Functional Neuroimaging in the New Era of Big Data

Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.

Neuroimaging Biomarkers in Addiction

As a neurobiological process, addiction involves pathological patterns of engagement with substances and a range of behaviors with a chronic and relapsing course. Neuroimaging technologies assess brain activity, structure, physiology, and metabolism at scales ranging from neurotransmitter receptors to large-scale brain networks, providing unique windows into the core neural processes implicated in substance use disorders. Identified aberrations in the neural substrates of reward and salience processing, response inhibition, interoception, and executive functions with neuroimaging can inform the development of pharmacological, neuromodulatory, and psychotherapeutic interventions to modulate the disordered neurobiology. Based on our systematic search, 409 protocols registered on ClinicalTrials.gov include the use of one or more neuroimaging paradigms as an outcome measure in addiction, with the majority (N=268) employing functional magnetic resonance imaging (fMRI), followed by positron emission tomography (PET) (N=71), electroencephalography (EEG) (N=50), structural magnetic resonance imaging (MRI) (N=35) and magnetic resonance spectroscopy (MRS) (N=35). Furthermore, in a PubMed systematic review, we identified 61 meta-analyses including 30 fMRI, 22 structural MRI, 8 EEG, 7 PET, and 3 MRS meta-analyses suggesting potential biomarkers in addictions. These studies can facilitate the development of a range of biomarkers that may prove useful in the arsenal of addiction treatments in the coming years. There is evidence that these markers of large-scale brain structure and activity may indicate vulnerability or separate disease subtypes, predict response to treatment, or provide objective measures of treatment response or recovery. Neuroimaging biomarkers can also suggest novel targets for interventions. Closed or open loop interventions can integrate these biomarkers with neuromodulation in real-time or offline to personalize stimulation parameters and deliver the precise intervention. This review provides an overview of neuroimaging modalities in addiction, potential neuroimaging biomarkers, and their physiologic and clinical relevance. Future directions and challenges in bringing these putative biomarkers from the bench to the bedside are also discussed.

Events in context-The HED framework for the study of brain, experience and behavior

The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity. Here we consider an event-related data modeling approach that seeks to parse experience and behavior into a set of time-delimited events. We distinguish between event processes themselves, that unfold through time, and event markers that record the experiment timeline latencies of event onset, offset, and any other event phase transitions. Precise descriptions of experiment events (sensory, motor, or other) allow participant experience and behavior to be interpreted in the context either of the event itself or of all or any experiment events. We discuss how events in neuroimaging experiments have been, are currently, and should best be identified and represented with emphasis on the importance of modeling both events and event context for meaningful interpretation of relationships between brain dynamics, experience, and behavior. We show how text annotation of time series neuroimaging data using the system of Hierarchical Event Descriptors (HED; https://www.hedtags.org) can more adequately model the roles of both events and their ever-evolving context than current data annotation practice and can thereby facilitate data analysis, meta-analysis, and mega-analysis. Finally, we discuss ways in which the HED system must continue to expand to serve the evolving needs of neuroimaging research.

Audiovisualization of real-time neuroimaging data

Advancements in brain imaging techniques have significantly expanded the size and complexity of real-time neuroimaging and behavioral data. However, identifying patterns, trends and synchronies within these datasets presents a significant computational challenge. Here, we demonstrate an approach that can translate time-varying neuroimaging data into unique audiovisualizations consisting of audible representations of dynamic data merged with simplified, color-coded movies of spatial components and behavioral recordings. Multiple variables can be encoded as different musical instruments, letting the observer differentiate and track multiple dynamic parameters in parallel. This representation enables intuitive assimilation of these datasets for behavioral correlates and spatiotemporal features such as patterns, rhythms and motifs that could be difficult to detect through conventional data interrogation methods. These audiovisual representations provide a novel perception of the organization and patterns of real-time activity in the brain, and offer an intuitive and compelling method for complex data visualization for a wider range of applications.

Tumor characteristics, brain functional activity, and connectivity of tinnitus in patients with vestibular schwannoma: a pilot study

Background

The mechanism underlying tinnitus remains unclear, and when it coexists with vestibular schwannoma (VS), it can significantly diminish the quality of life for affected patients. This study aimed to determine the correlation between preoperative clinical characteristics of VS, postoperative changes in brain function, and tinnitus in patients with VS through a cohort study.

