RF power design optimization in MRI system

Magnetic resonance image quality and patient safety have been the focus of engineering and research ever since the invention of equipment in the early 1970s.In high field(or ultrahigh field)MRI systems,the emerging techniques induced by B1 field challenges have promoted various potential solutions.This paper describes the relationship between RF power and B1þfield performance,and the overall requirements considered in RF subsystem design.The design of the RF in the MR system is systematically summarized,including the entire transmission chain,sequence algorithm and RF pulse design,and the probabilities for improvement and optimization in the system design are indicated.At the same time,the radio frequency related issues of the human whole-body 7 T MR and animal MR systems are discussed,especially the promising future showed by the technologies such as radio frequency parallel transmission technology in the ultrahigh field.

DISCRIMINATIVE ANALYSIS OF FUNCTIONAL NEAR-INFRARED SPECTROSCOPY SIGNALS FOR DEVELOPMENT OF NEUROIMAGING BIOMARKERS OF ELDERLY DEPRESSION

Functional near-infrared spectroscopy(fNIRS)is a neuroimaging technology which is suitable for psychiatric patients.Several fNIRS studies have found abnormal brain activations during cognitive tasks in elderly depression.In this paper,we proposed a discriminative model of multivariate pattern classification based on fNIRS signals to distinguish elderly depressed patients from healthy controls.This model used the brain activation patterns during a verbal fluency task as features of classification.Then Pseudo-Fisher Linear Discriminant Analysis was performed on the feature space to generate discriminative model.Using leave-one-out(LOO)cross-validation,our results showed a correct classification rate of 88%.The discriminative model showed its ability to identify people with elderly depression and suggested that fNIRS may be an efficient clinical tool for diagnosis of depression.This study may provide the first step for the development of neuroimaging biomarkers based on fNIRS in psychiatric disorders.

Multimodal Fusion of Brain Imaging Data: Methods and Applications

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.

The Neural Mechanism of Knowledge Assembly in the Human Brain Inspires Artificial Intelligence Algorithm

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.

Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer’s Disease

Alzheimer’s disease(AD)is associated with the impairment of white matter(WM)tracts.The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets[321 patients with AD,265 patients with mild cognitive impairment(MCI),279 normal controls(NC)],a unified pipeline,and independent site cross-validation.Automated fiber quantification was used to extract diffusion profiles along tracts.Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC.Machine learning models using tract-based features showed good generalizability among independent site cross-validation.The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups.We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.

Different Regional Patterns in Gray Matter-based Age Prediction

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as living habit.

Retraction Note to:Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

The authors have retracted this article.After publication we found an error in the implementation code that resulted in data leakage in the age-prediction model training process.We have redesigned the prediction model and tested the mode with an extended dataset(around 2000 subjects,in contrast to the 600 subjects in this article).

EM-fMRI:A Promising Method for Mapping the Brain Functional Connectome

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.

Effects of childhood trauma on aggressive behaviors andhippocampal function:the modulation of COMT haplotypes

Background:Aggression is a commonly hostile behavior linked to the hippocampal activity.Childhood trauma(CT)exposure has been associated with altered sensitization of the hypothalamic-pituitary-adrenal(HPA)axis and hippocampal volumewhich could increase violent aggressive behaviors.Additionally,Catechol-O-methyltransferase(COMT),the major dopamine metabolism enzyme,is impli-cated in stress responsivity,including aggression.Hence,CT exposure may affect aggression through the effect on the hippocampal function,which might also be modulated by the COMT variations.Objectives:This study examined whether both CT and haplotypes of COMT moderate hippocampal function and thus affect human aggressive behavior.Methods:We obtained bilateral hippocampal functional connectivity maps using resting state functional magnetic resonance imag-ing(MRI)data.COMT haplotype estimation was performed using Haploview 4.2 and PHASE 2.1.Then we constructed a moderated mediation model to study the effect of the CTQ×COMT on aggressive behavior.Results:Three major haplotypes were generated from thirteen single nucleotide polymorphisms(SNPs)within the COMT gene and formed three haplotypes corresponding to high,medium,and low enzymatic activity of COMT.The results showed interactive re-lationships between the Childhood Trauma Questionnaire(CTQ)and COMT with respect to the functional connectivity(FC)of the bilateral hippocampus(HIP)-orbital frontal cortex(OFC).Specifically,CT experience predicted lower negative HIP-OFC coupling in the APS and HPS haplotypes corresponding to the medium and high enzymatic activity of COMT,but greater FC in the LPS haplotypes corresponding to the low enzymatic activity.We also observed a conditional mediation effect of the right HIP-OFC coupling in the link between COMT and aggressive behavior that was moderated by CT experience.Conclusions:These results suggest that CT and COMT have a combined effect on aggressive behavior through hippocampal function.This mediation analysis sheds light on the influence of childhood experience on aggressive behavior in different genetic backgrounds.

