project with a data-driven analysis approach that relied on independent component analysis (ICA) restricted to the MTL. We found that four different well-known resting-state networks co-activated with a unique configuration of MTL subcomponents. Specifically, we found that different sections of the parahippocampal cortex were involved in the default mode, visual and dorsal attention networks; sections of the hippocampus in the somatomotor and default mode networks; and the lateral entorhinal cortex in the dorsal attention network. We replicated this set of results in a validation sample. These results provide new insight into how the MTL and its subcomponents contribute to known resting-state networks. The participation of the MTL in an expanded range of resting-state networks is in line with recent proposals on MTL function.

Functional Connectivity Density in the Sensorimotor Area is Associated with Sleep Latency in Patients with Primary Insomnia.

ObjectiveIt is unclear whether the patterns of functional connectivity density (FCD) differ between patients with primary insomnia (PI) and healthy subjects. In the present study, we investigated the features of FCD in patients with PI using resting-state functional MRI (rsfMRI).MethodsrsfMRI datasets of 32 patients with PI and 34 healthy controls (HCs) were obtained using a 3-Tesla scanner. FCD analysis was performed to compare voxels with abnormal whole-brain functional connectivity with other voxels among patients with PI and HCs. Abnormal brain regions were then used as seed points for FC analysis.ResultsCompared with HCs, patients with PI exhibited significantly decreased FCD in the left medial frontal gyrus and increased FCD in the left supplementary motor area (SMA). With the left medial frontal gyrus as the seed point, patients with PI showed decreased FC between the left medial frontal gyrus and the left fusiform gyrus compared with HCs. With the left SMA as the seed point, patients with PI exhibited increased FC between the left SMA and the right anterior cingulate gyrus. Correlation analysis revealed that the increased FCD values in the left SMA were positively correlated with sleep latency in patients with PI.ConclusionDefault-mode network and SMA dysfunctions may be related to the pathophysiology of PI.

Identity-mapping cascaded network for fMRI registration

Neuroscience researches based on functional magnetic resonance imaging (fMRI) rely on accurate inter-subject image registration of functional regions. The intersubject alignment of fMRI can improve the statistical power of group analyses. Recent studies have shown the deep learning-based registration methods can be used for registration. In our work, we proposed a 30-Identity-Mapping Cascaded network (30-IMCNet) for rs-fMRI registration. It is a cascaded network that can warp the moving image progressively and finally align to the fixed image. A Combination unit with an identity-mapping path is added to the inputs of each IMCNet to guide the network training. We implemented 30-IMCNet on an rs-fMRI dataset (1000 Functional Connectomes Project dataset) and a task-related fMRI dataset (Eyes Open Eyes Closed fMRI dataset). To evaluate our method, a group-level analysis was implemented in the testing dataset. For rs-fMRI, the criterions such as peak t-value of group-level t-maps, cluster-level evaluation, and intersubject functional network correlation were used to evaluate the quality of the registrations. For task-related fMRI, peak t-value in ALFF paired-t map and peak t-value in ReHo paired-t maps were used. Compared with traditional algorithm FSL, SPM, and deep learning algorithm Kim et al, Zhao et al our method has improvements of 48.90%, 30.73%, 36.38%, and 16.73% in the peak t value of t-maps. Our proposed method can achieve superior functional registration performance and thus gain a significant improvement in functional consistency.

Hemodynamic evaluation of patients with Moyamoya Angiopathy: comparison of resting-state fMRI to breath-hold fMRI and [15O]water PET

PurposePatients with Moyamoya Angiopathy (MMA) require hemodynamic evaluation to assess the risk of stroke. Assessment of cerebral blood flow with [15O]water PET and acetazolamide challenge is the diagnostic standard for the evaluation of the cerebral perfusion reserve (CPR). Estimation of the cerebrovascular reactivity (CVR) by use of breath-hold-triggered fMRI (bh-fMRI) as an index of CPR has been proposed as a reliable and more readily available approach. Recent findings suggest the use of resting-state fMRI (rs-fMRI) which requires minimum patient compliance. The aim of this study was to compare rs-fMRI to bh-fMRI and [15O]water PET in patients with MMA.MethodsPatients with MMA underwent rs-fMRI and bh-fMRI in the same MRI session. Maps of the CVR gained by both modalities were compared retrospectively by calculating the correlation between the mean CVR of 12 volumes of interest. Additionally, the rs-maps of a subgroup of patients were compared to CPR-maps gained by [15O]water PET.ResultsThe comparison of the rs-maps and the bh-maps of 24 patients revealed a good correlation (Pearson’s r = 0.71 ± 0.13; preoperative patients: Pearson’s r = 0.71 ± 0.17; postoperative patients: Pearson’s r = 0.71 ± 0.11). The comparison of 7 rs-fMRI data sets to the corresponding [15O]water PET data sets also revealed a high level of agreement (Pearson’s r = 0.80 ± 0.19).ConclusionThe present analysis indicates that rs-fMRI might be a promising non-invasive method with almost no patient cooperation needed to evaluate the CVR. Further prospective studies are required.