Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.

OBJECTIVE: Brain disorders are one of the major global mortality issues, and their early detection is crucial for healing. Machine learning, specifically deep learning, is a technology that is increasingly being used to detect and diagnose brain disorders. Our objective is to provide a quantitative bibliometric analysis of the field to inform researchers about trends that can inform their Research directions in the future.
METHODS: We carried out a bibliometric analysis to create an overview of brain disorder detection and diagnosis using machine learning and deep learning. Our bibliometric analysis includes 1550 articles gathered from the Scopus database on automated brain disorder detection and diagnosis using machine learning and deep learning published from 2015 to May 2023. A thorough bibliometric análisis is carried out with the help of Biblioshiny and the VOSviewer platform. Citation analysis and various measures of collaboration are analyzed in the study.
RESULTS: According to a study, maximum research is reported in 2022, with a consistent rise from preceding years. The majority of the authors referenced have concentrated on multiclass classification and innovative convolutional neural network models that are effective in this field. A keyword analysis revealed that among the several brain disorder types, Alzheimer\'s, autism, and Parkinson\'s disease had received the greatest attention. In terms of both authors and institutes, the USA, China, and India are among the most collaborating countries. We built a future research agenda based on our findings to help progress research on machine learning and deep learning for brain disorder detection and diagnosis.
CONCLUSIONS: In summary, our quantitative bibliometric analysis provides useful insights about trends in the field and points them to potential directions in applying machine learning and deep learning for brain disorder detection and diagnosis.

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Resting-state functional MRI (rs-fMRI) is increasingly used to detect altered functional connectivity patterns caused by brain disorders, thereby facilitating objective quantification of brain pathology. Existing studies typically extract fMRI features using various machine/deep learning methods, but the generated imaging biomarkers are often challenging to interpret. Besides, the brain operates as a modular system with many cognitive/topological modules, where each module contains subsets of densely inter-connected regions-of-interest (ROIs) that are sparsely connected to ROIs in other modules. However, current methods cannot effectively characterize brain modularity. This paper proposes a modularity-constrained dynamic representation learning (MDRL) framework for interpretable brain disorder analysis with rs-fMRI. The MDRL consists of 3 parts: (1) dynamic graph construction, (2) modularity-constrained spatiotemporal graph neural network (MSGNN) for dynamic feature learning, and (3) prediction and biomarker detection. In particular, the MSGNN is designed to learn spatiotemporal dynamic representations of fMRI, constrained by 3 functional modules (i.e., central executive network, salience network, and default mode network). To enhance discriminative ability of learned features, we encourage the MSGNN to reconstruct network topology of input graphs. Experimental results on two public and one private datasets with a total of 1,155 subjects validate that our MDRL outperforms several state-of-the-art methods in fMRI-based brain disorder analysis. The detected fMRI biomarkers have good explainability and can be potentially used to improve clinical diagnosis.

Nanobodies (Nbs) hold significant potential in molecular imaging due to their unique characteristics. However, there are challenges to overcome when it comes to brain imaging. To address these obstacles, collaborative efforts and interdisciplinary research are needed. This article aims to raise awareness and encourage collaboration among researchers from various fields to find solutions for effective brain imaging using Nbs. By fostering cooperation and knowledge sharing, we can make progress in overcoming the existing limitations and pave the way for improved molecular imaging techniques in the future.

The central nervous system relies heavily on neurotransmitters (NTMs), and NTM imbalances have been linked to a wide range of neurological conditions. Thus, the development of reliable detection techniques is essential for advancing brain studies. This review offers a comprehensive analysis of metal-organic frameworks (MOFs), transition metal oxides (TMOs), and MOFs-derived TMOs (MOFs/TMOs) as materials for electrochemical (EC) sensors targeting the detection of key NTMs, specifically dopamine (DA), epinephrine (EP), and serotonin (SR). The unique properties and diverse families of MOFs and TMOs, along with their nanostructured hybrids, are discussed in the context of EC sensing. The review also addresses the challenges in detecting NTMs and proposes a systematic approach to tackle these obstacles. Despite the vast amount of research on MOFs and TMOs-based EC sensors for DA detection, the review highlights the gaps in the literature for MOFs/TMOs-based EC sensors specifically for EP and SR detection, as well as the limited research on microneedles (MNs)-based EC sensors modified with MOFs, TMOs, and MOFs/TMOs for NTMs detection. This review serves as a foundation to encourage researchers to further explore the potential applications of MOFs, TMOs, and MOFs/TMOs-based EC sensors in the context of neurological disorders and other health conditions related to NTMs imbalances.