阿尔茨海默病多中心独立的、可重复的海马影像组学标志物:早期识别、纵向进展和生物学基础

脑内海马结构的形态学变化是阿尔茨海默病(AD)的影像学标记之一.海马的影像组学特征是否可以作为AD早期识别的生物标记以及是否可以预测轻度认知损害(MCI)的转化尚需进一步验证,并且海马影像组学特征是否有其神经生物学基础也需要进一步探索.本文的主要目的是研究海马的影像组学特征是否可以作为AD早期识别的影像学标记.为此,我们利用最常用的多变量分类器对AD(261例)和正常人(231例)进行独立中心识别的交叉验证,6个中心的平均准确率为88.21%,更进一步,利用完全独立的ADNI数据集(1228例被试)也验证了这一结果.通过对MCI人群的系统研究发现,部分重要海马影像组学特征与被试的生物学评分(主要包括APOE基因型、多基因风险分数、脑脊液的生物标记物Aβ和Tau,以及认知能力的变化等)具有显著的相关性.更为重要的是,通过5年左右的纵向跟踪研究发现影像组学特征的变化与认知水平的变化具有高度一致性.本研究结果表明海马的影像组学特征有希望用于早期识别AD,具有预测MCI病人是否会转化为AD的潜在能力.综上,研究结果将有可能应用于MCI和AD的早期辅助识别,具有重要的临床意义.

A review of functional magnetic resonance imaging for Brainnetome

The functional brain network using blood-oxygen-level-dependent（BOLD） functional magnetic resonance imaging（fMRI） has revealed the potentials for probing brain architecture,as well as for identifying clinical biomarkers for brain diseases.In the general context of Brainnetome,this review focuses on the development of approaches for modeling and analyzing functional brain networks with BOLD fMRI.The prospects for these approaches are also discussed.

Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia

Schizophrenia is hypothesized to arise from disrupted brain connectivity. This ＂dysconnectivity hypothesis＂ has generated interest in discovering whether there is anatomical and functional dysconnectivity between the prefrontal cortex （PFC） and other brain regions, and how this dysconnectivity is linked to the impaired cognitive functions and aberrant behaviors of schizophrenia. Critical advances in neuroimaging technologies, including diffusion tensor imaging （DTI） and functional magnetic resonance imaging （fMRI）, make it possible to explore these issues. DTI affords the possibility to explore anatomical connectivity in the human brain in vivo and fMRI can be used to make inferences about functional connections between brain regions. In this review, we present major advances in the understanding of PFC anatomical and functional dysconnectivity and their implications in schizophrenia. We then briefly discuss future prospects that need to be explored in order to move beyond simple mapping of connectivity changes to elucidate the neuronal mechanisms underlying schizophrenia.

Spinal Cord Stimulation Frequency Influences the Hemodynamic Response in Patients with Disorders of Consciousness

Spinal cord stimulation （SCS） is a promising technique for treating disorders of consciousness （DOCs）. However, differences in the spatio-temporal responsiveness of the brain under varied SCS parameters remain unclear. In this pilot study, functional near-infrared spectroscopy was used to measure the hemodynamic responses of 10 DOC patients to different SCS frequencies （5 Hz, 10 Hz, 50 Hz, 70 Hz, and 100 Hz）. In the prefrontal cortex, a key area in consciousness circuits, we found significantly increased hemodynamic responses at 70 Hz and 100 Hz, and significantly different hemodynamic responses between 50 Hz and 70 Hz/100 Hz. In addition, the functional connectivity between prefrontal and occipital areas was significantly improved with SCS at 70 Hz. These results demonstrated that SCS modulates the hemodynamic responses and long-range connectivity in a frequency-specific manner （with 70 Hz apparently better）, perhaps by improving the cerebral blood volume and information transmission through the reticular formation-thalamus-cortex pathway.

Brain Network Studies in Chronic Disorders of Consciousness:Advances and Perspectives

Neuroimaging has opened new opportunities to study the neural correlates of consciousness, and provided additional information concerning diagnosis, prognosis, and therapeutic interventions in patients with disorders of consciousness. Here, we aim to review neuroimaging studies in chronic disorders of consciousness from the viewpoint of the brain network, focusing on positron emission tomogra- phy, functional MRI, functional near-infrared spectroscopy, electrophysiology, and diffusion MRI. To accelerate basic research on disorders of consciousness and provide a panoramic view of unconsciousness, we propose that it is urgent to integrate different techniques at various spatiotemporal scales, and to merge fragmented findings into a uniform ＂Brainnetome＂ （Brain-net-ome） research framework.

