Modeling brain disorders has always been one of the key tasks in neurobiological studies. A wide range of organisms including worms, fruit ？ies, zebra？sh, and rodents have been used for modeling brain disorders. How...Modeling brain disorders has always been one of the key tasks in neurobiological studies. A wide range of organisms including worms, fruit ？ies, zebra？sh, and rodents have been used for modeling brain disorders. However,whether complicated neurological and psychiatric symptoms can be faithfully mimicked in animals is still debatable.In this review, we discuss key ？ndings using non-human primates to address the neural mechanisms underlying stress and anxiety behaviors, as well as technical advances for establishing genetically-engineered non-human primate models of autism spectrum disorders and other disorders.Considering the close evolutionary connections and similarity of brain structures between non-human primates and humans, together with the rapid progress in genome-editing technology, non-human primates will be indispensable for pathophysiological studies and exploring potential therapeutic methods for treating brain disorders.展开更多
The blood-brain barrier(BBB)is a highly selective and critical interface between the blood and the central nervous system(CNS),essential for maintaining the microenvironment necessary for normal brain function and hom...The blood-brain barrier(BBB)is a highly selective and critical interface between the blood and the central nervous system(CNS),essential for maintaining the microenvironment necessary for normal brain function and homeostasis[1].The BBB is primarily formed by brain endothelial cells(bECs),pericytes,and astrocytes,and it operates in concert with microglia/macrophages and neurons[2,3]to constitute the neurovascular unit(Fig.1).Disruption of the BBB or the presence of systemic inflammation can precipitate or exacerbate various CNS pathologies,including Alzheimer’s disease[4],amyotrophic lateral sclerosis[5],Huntington’s disease[6]and multiple sclerosis.展开更多
Brain organoids are artificial neural tissues derived in vitro,containing a variety of cell types,as well as structural and/or functional brain regions.They can partially mimic brain physiological activities and disea...Brain organoids are artificial neural tissues derived in vitro,containing a variety of cell types,as well as structural and/or functional brain regions.They can partially mimic brain physiological activities and diseased processes.Owing to their operability and sample accessibility,brain organoids serve as a bridge between in vitro monolayer cell culture models and in vivo animal models.An increasing number of induction protocols for brain organoids have been developed over the preceding decade.A key future research direction will focus on ensuring the complexity and quality of brain organoids.The integration of powerful technologies,such as the CRISP R/Cas9 genome editing and lineage tra cing systems,shall precipitate practical and broad applications of brain organoids.In this review,we discuss the generation and application of brain organoids,as well as their integration with genome editing technologies,in the study of neural development,disease modeling,and mechanistic investigations.The innovative combination of these two technologies may offer a fresh perspective for exploring the fundamental aspects of the human nervous system and related diseases.展开更多
Plant-derived nanovesicles(PDNVs),including plant extracellular vesicles(EVs)and plant exosome-like nanovesicles(ELNs),are natural nano-sized membranous vesicles containing bioactive molecules.PDNVs consist of a bilay...Plant-derived nanovesicles(PDNVs),including plant extracellular vesicles(EVs)and plant exosome-like nanovesicles(ELNs),are natural nano-sized membranous vesicles containing bioactive molecules.PDNVs consist of a bilayer of lipids that can effectively encapsulate hydrophilic and lipophilic drugs,improving drug stability and solubility as well as providing increased bioavailability,reduced systemic toxicity,and enhanced target accumulation.Bioengineering strategies can also be exploited to modify the PDNVs to achieve precise targeting,controlled drug release,and massive production.Meanwhile,they are capable of crossing the blood-brain barrier(BBB)to transport the cargo to the lesion sites without harboring human pathogens,making them excellent therapeutic agents and drug delivery nanoplatform candidates for brain diseases.Herein,this article provides an initial exposition on the fundamental characteristics of PDNVs,including biogenesis,uptake process,isolation,purification,characterization methods,and source.Additionally,it sheds light on the investigation of PDNVs’utilization in brain diseases while also presenting novel perspectives on the obstacles and clinical advancements associated with PDNVs.展开更多
Despite noteworthy technological progress and promising preclinical trials,brain disorders are still the leading causes of death globally.Extracellular vesicles(EVs),nano-/micro-sized membrane vesicles carrying bioact...Despite noteworthy technological progress and promising preclinical trials,brain disorders are still the leading causes of death globally.Extracellular vesicles(EVs),nano-/micro-sized membrane vesicles carrying bioactive molecules,are involved in cellular communication.Based on their unique properties,including superior biocompatibility,non-immunogenicity,and blood-brain barrier(BBB)penetration,EVs can shield their cargos from immune clearance and transport them to specific site,which have attracted increasing interests as novel nanocarriers for brain disorders.However,considering the limitations of native EVs,such as poor encapsulation efficiency,inadequate targeting capability,uncontrolled drug release,and limited production,researchers bioengineer EVs to fully exploit the clinical potential.Herein,this review initially describes the basic properties,biogenesis,and uptake process of EVs from different subtypes.Then,we highlight the application of EVs derived from different sources for personalized therapy and novel strategies to construct bioengineered EVs for enhanced diagnosis and treatment of brain disorders.Besides,it also presents a systematic comparison between EVs and other brain-targeted nanocarriers.Finally,existing challenges and future perspectives of EVs have been discussed,hoping to bolster the research from benchtop to bedside.展开更多
