When new information enters the brain,a human's prior knowledge of the world can change rapidly through a process referred to as"knowledge assembly".Recently,Nelli et al.investigated the neural correlate...When new information enters the brain,a human's prior knowledge of the world can change rapidly through a process referred to as"knowledge assembly".Recently,Nelli et al.investigated the neural correlates of knowledge assembly in the human brain using functional MRI.Further,inspired by the neural mechanism,the authors developed an artificial neural network algorithm to permit rapid knowledge assembly,improving the flexibility of the system[1].Once again,this research demonstrates that studying how the brain works can lead to better computational algorithms.展开更多
Electrical microstimulation(EM)can be used to locally stimulate the cerebral cortex or subcortical nuclei.Meanwhile,functional magnetic resonance imaging(fMRI)can noninvasively visualize the activity of the whole brai...Electrical microstimulation(EM)can be used to locally stimulate the cerebral cortex or subcortical nuclei.Meanwhile,functional magnetic resonance imaging(fMRI)can noninvasively visualize the activity of the whole brain.When EM is combined with fMRI(EM-fMRI),it is possible to measure the changes of the whole-brain neural activity using fMRI while applying electrical stimulation to a specific brain site,and accordingly infer the causal links between the stimulated site and the activated brain areas.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 STI2030-Major Projects 2021ZD0200201the Scientific Research and Equipment Development Project of the Chinese Academy of Sciences(YJKYYQ20190040)。
文摘When new information enters the brain,a human's prior knowledge of the world can change rapidly through a process referred to as"knowledge assembly".Recently,Nelli et al.investigated the neural correlates of knowledge assembly in the human brain using functional MRI.Further,inspired by the neural mechanism,the authors developed an artificial neural network algorithm to permit rapid knowledge assembly,improving the flexibility of the system[1].Once again,this research demonstrates that studying how the brain works can lead to better computational algorithms.
基金supported by the National Natural Science Foundation of China(31870984)the Scientific Research and Equipment Development Project of Chinese Academy of Sciences(YJKYYQ20190040)the Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project(2021ZD0200201).
文摘Electrical microstimulation(EM)can be used to locally stimulate the cerebral cortex or subcortical nuclei.Meanwhile,functional magnetic resonance imaging(fMRI)can noninvasively visualize the activity of the whole brain.When EM is combined with fMRI(EM-fMRI),it is possible to measure the changes of the whole-brain neural activity using fMRI while applying electrical stimulation to a specific brain site,and accordingly infer the causal links between the stimulated site and the activated brain areas.
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.