Separate Neural Networks for Gains and Losses in Intertemporal Choice

An important and unresolved question is how human brain regions process information and interact with each other in intertemporal choice related to gains and losses. Using psychophysiological interaction and dynamic causal modeling analyses, we investigated the functional interactions between regions involved in the decision- making process while participants performed temporal discounting tasks in both the gains and losses domains. We found two distinct intrinsic valuation systems underlying temporal discounting in the gains and losses domains： gains were specifically evaluated in the medial regions, including the medial prefrontal and orbitofrontal cortices, and losses were evaluated in the lateral dorsolateral prefrontal cortex. In addition, immediate reward or pun- ishment was found to modulate the functional interactions between the dorsolateral prefrontal cortex and distinct regions in both the gains and losses domains： in the gains domain, the mesolimbic regions; in the losses domain, the medial prefrontal cortex, anterior cingulate cortex, and insula. These findings suggest that intertemporal choice of gains and losses might involve distinct valuation systems, and more importantly, separate neural interactions may implement the intertemporal choices of gains and losses. These findings may provide a new biological perspective for understanding the neural mechanisms underlying intertemporal choice of gains and losses.

Mapping Underlying Maturational Changes in Human Brain

Human brain development is a complex process that continues between birth and maturity, and monitoring the underlying maturational changes at these stages is crucial for our understanding of typical development as well as neurodevelopmental disorders. During the critical periods of brain development, on one hand, many human capacities originate, but on the other hand, a brain undergoing rapid plastic changes may also be vulnerable to neuropsychiatric disorders . Multi-modal magnetic resonance imaging （MRI） has been increasingly used for its ability to noninvasively reveal structural and functional changes in the brain. However, interpretation of the neurobiological processes underlying the findings obtained with MRI is very limited .

ASAF: altered spontaneous activity fingerprinting in Alzheimer’s disease based on multisite fMRI

Several monocentric studies have noted alterations in spontaneous brain activity in Alzheimer's disease (AD), although there is no consensus on the altered amplitude of low-frequency fluctuations in AD patients. The main aim of the present study was to identify a reliable and reproducible abnormal brain activity pattern in AD. The amplitude of local brain activity (AM), which can provide fast mapping of spontaneous brain activity across the whole brain, was evaluated based on multisite rs-fMRI data for 688 subjects (215 normal controls (NCs), 221 amnestic mild cognitive impairment (aMCI) 252 AD). Two-sample t-tests were used to detect group differences between AD patients and NCs from the same site. Differences in the AM maps were statistically analyzed via the Stouffer's meta-analysis. Consistent regions of lower spontaneous brain activity in the default mode network and increased activity in the bilateral hippocampus/parahippocampus, thalamus, caudate nucleus, orbital part of the middle frontal gyrus and left fusiform were observed in the AD patients compared with those in NCs. Significant correlations (P?<?0.05, Bonferroni corrected) between the normalized amplitude index and Mini-Mental State Examination scores were found in the identified brain regions, which indicates that the altered brain activity was associated with cognitive decline in the patients. Multivariate analysis and leave-one-site-out cross-validation led to a 78.49% prediction accuracy for single-patient classification. The altered activity patterns of the identified brain regions were largely correlated with the FDG-PET results from another independent study. These results emphasized the impaired brain activity to provide a robust and reproducible imaging signature of AD.

Precise Modulation Strategies for Transcranial Magnetic Stimulation:Advances and Future Directions

Transcranial magnetic stimulation(TMS)is a popular modulatory technique for the noninvasive diagnosis and therapy of neurological and psychiatric diseases.Unfortunately,current modulation strategies are only modestly effective.The literature provides strong evidence that the modulatory effects of TMS vary depending on device components and stimulation protocols.These differential effects are important when designing precise modulatory strategies for clinical or research applications.Developments in TMS have been accompanied by advances in combining TMS with neuroimaging techniques,including electroencephalography,functional nearinfrared spectroscopy,functional magnetic resonance imaging,and positron emission tomography.Such studies appear particularly promising as they may not only allow us to probe affected brain areas during TMS but also seem to predict underlying research directions that may enable us to precisely target and remodel impaired cortices or circuits.However,few precise modulation strategies are available,and the long-term safety and efficacy of these strategies need to be confirmed.Here,we review the literature on possible technologies for precise modulation to highlight progress along with limitations with the goal of suggesting future directions for this field.

Diffusion magnetic resonance imaging for Brainnetome:A critical review

Increasing evidence shows that the human brain is a highly self-organized system that shows attributes of smallworldness,hierarchy and modularity.The ＂connectome＂ was conceived several years ago to identify the underpinning physical connectivities of brain networks.The need for an integration of multi-spatial and-temporal approaches is becoming apparent.Therefore,the ＂Brainnetome＂（brain-net-ome） project was proposed.Diffusion magnetic resonance imaging（dMRI） is a non-invasive way to study the anatomy of brain networks.Here,we review the principles of dMRI,its methodologies,and some of its clinical applications for the Brainnetome.Future research in this field is discussed.