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Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases remain poorly understood. In this study, we recruited 14 term-born infants with mild hypoxic ischemic encephalopathy and 14 term-born infants with severe hypoxic ischemic encephalopathy from Changzhou Children's Hospital, China. Resting-state functional magnetic resonance imaging data showed efficient small-world organization in whole-brain networks in both the mild and severe hypoxic ischemic encephalopathy groups. However, compared with the mild hypoxic ischemic encephalopathy group, the severe hypoxic ischemic encephalopathy group exhibited decreased local efficiency and a low clustering coefficient. The distribution of hub regions in the functional networks had fewer nodes in the severe hypoxic ischemic encephalopathy group compared with the mild hypoxic ischemic encephalopathy group. Moreover, nodal efficiency was reduced in the left rolandic operculum, left supramarginal gyrus, bilateral superior temporal gyrus, and right middle temporal gyrus. These results suggest that the topological structure of the resting state functional network in children with severe hypoxic ischemic encephalopathy is clearly distinct from that in children with mild hypoxic ischemic encephalopathy, and may be associated with impaired language, motion, and cognition. These data indicate that it may be possible to make early predictions regarding brain development in children with severe hypoxic ischemic encephalopathy, enabling early interventions targeting brain function. This study was approved by the Regional Ethics Review Boards of the Changzhou Children's Hospital (approval No. 2013-001) on January 31, 2013. Informed consent was obtained from the family members of the children. The trial was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR1800016409) and the protocol version is 1.0.

The specific mechanisms by which acupuncture affects the central nervous system are unclear. In the International Standard Scalp Acupuncture system, acupuncture needles are applied at the middle line of the vertex, anterior parietal-temporal oblique line, and the posterior parietal-temporal oblique line. We conducted a single-arm prospective clinical trial in which seven healthy elderly volunteers (three men and four women; 50-70 years old) received International Standard Scalp Acupuncture at MS5 (the mid-sagittal line between Baihui (DU20) and Qianding (DU21)), the left MS6 (line joining Sishencong (EX-HN1) and Xuanli (GB6)), and the left MS7 (line joining DU20 and Qubin (GB7)). After acupuncture, resting-state functional magnetic resonance imaging demonstrated changes in the fractional amplitude of low frequency fluctuations and regional homogeneity in various areas, showing remarkable enhancement of regional homogeneity in the bilateral anterior cingulate, left medial frontal gyrus, supramarginal gyrus, right middle frontal gyrus, and inferior frontal gyrus. Functional connectivity based on a seed region at the right middle frontal gyrus (42, 51, 9) decreased at the bilateral medial superior frontal gyrus. Our data preliminarily indicates that the international standard scalp acupuncture in healthy elderly participants specifcally enhances the correlation between the brain regions involved in cognition and implementation of the brain network regulation system and the surrounding adjacent brain regions. The study was approved by the Ethics Committee of the China-Japan Union Hospital at Jilin University, China, on July 18, 2016 (approval No. 2016ks043).

•A functional ultrasound imaging in an fMRI-like environment with acoustic noise.•Positive rCBV response in auditory cortex and negative response in motor cortex.•Greater acoustic noise reduces functional connectivity in auditory and motor networks.•Functional connectivity by rsfUS under acoustic noise is similar to that by rsfMRI. Loud acoustic noise from the scanner during functional magnetic resonance imaging (fMRI) can affect functional connectivity (FC) observed in the resting state, but the exact effect of the MRI acoustic noise on resting state FC is not well understood. Functional ultrasound (fUS) is a neuroimaging method that visualizes brain activity based on relative cerebral blood volume (rCBV), a similar neurovascular coupling response to that measured by fMRI, but without the audible acoustic noise. In this study, we investigated the effects of different acoustic noise levels (silent, 80 dB, and 110 dB) on FC by measuring resting state fUS (rsfUS) in awake mice in an environment similar to fMRI measurement. Then, we compared the results to those of resting state fMRI (rsfMRI) conducted using an 11.7 Tesla scanner. RsfUS experiments revealed a significant reduction in FC between the retrosplenial dysgranular and auditory cortexes (0.56 ± 0.07 at silence vs 0.05 ± 0.05 at 110 dB, p=.01) and a significant increase in FC anticorrelation between the infralimbic and motor cortexes (−0.21 ± 0.08 at silence vs −0.47 ± 0.04 at 110 dB, p=.017) as acoustic noise increased from silence to 80 dB and 110 dB, with increased consistency of FC patterns between rsfUS and rsfMRI being found with the louder noise conditions. Event-related auditory stimulation experiments using fUS showed strong positive rCBV changes (16.5% ± 2.9% at 110 dB) in the auditory cortex, and negative rCBV changes (−6.7% ± 0.8% at 110 dB) in the motor cortex, both being constituents of the brain network that was altered by the presence of acoustic noise in the resting state experiments. Anticorrelation between constituent brain regions of the default mode network (such as the infralimbic cortex) and those of task-positive sensorimotor networks (such as the motor cortex) is known to be an important feature of brain network antagonism, and has been studied as a biological marker of brain disfunction and disease. This study suggests that attention should be paid to the acoustic noise level when using rsfMRI to evaluate the anticorrelation between the default mode network and task-positive sensorimotor network.

