Importance:Precisely decoding brain dysfunction from high-dimensional functional recordings is crucial for advancing our understanding of brain dysfunction in brain disorders.Self-supervised learning(SSL)models offer ...Importance:Precisely decoding brain dysfunction from high-dimensional functional recordings is crucial for advancing our understanding of brain dysfunction in brain disorders.Self-supervised learning(SSL)models offer a transformative approach for mapping dependencies in functional neuroimaging data.Leveraging the intrinsic organization of brain signals for comprehensive feature extraction,these models enable the analysis of critical neurofunctional features within a clinically relevant framework,overcoming challenges related to data heterogeneity and the scarcity of labeled data.Highlight:This paper provides a comprehensive overview of SSL techniques applied to functional neuroimaging data,such as functional magnetic resonance imaging and electroencephalography,with a specific focus on their applications in various neuropsychiatric disorders.We discuss 3 main categories of SSL methods:contrastive learning,generative learning,and generative-contrastive methods,outlining their basic principles and representative methods.Critically,we highlight the potential of SSL in addressing data scarcity,multimodal integration,and dynamic network modeling for disease detection and prediction.We showcase successful applications of these techniques in understanding and classifying conditions such as Alzheimer’s disease,Parkinson’s disease,and epilepsy,demonstrating their potential in downstream neuropsychological applications.Conclusion:SSL models provide a scalable and effective methodology for individual detection and prediction in brain disorders.Despite current limitations in interpretability and data heterogeneity,the potential of SSL for future clinical applications,particularly in the areas of transdiagnostic psychosis subtyping and decoding task-based brain functional recordings,is substantial.展开更多
Persistent motor deficits are highly prevalent among post-stroke survivors,contributing significantly to disability.Despite the prevalence of these deficits,the precise mechanisms underlying motor recovery after strok...Persistent motor deficits are highly prevalent among post-stroke survivors,contributing significantly to disability.Despite the prevalence of these deficits,the precise mechanisms underlying motor recovery after stroke remain largely elusive.The exploration of motor system reorganization using functional neuroimaging techniques represents a compelling yet challenging avenue of research.Quantitative electroencephalography(qEEG)parameters,including the power ratio index,brain symmetry index,and phase synchrony index,have emerged as potential prognostic markers for overall motor recovery post-stroke.Current evidence suggests a correlation between qEEG parameters and functional motor outcomes in stroke recovery.However,accurately identifying the source activity poses a challenge,prompting the integration of EEG with other neuroimaging modalities,such as functional near-infrared spectroscopy(fNIRS).fNIRS is nowadays widely employed to investigate brain function,revealing disruptions in the functional motor network induced by stroke.Combining these two methods,referred to as integrated fNIRS-EEG,neural activity and hemodynamics signals can be pooled out and offer new types of neurovascular coupling-related features,which may be more accurate than the individual modality alone.By harnessing integrated fNIRS-EEG source localization,brain connectivity analysis could be applied to characterize cortical reorganization associated with stroke,providing valuable insights into the assessment and treatment of poststroke motor recovery.展开更多
Deep brain stimulation(DBS) is emerging as a pow-erful tool for the alleviation of targeted symptoms in treatment-resistant neuropsychiatric disorders. Despite the expanding use of neuropsychiatric DBS, the mecha-nism...Deep brain stimulation(DBS) is emerging as a pow-erful tool for the alleviation of targeted symptoms in treatment-resistant neuropsychiatric disorders. Despite the expanding use of neuropsychiatric DBS, the mecha-nisms responsible for its effects are only starting to be elucidated. Several modalities such as quantitative elec-troencephalography as well a intraoperative recordings have been utilized to attempt to understand the under-pinnings of this new treatment modality, but functional imaging appears to offer several unique advantages. Functional imaging techniques like positron emission tomography, single photon emission computed tomog-raphy and functional magnetic resonance imaging have been used to examine the effects of focal DBS on activ-ity in a distributed neural network. These investigations