We refute the controversial statement that addiction is not a brain disorder. Extensive peer-reviewed studies support the underlying neurobiological and neurogenetic basis of addiction’s “disease model”. In the 70s...We refute the controversial statement that addiction is not a brain disorder. Extensive peer-reviewed studies support the underlying neurobiological and neurogenetic basis of addiction’s “disease model”. In the 70s and 80s, a few clinical scientists suggested that it is possible to use behavioral training to teach controlled drinking. However, this controversial model failed drastically and increased labeling and stigmatization. Additionally, it was unhelpful in the search for treatment. Instead, we assert that addiction is a neuropsychiatric disorder characterized by a recurring desire to continue taking substances despite harmful physical and mental consequences. Work from our laboratory in 1995 supported the Reward Deficiency Syndrome (RDS) concept based on a common neurogenetic mechanism (hypodopaminergia) that underlies all substance and non-substance addictions. Non-substance addictions include behaviors like pathological gambling, internet addiction, and mobile phone addiction. Certain impulsive and compulsive behaviors or the acute intake of psychoactive substances result in heightened dopaminergic activity, while the opposite, hypodopaminergia, occurs following chronic abuse. Patients with Substance Use Disorder (SUD) can have a genetic predisposition compounded by stress or other epigenetic insults that can impact recovery. Relapse will occur post-short-term recovery if dopaminergic dysfunction remains untreated. Addiction, a brain disorder, requires treatment with DNA-directed pro-dopamine regulation and rehabilitation.展开更多
Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while ...Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.展开更多
Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representativ...Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representative signals from brain regions of interest (ROIs) is important. In the past decades, the common method is generally to take a ROI as a node, averaging all the voxel time series inside it to extract a representative signal. However, one node does not represent the entire information of this ROI, and averaging method often leads to signal cancellation and information loss. Inspired by this, we propose a novel model extraction method based on an assumption that a ROI can be represented by multiple nodes. Methods: In this paper, we first extract multiple nodes (the number is user-defined) from the ROI based on two traditional methods, including principal component analysis (PCA), and K-means (Clustering according to the spatial position of voxels). Then, canonical correlation analysis (CCA) was issued to construct BFNs by maximizing the correlation between the representative signals corresponding to the nodes in any two ROIs. Finally, to further verify the effectiveness of the proposed method, the estimated BFNs are applied to identify subjects with autism spectrum disorder (ASD) and mild cognitive impairment (MCI) from health controls (HCs). Results: Experimental results on two benchmark databases demonstrate that the proposed method outperforms the baseline method in the sense of classification performance. Conclusions: We propose a novel method for obtaining nodes of ROId based on the hypothesis that a ROI can be represented by multiple nodes, that is, to extract the node signals of ROIs with K-means or PCA. Then, CCA is used to construct BFNs.展开更多
Yoga is a therapeutic practice renowned for its multifaceted benefits across the body's systems.Its positive impact spans the physical,mental and emotional realms,fostering harmony and well-being.Through a combina...Yoga is a therapeutic practice renowned for its multifaceted benefits across the body's systems.Its positive impact spans the physical,mental and emotional realms,fostering harmony and well-being.Through a combination of postures,breathing techniques and meditation,yoga offers profound effects,enhancing flexibility,strength and balance while simultaneously promoting relaxation and reducing stress.This integrative approach not only cultivates physical resilience but also supports mental clarity,emotional balance and overall vitality,showcasing yoga as a comprehensive and impactful system for holistic health.The review delved into the multifaceted ways in which yoga exerts a positive influence on the body's various systems.It highlights how yoga serves as a beneficial tool in addressing and counteracting the underlying factors associated with different diseases.By examining yoga's effects on these systems and its potential in combating illness,the paper sheds light on the comprehensive therapeutic benefits that yoga offers.Please cite this article as:Pandey S,Pandey AC,Kotecha VR.YogaDA complementary and traditional medicine for human health.展开更多
Dear Editor,Growing clinical evidence shows that brain disorders are heterogeneous in phenotype,genetics,and neuropathology[1].Diagnosis and treatment tend to be affected by symptom presentation and the heterogeneity ...Dear Editor,Growing clinical evidence shows that brain disorders are heterogeneous in phenotype,genetics,and neuropathology[1].Diagnosis and treatment tend to be affected by symptom presentation and the heterogeneity of pathology,potentially hindering clinical trials in the development of medical treatment.Brain-based subtyping studies utilize magnetic resonance imaging(MRI)and data-driven methods to discover the subtypes of diseases,providing a new perspective on disease heterogeneity.展开更多
Transcranial focused ultrasound(tFUS)is an emerging modality with strong potential for non-invasively treating brain disorders.However,the inhomogeneity and complex structure of the skull induce substantial phase aber...Transcranial focused ultrasound(tFUS)is an emerging modality with strong potential for non-invasively treating brain disorders.However,the inhomogeneity and complex structure of the skull induce substantial phase aberrations and pressure attenuation;these can distort and shift the acoustic focus,thus hindering the efficiency of tFUS therapy.To achieve effective treatments,phased array transducers combined with aberration correction algorithms are commonly implemented.The present report aims to provide a comprehensive review of the current methods used for tFUS phase aberration correction.We first searched the PubMed and Web of Science databases for studies on phase aberration correction algorithms,identifying 54 articles for review.Relevant information,including the principles of algorithms and refocusing performances,were then extracted from the selected articles.The phase correction algorithms involved two main steps:acoustic field estimation and transmitted pulse adjustment.Our review identified key benchmarks for evaluating the effectiveness of these algorithms,each of which was used in at least three studies.These benchmarks included pressure and intensity,positioning error,focal region size,peak sidelobe ratio,and computational efficiency.Algorithm performances varied under different benchmarks,thus highlighting the importance of application-specific algorithm selection for achieving optimal tFUS therapy outcomes.The present review provides a thorough overview and comparison of various phase correction algorithms,and may offer valuable guidance to tFUS researchers when selecting appropriate phase correction algorithms for specific applications.展开更多