INTRODUCTION Amnestic mild cognitive impairment (aMCI) exhibits biased functional connectivity (FC) abnormalities impairing neural plasticity modulation. This study aimed to identify unbiased FC deficits using a brain‐wide association study (BWAS) and investigate repetitive transcranial magnetic stimulation (rTMS)‐driven plasticity restoration. METHODS BWAS identified unbiased FC‐altered voxels (robustness‐validated). Region‐of‐interest (ROI)‐wise FC analysis localized disrupted circuits, which were modulated through precuneus‐targeted rTMS. Effective connectivity (EC) tested whether the precuneus exerted a causal influence on these disrupted circuits. Correlation analyses linked FC plasticity to cognitive and clinical outcomes. RESULTS Eleven brain regions with 31 altered unbiased FC circuits were robustly identified, centered on the bilateral anterior cingulate cortex (ACC). rTMS causally restored FC in the right supracallosal ACC and postcentral gyrus, correlating with improved information processing speed (IPS). Remarkably, 80.77% (21/26) of aMCI responded clinically to rTMS. DISCUSSION This study first maps unbiased FC lesions in aMCI, confirming rTMS‐mediated ACC plasticity causally enhances IPS. These findings inform network‐targeted therapies to delay Alzheimer's disease (AD) progression. Highlights This is the first study to robustly map unbiased FC lesions in aMCI patients using a BWAS. rTMS causally restored FC in right supracallosal ACC in aMCI patients. FC and EC recovery demonstrated causal links to improvements in IPS and MoCA scores. Remarkably, 80.77% (21/26) of aMCI patients responded clinically to rTMS modulation.

Previous studies on brain functional connectivity networks in children have mainly focused on changes in function in specific brain regions, as opposed to whole brain connectivity in healthy children. By analyzing the independent components of activation and network connectivity between brain regions, we examined brain activity status and development trends in children aged 3 and 5 years. These data could provide a reference for brain function rehabilitation in children with illness or abnormal function. We acquired functional magnetic resonance images from 15 3-year-old children and 15 5-year-old children under natural sleep conditions. The participants were recruited from five kindergartens in the Nanshan District of Shenzhen City, China. The parents of the participants signed an informed consent form with the premise that they had been fully informed regarding the experimental protocol. We used masked independent component analysis and BrainNet Viewer software to explore the independent components of the brain and correlation connections between brain regions. We identified seven independent components in the two groups of children, including the executive control network, the dorsal attention network, the default mode network, the left frontoparietal network, the right frontoparietal network, the salience network, and the motor network. In the default mode network, the posterior cingulate cortex, medial frontal gyrus, and inferior parietal lobule were activated in both 3- and 5-year-old children, supporting the \"three-brain region theory\" of the default mode network. In the frontoparietal network, the frontal and parietal gyri were activated in the two groups of children, and functional connectivity was strengthened in 5-year-olds compared with 3-year-olds, although the nodes and network connections were not yet mature. The high-correlation network connections in the default mode networks and dorsal attention networks had been significantly strengthened in 5-year-olds vs. 3-year-olds. Further, the salience network in the 3-year-old children included an activated insula/inferior frontal gyrus-anterior cingulate cortex network circuit and an activated thalamus-parahippocampal-posterior cingulate cortex-subcortical regions network circuit. By the age of 5 years, nodes and high-correlation network connections (edges) were reduced in the salience network. Overall, activation of the dorsal attention network, default mode network, left frontoparietal network, and right frontoparietal network increased (the volume of activation increased, the signals strengthened, and the high-correlation connections increased and strengthened) in 5-year-olds compared with 3-year-olds, but activation in some brain nodes weakened or disappeared in the salience network, and the network connections (edges) were reduced. Between the ages of 3 and 5 years, we observed a tendency for function in some brain regions to be strengthened and for the generalization of activation to be reduced, indicating that specialization begins to develop at this time. The study protocol was approved by the local ethics committee of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences in China with approval No. SIAT-IRB-131115-H0075 on November 15, 2013.

With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non-matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods. •Metric learning approach for similarity estimation between brain connectivity graphs.•The method employs spectral graph convolutions to learn localised feature maps.•Quantitative and qualitative evaluation on ABIDE and UK Biobank databases.•Global loss function leads to improved results on heterogeneous datasets.