are critical for advancing the field of invasive neuro-modulation in a safe and effective manner, particularly in terms of defining the neuroanatomical targets and refining the stimulation protocols. The purpose of this review is to summarize the current functional neuroim-aging findings from neuropsychiatric DBS implantation for three disorders: treatment-resistant depression, obsessive-compulsive disorder, and Tourette syndrome. All of the major targets will be discussed(Nucleus ac-cumbens, anterior limb of internal capsule, subcallosal cingulate, Subthalamic nucleus, Centromedial nucleus of the thalamus-Parafasicular complex, frontal pole, and dorsolateral prefrontal cortex). We will also address some apparent inconsistencies within this literature, and suggest potential future directions for this promis-ing area.展开更多
The fundamental limitations of most vascular-based functional neuroimaging techniques are placed by the fact how fine the brain regulates the blood supply system.In vivo mapping of the cerebral microcirculation with h...The fundamental limitations of most vascular-based functional neuroimaging techniques are placed by the fact how fine the brain regulates the blood supply system.In vivo mapping of the cerebral microcirculation with high resolution and sensitivity hence becomes unprecedentedly compelling.This paper reviews the theoretical background of the laser speckle contrast imaging(LSCI)technique and attempts to present a complete framework stemming from a simple biophysical model.Through the sensitivity analysis,more insights into the tool optimization are attained for in vivo applications.Open questions of the technical aspects are discussed within this unified framework.Finally,it concludes with a brief perspective of future research in a way analogous to the magnetic resonance imaging(MRI)technique.Such exploration could catalyze their development and initiate a technological fusion for precise assessment of blood flow across various spatial scales.展开更多
Functional magnetic resonance imaging has been widely used to investigate the effects of acupuncture on neural activity. However, most functional magnetic resonance imaging studies have focused on acute changes in bra...Functional magnetic resonance imaging has been widely used to investigate the effects of acupuncture on neural activity. However, most functional magnetic resonance imaging studies have focused on acute changes in brain activation induced by acupuncture. Thus, the time course of the therapeutic effects of acupuncture remains unclear. In this study, 32 patients with amnestic mild cognitive impairment were randomly divided into two groups, where they received either Tiaoshen Yizhi acupuncture or sham acupoint acupuncture. The needles were either twirled at Tiaoshen Yizhi acupoints, including Sishencong（EX-HN1）, Yintang（EX-HN3）, Neiguan（PC6）, Taixi（KI3）, Fenglong（ST40）, and Taichong（LR3）, or at related sham acupoints at a depth of approximately 15 mm, an angle of ± 60°, and a rate of approximately 120 times per minute. Acupuncture was conducted for 4 consecutive weeks, five times per week, on weekdays. Resting-state functional magnetic resonance imaging indicated that connections between cognition-related regions such as the insula, dorsolateral prefrontal cortex, hippocampus, thalamus, inferior parietal lobule, and anterior cingulate cortex increased after acupuncture at Tiaoshen Yizhi acupoints. The insula, dorsolateral prefrontal cortex, and hippocampus acted as central brain hubs. Patients in the Tiaoshen Yizhi group exhibited improved cognitive performance after acupuncture. In the sham acupoint acupuncture group, connections between brain regions were dispersed, and we found no differences in cognitive function following the treatment. These results indicate that acupuncture at Tiaoshen Yizhi acupoints can regulate brain networks by increasing connectivity between cognition-related regions, thereby improving cognitive function in patients with mild cognitive impairment.展开更多
Searching for effective biomarkers is one of the most challenging tasks in the research ？eld of Autism Spectrum Disorder（ASD）. Magnetic resonance imaging（MRI） provides a non-invasive and powerful tool for investi...Searching for effective biomarkers is one of the most challenging tasks in the research ？eld of Autism Spectrum Disorder（ASD）. Magnetic resonance imaging（MRI） provides a non-invasive and powerful tool for investigating changes in the structure, function, maturation,connectivity, and metabolism of the brain of children with ASD. Here, we review the more recent MRI studies in young children with ASD, aiming to provide candidate biomarkers for the diagnosis of childhood ASD. The review covers structural imaging methods, diffusion tensor imaging, resting-state functional MRI, and magnetic resonance spectroscopy. Future advances in neuroimaging techniques, as well as cross-disciplinary studies and largescale collaborations will be needed for an integrated approach linking neuroimaging, genetics, and phenotypic data to allow the discovery of new, effective biomarkers.展开更多
