Functional Imaging Data Research Articles

Altered local spontaneous brain activity pattern in children with right-eye amblyopia of varying degrees: evidence from fMRI.

To investigate the abnormal changes of local brain activity in children with right-eye amblyopia of varying degrees. Data of resting-state functional magnetic resonance imaging were collected from 16 children with severe amblyopia, 17 children with mild to moderate amblyopia, and 15 children with normal binocular vision. Local brain activity was analyzed using the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo). There were extensive ALFF differences among the three groups in 10 brain regions. There were extensive differences in ReHo among the three groups in 11 brain regions. The ALFF and ReHo of the right orbital part of the middle frontal gyrus displayed a significantly positive correlation with the best-corrected visual acuity of the right eye, respectively. The ALFF value and ReHo value of the right orbital part of the middle frontal gyrus followed the pattern of normal control < mild to moderate amblyopia < severe amblyopia. This study demonstrated that there were changes in specific patterns of ALFF and ReHo in children with right-eye amblyopia of different degrees in brain regions performing visual sensorimotor and attentional control functions.

VoDEx: a Python library for time annotation and management of volumetric functional imaging data.

In functional imaging studies, accurately synchronizing the time course of experimental manipulations and stimulus presentations with resulting imaging data is crucial for analysis. Current software tools lack such functionality, requiring manual processing of the experimental and imaging data, which is error-prone and potentially non-reproducible. We present VoDEx, an open-source Python library that streamlines the data management and analysis of functional imaging data. VoDEx synchronizes the experimental timeline and events (e.g. presented stimuli, recorded behavior) with imaging data. VoDEx provides tools for logging and storing the timeline annotation, and enables retrieval of imaging data based on specific time-based and manipulation-based experimental conditions. VoDEx is an open-source Python library and can be installed via the "pip install" command. It is released under a BSD license, and its source code is publicly accessible on GitHub (https://github.com/LemonJust/vodex). A graphical interface is available as a napari-vodex plugin, which can be installed through the napari plugins menu or using "pip install." The source code for the napari plugin is available on GitHub (https://github.com/LemonJust/napari-vodex). The software version at the time of submission is archived at Zenodo (version v1.0.18, https://zenodo.org/record/8061531).

Application of functional connectivity neurofeedback in patients with treatment-resistant depression: A preliminary report

Functional connectivity neurofeedback (FCNef) is a technique that modulates synchronous neural activity through training and is being investigated as a potential novel treatment for patients suffering from treatment-resistant depression (TRD). An FCNef protocol, based on the analysis of resting-state functional imaging data from a large cohort of depressed individuals, has been proposed to promote negative functional connectivity between the dorsolateral prefrontal cortex and the posterior cingulate cortex (DLPFC-PCC FC). This study aimed to assess the therapeutic efficacy and practicality of the protocol in a small sample of TRD patients. Of the six patients recruited, five completed the FCNef sessions. Depression and rumination scores significantly improved post-treatment, however, there were no significant changes in DLPFC-PCC FC. The study demonstrated efficacy of FCNef in ameliorating depressive symptoms, yet, it also indicated that the training itself may be burdensome for depressed patients, as evidenced by participants reporting fatigue (one of whom dropped out). Thus, a more efficient and less burdensome protocol is needed for future investigations and applications.

Carbon Nanotube Sensors Enable Visualization of Dopamine Neuromodulation at the Resolution of a Single Chemical Synapse