Resting-state functional magnetic resonance imaging (rs-fMRI) helps characterize regional interactions that occur in the human brain at a resting state. Existing research often attempts to explore fMRI biomarkers that best predict brain disease progression using machine/deep learning techniques. Previous fMRI studies have shown that learning-based methods usually require a large amount of labeled training data, limiting their utility in clinical practice where annotating data is often time-consuming and labor-intensive. To this end, we propose an unsupervised contrastive graph learning (UCGL) framework for fMRI-based brain disease analysis, in which a pretext model is designed to generate informative fMRI representations using unlabeled training data, followed by model fine-tuning to perform downstream disease identification tasks. Specifically, in the pretext model, we first design a bi-level fMRI augmentation strategy to increase the sample size by augmenting blood-oxygen-level-dependent (BOLD) signals, and then employ two parallel graph convolutional networks for fMRI feature extraction in an unsupervised contrastive learning manner. This pretext model can be optimized on large-scale fMRI datasets, without requiring labeled training data. This model is further fine-tuned on to-be-analyzed fMRI data for downstream disease detection in a task-oriented learning manner. We evaluate the proposed method on three rs-fMRI datasets for cross-site and cross-dataset learning tasks. Experimental results suggest that the UCGL outperforms several state-of-the-art approaches in automated diagnosis of three brain diseases (i.e., major depressive disorder, autism spectrum disorder, and Alzheimer\'s disease) with rs-fMRI data.

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The buildup of beta-amyloid plaques in the brain results in Alzheimer\'s disease (AD), a neurodegenerative condition. A permanent treatment for AD is not yet available. Only a slowing down of its advancement is possible with the current pharmaceutical options. Nevertheless, nanotechnology has proven to be advantageous in medical applications. It has a lot of potential for AD therapy, particularly in diagnosing the condition and providing an alternative course of treatment. In this review, we outline the developments and benefits of nanomedicines in treating AD. Prospective nanomedicines for diagnosing and surveillance therapeutic interventions for AD and other diseases of the central nervous system (CNS) may be clinically accessible, persuading the development of investigation in this field.

Functional connectivity networks (FCN) analysis is instructive for the diagnosis of brain diseases, such as mild cognitive impairment (MCI) and major depressive disorder (MDD) at their early stages. As the critical step of FCN analysis, feature representation provides the basis for finding potential biomarkers of brain diseases. In previous studies, different node statistics (e.g. local efficiency and local clustering coefficients) are usually extracted from FCNs as features for the diagnosis/classification task, which can specifically locate disease-related regions on the node level, so as to help us understand the neurodevelopmental roots of brain disorders. However, each node statistic is proposed only considering a kind of specific network property, which has one-sidedness and limitations. As a result, it is incomplete to represent a node with only one statistic. To resolve this issue, we put forward a novel scheme to select multiple node statistics jointly from the estimated FCNs for automated classification, called multiple node statistics feature selection (MNSFS). Specifically, we first extract multiple statistics from the same nodes and assign each kind of statistic into a group. Then, sparse group least absolute shrinkage and selection operator (sgLASSO) is used to select groups (nodes) and statistics in the groups towards a better classification performance. Such a technique enables us to simultaneously locate the discriminative brain regions, as well as the specific statistics associated with these brain regions, making the classification results more interpretable. We conducted our scheme on two public databases for identifying subjects with MCI and MDD from normal controls. Experimental results show that the proposed scheme achieves superior classification accuracy and features interpreted on the benchmark datasets.

Neuromodulation technology is one of the medical fields currently experiencing the most rapid development, witnessing a surge in the types of modulation techniques and a constant expansion of indications. Consequently, hundreds of thousands of patients with functional neurological disorders have benefited from the advancements in the field all over the world. Nevertheless, some challenges remain, for exmaple, the lack of a thorough understanding of the mechanism of neuromodulation, the long-standing controversy over the optimal targets of neuromodulation, the lack of reliable efficacy predictors, and the cumbersome and inefficient mode of postoperative programming. We anticipate that these issues will be resolved with the continued advancement in medical technology and the gradual revelation of the neural network mechanism of brain disorders. More individualized, precise, and intelligent neuromodulation technology will be the main direction of development in the future. Herein, we reviewed and commented on the evolution of neuromodulation technology, the current status of its applications, and its prospective development.