A critical gap currently exists in systematic understanding and experimental validation of the role of astrocytes in neurovascular coupling and their functional links with other brain cells.Despite a broad selection o...A critical gap currently exists in systematic understanding and experimental validation of the role of astrocytes in neurovascular coupling and their functional links with other brain cells.Despite a broad selection of functional neuroimaging tools for multi-scale brain interrogations,no methodology currently exists that can discern responses from neural and glial cells while simultaneously mapping the associated hemodynamic activity on a large scale.We present a hybrid multiplexed fluorescence and magnetic resonance imaging(HyFMRI)platform for measuring neuronal and astrocytic activity registered to concurrently recorded brain-wide hemodynamic responses.It features a fiberscope-based imaging system for multichannel fluorescence and optical intrinsic signal recordings and a custom surface radiofrequency coil,which are incorporated into the bore of a preclinical magnetic resonance imaging(MRI)scanner.We used HyFMRI to study peripheral-stimulus-evoked brain responses in mice differentially labeled with RCaMP and GCaMP genetically-encoded calcium indicators.Stimulation-evoked neuronal responses displayed the fastest kinetics and highest activation amplitude followed by astrocytic signals and the hemodynamic responses simultaneously recorded with functional MRI.In addition,the activation traces from neurons and astrocytes exhibited high linear correlation,thus providing direct evidence of astrocytic mediation in neurovascular coupling.This newly developed capacity to capture cell-type-specific calcium signaling alongside whole-brain hemodynamics enables the simultaneous investigation of neuro-glial-vascular interactions in health and disease.HyFMRI thus expands the current neuroimaging toolbox for a wide range of studies into synaptic plasticity,neural circuitry,brain function and disorders.展开更多
Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep...Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks(DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain's neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.展开更多
基金supported by grants from the National Natural Science Foundation of P.R.China(62276081 and 62106113)Guangdong Basic and Applied Basic Research Foundation(2023A1515010792 and 2023B1515120065)Shenzhen Science and Technology Program(GXWD20231129121139001 and JCYJ20240813110522029).
文摘Importance:Precisely decoding brain dysfunction from high-dimensional functional recordings is crucial for advancing our understanding of brain dysfunction in brain disorders.Self-supervised learning(SSL)models offer a transformative approach for mapping dependencies in functional neuroimaging data.Leveraging the intrinsic organization of brain signals for comprehensive feature extraction,these models enable the analysis of critical neurofunctional features within a clinically relevant framework,overcoming challenges related to data heterogeneity and the scarcity of labeled data.Highlight:This paper provides a comprehensive overview of SSL techniques applied to functional neuroimaging data,such as functional magnetic resonance imaging and electroencephalography,with a specific focus on their applications in various neuropsychiatric disorders.We discuss 3 main categories of SSL methods:contrastive learning,generative learning,and generative-contrastive methods,outlining their basic principles and representative methods.Critically,we highlight the potential of SSL in addressing data scarcity,multimodal integration,and dynamic network modeling for disease detection and prediction.We showcase successful applications of these techniques in understanding and classifying conditions such as Alzheimer’s disease,Parkinson’s disease,and epilepsy,demonstrating their potential in downstream neuropsychological applications.Conclusion:SSL models provide a scalable and effective methodology for individual detection and prediction in brain disorders.Despite current limitations in interpretability and data heterogeneity,the potential of SSL for future clinical applications,particularly in the areas of transdiagnostic psychosis subtyping and decoding task-based brain functional recordings,is substantial.
基金supported by grants from the National Natural Science Foundation of China(82272591,82072534)Xijing Hospital Medical Staff Training&Boost Project(XJZT24JC19,XJZT24LY11,XJZT24QN20).