Among the organs in our body, the brain easily remains the most intriguing in terms of its complexity and function. Nerve cells, which are the functional building blocks of the brain, operate in complex networks that underpin most of the brain’s capabilities, including ability to learn, remember, initiate and orchestrate complex movement. In systems neuroscience, behavioral assays and large-scale neuronal activity recording have broadened our understanding of the role that specific brain regions and neuronal circuits play in a behaving animal. An equally exciting aspect of neuroscience concerns itself with the cellular and molecular underpinnings of nerve cell function. Unique among all the organs, the brain’s constituent cells, neurons, need to communicate with another in intricate networks that are responsible for circuit function, and most of this communication is enabled by chemical cues that are released between neurons. Better understanding of neuronal communication via chemical release from synapses requires technologies that can help us visualize and measure the spatial and temporal dynamics of these chemical signals. Our lab seeks to address this challenge by developing optical biosensors with ultra-low detection capabilities, high spatial resolution and signal-to-noise ratio.To study chemical synapses at high spatial resolution, we use primary dopamine neuron cultures derived from rats and mice as a model system. Dopamine neuron signaling is critical for learning and motor control, and its aberration is implicated in a wide range of neurological and psychiatric disorders. Furthermore, dopamine is a neuromodulator, and it is a neurochemical that is predicted to be highly diffusive in its spatial signaling. Our lab and several others have developed ssDNA functionalized SWCNT optical sensors to study catecholaminergic neurotransmitters, including dopamine. In a recent publication we reported an assay to study dopamine effluxes from primary dopaminergic neurons using a chemi-sensitive 2D substrate (DopaFilm). DopaFilm is fabricated from solution phase SWCNT-sensors in two simple steps: (1) surface functionalization of glass coverslips using silane chemistry to anchor nanosensors (2) drop casting of nanotube sensor solution to create the 2D nanofilm followed by passivation with a thin layer of poly(D-lysine). Primary dopamine neurons plated on DopaFilm are allowed to grow and mature (2 – 6 weeks depending on the experiment). Dopamine release activity can be imaged by recording the NIR emission (900-1400 nm) and at excitation with a 785nm laser. With this technology we were able to faithfully measure dopamine concentrations as low as 1nM with DopaFilm, which has an apparent dissociation constant of 268nM. In 2D cultures, we routinely observed spontaneous and evoked dendritic and axonal DA release events with bouton level spatial resolutions and sub-second temporal resolutions. With signal-to-noise ratios above 50 and comparable on and off-kinetics to latest genetically encoded dopamine sensors (dLight1 and GRABDA), DopaFilm is by far the most sensitive technology to study dopamine chemical signaling with the resolution of a single synapse. The technology allows the study of an entire neuron at the resolution of a single synapse. We combine functional imaging data with pharmacological and genetic perturbations to dissect the role that circuit effects and specific genes (proteins) play in dopamine synthesis, packaging, and release. Additionally, by taking advantage of the NIR emission of SWCNTs, we are able to multiplex dopamine release imaging with Ca2+ activity in orthogonal channels, a feat that is yet to be demonstrated with any competing technology.

Functional brain activity in patients with amnestic mild cognitive impairment: an rs-fMRI study.

Amnestic mild cognitive impairment (aMCI) is an early stage of Alzheimer's disease (AD). Regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF) are employed to explore spontaneous brain function in patients with aMCI. This study applied ALFF and ReHo indicators to analyze the neural mechanism of aMCI by resting-state functional magnetic resonance imaging (rs-fMRI). Twenty-six patients with aMCI were included and assigned to the aMCI group. The other 26 healthy subjects were included as a healthy control (HC) group. Rs-fMRI was performed for all participants in both groups. Between-group comparisons of demographic data and neuropsychological scores were analyzed using SPSS 25.0. Functional imaging data were analyzed using DPARSF and SPM12 software based on MATLAB 2017a. Gender, age, and years of education were used as covariates to obtain ALFF and ReHo indices. Compared with HC group, ALFF decreased in the left fusiform gyrus, left superior temporal gyrus, and increased in the left cerebellum 8, left inferior temporal gyrus, left superior frontal gyrus (BA11), and right inferior temporal gyrus (BA20) in the aMCI group (p < 0.05, FWE correction). In addition, ReHo decreased in the right middle temporal gyrus and right anterior cuneiform lobe, while it increased in the left middle temporal gyrus, left inferior temporal gyrus, cerebellar vermis, right parahippocampal gyrus, left caudate nucleus, right thalamus, and left superior frontal gyrus (BA6) (p < 0.05, FWE correction). In the aMCI group, the ALFF of the left superior frontal gyrus was negatively correlated with Montreal Cognitive Assessment (MoCA) score (r = -0.437, p = 0.026), and the ALFF of the left superior temporal gyrus was positively correlated with the MoCA score (r = 0.550, p = 0.004). The ReHo of the right hippocampus was negatively correlated with the Mini-Mental State Examination (MMSE) score (r = -0.434, p = 0.027), and the ReHo of the right middle temporal gyrus was positively correlated with MMSE score (r = 0.392, p = 0.048). Functional changes in multiple brain regions rather than in a single brain region have been observed in patients with aMCI. The abnormal activity of multiple specific brain regions may be a manifestation of impaired central function in patients with aMCI.

