Human-Guided Functional Connectivity Network Estimation for Chronic Tinnitus Identification: A Modularity View

Optimizing functional brain network analysis by incorporating nonlinear factors and frequency band selection with machine learning models

The accurate assessment of the brain's functional network is seen as crucial for the understanding of complex relationships between different brain regions. Hidden information within different frequency bands, which is often overlooked by traditional linear correlation-based methods such as Pearson correlation (PC) and partial correlation, fails to be revealed, leading to the neglect of more intricate nonlinear factors. These limitations were aimed to be overcome in this study by the combination of fast continuous wavelet transform and normalized mutual information (NMI) to develop a novel approach. Original time-domain signals from resting-state functional magnetic resonance imaging were decomposed into different frequency domains using fast continuous wavelet transform, and adjacency matrices were constructed to enhance feature separation across brain regions. Both linear and nonlinear aspects between brain regions were comprehensively considered through the integration of complex correlation coefficient and NMI. The construction of functional brain networks was enabled by the adaptive selection of optimal frequency band combinations. The construction of the model was facilitated by feature extraction using tree models with extreme gradient boosting. It was demonstrated through comparative analysis that the method outperformed baseline methods such as PC and NMI, achieving an area under the curve of 0.9054. The introduction of nonlinear factors was found to increase precision by 14.25% and recall by 17.14%. Importantly, the approach optimized the original data without significantly altering the feature topology. Overall, this innovation advances the understanding of brain function, offering more accurate potential for future research and clinical applications.

Urine Albumin-to-Creatinine Ratio as an Indicator of Brain Activity Changes in Chronic Kidney Disease: A Resting-State fMRI Study

OBJECTIVE

Chronic kidney disease (CKD) is increasingly recognized as a risk factor for alterations in brain function. However, detecting early-stage symptoms and structural changes remains challenging, potentially leading to delayed treatment. In our study, we aimed to investigate spontaneous brain activity changes in CKD patients using resting-state functional magnetic resonance imaging (fMRI). Additionally, we explored the correlation between common biomarkers reflecting CKD severity and brain activity.

METHODS

We recruited a cohort of 22 non-dialysis-dependent CKD patients and 22 controls for resting-state fMRI scans. Amplitude of low-frequency fluctuations (ALFFs) and regional homogeneity (ReHo) were calculated to evaluate brain activity. Regression analysis was conducted to explore the correlations between biomarkers reflecting the severity of CKD and brain activity.

RESULTS

CKD patients exhibited reduced z-scored ALFF (zALFF) and mean ALFF (mALFF) in the bilateral putamen, right caudate nucleus, left anterior cingulate, and right precuneus. Changes in bilateral putamen were also found in smCohe-ReHo and szCohe-ReHo analyses. Urine albumin-to-creatinine ratio (UACR), urine protein-to-creatinine ratio (UPCR), and serum albumin levels were associated with attenuated putamen activity.

CONCLUSION

Non-dialysis-dependent CKD patients had changes in zALFF, mALFF, smCohe-ReHo, and szCohe-ReHo values in specific brain regions, especially bilateral putamen. UACR, UPCR, and serum albumin levels are associated with putamen activity attenuation in rs-fMRI.

A multi-frequency approach of the altered functional connectome for autism spectrum disorder identification

Autism spectrum disorder stands as a multifaceted and heterogeneous neurodevelopmental condition. The utilization of functional magnetic resonance imaging to construct functional brain networks proves instrumental in comprehending the intricate interplay between brain activity and autism spectrum disorder, thereby elucidating the underlying pathogenesis at the cerebral level. Traditional functional brain networks, however, typically confine their examination to connectivity effects within a specific frequency band, disregarding potential connections among brain areas that span different frequency bands. To harness the full potential of interregional connections across diverse frequency bands within the brain, our study endeavors to develop a novel multi-frequency analysis method for constructing a comprehensive functional brain networks that incorporates multiple frequencies. Specifically, our approach involves the initial decomposition of functional magnetic resonance imaging into distinct frequency bands through wavelet transform. Subsequently, Pearson correlation is employed to generate corresponding functional brain networks and kernel for each frequency band. Finally, the classification was performed by a multi-kernel support vector machine, to preserve the connectivity effects within each band and the connectivity patterns shared among the different bands. Our proposed multi-frequency functional brain networks method yielded notable results, achieving an accuracy of 89.1%, a sensitivity of 86.67%, and an area under the curve of 0.942 in a publicly available autism spectrum disorder dataset.