The global incidence of Alzheimer's Disease(AD)is on a swift rise.The Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using ma...The global incidence of Alzheimer's Disease(AD)is on a swift rise.The Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning models.Analysis of AD using EEG involves multi-channel analysis.However,the use of multiple channels may impact the classification performance due to data redundancy and complexity.In this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition methods.Empirical Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG analysis.We extracted thirty-four features from each subband of EEG signals.Finally,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel selection.The effectiveness of this model is assessed by two publicly accessible AD EEG datasets.An accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG channels.Moreover,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)channels.The performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.展开更多
The launch of the Brain Network Disorders journal marks a pivotal moment in neuroscience,addressing the urgent need to unravel the brain’s complex network functions.The brain orchestrates cognitive,emotional,and beha...The launch of the Brain Network Disorders journal marks a pivotal moment in neuroscience,addressing the urgent need to unravel the brain’s complex network functions.The brain orchestrates cognitive,emotional,and behavioral processes,yet its underlying mechanisms—especially how it transitions between health and disease—remain elusive due to the intricate connectivity of its networks.1,2 This journal aims to foster interdisciplinary collaboration,bridging gaps across neurology,computational neuroscience,molecular biology,and clinical research.By focusing on how brain network dynamics influence both physiological and pathological states,we aim to fundamentally reshape our understanding of brain function and disease.展开更多
1.Introduction.This perspective highlights the need for a specialized publication dedicated to neuropsychiatric disorders,collectively termed“dysconnectivity syndromes.”These conditions,including dementia-Alzheimer...1.Introduction.This perspective highlights the need for a specialized publication dedicated to neuropsychiatric disorders,collectively termed“dysconnectivity syndromes.”These conditions,including dementia-Alzheimer’s disease(AD),schizophrenia,and autism spectrum disorders,result from failures in interconnected physiological systems and neural circuits rather than isolated lesions.The proposed Brain Network Disorders(BND)journal aims to promote innovative paradigms for understanding these complex brain disorders by applying general systems theory and complexity sciences.The aim is to broaden the traditional conceptualization of these diseases by stressing the multifactorial roots of these disorders where the focus is on the dynamic interactions among several influences and the failure of multiple overlapping nets that drive clinical manifestations。展开更多
Brain disorders have imposed an escalating socioeconomic burden in recent years,yet critical gaps persist in understanding their pathogenesis and developing effective therapies.Nonhuman primate(NHP)models provide inva...Brain disorders have imposed an escalating socioeconomic burden in recent years,yet critical gaps persist in understanding their pathogenesis and developing effective therapies.Nonhuman primate(NHP)models provide invaluable insights into human brain disorders due to their unique neuroanatomical and neurophysiological similarities with humans.Here,we review the current landscape of genetically modified NHP-based research in brain disorders,emphasizing the pivotal role of gene editing in disease modeling and the distinct advantages of transgenic NHP models in deciphering disease mechanisms.While key mechanistic questions and technical hurdles remain,NHP models hold immense promise in overcoming challenges and accelerating the development of therapeutics for brain disorders.展开更多
Microtubules play a central role in cytoskeletal changes during neuronal development and maintenance.Microtubule dynamics is essential to polarity and shape transitions underlying neural cell division,differentiation,...Microtubules play a central role in cytoskeletal changes during neuronal development and maintenance.Microtubule dynamics is essential to polarity and shape transitions underlying neural cell division,differentiation,motility,and maturation.Kinesin superfamily protein 2A is a member of human kinesin 13 gene family of proteins that depolymerize and destabilize microtubules.In dividing cells,kinesin superfamily protein 2A is involved in mitotic progression,spindle assembly,and chromosome segregation.In postmitotic neurons,it is required for axon/dendrite specification and extension,neuronal migration,connectivity,and survival.Humans with kinesin superfamily protein 2A mutations suffer from a variety of malformations of cortical development,epilepsy,autism spectrum disorder,and neurodegeneration.In this review,we discuss how kinesin superfamily protein 2A regulates neuronal development and function,and how its deregulation causes neurodevelopmental and neurological disorders.展开更多