The NEF-SPA Approach as a Framework for Developing a Neurobiologically Inspired Spiking Neural Network Model for Speech Production.

The computer-based simulation of the whole processing route for speech production and speech perception in a neurobiologically inspired way remains a challenge. Only a few neural based models of speech production exist, and these models either concentrate on the cognitive-linguistic component or the lower-level sensorimotor component of speech production and speech perception. Moreover, these existing models are second-generation neural network models using rate-based neuron approaches. The aim of this paper is to describe recent work developing a third-generation spiking-neuron neural network capable of modeling the whole process of speech production, including cognitive and sensorimotor components. Our neural model of speech production was developed within the Neural Engineering Framework (NEF), incorporating the concept of Semantic Pointer Architecture (SPA), which allows the construction of large-scale neural models of the functioning brain based on only a few essential and neurobiologically well-grounded modeling or construction elements (i.e., single spiking neuron elements, neural connections, neuron ensembles, state buffers, associative memories, modules for binding and unbinding of states, modules for time scale generation (oscillators) and ramp signal generation (integrators), modules for input signal processing, modules for action selection, etc.). We demonstrated that this modeling approach is capable of constructing a fully functional model of speech production based on these modeling elements (i.e., biologically motivated spiking neuron micro-circuits or micro-networks). The model is capable of (i) modeling the whole processing chain of speech production and, in part, for speech perception based on leaky-integrate-and-fire spiking neurons and (ii) simulating (macroscopic) speaking behavior in a realistic way, by using neurobiologically plausible (microscopic) neural construction elements. The model presented here is a promising approach for describing speech processing in a bottom-up manner based on a set of micro-circuit neural network elements for generating a large-scale neural network. In addition, the model conforms to a top-down design, as it is available in a condensed form in box-and-arrow models based on functional imaging and electrophysiological data recruited from speech processing tasks.

Altered functional connectivity of the hippocampus with the sensorimotor cortex induced by long-term experience of virtual hand illusion

Synchronizing the movement of a virtual hand with an unseen real hand in a virtual environment is an effective method to induce a sense of ownership of the virtual hand. Although several neuroimaging studies have revealed neural mechanisms related to the ongoing process of ownership illusion, the effect of the long-term experience of ownership illusion on brain activity remains to be investigated. Here, we developed an apparatus based on real-time image matting and virtual reality technology which allowed us to use the image of participants’ real hands as their virtual hands and synchronize the movement of the virtual hands with the unseen real hands in a virtual scene. Resting-state functional imaging data were acquired immediately after participants completed several light office tasks with the virtual hands in either the virtual environment (virtual hand condition) or real environment (real hand condition). Significant positive functional connectivity of the hippocampus with the primary somatosensory and motor cortex was only observed in the virtual hand condition. The results provided novel evidence for the involvement of hippocampal-sensorimotor connection in the long-term experience of virtual hand ownership. The functional connection reorganization of the hippocampus might promote multi-sensory information integration into memory and updated the sense of body ownership, which offered important insights into the neural network underlying the availability of long-term use of VR technology in healthcare and rehabilitation.