Objective Few effective treatments improve upper extremity (UE) function after stroke. Immersive virtual reality (imVR) is a novel and promising strategy for stroke UE recovery. We assessed the extent to which imVR-based UE rehabilitation can augment conventional treatment and explored changes in brain functional connectivity (FC) that were related to the rehabilitation. Methods An assessor-blinded, parallel-group randomized controlled trial was performed with 40 subjects randomly assigned to either imVR or Control group (1:1 allocation), each receiving rehabilitation 5 times per week for 3 weeks. Subjects in the imVR received both imVR and conventional rehabilitation, while those in the Control received conventional rehabilitation only. Our primary and secondary outcomes were the Fugl-Meyer assessment’s upper extremity subscale (FMA-UE) and the Barthel Index (BI), respectively. Both intention-to-treat (ITT) and per-protocol (PP) analyses were performed to assess the effectiveness of the trial. For both the FMA-UE/BI, a one-way analysis of covariance (ANCOVA) model was used, with the FMA-UE/BI at post-intervention or at follow-up, respectively, as the dependent variable, the two groups as the independent variable, baseline FMA-UE/BI, age, sex, site, time since onset, hypertension and diabetes as covariates. Results Both ITT and PP analyses demonstrated the effectiveness of imVR-based rehabilitation. The FMA-UE score was greater in the imVR compared with the Control at the post-intervention (mean difference: 9.1 (95% CI 1.6, 16.6); P  = 0.019) and follow-up (mean difference:11.5 (95% CI 1.9, 21.0); P  = 0.020). The results were consistent for BI scores. Moreover, brain FC analysis found that the motor function improvements were associated with a change in degree in ipsilesional premotor cortex and ipsilesional dorsolateral prefrontal cortex immediately following the intervention and in ipsilesional visual region and ipsilesional middle frontal gyrus after the 12-week follow-up. Conclusions ImVR-based rehabilitation is an effective tool that can improve the recovery of UE functional capabilities of subacute stroke patients when added to standard care. These improvements were associated with distinctive brain changes at two post-stroke timepoints. The study results will benefit future patients with stroke and provide evidence for a promising new method of stroke rehabilitation. Trial registration ClinicalTrials.gov identifier: NCT03086889.

Internalizing and externalizing problems that emerge during adolescence differentially increase boys’ and girls’ risk for developing psychiatric disorders. It is not clear, however, whether there are sex differences in the intrinsic functional architecture of the brain that underlie changes in the severity of internalizing and externalizing problems in adolescents. Using resting-state fMRI data and self-reports of behavioral problems obtained from 128 adolescents (73 females; 9–14 years old) at two timepoints, we conducted multivoxel pattern analysis to identify resting-state functional connectivity markers at baseline that predict changes in the severity of internalizing and externalizing problems in boys and girls 2 years later. We found sex-differentiated involvement of the default mode network in changes in internalizing and externalizing problems. Whereas changes in internalizing problems were associated with the dorsal medial subsystem in boys and with the medial temporal subsystem in girls, changes in externalizing problems were predicted by hyperconnectivity between core nodes of the DMN and frontoparietal network in boys and hypoconnectivity between the DMN and affective networks in girls. Our results suggest that different neural mechanisms predict changes in internalizing and externalizing problems in adolescent boys and girls and offer insights concerning mechanisms that underlie sex differences in the expression of psychopathology in adolescence.

Functional and structural maturation of networks comprised of discrete regions is an important aspect of brain development. The default-mode network (DMN) is a prominent network which includes the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), medial temporal lobes (MTL), and angular gyrus (AG). Despite increasing interest in DMN function, little is known about its maturation from childhood to adulthood. Here we examine developmental changes in DMN connectivity using a multimodal imaging approach by combining resting-state fMRI, voxel-based morphometry and diffusion tensor imaging-based tractography. We found that the DMN undergoes significant developmental changes in functional and structural connectivity, but these changes are not uniform across all DMN nodes. Convergent structural and functional connectivity analyses suggest that PCC-mPFC connectivity along the cingulum bundle is the most immature link in the DMN of children. Both PCC and mPFC also showed gray matter volume differences, as well as prominent macrostructural and microstructural differences in the dorsal cingulum bundle linking these regions. Notably, structural connectivity between PCC and left MTL was either weak or non-existent in children, even though functional connectivity did not differ from that of adults. These results imply that functional connectivity in children can reach adult-like levels despite weak structural connectivity. We propose that maturation of PCC-mPFC structural connectivity plays an important role in the development of self-related and social-cognitive functions that emerge during adolescence. More generally, our study demonstrates how quantitative multimodal analysis of anatomy and connectivity allows us to better characterize the heterogeneous development and maturation of brain networks.