Comparison of brain gray matter volume changes in peritoneal dialysis and hemodialysis patients with chronic kidney disease: a VBM study

Objective

This study aims to compare gray matter volume changes in patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD) and hemodialysis (HD) using voxel-based morphometry (VBM).

Methods

A total of 27 PD patients, 25 HD patients, and 42 healthy controls were included. VBM analysis was performed, and cognitive function was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment Scale (MoCA). The correlation between cognitive function and changes in brain gray matter volume was analyzed.

Results

Both peritoneal dialysis and hemodialysis patients had partial gray matter volume reduction compared to the controls, but the affected brain regions were not uniform. The hemodialysis patients had greater volume reduction in certain brain regions than the PD patients. The MMSE and MoCA scores were positively correlated with gray matter volume changes.

Conclusion

Different dialysis modalities cause damage to specific areas of the brain, which can be detected using VBM. VBM, combined with cognitive function assessment, can help detect structural brain changes and cognitive impairment in patients with different dialysis modalities. The comprehensive application of VBM in the field of neurological function deserves further exploration.

Cortical-cerebellar circuits changes in preschool ASD children by multimodal MRI

OBJECTIVE

To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with autism spectrum disorder using multimodal magnetic resonance imaging.

METHODS

We utilized diffusion basis spectrum imaging approaches, namely DBSI_20 and DBSI_combine, alongside 3D structural imaging to examine 31 autism spectrum disorder diagnosed patients and 30 healthy controls. The participants' brains were segmented into 120 anatomical regions for this analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification.

RESULTS

The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal and subcortical pathways, whereas negative consensus connections were mainly observed in frontotemporal and subcortical pathways. Among the models tested, DBSI_20 showed the highest accuracy rate of 86.88%. In addition, further analysis indicated that combining the 3 models resulted in the most effective performance.

CONCLUSION

The connectivity network analysis of the multimodal brain data identified significant abnormalities in the cortical-cerebellar circuits in autism spectrum disorder patients. The DBSI_20 model not only provided the highest accuracy but also demonstrated efficiency, suggesting its potential for clinical application in autism spectrum disorder diagnosis.

Classification of Alzheimer's disease: application of a transfer learning deep Q-network method

Early diagnosis is crucial to slowing the progression of Alzheimer's disease (AD), so it is urgent to find an effective diagnostic method for AD. This study intended to investigate whether the transfer learning approach of deep Q-network (DQN) could effectively distinguish AD patients using local metrics of resting-state functional magnetic resonance imaging (rs-fMRI) as features. This study included 1310 subjects from the Consortium for Reliability and Reproducibility (CoRR) and 50 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) GO/2. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF) and percent amplitude of fluctuation (PerAF) were extracted as features using the Power 264 atlas. Based on gender bias in AD, we searched for transferable similar parts between the CoRR feature matrix and the ADNI feature matrix, resulting in the CoRR similar feature matrix served as the source domain and the ADNI similar feature matrix served as the target domain. A DQN classifier was pre-trained in the source domain and transferred to the target domain. Finally, the transferred DQN classifier was used to classify AD and healthy controls (HC). A permutation test was performed. The DQN transfer learning achieved a classification accuracy of 86.66% (p < 0.01), recall of 83.33% and precision of 83.33%. The findings suggested that the transfer learning approach using DQN could be an effective way to distinguish AD from HC. It also revealed the potential value of local brain activity in AD clinical diagnosis.

Neuromelanin-sensitive MRI of the substantia nigra distinguishes bipolar from unipolar depression

Depression in bipolar disorder (BD-II) is frequently misdiagnosed as unipolar depression (UD) leading to inappropriate treatment and downstream complications for many bipolar sufferers. In this study, we evaluated whether neuromelanin-MR signal and volume changes in the substantia nigra (SN) can be used as potential biomarkers to differentiate BD-II from UD. The signal intensities and volumes of the SN regions were measured, and contrast-to-noise ratio (CNR) to the decussation of the superior cerebellar peduncles were calculated and compared between healthy controls (HC), BD-II and UD subjects. Results showed that compare to HC, both BD-II and UD subjects had significantly decreased CNR and increased volume on the right and left sides. Moreover, the volume in BD-II group was significantly increased compared to UD group. The area under the receiver operating characteristic curve (AUC) for discriminating BD from HC was the largest for the Volume-L (AUC, 0.85; 95% confidence interval [CI]: 0.77, 0.93). The AUC for discriminating UD from HC was the largest for the Volume-L (AUC, 0.76; 95% CI: 0.65, 0.86). Furthermore, the AUC for discriminating BD from UD was the largest for the Volume-R (AUC, 0.73; 95% CI: 0.62, 0.84). Our findings suggest that neuromelanin-sensitive magnetic resonance imaging techniques can be used to differentiate BD-II from UD.