Methods

We collected data from 80 patients with VS preoperatively and 28 patients with VS preoperatively and postoperatively, and recruited 28 healthy controls. We used Chi-squared tests and unpaired t-tests to identify clinical characteristics with a significant preoperative effect. We used paired t-tests to identify brain regions where patients demonstrated significant changes in amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) postoperatively. Tinnitus severity was evaluated using the Tinnitus Handicap Inventory (THI) and Visual Analogue Scale (VAS). Pearson correlation coefficients were applied to assess the relationship between the changes in ALFF and ReHo and the changes in THI and VAS scores postoperatively. We also conducted seed- and region of interest (ROI)-based functional connectivity (FC) analyses.

Results

Before surgery, patients with VS with tinnitus (n=49) had smaller tumors (t=3.293; P<0.001), more solid tumor (χ2=4.559; P=0.033), and less extrusion into the cerebellum brain stem (χ2=10.345; P=0.001) than those without tinnitus (n=31). After surgery, the 28 patients with VS showed a significant reduction in ALFF in the left Cerebellum_Crus2 (a lobule in the cerebellum anatomy) (ROI 1) and a significant reduction in ReHo in the left Cerebellum_Crus1 (a lobule in the cerebellum anatomy) (ROI 2) and the right precuneus (ROI 3). Conversely, ReHo was significantly increased in the right precentral gyrus (ROI 4) [cluster-level P value family-wise error (PFWE) <0.05]. The changes in ALFF values were negatively correlated with changes in the VAS score (r=-0.32; P<0.05). The FC strengths of patients between ROI 2 and the left and right posterior cingulate gyrus were significantly decreased postoperatively [false discovery rate (FDR) correction; P<0.05].

Conclusions

Preoperative tinnitus in patients with VS may be influenced by tumor characteristics. The functional activities of brain regions are possibly altered postoperatively, which may be involved in the maintenance of postoperative tinnitus. Notably, the changes in ALFF are correlated with tinnitus.

Machine learning applied to functional magnetic resonance imaging in anxiety disorders

BACKGROUND

Brain functional abnormalities have been commonly reported in anxiety disorders, including generalized anxiety disorder, social anxiety disorder, panic disorder, agoraphobia, and specific phobias. The role of functional abnormalities in the discrimination of these disorders can be tested with machine learning (ML) techniques. Here, we aim to provide a comprehensive overview of ML studies exploring the potential discriminating role of functional brain alterations identified by functional magnetic resonance imaging (fMRI) in anxiety disorders.

METHODS

We conducted a search on PubMed, Web of Science, and Scopus of ML investigations using fMRI as features in patients with anxiety disorders. A total of 12 studies (resting-state fMRI n = 5, task-based fMRI n = 6, resting-state and task-based fMRI n=1) met our inclusion criteria.

RESULTS

Overall, the studies showed that, regardless of the classifiers, alterations in functional connectivity and aberrant neural activation involving the amygdala, anterior cingulate cortex, hippocampus, insula, orbitofrontal cortex, temporal pole, cerebellum, default mode network, dorsal attention network, sensory network, and affective network were able to discriminate patients with anxiety from controls, with accuracies spanning from 36 % to 94 %.

LIMITATIONS

The small sample size, different ML approaches and heterogeneity in the selection of regions included in the multivariate pattern analyses limit the conclusions of the present review.

CONCLUSIONS

ML methods using fMRI as features can distinguish patients with anxiety disorders from healthy controls, indicating that these techniques could be used as a helpful tool for the diagnosis and the development of more targeted treatments for these disorders.

The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis

Objective

Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD.

Methods

English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity.

Results

Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: p < 0.01, specificity: p < 0.001), graph theory (sensitivity: p < 0.05, specificity: p < 0.01), n > 100 (sensitivity: p < 0.001, specificity: p < 0.001), simens equipment (sensitivity: p < 0.01, specificity: p < 0.001), 3.0T field strength (Sensitivity: p < 0.001, specificity: p = 0.04), and Beck Depression Inventory (BDI) (sensitivity: p = 0.04, specificity: p = 0.06) might be the sources of heterogeneity. Furthermore, the subgroup analysis showed that the sample size (n > 100: sensitivity: 0.71, specificity: 0.72, n < 100: sensitivity: 0.81, specificity: 0.79), the different levels of disease evaluated by the Hamilton Depression Rating Scale (HDRS/HAMD) (mild vs. moderate vs. severe: sensitivity: 0.52 vs. 0.86 vs. 0.89, specificity: 0.62 vs. 0.78 vs. 0.82, respectively), the depression scales in patients with comparable levels of severity. (BDI vs. HDRS/HAMD: sensitivity: 0.86 vs. 0.87, specificity: 0.78 vs. 0.80, respectively), and the features (graph vs. functional connectivity: sensitivity: 0.84 vs. 0.86, specificity: 0.76 vs. 0.78, respectively) selected might be the causes of heterogeneity.