An emerging paradigm shift for disease diagnosis is to rely on molecular characterization beyond traditional clinical and symptom-based examinations. Although genetic alterations and transcription signature were first...An emerging paradigm shift for disease diagnosis is to rely on molecular characterization beyond traditional clinical and symptom-based examinations. Although genetic alterations and transcription signature were first introduced as potential biomarkers, clinical implementations of these markers are limited due to low reproducibility and accuracy. Instead, epigenetic changes are considered as an alternative approach to disease diagnosis. Complex epigenetic regulation is required for normal biological functions and it has been shown that distinctive epigenetic disruptions could contribute to disease pathogenesis. Disease-specific epigenetic changes, especially DNA methylation, have been observed,suggesting its potential as disease biomarkers for diagnosis. In addition to specificity, the feasibility of detecting disease-associated methylation marks in the biological specimens collcted noninvasively,such as blood samples, has driven the clinical studies to validate disease-specific DNA methylation changes as a diagnostic biomarker. Here, we highlight the advantages of DNA methylation signature for diagnosis in different diseases and discuss the statistical and technical challenges to be overcome before clinical implementation.展开更多
Parkinson’s disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system.The cause of such brain damage seems to be fully explained in many res...Parkinson’s disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system.The cause of such brain damage seems to be fully explained in many research studies,but the understanding of its functionality remains to be impractical.Specifically,the development of a quantitative disease prediction model has evolved in recent decades.Moreover,accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson’s disease.This type of minimal infrastructure equipment helps in analyzing the Parkinson’s gait properties without affecting the common behavioral patterns during the clinical practices.Therefore,the Accelerometer Sensor-based Parkinson’s Disease Identi-fication System(ASPDIS)is introduced with a kernel-based support vector machine classifier model to make an early prediction of the disease.consequently,the proposed classifier can easily predict various severity levels of Parkinson’s disease from the sensor data.The performance of the proposed classifier is com-pared against the existing models such as random forest,decision tree,and k-near-est neighbor classifiers respectively.As per the experimental observation,the proposed classifier has more capability to differentiate Parkinson’s from non-Parkinson patients depending upon the severity levels.Also,it is found that the model has outperformed the existing classifiers concerning prediction time and accuracy respectively.展开更多
Electroencephalography is a sensitive indicator for measuring brain condition, and can reflect early changes in brain function and severity of cerebral ischemia. However, it is not yet known whether electroencephalogr...Electroencephalography is a sensitive indicator for measuring brain condition, and can reflect early changes in brain function and severity of cerebral ischemia. However, it is not yet known whether electroencephalography can predict development of post-cerebral infarc- tion depression. A total of 321 patients with ischemic stroke underwent electroencephalography and Hamilton Depression Rating Scale assessment to analyze the relationship between electroencephalography and post-cerebral infarction depression. Our results show that electroencephalograms of ischemic stroke patients with depression exhibit low-amplitude alpha activity and slow theta activity. In con- trast, electroencephalograms of ischemic stroke patients without depression show fast beta activity and slow delta activity. ＂Ihese findings confirm that low-amplitude alpha activity and slow theta activity can be considered as independent predictors for post-cerebral infarction depression.展开更多
Neuroimaging data typically include multiple modalities,such as structural or functional magnetic resonance imaging,dif-fusion tensor imaging,and positron emission tomography,which provide multiple views for observing...Neuroimaging data typically include multiple modalities,such as structural or functional magnetic resonance imaging,dif-fusion tensor imaging,and positron emission tomography,which provide multiple views for observing and analyzing the brain.To lever-age the complementary representations of different modalities,multimodal fusion is consequently needed to dig out both inter-modality and intra-modality information.With the exploited rich information,it is becoming popular to combine multiple modality data to ex-plore the structural and functional characteristics of the brain in both health and disease status.In this paper,we first review a wide spectrum of advanced machine learning methodologies for fusing multimodal brain imaging data,broadly categorized into unsupervised and supervised learning strategies.Followed by this,some representative applications are discussed,including how they help to under-stand the brain arealization,how they improve the prediction of behavioral phenotypes and brain aging,and how they accelerate the biomarker exploration of brain diseases.Finally,we discuss some exciting emerging trends and important future directions.Collectively,we intend to offer a comprehensive overview of brain imaging fusion methods and their successful applications,along with the chal-lenges imposed by multi-scale and big data,which arises an urgent demand on developing new models and platforms.展开更多
基金supported by the Chinese Academy of Sciences Strategic Priority Research Program (XDB02050400)the National Natural Science Foundation of China (91432111)
文摘Modeling brain disorders has always been one of the key tasks in neurobiological studies. A wide range of organisms including worms, fruit ？ies, zebra？sh, and rodents have been used for modeling brain disorders. However,whether complicated neurological and psychiatric symptoms can be faithfully mimicked in animals is still debatable.In this review, we discuss key ？ndings using non-human primates to address the neural mechanisms underlying stress and anxiety behaviors, as well as technical advances for establishing genetically-engineered non-human primate models of autism spectrum disorders and other disorders.Considering the close evolutionary connections and similarity of brain structures between non-human primates and humans, together with the rapid progress in genome-editing technology, non-human primates will be indispensable for pathophysiological studies and exploring potential therapeutic methods for treating brain disorders.