Wavelet transform-based frequency self-adaptive model for functional brain network

The accurate estimation of functional brain networks is essential for comprehending the intricate relationships between different brain regions. Conventional methods such as Pearson Correlation and Sparse Representation often fail to uncover concealed information within diverse frequency bands. To address this limitation, we introduce a novel frequency-adaptive model based on wavelet transform, enabling selective capture of highly correlated frequency band sequences. Our approach involves decomposing the original time-domain signal from resting-state functional magnetic resonance imaging into distinct frequency domains, thus constructing an adjacency matrix that offers enhanced separation of features across brain regions. Comparative analysis demonstrates the superior performance of our proposed model over conventional techniques, showcasing improved clarity and distinctiveness. Notably, we achieved the highest accuracy rate of 89.01% using Sparse Representation based on Wavelet Transform, outperforming Pearson Correlation based on Wavelet Transform with an accuracy of 81.32%. Importantly, our method optimizes raw data without significantly altering feature topology, rendering it adaptable to various functional brain network estimation approaches. Overall, this innovation holds the potential to advance the understanding of brain function and furnish more accurate samples for future research and clinical applications.

The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification

18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on 18F-FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen-Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual-level metabolic connectome of 18F-FDG PET, providing more dimensional and systematic mechanism insights for PD.

Morphologic brain network predicts levodopa responsiveness in Parkinson disease

Background

The levodopa challenge test (LCT) has been routinely used in Parkinson disease (PD) evaluation and predicts the outcome of deep brain stimulation (DBS). Guidelines recommend that patients with an improvement in Unified Parkinson's Disease Rating Scale (UPDRS)-III score > 33% in the LCT receive DBS treatment. However, LCT results are affected by many factors, and only provide information on the immediate effectiveness of dopamine. The aim of the present study was to investigate the relationship between LCT outcome and brain imaging features of PD patients to determine whether the latter can be used to identify candidates for DBS.

Methods

A total of 38 PD patients were enrolled in the study. Based on improvement in UPDRS-III score in the LCT, patients were divided into low improvement (PD-LCT-L) and high improvement (PD-LCT-H) groups. Each patient's neural network was reconstructed based on T1-weighted magnetic resonance imaging data using the Jensen-Shannon divergence similarity estimation method. The network was established with the multiple kernel support vector machine technique. We analyzed differences in individual morphologic brain networks and their global and local metrics to determine whether there were differences in the connectomes of PD-LCT-L and PD-LCT-H groups.

Results

The 2 groups were similar in terms of demographic and clinical characteristics. Mean ± SD levodopa responsiveness was 26.52% ± 3.47% in the PD-LCT-L group (N = 13) and 58.66% ± 4.09% in the PD-LCT-H group (N = 25). There were no significant differences between groups in global and local metrics. There were 43 consensus connections that were affected in both groups; in PD-LCT-L patients, most of these connections were decreased whereas those related to the dorsolateral superior frontal gyrus and left cuneus were significantly increased.

Conclusion

Morphologic brain network assessment is a valuable method for predicting levodopa responsiveness in PD patients, which can facilitate the selection of candidates for DBS.

Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder

Depression (major depressive disorder, MDD) is a common and serious medical illness. Globally, it is estimated that 5% of adults suffer from depression. Recently, imaging genetics receives growing attention and become a powerful strategy for discoverying the associations between genetic variants (e.g., single-nucleotide polymorphisms, SNPs) and multi-modality brain imaging data. However, most of the existing MDD imaging genetic research studies conducted by clinicians usually utilize simple statistical analysis methods and only consider single-modality brain imaging, which are limited in the deeper discovery of the mechanistic understanding of MDD. It is therefore imperative to utilize a powerful and efficient technology to fully explore associations between genetic variants and multi-modality brain imaging. In this study, we developed a novel imaging genetic association framework to mine the multi-modality phenotype network between genetic risk variants and multi-stage diagnosis status. Specifically, the multi-modality phenotype network consists of voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI). Thereafter, an association model based on multi-task learning strategy was adopted to fully explore the relationship between the MDD risk SNP and the multi-modality phenotype network. The multi-stage diagnosis status was introduced to further mine the relation among the multiple modalities of different subjects. A multi-modality brain imaging data and genotype data were collected by us from two hospitals. The experimental results not only demonstrate the effectiveness of our proposed method but also identify some consistent and stable brain regions of interest (ROIs) biomarkers from the node and edge features of multi-modality phenotype network. Moreover, four new and potential risk SNPs associated with MDD were discovered.