Conclusion

ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion

To embrace big-data neuroimaging, harmonizing the site effect in resting-state functional magnetic resonance imaging (R-fMRI) data fusion is a fundamental challenge. A comprehensive evaluation of potentially effective harmonization strategies, particularly with specifically collected data, has been scarce, especially for R-fMRI metrics. Here, we comprehensively assess harmonization strategies from multiple perspectives, including tests on residual site effect, individual identification, test-retest reliability, and replicability of group-level statistical results, on widely used R-fMRI metrics across various datasets, including data obtained from participants with repetitive measures at different scanners. For individual identifiability (i.e., whether the same subject could be identified across R-fMRI data scanned across different sites), we found that, while most methods decreased site effects, the Subsampling Maximum-mean-distance based distribution shift correction Algorithm (SMA) and parametric unadjusted CovBat outperformed linear regression models, linear mixed models, ComBat series and invariant conditional variational auto-encoder in clustering accuracy. Test-retest reliability was better for SMA and parametric adjusted CovBat than unadjusted ComBat series and parametric unadjusted CovBat in the number of overlapped voxels. At the same time, SMA was superior to the latter in replicability in terms of the Dice coefficient and the scale of brain areas showing sex differences reproducibly observed across datasets. Furthermore, SMA better detected reproducible sex differences of ALFF under the site-sex confounded situation. Moreover, we designed experiments to identify the best target site features to optimize SMA identifiability, test-retest reliability, and stability. We noted both sample size and distribution of the target site matter and introduced a heuristic formula for selecting the target site. In addition to providing practical guidelines, this work can inform continuing improvements and innovations in harmonizing methodologies for big R-fMRI data.

Systematic bibliometric and visualized analysis of research hotspots and trends in attention-deficit hyperactivity disorder neuroimaging

Introduction

This study focused on the research hotspots and development trends of the neuroimaging of attention deficit hyperactivity disorder (ADHD) in the past thirty years.

Methods

The Web of Science database was searched for articles about ADHD neuroimaging from January 1992 to September 2022. CiteSpace was used to analyze the co-occurrence of keywords in literature, partnerships between authors, institutions, and countries, the sudden occurrence of keywords, clustering of keywords over time, and analysis of references, cited authors, and cited journals.

Results

2,621 articles were included. More and more articles have been published every year in the last years. These articles mainly come from 435 institutions and 65 countries/regions led by the United States. King's College London had the highest number of publications. The study identified 634 authors, among which Buitelaar, J. K. published the largest number of articles and Castellanos, F. X. was co-cited most often. The most productive and cited journal was Biological psychiatry. In recent years, burst keywords were resting-state fMRI, machine learning, functional connectivity, and networks. And a timeline chart of the cluster of keywords showed that “children” had the longest time span.

Conclusions

Increased attention has been paid to ADHD neuroimaging. This work might assist researchers to identify new insight on potential collaborators and cooperative institutions, hot topics, and research directions.

Effect of transcranial direct current stimulation on postpartum depression: A study protocol for a randomized controlled trial

Postpartum depression (PPD) is a complex combination of physiological, emotional, and behavioral alterations associated with postpartum chemical, social, and psychological variations. It does harm to the relationship between family members that could potentially last for years. However, standard depression treatments are not ideal for PPD, and the outcomes of these treatments are debatable. Transcranial direct current stimulation (tDCS) is an emerging technology that could provide patients with PPD with a safe and non-pharmacological treatment. tDCS can relieve depression by directly stimulating the prefrontal cortex through the excitatory effect of the anode. It may also ease depression indirectly by promoting the production and release of the neurotransmitter GABA. The mechanism of tDCS makes it an ideal therapeutic approach to treat PPD, although it has not been widely used, and its effect has not been evaluated systematically and effectively. A double-blind, randomized controlled trial will be conducted involving 240 tDCS-naive patients with PPD, who will be randomly divided into two groups. One group will receive routine clinical treatment and care with active tDCS, and the other group will receive routine clinical treatment and care with sham tDCS. Each group of patients will receive a 3-week intervention during which they will receive 20 min of active or sham tDCS 6 days per week. The Montgomery-Åsberg Depression Rating Scale will be administered before the intervention as a baseline and on each weekend throughout the intervention phase. Before and after the intervention, the Perceived Stress Scale and the Positive and Negative Affect Schedule will be evaluated. Side effects and abnormal reactions will be recorded during each treatment. As antidepressants are banned in the study, the results will not be affected by drugs and will therefore be more accurate. Nonetheless, this experiment will be conducted in a single center as a small sample experiment. Therefore, future studies are required to confirm the effectiveness of tDCS in treating PPD.