基金supported by the National Natural Science Foundation of China(Nos.82174010 and 81973512).
文摘The blood-brain barrier(BBB)is a highly selective and critical interface between the blood and the central nervous system(CNS),essential for maintaining the microenvironment necessary for normal brain function and homeostasis[1].The BBB is primarily formed by brain endothelial cells(bECs),pericytes,and astrocytes,and it operates in concert with microglia/macrophages and neurons[2,3]to constitute the neurovascular unit(Fig.1).Disruption of the BBB or the presence of systemic inflammation can precipitate or exacerbate various CNS pathologies,including Alzheimer’s disease[4],amyotrophic lateral sclerosis[5],Huntington’s disease[6]and multiple sclerosis.
基金Special Projectfor Clinical Research of Shanghai Municipal Health Commission,No.202140403Key Disciplines Group Construction Project of Pudong Health Bureau of Shanghai,No.PWZxq2022-05+2 种基金Natural Science Foundation of Ningxia Hui Autonomous Region,No.2024AAC05084Ningxia Hui Autonomous Region Key Research and Development Program,No.2021BEG03084National Natural Science Foundation of China,Nos.32370895,32070862。
文摘Brain organoids are artificial neural tissues derived in vitro,containing a variety of cell types,as well as structural and/or functional brain regions.They can partially mimic brain physiological activities and diseased processes.Owing to their operability and sample accessibility,brain organoids serve as a bridge between in vitro monolayer cell culture models and in vivo animal models.An increasing number of induction protocols for brain organoids have been developed over the preceding decade.A key future research direction will focus on ensuring the complexity and quality of brain organoids.The integration of powerful technologies,such as the CRISP R/Cas9 genome editing and lineage tra cing systems,shall precipitate practical and broad applications of brain organoids.In this review,we discuss the generation and application of brain organoids,as well as their integration with genome editing technologies,in the study of neural development,disease modeling,and mechanistic investigations.The innovative combination of these two technologies may offer a fresh perspective for exploring the fundamental aspects of the human nervous system and related diseases.
基金the National Natural Science Foundation of China(82125037,82274104,82074024,82374042)National Key R&D Program of China(2023YFC2308200)+3 种基金Jiangsu Provincial Medical Innovation Center(CXZX202225)the Natural Science Foundation of Jiangsu Province(BK20240144)the Innovation Projects of State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture(NZYSKL240103)Nanjing University of Chinese Medicine’s Project(RC202407).
文摘Plant-derived nanovesicles(PDNVs),including plant extracellular vesicles(EVs)and plant exosome-like nanovesicles(ELNs),are natural nano-sized membranous vesicles containing bioactive molecules.PDNVs consist of a bilayer of lipids that can effectively encapsulate hydrophilic and lipophilic drugs,improving drug stability and solubility as well as providing increased bioavailability,reduced systemic toxicity,and enhanced target accumulation.Bioengineering strategies can also be exploited to modify the PDNVs to achieve precise targeting,controlled drug release,and massive production.Meanwhile,they are capable of crossing the blood-brain barrier(BBB)to transport the cargo to the lesion sites without harboring human pathogens,making them excellent therapeutic agents and drug delivery nanoplatform candidates for brain diseases.Herein,this article provides an initial exposition on the fundamental characteristics of PDNVs,including biogenesis,uptake process,isolation,purification,characterization methods,and source.Additionally,it sheds light on the investigation of PDNVs’utilization in brain diseases while also presenting novel perspectives on the obstacles and clinical advancements associated with PDNVs.
基金support from National Natural Science Foundation of China(Nos.82274104,81903557,and 82074024)Natural Science Foundation of Jiangsu Province(No.BK20190802)+3 种基金Young Elite Scientists Sponsorship Program by CACM(No.2021-QNRC2-A01)Natural Science Foundation Youth Project of Nanjing University of Chinese Medicine(No.NZY81903557)College Students’Innovative Entrepreneurial Training of Jiangsu Province(No.202110315021)College Students’Innovative Entrepreneurial Training of Kangyuan School of Chinese Herbal Medicine of Nanjing University of Chinese Medicine(No.kyxysc12).
文摘Despite noteworthy technological progress and promising preclinical trials,brain disorders are still the leading causes of death globally.Extracellular vesicles(EVs),nano-/micro-sized membrane vesicles carrying bioactive molecules,are involved in cellular communication.Based on their unique properties,including superior biocompatibility,non-immunogenicity,and blood-brain barrier(BBB)penetration,EVs can shield their cargos from immune clearance and transport them to specific site,which have attracted increasing interests as novel nanocarriers for brain disorders.However,considering the limitations of native EVs,such as poor encapsulation efficiency,inadequate targeting capability,uncontrolled drug release,and limited production,researchers bioengineer EVs to fully exploit the clinical potential.Herein,this review initially describes the basic properties,biogenesis,and uptake process of EVs from different subtypes.Then,we highlight the application of EVs derived from different sources for personalized therapy and novel strategies to construct bioengineered EVs for enhanced diagnosis and treatment of brain disorders.Besides,it also presents a systematic comparison between EVs and other brain-targeted nanocarriers.Finally,existing challenges and future perspectives of EVs have been discussed,hoping to bolster the research from benchtop to bedside.