Abnormal regional homogeneity and amplitude of low frequency fluctuation in chronic kidney patients with and without dialysis

Purpose

The study characterizes regional homogeneity (ReHo) and amplitude of low frequency fluctuations (ALFF) in abnormal regions of brain in patients of chronic kidney disease (CKD).

Materials and methods

A total of 64 patients of CKD were divided into 26 cases of non-dialysis-dependent chronic kidney disease (NDD-CKD), and 38 cases of dialysis-dependent chronic kidney disease (DD-CKD). A total of 43 healthy controls (normal control, NC) were also included. All subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI). ALFF and ReHo data was processed for monitoring the differences in spontaneous brain activity between the three groups. ALFF and ReHo values of extracted differential brain regions were correlated to the clinical data and cognitive scores of CKD patients.

Results

Non-dialysis-dependent group has increased ALFF levels in 13 brain regions while that of DD group in 28 brain regions as compared with NC group. ReHo values are altered in six brain regions of DD group. ALFF is correlated with urea nitrogen and ReHo with urea nitrogen and creatinine. DD group has altered ReHo in two brain regions compared with NDD group. The differences are located in basal ganglia, cerebellar, and hippocampus regions.

Conclusion

Abnormal activity in basal ganglia, cerebellar, and hippocampal regions may be involved in the cognitive decline of CKD patients. This link can provide theoretical basis for understanding the cognitive decline.

Adaptive noise depression for functional brain network estimation

Autism spectrum disorder (ASD) is one common psychiatric illness that manifests in neurological and developmental disorders, which can last throughout a person's life and cause challenges in social interaction, communication, and behavior. Since the standard ASD diagnosis is highly based on the symptoms of the disease, it is difficult to make an early diagnosis to take the best cure opportunity. Compared to the standard methods, functional brain network (FBN) could reveal the statistical dependence among neural architectures in brains and provide potential biomarkers for the early neuro-disease diagnosis and treatment of some neurological disorders. However, there are few FBN estimation methods that take into account the noise during the data acquiring process, resulting in poor quality of FBN and thus poor diagnosis results. To address such issues, we provide a brand-new approach for estimating FBNs under a noise modeling framework. In particular, we introduce a noise term to model the representation errors and impose a regularizer to incorporate noise prior into FBNs estimation. More importantly, the proposed method can be formulated as conducting traditional FBN estimation based on transformed fMRI data, which means the traditional methods can be elegantly modified to support noise modeling. That is, we provide a plug-and-play noise module capable of being embedded into different methods and adjusted according to different noise priors. In the end, we conduct abundant experiments to identify ASD from normal controls (NCs) based on the constructed FBNs to illustrate the effectiveness and flexibility of the proposed method. Consequently, we achieved up to 13.04% classification accuracy improvement compared with the baseline methods.

Multi-band network fusion for Alzheimer's disease identification with functional MRI

INTRODUCTION

The analysis of functional brain networks (FBNs) has become a promising and powerful tool for auxiliary diagnosis of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Previous studies usually estimate FBNs using full band Blood Oxygen Level Dependent (BOLD) signal. However, a single band is not sufficient to capture the diagnostic and prognostic information contained in multiple frequency bands.

METHOD

To address this issue, we propose a novel multi-band network fusion framework (MBNF) to combine the various information (e.g., the diversification of structural features) of multi-band FBNs. We first decompose the BOLD signal adaptively into two frequency bands named high-frequency band and low-frequency band by the ensemble empirical mode decomposition (EEMD). Then the similarity network fusion (SNF) is performed to blend two networks constructed by two frequency bands together into a multi-band fusion network. In addition, we extract the features of the fused network towards a better classification performance.

RESULT

To verify the validity of the scheme, we conduct our MBNF method on the public ADNI database for identifying subjects with AD/MCI from normal controls.

DISCUSSION

Experimental results demonstrate that the proposed scheme extracts rich multi-band network features and biomarker information, and also achieves better classification accuracy.