Neuroanatomical and functional substrates of the hypomanic personality trait and its prediction on aggression

Hypomanic personality manifests a close link with several psychiatric disorders and its abnormality is a risk indicator for developing bipolar disorders. We systematically investigated the potential neuroanatomical and functional substrates underlying hypomanic personality trait (HPT) and its sub-dimensions (i.e., Social Vitality, Mood Volatility, and Excitement) combined with structural and functional imaging data as well as their corresponding brain networks in a large non-clinical sample across two studies (n = 464). Behaviorally, HPT, specifically Mood Volatility and Excitement, was positively associated with aggressive behaviors in both studies. Structurally, sex-specific morphological characteristics were further observed in the motor and top-down control networks especially for Mood Volatility, although HPT was generally positively associated with grey matter volumes (GMVs) in the prefrontal, temporal, visual, and limbic systems. Functionally, brain activations related to immediate or delayed losses were found to predict individual variability in HPT, specifically Social Vitality and Excitement, on the motor and prefrontal-parietal cortices. Topologically, connectome-based prediction model analysis further revealed the predictive role of individual-level morphological and resting-state functional connectivity on HPT and its sub-dimensions, although it did not reveal any links with general brain topological properties. GMVs in the temporal, limbic (e.g., amygdala), and visual cortices mediated the effects of HPT on behavioral aggression. These findings suggest that the imbalance between motor and control circuits may be critical for HPT and provide novel insights into the neuroanatomical, functional, and topological mechanisms underlying the specific temperament and its impacts on aggression.

Effect of auricular acupuncture on neuroplasticity of stroke patients with motor dysfunction: A fNIRS study

Cerebral Stroke is an acute cerebrovascular disease, a disease of brain tissue damage caused by the sudden rupture or blockage of blood vessels in the brain that prevents blood flow to the brain. Acupuncture has become a popular treatment for stroke, with auricular acupuncture providing a new idea for stroke treatment. However, the neuromodulatory mechanism of auricular acupuncture in the brain is still unclear. The aim of this study was to investigate the effect of auricular acupuncture in the treatment of upper limb dysfunction and the activation of specific brain regions in stroke patients. Forty patients with stroke hemiplegia who met the nerf criteria were included in the experiment and randomly assigned into two groups (20 patients in each group): the auricular acupuncture group and the control group. Fugl-Meyer score (FMA) assessment of upper limb motor function, motor evoked potential (MEP) measurement, and functional near-infrared brain function imaging (fNIRS) data acquisition in the primary motor M1 area of the brain at rest were performed before and after treatment, respectively. It was found that: 1) after auricular acupuncture treatment, the patients in the auricular acupuncture group showed significantly greater peak MEP and significantly higher oxyhemoglobin content in the M1 region of the brain compared with the control group, with a significant activation effect (MEP: P-value = 0.032, t = -2.22; HbO2; f = 4.225, p = 0.046); 2) in the clinical efficacy assessment, the FMA score in the auricular acupuncture group after treatment (p = 0.0122, t = 2.769). The results suggest that auricular acupuncture has an ameliorative effect on upper limb motor deficits after stroke and that activation of the M1 region of the brain may be a key node in auricular acupuncture for treating upper limb dysfunction in stroke patients, a finding that emphasizes the potential for clinical application of auricular acupuncture therapy for stroke patients with potential mechanisms influencing the outcome.

A Multi-Site Anti-Interference Neural Network for ASD Classification

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that can reduce quality of life and burden families. However, there is a lack of objectivity in clinical diagnosis, so it is very important to develop a method for early and accurate diagnosis. Multi-site data increases sample size and statistical power, which is convenient for training deep learning models. However, heterogeneity between sites will affect ASD recognition. To solve this problem, we propose a multi-site anti-interference neural network for ASD classification. The resting state brain functional image data provided by the multi-site is used to train the ASD classification model. The model consists of three modules. First, the site feature extraction module is used to quantify the inter-site heterogeneity, in which the autoencoder is used to reduce the feature dimension. Secondly, the presentation learning module is used to extract classification features. Finally, the anti-interference classification module uses the output of the first two modules as labels and inputs for multi-task adversarial training to complete the representation learning that is not affected by the confounding of sites, so as to realize the adaptive anti-interference ASD classification. The results show that the average accuracy of ten-fold cross validation is 75.56%, which is better than the existing studies. The innovation of our proposed method lies in the problem that the traditional single-task deep learning ASD classification model will be affected by the heterogeneity of multi-site data and interfere with the classification. Our method eliminates the influence of multi-site factors on feature extraction through multi-task adversarial training, so that the model can better adapt to the heterogeneity of multi-site data. Meanwhile, large-scale 1DconV is introduced to extract features of brain functional network, which provides support for the interpretability of the model. This method is expected to take advantage of multiple sites and provide reference for early diagnosis and treatment of ASD.