文摘Persistent motor deficits are highly prevalent among post-stroke survivors,contributing significantly to disability.Despite the prevalence of these deficits,the precise mechanisms underlying motor recovery after stroke remain largely elusive.The exploration of motor system reorganization using functional neuroimaging techniques represents a compelling yet challenging avenue of research.Quantitative electroencephalography(qEEG)parameters,including the power ratio index,brain symmetry index,and phase synchrony index,have emerged as potential prognostic markers for overall motor recovery post-stroke.Current evidence suggests a correlation between qEEG parameters and functional motor outcomes in stroke recovery.However,accurately identifying the source activity poses a challenge,prompting the integration of EEG with other neuroimaging modalities,such as functional near-infrared spectroscopy(fNIRS).fNIRS is nowadays widely employed to investigate brain function,revealing disruptions in the functional motor network induced by stroke.Combining these two methods,referred to as integrated fNIRS-EEG,neural activity and hemodynamics signals can be pooled out and offer new types of neurovascular coupling-related features,which may be more accurate than the individual modality alone.By harnessing integrated fNIRS-EEG source localization,brain connectivity analysis could be applied to characterize cortical reorganization associated with stroke,providing valuable insights into the assessment and treatment of poststroke motor recovery.
文摘Deep brain stimulation(DBS) is emerging as a pow-erful tool for the alleviation of targeted symptoms in treatment-resistant neuropsychiatric disorders. Despite the expanding use of neuropsychiatric DBS, the mecha-nisms responsible for its effects are only starting to be elucidated. Several modalities such as quantitative elec-troencephalography as well a intraoperative recordings have been utilized to attempt to understand the under-pinnings of this new treatment modality, but functional imaging appears to offer several unique advantages. Functional imaging techniques like positron emission tomography, single photon emission computed tomog-raphy and functional magnetic resonance imaging have been used to examine the effects of focal DBS on activ-ity in a distributed neural network. These investigations are critical for advancing the field of invasive neuro-modulation in a safe and effective manner, particularly in terms of defining the neuroanatomical targets and refining the stimulation protocols. The purpose of this review is to summarize the current functional neuroim-aging findings from neuropsychiatric DBS implantation for three disorders: treatment-resistant depression, obsessive-compulsive disorder, and Tourette syndrome. All of the major targets will be discussed(Nucleus ac-cumbens, anterior limb of internal capsule, subcallosal cingulate, Subthalamic nucleus, Centromedial nucleus of the thalamus-Parafasicular complex, frontal pole, and dorsolateral prefrontal cortex). We will also address some apparent inconsistencies within this literature, and suggest potential future directions for this promis-ing area.
基金supported by grant 358/04-3 of“The Israeli Science Foundation”.
文摘The fundamental limitations of most vascular-based functional neuroimaging techniques are placed by the fact how fine the brain regulates the blood supply system.In vivo mapping of the cerebral microcirculation with high resolution and sensitivity hence becomes unprecedentedly compelling.This paper reviews the theoretical background of the laser speckle contrast imaging(LSCI)technique and attempts to present a complete framework stemming from a simple biophysical model.Through the sensitivity analysis,more insights into the tool optimization are attained for in vivo applications.Open questions of the technical aspects are discussed within this unified framework.Finally,it concludes with a brief perspective of future research in a way analogous to the magnetic resonance imaging(MRI)technique.Such exploration could catalyze their development and initiate a technological fusion for precise assessment of blood flow across various spatial scales.