文摘We refute the controversial statement that addiction is not a brain disorder. Extensive peer-reviewed studies support the underlying neurobiological and neurogenetic basis of addiction’s “disease model”. In the 70s and 80s, a few clinical scientists suggested that it is possible to use behavioral training to teach controlled drinking. However, this controversial model failed drastically and increased labeling and stigmatization. Additionally, it was unhelpful in the search for treatment. Instead, we assert that addiction is a neuropsychiatric disorder characterized by a recurring desire to continue taking substances despite harmful physical and mental consequences. Work from our laboratory in 1995 supported the Reward Deficiency Syndrome (RDS) concept based on a common neurogenetic mechanism (hypodopaminergia) that underlies all substance and non-substance addictions. Non-substance addictions include behaviors like pathological gambling, internet addiction, and mobile phone addiction. Certain impulsive and compulsive behaviors or the acute intake of psychoactive substances result in heightened dopaminergic activity, while the opposite, hypodopaminergia, occurs following chronic abuse. Patients with Substance Use Disorder (SUD) can have a genetic predisposition compounded by stress or other epigenetic insults that can impact recovery. Relapse will occur post-short-term recovery if dopaminergic dysfunction remains untreated. Addiction, a brain disorder, requires treatment with DNA-directed pro-dopamine regulation and rehabilitation.
基金supported by the Deanship of Scientific Research at Prince Sattam bin Aziz University under the Research Project (PSAU/2023/01/22425).
文摘Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.
文摘Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representative signals from brain regions of interest (ROIs) is important. In the past decades, the common method is generally to take a ROI as a node, averaging all the voxel time series inside it to extract a representative signal. However, one node does not represent the entire information of this ROI, and averaging method often leads to signal cancellation and information loss. Inspired by this, we propose a novel model extraction method based on an assumption that a ROI can be represented by multiple nodes. Methods: In this paper, we first extract multiple nodes (the number is user-defined) from the ROI based on two traditional methods, including principal component analysis (PCA), and K-means (Clustering according to the spatial position of voxels). Then, canonical correlation analysis (CCA) was issued to construct BFNs by maximizing the correlation between the representative signals corresponding to the nodes in any two ROIs. Finally, to further verify the effectiveness of the proposed method, the estimated BFNs are applied to identify subjects with autism spectrum disorder (ASD) and mild cognitive impairment (MCI) from health controls (HCs). Results: Experimental results on two benchmark databases demonstrate that the proposed method outperforms the baseline method in the sense of classification performance. Conclusions: We propose a novel method for obtaining nodes of ROId based on the hypothesis that a ROI can be represented by multiple nodes, that is, to extract the node signals of ROIs with K-means or PCA. Then, CCA is used to construct BFNs.
基金provided by Inter University Centre for Yogic Science。
文摘Yoga is a therapeutic practice renowned for its multifaceted benefits across the body's systems.Its positive impact spans the physical,mental and emotional realms,fostering harmony and well-being.Through a combination of postures,breathing techniques and meditation,yoga offers profound effects,enhancing flexibility,strength and balance while simultaneously promoting relaxation and reducing stress.This integrative approach not only cultivates physical resilience but also supports mental clarity,emotional balance and overall vitality,showcasing yoga as a comprehensive and impactful system for holistic health.The review delved into the multifaceted ways in which yoga exerts a positive influence on the body's various systems.It highlights how yoga serves as a beneficial tool in addressing and counteracting the underlying factors associated with different diseases.By examining yoga's effects on these systems and its potential in combating illness,the paper sheds light on the comprehensive therapeutic benefits that yoga offers.Please cite this article as:Pandey S,Pandey AC,Kotecha VR.YogaDA complementary and traditional medicine for human health.
基金supported by the National Natural Science Foundation of China(82102018,62333002,T2425027,and 82327809)Data collection and sharing for this project were supported by the National Natural Science Foundation of China(61633018,81571062,81471120,and 81901101)+30 种基金Data collection and sharing for this project were funded by the ADNI(National Institutes of Health Grant U01 AG024904)the Department of Defense ADNI(award number W81XWH-12-2-0012).The ADNI is funded by the National Institute on Aging,the National Institute of Biomedical Imaging and Bioengineering,and through generous contributions from the following:AbbVie,Alzheimer’s AssociationAlzheimer’s Drug Discovery FoundationAraclon BiotechBioClinica,Inc.BiogenBristol-Myers Squibb Co.CereSpir,Inc.CogstateEisai Inc.Elan Pharmaceuticals,Inc.Eli Lilly and Co.EuroImmunF.Hoffmann-La Roche Ltd and its affiliated company Genentech,Inc.FujirebioG.E.HealthcareIXICO Ltd.Janssen Alzheimer Immunotherapy Research&Development,LLC.Johnson&Johnson Pharmaceutical Research&Development LLC.LumosityLundbeckMerck&Co.,Inc.Meso Scale Diagnostics,LLC.NeuroRx ResearchNeurotrack TechnologiesNovartis Pharmaceuticals Corp.Pfizer Inc.Piramal ImagingServierTakeda Pharmaceutical Co.and Transition Therapeutics.The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada.Private sector contributions are facilitated by the Foundation for the National Institutes of Health(www.fnih.org).The grantee organization was the Northern California Institute for Research and Education,and the study was coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California.ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
文摘Dear Editor,Growing clinical evidence shows that brain disorders are heterogeneous in phenotype,genetics,and neuropathology[1].Diagnosis and treatment tend to be affected by symptom presentation and the heterogeneity of pathology,potentially hindering clinical trials in the development of medical treatment.Brain-based subtyping studies utilize magnetic resonance imaging(MRI)and data-driven methods to discover the subtypes of diseases,providing a new perspective on disease heterogeneity.