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.

FRAT: an interactive, Python-based tool for region-of-interest summaries of functional imaging data

fRAT: an interactive, Python-based tool for region-of-interest summaries of functional imaging data

Disrupted longitudinal restoration of brain connectivity during weight normalization in severe anorexia nervosa

Altered intrinsic brain connectivity of patients with anorexia nervosa has been observed in the acute phase of the disorder, but it remains unclear to what extent these alterations recover during weight normalization. In this study, we used functional imaging data from three time points to probe longitudinal changes in intrinsic connectivity patterns in patients with severe anorexia nervosa (BMI ≤ 15.5 kg/m2) over the course of weight normalization. At three distinct stages of inpatient treatment, we examined resting-state functional connectivity in 27 women with severe anorexia nervosa and 40 closely matched healthy controls. Using network-based statistics and graph-theoretic measures, we examined differences in global network strength, subnetworks with altered intrinsic connectivity, and global network topology. Patients with severe anorexia nervosa showed weakened intrinsic connectivity and altered network topology which did not recover during treatment. The persistent disruption of brain networks suggests sustained alterations of information processing in weight-recovered severe anorexia nervosa.

It is the locus coeruleus! Or… is it?: a proposition for analyses and reporting standards for structural and functional magnetic resonance imaging of the noradrenergic locus coeruleus

The noradrenergic locus coeruleus (LC) is one of the protein pathology epicenters in neurodegenerative diseases. In contrast to PET (positron emission tomography), MRI (magnetic resonance imaging) offers the spatial resolution necessary to investigate the 3–4 mm wide and 1.5 cm long LC. However, standard data postprocessing is often too spatially imprecise to allow investigating the structure and function of the LC at the group level. Our analysis pipeline uses a combination of existing toolboxes (SPM12, ANTs, FSL, FreeSurfer), and is tailored towards achieving suitable spatial precision in the brainstem area. Its effectiveness is demonstrated using 2 datasets comprising both younger and older adults. We also suggest quality assessment procedures which allow to quantify the spatial precision obtained. Spatial deviations below 2.5 mm in the LC area are achieved, which is superior to current standard approaches. Relevant for ageing and clinical researchers interested in brainstem imaging, we provide a tool for more reliable analyses of structural and functional LC imaging data which can be also adapted for investigating other nuclei of the brainstem.

Structural and functional activities of brain in patients with vascular cognitive impairment: A case-controlled magnetic resonance imaging study.

This study aimed to identify abnormal brain regions and imaging indices of vascular cognitive impairment (VCI) and explore specific imaging diagnostic markers of VCI. In this study, 24 patients with VCI were allocated to the VCI group and 25 healthy subjects were assigned to the healthy control (HC) group. Demographic data and neuropsychological test scores were compared using SPSS 25.0. The structural and functional imaging data were post-processed and statistically analyzed using CAT12, DPARSF and SPM12 software, based on the MATLAB platform. The structural and functional indices of gray matter volume (GMV) and regional homogeneity (ReHo) were obtained, and inter-group data were analyzed using an independent-sample t test. Sex, age, years of education, and total brain volume were used as covariates. Compared to the HC group, the GMV of VCI in the VCI group decreased significantly in the rectus muscles of the bilateral gyrus, left superior temporal gyrus, left supplementary motor area (SMA), right insula, right superior temporal gyrus, right anterior cuneiform lobe, and right anterior central gyrus (PRECG) (P < .05, FWE correction), without GMV enlargement in the brain area. ReHo decreased in the right inferior temporal gyrus (ITG), right parahippocampal gyrus, and left temporal pole (middle temporal gyrus, right lingual gyrus, left posterior central gyrus, and right middle temporal gyrus), the areas of increased ReHo were the left caudate nucleus, left rectus gyrus, right anterior cingulate gyrus and lateral cingulate gyrus (P < .05, FWE correction). Correlation analysis showed that the GMV of the left superior temporal gyrus was positively correlated with the Montreal Cognitive Assessment (MoCA) score (P < .05), and the GMV of the right insula was positively correlated with the MESE and long delayed memory scores (P < .05). There was a significant positive correlation between the ReHo and short-term delayed memory scores in the middle temporal gyrus of the left temporal pole (P < .05). The volume of GMV and ReHo decreased in VCI patients, suggesting that impairment of brain structure and function in specific regions is the central mechanism of cognitive impairment in these patients. Meanwhile, the functional indices of some brain regions were increased, which may be a compensatory mechanism for the cognitive impairment associated with VCI.