基金supported by the National Natural Science Foundation of China,No.81173354a grant from the Science and Technology Plan Project of Guangdong Province of China,No.2013B021800099a grant from the Science and Technology Plan Project of Shenzhen City of China,No.JCYJ20150402152005642
文摘Functional magnetic resonance imaging has been widely used to investigate the effects of acupuncture on neural activity. However, most functional magnetic resonance imaging studies have focused on acute changes in brain activation induced by acupuncture. Thus, the time course of the therapeutic effects of acupuncture remains unclear. In this study, 32 patients with amnestic mild cognitive impairment were randomly divided into two groups, where they received either Tiaoshen Yizhi acupuncture or sham acupoint acupuncture. The needles were either twirled at Tiaoshen Yizhi acupoints, including Sishencong（EX-HN1）, Yintang（EX-HN3）, Neiguan（PC6）, Taixi（KI3）, Fenglong（ST40）, and Taichong（LR3）, or at related sham acupoints at a depth of approximately 15 mm, an angle of ± 60°, and a rate of approximately 120 times per minute. Acupuncture was conducted for 4 consecutive weeks, five times per week, on weekdays. Resting-state functional magnetic resonance imaging indicated that connections between cognition-related regions such as the insula, dorsolateral prefrontal cortex, hippocampus, thalamus, inferior parietal lobule, and anterior cingulate cortex increased after acupuncture at Tiaoshen Yizhi acupoints. The insula, dorsolateral prefrontal cortex, and hippocampus acted as central brain hubs. Patients in the Tiaoshen Yizhi group exhibited improved cognitive performance after acupuncture. In the sham acupoint acupuncture group, connections between brain regions were dispersed, and we found no differences in cognitive function following the treatment. These results indicate that acupuncture at Tiaoshen Yizhi acupoints can regulate brain networks by increasing connectivity between cognition-related regions, thereby improving cognitive function in patients with mild cognitive impairment.
文摘Searching for effective biomarkers is one of the most challenging tasks in the research ？eld of Autism Spectrum Disorder（ASD）. Magnetic resonance imaging（MRI） provides a non-invasive and powerful tool for investigating changes in the structure, function, maturation,connectivity, and metabolism of the brain of children with ASD. Here, we review the more recent MRI studies in young children with ASD, aiming to provide candidate biomarkers for the diagnosis of childhood ASD. The review covers structural imaging methods, diffusion tensor imaging, resting-state functional MRI, and magnetic resonance spectroscopy. Future advances in neuroimaging techniques, as well as cross-disciplinary studies and largescale collaborations will be needed for an integrated approach linking neuroimaging, genetics, and phenotypic data to allow the discovery of new, effective biomarkers.
基金support from the Swiss National Science Foundation(SNSF)grant 310030_192757the Fundamental Research Funds for the Central Universities 13702150142the Shanghai Science and Technology Program Project 24ZR1468000.
文摘A critical gap currently exists in systematic understanding and experimental validation of the role of astrocytes in neurovascular coupling and their functional links with other brain cells.Despite a broad selection of functional neuroimaging tools for multi-scale brain interrogations,no methodology currently exists that can discern responses from neural and glial cells while simultaneously mapping the associated hemodynamic activity on a large scale.We present a hybrid multiplexed fluorescence and magnetic resonance imaging(HyFMRI)platform for measuring neuronal and astrocytic activity registered to concurrently recorded brain-wide hemodynamic responses.It features a fiberscope-based imaging system for multichannel fluorescence and optical intrinsic signal recordings and a custom surface radiofrequency coil,which are incorporated into the bore of a preclinical magnetic resonance imaging(MRI)scanner.We used HyFMRI to study peripheral-stimulus-evoked brain responses in mice differentially labeled with RCaMP and GCaMP genetically-encoded calcium indicators.Stimulation-evoked neuronal responses displayed the fastest kinetics and highest activation amplitude followed by astrocytic signals and the hemodynamic responses simultaneously recorded with functional MRI.In addition,the activation traces from neurons and astrocytes exhibited high linear correlation,thus providing direct evidence of astrocytic mediation in neurovascular coupling.This newly developed capacity to capture cell-type-specific calcium signaling alongside whole-brain hemodynamics enables the simultaneous investigation of neuro-glial-vascular interactions in health and disease.HyFMRI thus expands the current neuroimaging toolbox for a wide range of studies into synaptic plasticity,neural circuitry,brain function and disorders.
基金supported by the National Natural Science Foundation of China (62236009,61876032,32371154)Shenzhen Science and Technology Program (JCYJ20210324140807019)Shanghai Pujiang Program (22PJ1410500)。
文摘Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks(DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain's neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.