基金supported by Start-Up Grant From ShanghaiTech University,2021F0209-000-09Natural Science Foundation of Shanghai Municipality,23ZR1442000。
文摘Transcranial focused ultrasound(tFUS)is an emerging modality with strong potential for non-invasively treating brain disorders.However,the inhomogeneity and complex structure of the skull induce substantial phase aberrations and pressure attenuation;these can distort and shift the acoustic focus,thus hindering the efficiency of tFUS therapy.To achieve effective treatments,phased array transducers combined with aberration correction algorithms are commonly implemented.The present report aims to provide a comprehensive review of the current methods used for tFUS phase aberration correction.We first searched the PubMed and Web of Science databases for studies on phase aberration correction algorithms,identifying 54 articles for review.Relevant information,including the principles of algorithms and refocusing performances,were then extracted from the selected articles.The phase correction algorithms involved two main steps:acoustic field estimation and transmitted pulse adjustment.Our review identified key benchmarks for evaluating the effectiveness of these algorithms,each of which was used in at least three studies.These benchmarks included pressure and intensity,positioning error,focal region size,peak sidelobe ratio,and computational efficiency.Algorithm performances varied under different benchmarks,thus highlighting the importance of application-specific algorithm selection for achieving optimal tFUS therapy outcomes.The present review provides a thorough overview and comparison of various phase correction algorithms,and may offer valuable guidance to tFUS researchers when selecting appropriate phase correction algorithms for specific applications.
文摘The global incidence of Alzheimer's Disease(AD)is on a swift rise.The Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning models.Analysis of AD using EEG involves multi-channel analysis.However,the use of multiple channels may impact the classification performance due to data redundancy and complexity.In this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition methods.Empirical Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG analysis.We extracted thirty-four features from each subband of EEG signals.Finally,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel selection.The effectiveness of this model is assessed by two publicly accessible AD EEG datasets.An accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG channels.Moreover,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)channels.The performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.
文摘The launch of the Brain Network Disorders journal marks a pivotal moment in neuroscience,addressing the urgent need to unravel the brain’s complex network functions.The brain orchestrates cognitive,emotional,and behavioral processes,yet its underlying mechanisms—especially how it transitions between health and disease—remain elusive due to the intricate connectivity of its networks.1,2 This journal aims to foster interdisciplinary collaboration,bridging gaps across neurology,computational neuroscience,molecular biology,and clinical research.By focusing on how brain network dynamics influence both physiological and pathological states,we aim to fundamentally reshape our understanding of brain function and disease.
基金CLARA project is jointly funded by the European Union and Czech Republic under EU’s Horizon Europe Framework Program(grant agreement No.Editorial Brain Network Disorders 1(2025)3-65101136607).
文摘1.Introduction.This perspective highlights the need for a specialized publication dedicated to neuropsychiatric disorders,collectively termed“dysconnectivity syndromes.”These conditions,including dementia-Alzheimer’s disease(AD),schizophrenia,and autism spectrum disorders,result from failures in interconnected physiological systems and neural circuits rather than isolated lesions.The proposed Brain Network Disorders(BND)journal aims to promote innovative paradigms for understanding these complex brain disorders by applying general systems theory and complexity sciences.The aim is to broaden the traditional conceptualization of these diseases by stressing the multifactorial roots of these disorders where the focus is on the dynamic interactions among several influences and the failure of multiple overlapping nets that drive clinical manifestations。
基金National Key Research and Development Program of China(2024YFC3406700,2024YFF1206400)Shenzhen Medical Research Fund(B2302053,B2402029,D2403002)+3 种基金Youth Team in Basic Research Field of Chinese Academy of Sciences(YSBR-114)Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0930000)National Natural Science Foundation of China(81961128019,31871090)Shenzhen Municipal(KQTD20210811090117032,JCYJ20241202124921029)。
文摘Brain disorders have imposed an escalating socioeconomic burden in recent years,yet critical gaps persist in understanding their pathogenesis and developing effective therapies.Nonhuman primate(NHP)models provide invaluable insights into human brain disorders due to their unique neuroanatomical and neurophysiological similarities with humans.Here,we review the current landscape of genetically modified NHP-based research in brain disorders,emphasizing the pivotal role of gene editing in disease modeling and the distinct advantages of transgenic NHP models in deciphering disease mechanisms.While key mechanistic questions and technical hurdles remain,NHP models hold immense promise in overcoming challenges and accelerating the development of therapeutics for brain disorders.