Prostatectomy Bed Image-guided Dose-escalated Salvage Radiotherapy (SPIDER): An International Multicenter Retrospective Study

BackgroundManagement of macroscopic local recurrence (MLR) after radical prostatectomy is a challenging situation with no standardized approach. ObjectiveThe objective of our study was to assess the efficacy and safety of functional image-guided salvage radiotherapy (SRT) in patients with MLR in the prostate bed. Design, setting, and participantsIn this international multicenter retrospective study across 16 European centers, eligible patients were initially treated by radical prostatectomy (RP) with or without pelvic lymph node dissection for localized or locally advanced adenocarcinoma of the prostate. Outcome measurements and statistical analysisProstate-specific antigen (PSA) measured 4 wk after RP was <0.1 ng/ml. All patients presented a biochemical relapse after RP defined by an increase in PSA level of ≥0.2 ng/ml on two successive measures. Only patients with an MLR lesion in the prostatectomy bed visualized on functional imaging (multiparametric magnetic resonance imaging, positron emission tomography/computed tomography [PET/CT] choline, or PET/CT prostate-specific membrane antigen) were eligible. Patients with lymph node, bone, or visceral dissemination at restaging imaging (CT and/or bone scintigraphy and/or magnetic resonance imaging and/or PET) were excluded. Dose escalation was defined as a dose of >66 Gy prescribed to the prostate bed or to MLR. Toxicities were classified using the Common Terminology Criteria for Adverse Events scale, version 4.03. The primary endpoint was progression-free survival (PFS). Secondary outcomes were metastasis-free survival (MPFS), biochemical progression-free survival, and overall survival. Genitourinary (GU) and gastrointestinal (GI) toxicities were analyzed. Results and limitationsBetween January 2000 and December 2019, 310 patients received at least one dose escalation on MLR and 25 patients did not receive any dose escalation. The median PSA level before SRT was 0.63 ng/ml (interquartile range [IQR], 0.27–1.7). The median follow-up was 54 mo (IQR, 50–56). Five-year PFS and MPFS were 70% (95% confidence interval [CI]: [64; 75]) and 84% (95% CI: [78; 88]), respectively. Grade ≥2 GU and GI late toxicities were observed in 43 (12%) and 11 (3%) patients, respectively. When the prescribed dose on the MLR lesion was ≥72 Gy, an improvement in 5-yr PFS was found for patients received at least one dose escalation (73% [95% CI: 65–79]) vs 60% [95% CI: 48; 70]; p = 0.03). ConclusionsIn this contemporary study integrating functional imaging data, we found potential efficacy of SRT with dose escalation ≥72 Gy for patients with MLR in the prostate bed and with an acceptable toxicity profile. Prospective data exploring this MLR dose escalation strategy are awaited. Patient summaryIn this report, we looked at the outcomes from salvage radiotherapy for prostate cancer and macroscopic relapse in a large European population. We found that outcomes varied with prostate-specific antigen at relapse, Gleason score, and dose escalation. We found potential efficacy of salvage radiotherapy with dose escalation for macroscopic relapse in the prostate bed, with an acceptable toxicity profile.

Examining resting-state network connectivity in children exposed to perinatal maternal adversity using anatomically weighted functional connectivity (awFC) analyses; A preliminary report.

