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.

Face-Specific Activity in the Ventral Stream Visual Cortex Linked to Conscious Face Perception

When presented with visual stimuli of face images,the ventral stream visual cortex of the human brain exhibits face-specific activity that is modulated by the physical properties of the input images.However,it is still unclear whether this activity relates to conscious face perception.We explored this issue by using the human intracranial electroencephalography technique.Our results showed that face-specific activity in the ventral stream visual cortex was significantly higher when the subjects subjectively saw faces than when they did not,even when face stimuli were presented in both conditions.In addition,the face-specific neural activity exhibited a more reliable neural response and increased posterior-anterior direction information transfer in the“seen”condition than the“unseen”condition.Furthermore,the face-specific neural activity was significantly correlated with performance.These findings support the view that face-specific activity in the ventral stream visual cortex is linked to conscious face perception.

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.

Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer’s Disease:A Data-Driven Meta-Analysis with N=3,118

The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer’s disease(AD).We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns.Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD.Our results showed that the hippocampus and amygdala exhibit the most severe atrophy,followed by the temporal,frontal,and occipital lobes in mild cognitive impairment(MCI)and AD.The extent of atrophy in MCI was less severe than that in AD.A series of biological processes related to the glutamate signaling pathway,cellular stress response,and synapse structure and function were investigated through gene set enrichment analysis.Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy,providing new insight for further clinical research on AD.

Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s disease:diagnosis,longitudinal progress and biological basis

Hippocampal morphological change is one of the main hallmarks of Alzheimer’s disease(AD).However,whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment(MCI)to AD dementia and whether these features provide any neurobiological foundation remains unclear.The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging(MRI)markers for AD.Multivariate classifier-based support vector machine(SVM)analysis provided individual-level predictions for distinguishing AD patients(n=261)from normal controls(NCs;n=231)with an accuracy of 88.21%and intersite crossvalidation.Further analyses of a large,independent the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset(n=1228)reinforced these findings.In MCI groups,a systemic analysis demonstrated that the identified features were significantly associated with clinical features(e.g.,apolipoprotein E(APOE)genotype,polygenic risk scores,cerebrospinal fluid(CSF)Ab,CSF Tau),and longitudinal changes in cognition ability;more importantly,the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up.These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI,and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus.The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI.

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.

The long rather than the short allele of 5-HTTLPR predisposes Han Chinese to anxiety and reduced connectivity between prefrontal cortex and amygdala

The short allele of the serotonin-transporter gene is associated with higher risk for anxiety and depression in Caucasians, but this association is still unclear in Asians. Here, we addressed this issue using behavioral and multi-modal MRI approaches in a large group of healthy Han Chinese participants （n = 233）. In contrast to findings in Caucasians, we found that long-allele （L） carriers had higher anxiety scores. In another group （n = 64） experiencing significant levels of depression or anxiety, the L-allele frequency was also significantly higher. In healthy participants, L-carriers had reduced functional and anatomical connectivity between the amygdala and prefrontal cortex （PFC）, which was correlated with anxiety or depression scores. Our findings demonstrated that in Chinese Han participants, in contrast to Caucasians, the L-allele confers vulnerability to anxiety or depression and weakens top-down emotional control between the PFC and amygdala. Therefore, ethnic background should be taken into account in gene-related studies and their potential clinical applications.

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.

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.

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.

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 .

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.

Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles

The frontal pole cortex(FPC)plays key roles in various higher-order functions and is highly developed in non-human primates.An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results.This is important for understanding the functional architecture of the cerebral cortex.Here,combining cross-validation and principal component analysis,we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC(2 males and 6 females)into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4 T Bruker system,and then revealed their subregional connections.Furthermore,we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions,and found that the dorsolateral FPC,which contains an extension to the medial FPC,was mainly connected to regions of the default-mode network.The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network.These results enhance our understanding of the anatomy and circuitry of the macaque brain,and contribute to FPC-related clinical research.

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.

Reconstruction of behavior-relevant individual brain activity:an individualized fMRI study

Different patterns of brain activity are observed in various subjects across a wide functional domain.However,these individual differences,which are often neglected through the group average,are not yet completely understood.Based on the fundamental assumption that human behavior is rooted in the underlying brain function,we speculated that the individual differences in brain activity are reflected in the individual differences in behavior.Adopting 98 behavioral measures and assessing the brain activity induced at seven task functional magnetic resonance imaging states,we demonstrated that the individual differences in brain activity can be used to predict behavioral measures of individual subjects with high accuracy using the partial least square regression model.In addition,we revealed that behavior-relevant individual differences in brain activity transferred between different task states and can be used to reconstruct individual brain activity.Reconstructed individual brain activity retained certain individual differences which were lost in the group average and could serve as an individual functional localizer.Therefore,our results suggest that the individual differences in brain activity contain behavior-relevant information and should be included in group averaging.Moreover,reconstructed individual brain activity shows a potential use in precise and personalized medicine.