基金Fund for Scientific Research(FNRS)PDR T0236.20FNRS-Exellence of Science 30913351FNRS CDR J.0175.23(to FT)。
文摘Microtubules play a central role in cytoskeletal changes during neuronal development and maintenance.Microtubule dynamics is essential to polarity and shape transitions underlying neural cell division,differentiation,motility,and maturation.Kinesin superfamily protein 2A is a member of human kinesin 13 gene family of proteins that depolymerize and destabilize microtubules.In dividing cells,kinesin superfamily protein 2A is involved in mitotic progression,spindle assembly,and chromosome segregation.In postmitotic neurons,it is required for axon/dendrite specification and extension,neuronal migration,connectivity,and survival.Humans with kinesin superfamily protein 2A mutations suffer from a variety of malformations of cortical development,epilepsy,autism spectrum disorder,and neurodegeneration.In this review,we discuss how kinesin superfamily protein 2A regulates neuronal development and function,and how its deregulation causes neurodevelopmental and neurological disorders.
基金supported in part by NIH grants(NS051630, NS079625, MH102690 and NS097206 to P.J.)
文摘An emerging paradigm shift for disease diagnosis is to rely on molecular characterization beyond traditional clinical and symptom-based examinations. Although genetic alterations and transcription signature were first introduced as potential biomarkers, clinical implementations of these markers are limited due to low reproducibility and accuracy. Instead, epigenetic changes are considered as an alternative approach to disease diagnosis. Complex epigenetic regulation is required for normal biological functions and it has been shown that distinctive epigenetic disruptions could contribute to disease pathogenesis. Disease-specific epigenetic changes, especially DNA methylation, have been observed,suggesting its potential as disease biomarkers for diagnosis. In addition to specificity, the feasibility of detecting disease-associated methylation marks in the biological specimens collcted noninvasively,such as blood samples, has driven the clinical studies to validate disease-specific DNA methylation changes as a diagnostic biomarker. Here, we highlight the advantages of DNA methylation signature for diagnosis in different diseases and discuss the statistical and technical challenges to be overcome before clinical implementation.
文摘Parkinson’s disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system.The cause of such brain damage seems to be fully explained in many research studies,but the understanding of its functionality remains to be impractical.Specifically,the development of a quantitative disease prediction model has evolved in recent decades.Moreover,accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson’s disease.This type of minimal infrastructure equipment helps in analyzing the Parkinson’s gait properties without affecting the common behavioral patterns during the clinical practices.Therefore,the Accelerometer Sensor-based Parkinson’s Disease Identi-fication System(ASPDIS)is introduced with a kernel-based support vector machine classifier model to make an early prediction of the disease.consequently,the proposed classifier can easily predict various severity levels of Parkinson’s disease from the sensor data.The performance of the proposed classifier is com-pared against the existing models such as random forest,decision tree,and k-near-est neighbor classifiers respectively.As per the experimental observation,the proposed classifier has more capability to differentiate Parkinson’s from non-Parkinson patients depending upon the severity levels.Also,it is found that the model has outperformed the existing classifiers concerning prediction time and accuracy respectively.
基金supported by the National Natural Science Foundation of China,No.81372919the Natural Science Foundation of Guangdong Province of China,No.2014A030313016+1 种基金the Basic Key Research Project Fund of Shenzhen City of China,No.JCYJ20150324140036853the Science and Technology Program Fund of Shenzhen City of China,No.JCYJ20140418181958477
文摘Electroencephalography is a sensitive indicator for measuring brain condition, and can reflect early changes in brain function and severity of cerebral ischemia. However, it is not yet known whether electroencephalography can predict development of post-cerebral infarc- tion depression. A total of 321 patients with ischemic stroke underwent electroencephalography and Hamilton Depression Rating Scale assessment to analyze the relationship between electroencephalography and post-cerebral infarction depression. Our results show that electroencephalograms of ischemic stroke patients with depression exhibit low-amplitude alpha activity and slow theta activity. In con- trast, electroencephalograms of ischemic stroke patients without depression show fast beta activity and slow delta activity. ＂Ihese findings confirm that low-amplitude alpha activity and slow theta activity can be considered as independent predictors for post-cerebral infarction depression.
文摘Neuroimaging data typically include multiple modalities,such as structural or functional magnetic resonance imaging,dif-fusion tensor imaging,and positron emission tomography,which provide multiple views for observing and analyzing the brain.To lever-age the complementary representations of different modalities,multimodal fusion is consequently needed to dig out both inter-modality and intra-modality information.With the exploited rich information,it is becoming popular to combine multiple modality data to ex-plore the structural and functional characteristics of the brain in both health and disease status.In this paper,we first review a wide spectrum of advanced machine learning methodologies for fusing multimodal brain imaging data,broadly categorized into unsupervised and supervised learning strategies.Followed by this,some representative applications are discussed,including how they help to under-stand the brain arealization,how they improve the prediction of behavioral phenotypes and brain aging,and how they accelerate the biomarker exploration of brain diseases.Finally,we discuss some exciting emerging trends and important future directions.Collectively,we intend to offer a comprehensive overview of brain imaging fusion methods and their successful applications,along with the chal-lenges imposed by multi-scale and big data,which arises an urgent demand on developing new models and platforms.