Environmental perturbations during critical periods can have pervasive, organizational effects on neurodevelopment. To date, the literature examining the long-term impact of early life adversity has largely investigated structural and functional imaging data outcomes independently. However, emerging research points to a relationship between functional connectivity and the brain's underlying structural architecture. For instance, functional connectivity can be mediated by the presence of direct or indirect anatomical pathways. Such evidence warrants the use of structural and functional imaging in tandem to study network maturation. Accordingly, this study examines the impact of poor maternal mental health and socioeconomic context during the perinatal period on network connectivity in middle childhood using an anatomically weighted functional connectivity (awFC) approach. awFC is a statistical model that identifies neural networks by incorporating information from both structural and functional imaging data. Resting-state fMRI and DTI scans were acquired from children aged 7-9 years old. Our results indicate that maternal adversity during the perinatal period can affect offspring's resting-state network connectivity during middle childhood. Specifically, in comparison to controls, children of mothers who had poor perinatal maternal mental health and/or low socioeconomic status exhibited greater awFC in the ventral attention network. These group differences were discussed in terms of the role this network plays in attention processing and maturational changes that may accompany the consolidation of a more adult-like functional cortical organization. Furthermore, our results suggest that there is value in using an awFC approach as it may be more sensitive in highlighting connectivity differences in developmental networks associated with higher-order cognitive and emotional processing, as compared to stand-alone FC or SC analyses.

Disruption of Periaqueductal Gray-default Mode Network Functional Connectivity in Patients with Crohn's Disease with Abdominal Pain

Abdominal pain in Crohn's disease (CD) has been known to be associated with changes in the central nervous system. The periaqueductal gray (PAG) plays a well-established role in pain processing. However, the role of PAG-related network and the effect of pain on the network in CD remain unclear.Resting-state functional magnetic imaging (fMRI) data were collected from 24 CD patients in remission with abdominal pain, 24 CD patients without abdominal pain and 28 healthy controls (HCs). Using the subregions of PAG (dorsomedial (dmPAG), dorsolateral (dlPAG), lateral (lPAG) and ventrolateral (vlPAG)) as seeds, the seed-based FC maps were calculated and one-way analysis of variance (ANOVA) was performed to investigate the differences among the three groups.Results showed that the group differences were mainly involved in the FC of the vlPAG with the precuneus, medial prefrontal cortex (mPFC) as well as orbitofrontal cortex (OFC), and the FC of the right lateral PAG (lPAG) with the precuneus, inferior parietal lobule (IPL), angular gyrus and premotor cortex. The FC values of all these regions decreased successively in the order of HCs, CD without abdominal pain and CD with abdominal pain. The pain score was negatively correlated with the FC of the l/vlPAG with the precuneus, angular gyrus and mPFC in CD patients with abdominal pain.This study implicated the disrupt communication between the PAG and the default mode network (DMN). These findings complemented neuroimaging evidence for the pathophysiology of visceral pain in CD patients.

Effects of integrated action and sensory observation therapy based on mirror neuron and embodied cognition theory on upper limb sensorimotor function in chronic stroke: a study protocol for a randomised controlled trial

IntroductionThis study protocol aims to explore the effectiveness and neural mechanism of the integration of action observation therapy (AOT) and sensory observation therapy (SOT) for post-stroke patients on upper limb...

Identification of Autism Spectrum Disorder using Residual Attention Network for Facial Image Analysis.

The goal of the present paper is to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from a face image dataset based solely on the patient's face activation patterns. We investigated ASD Patients' face imaging data from a worldwide multispecialty database known as Autism Face Imaging Data Exchange. ASD is a brain-based disorder characterized by social deficiency and the symptoms are different scenarios. According to recent Centers for Disease Control data, ASD affects About 1 in 54 children who have been identified with autism spectrum disorder (ASD) according to estimates from CDC’s Autism and Developmental Disabilities Monitoring (ADDM) Network. We investigated patterns of functional connectivity that objectively identify ASD participants from functional face imaging data and attempted to unveil the facial patterns that emerged from the classification. With the proposed module, standard CNNs are made, like ResNet-50 have more discriminative power for deep face recognition, and results improved the state-of-the-art by achieving 99% accuracy in the identification of ASD versus control patients in the dataset. We present the results and identify the areas of facial expressions that contributed most to differentiating ASD from typically developing controls as per our deep learning method. For verification purposes, the videos collected in real-time manually from different children we retested and an accuracy score of 99.90% and an F1 score of 99.67% were achieved.