BLMM: Parallelised computing for big linear mixed models

Within neuroimaging large-scale, shared datasets are becoming increasingly commonplace, challenging existing tools both in terms of overall scale and complexity of the study designs. As sample sizes grow, researchers are presented with new opportunities to detect and account for grouping factors and covariance structure present in large experimental designs. In particular, standard linear model methods cannot account for the covariance and grouping structures present in large datasets, and the existing linear mixed models (LMM) tools are neither scalable nor exploit the computational speed-ups afforded by vectorisation of computations over voxels. Further, nearly all existing tools for imaging (fixed or mixed effect) do not account for variability in the patterns of missing data near cortical boundaries and the edge of the brain, and instead omit any voxels with any missing data. Yet in the large-n setting, such a voxel-wise deletion missing data strategy leads to severe shrinkage of the final analysis mask. To counter these issues, we describe the "Big" Linear Mixed Models (BLMM) toolbox, an efficient Python package for large-scale fMRI LMM analyses. BLMM is designed for use on high performance computing clusters and utilizes a Fisher Scoring procedure made possible by derivations for the LMM Fisher information matrix and score vectors derived in our previous work, Maullin-Sapey and Nichols (2021).

Multi-modal intermediate integrative methods in neuropsychiatric disorders: A review

The etiology of neuropsychiatric disorders involves complex biological processes at different omics layers, such as genomics, transcriptomics, epigenetics, proteomics, and metabolomics. The advent of high-throughput technology, as well as the availability of large open-source datasets, has ushered in a new era in system biology, necessitating the integration of various types of omics data. The complexity of biological mechanisms, the limitations of integrative strategies, and the heterogeneity of multi-omics data have all presented significant challenges to computational scientists. In comparison to early and late integration, intermediate integration may transform each data type into appropriate intermediate representations using various data transformation techniques, allowing it to capture more complementary information contained in each omics and highlight new interactions across omics layers. Here, we reviewed multi-modal intermediate integrative techniques based on component analysis, matrix factorization, similarity network, multiple kernel learning, Bayesian network, artificial neural networks, and graph transformation, as well as their applications in neuropsychiatric domains. We depicted advancements in these approaches and compared the strengths and weaknesses of each method examined. We believe that our findings will aid researchers in their understanding of the transformation and integration of multi-omics data in neuropsychiatric disorders.

The way forward for neuroethics in Japan: A review of five topics surrounding present challenges

Neuroethics is the study of how neuroscience impacts humans and society. About 15 years have passed since neuroethics was introduced to Japan, yet the field of neuroethics still seeks developed methodologies and an established academic identity. In light of progress in neuroscience and neurotechnology, the challenges for Japanese neuroethics in the 2020s can be categorized into five topics. (1) The need for further research into the importance of informed consent in psychiatric research and the promotion of public-patient engagement. (2) The need for a framework that constructs a global environment for neuroscience research that utilizes reliable samples and data. (3) The need for ethical support within a Japanese context regarding the construction of brain banks and the research surrounding their use. It is also important to reconsider the moral value of the human neural system and make comparisons with non-human primates. (4) An urgent need to study neuromodulation technologies that intervene in emotions. (5) The need to reconsider neuroscience and neurotechnology from social points of view. Rules for neuroenhancements and do-it-yourself neurotechnologies are urgently needed, while from a broader perspective, it is essential to study the points of contact between neuroscience and public health.

Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution

Objective. Objective of this work is the development and evaluation of a cortical parcellation framework based on tractography-derived brain structural connectivity. Impact Statement. The proposed framework utilizes novel spatial-graph representation learning methods for solving the task of cortical parcellation, an important medical image analysis and neuroscientific problem. Introduction. The concept of "connectional fingerprint" has motivated many investigations on the connectivity-based cortical parcellation, especially with the technical advancement of diffusion imaging. Previous studies on multiple brain regions have been conducted with promising results. However, performance and applicability of these models are limited by the relatively simple computational scheme and the lack of effective representation of brain imaging data. Methods. We propose the Spatial-graph Convolution Parcellation (SGCP) framework, a two-stage deep learning-based modeling for the graph representation brain imaging. In the first stage, SGCP learns an effective embedding of the input data through a self-supervised contrastive learning scheme with the backbone encoder of a spatial-graph convolution network. In the second stage, SGCP learns a supervised classifier to perform voxel-wise classification for parcellating the desired brain region. Results. SGCP is evaluated on the parcellation task for 5 brain regions in a 15-subject DWI dataset. Performance comparisons between SGCP, traditional parcellation methods, and other deep learning-based methods show that SGCP can achieve superior performance in all the cases. Conclusion. Consistent good performance of the proposed SGCP framework indicates its potential to be used as a general solution for investigating the regional/subregional composition of human brain based on one or more connectivity measurements.

Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use

Large, open datasets have emerged as important resources in the field of human connectomics. In this review, the evolution of data sharing involving magnetic resonance imaging is described. A summary of the challenges and progress in conducting reproducible data analyses is provided, including description of recent progress made in the development of community guidelines and recommendations, software and data management tools, and initiatives to enhance training and education. Finally, this review concludes with a discussion of ethical conduct relevant to analyses of large, open datasets and a researcher's responsibility to prevent further stigmatization of historically marginalized racial and ethnic groups. Moving forward, future work should include an enhanced emphasis on the social determinants of health, which may further contextualize findings among diverse population-based samples. Leveraging the progress to date and guided by interdisciplinary collaborations, the future of connectomics promises to be an impressive era of innovative research, yielding a more inclusive understanding of brain structure and function.

Fisher Scoring for crossed factor linear mixed models

The analysis of longitudinal, heterogeneous or unbalanced clustered data is of primary importance to a wide range of applications. The linear mixed model (LMM) is a popular and flexible extension of the linear model specifically designed for such purposes. Historically, a large proportion of material published on the LMM concerns the application of popular numerical optimization algorithms, such as Newton-Raphson, Fisher Scoring and expectation maximization to single-factor LMMs (i.e. LMMs that only contain one "factor" by which observations are grouped). However, in recent years, the focus of the LMM literature has moved towards the development of estimation and inference methods for more complex, multi-factored designs. In this paper, we present and derive new expressions for the extension of an algorithm classically used for single-factor LMM parameter estimation, Fisher Scoring, to multiple, crossed-factor designs. Through simulation and real data examples, we compare five variants of the Fisher Scoring algorithm with one another, as well as against a baseline established by the R package lme4, and find evidence of correctness and strong computational efficiency for four of the five proposed approaches. Additionally, we provide a new method for LMM Satterthwaite degrees of freedom estimation based on analytical results, which does not require iterative gradient estimation. Via simulation, we find that this approach produces estimates with both lower bias and lower variance than the existing methods.

SUPPLEMENTARY INFORMATION

The online version supplementary material available at 10.1007/s11222-021-10026-6.

Characterization of Brain Iron Deposition Pattern and Its Association With Genetic Risk Factor in Alzheimer's Disease Using Susceptibility-Weighted Imaging

The presence of iron is an important factor for normal brain functions, whereas excessive deposition of iron may impair normal cognitive function in the brain and lead to Alzheimer’s disease (AD). MRI has been widely applied to characterize brain structural and functional changes caused by AD. However, the effectiveness of using susceptibility-weighted imaging (SWI) for the analysis of brain iron deposition is still unclear, especially within the context of early AD diagnosis. Thus, in this study, we aim to explore the relationship between brain iron deposition measured by SWI with the progression of AD using various feature selection and classification methods. The proposed model was evaluated on a 69-subject SWI imaging dataset consisting of 24 AD patients, 21 mild cognitive impairment patients, and 24 normal controls. The identified AD progression-related regions were then compared with the regions reported from previous genetic association studies, and we observed considerable overlap between these two. Further, we have identified a new potential AD-related gene (MEF2C) closely related to the interaction between iron deposition and AD progression in the brain.

DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation

One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurately. This work devises a new method, named DTFLOW, for determining the pseudo-temporal trajectories with multiple branches. DTFLOW consists of two major steps: a new method called Bhattacharyya kernel feature decomposition (BKFD) to reduce the data dimensions, and a novel approach named Reverse Searching on k-nearest neighbor graph (RSKG) to identify the multi-branching processes of cellular differentiation. In BKFD, we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm, and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix. The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets. We compare the efficiency of DTFLOW with the published state-of-the-art methods. Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories. The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.

Promoting global collaboration for brain health research

Liping Liu and colleagues discuss the challenges of global collaboration for brain health research and promising future opportunities for improvement of brain health worldwide