Search

Individuals with gambling disorder display deficits in decision-making in the Iowa Gambling Task. The rat Gambling Task (rGT) is a rodent analogue that can be used to investigate the neurobiological mechanisms underlying gambling behaviour. The aim of this explorative study was to examine individual strategies in the rGT and investigate possible behavioural and neural correlates associated with gambling strategies. Thirty-two adult male Lister hooded rats underwent behavioural testing in the multivariate concentric square field™ (MCSF) and the novel cage tests, were trained on and performed the rGT and subsequently underwent resting-state functional magnetic resonance imaging (R-fMRI). In the rGT, stable gambling strategies were found with subgroups of rats that preferred the suboptimal safest choice as well as the disadvantageous choice, that is, the riskiest gambling strategy. R-fMRI results revealed associations between gambling strategies and brain regions central for reward networks. Moreover, rats with risky gambling strategies differed from those with strategic and intermediate strategies in brain functional connectivity. No differences in behavioural profiles, as assessed with the MCSF and novel cage tests, were observed between the gambling strategy groups. In conclusion, stable individual differences in gambling strategies were found. Intrinsic functional connectivity using R-fMRI provides novel evidence to support the notion that individual differences in gambling strategies are associated with functional connectivity in brain regions important for reward networks.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is an illness characterized by a diverse range of debilitating symptoms including autonomic, immunologic, and cognitive dysfunction. Although neurological and cognitive aberrations have been consistently reported, relatively little is known regarding the regional cerebral blood flow (rCBF) in ME/CFS. In this study, we studied a cohort of 31 ME/CSF patients (average age: 42.8 ± 13.5 years) and 48 healthy controls (average age: 42.9 ± 12.0 years) using the pseudo-continuous arterial spin labeling (PCASL) technique on a whole-body clinical 3T MRI scanner. Besides routine clinical MRI, the protocol included a session of over 8 min-long rCBF measurement. The differences in the rCBF between the ME/CSF patients and healthy controls were statistically assessed with voxel-wise and AAL ROI-based two-sample t-tests. Linear regression analysis was also performed on the rCBF data by using the symptom severity score as the main regressor. In comparison with the healthy controls, the patient group showed significant hypoperfusion (uncorrected voxel wise p ≤ 0.001, FWE p ≤ 0.01) in several brain regions of the limbic system, including the anterior cingulate cortex, putamen, pallidum, and anterior ventral insular area. For the ME/CFS patients, the overall symptom severity score at rest was significantly associated with a reduced rCBF in the anterior cingulate cortex. The results of this study show that brain blood flow abnormalities in the limbic system may contribute to ME/CFS pathogenesis.

Skill learning induces changes in estimates of gray matter volume (GMV) in the human brain, commonly detectable with magnetic resonance imaging (MRI). Rapid changes in GMV estimates while executing tasks may however confound between- and within-subject differences. Fluctuations in arterial blood flow are proposed to underlie this apparent task-related tissue plasticity. To test this hypothesis, we acquired multiple repetitions of structural T1-weighted and functional blood-oxygen level-dependent (BOLD) MRI measurements from 51 subjects performing a finger-tapping task (FTT; á 2 min) repeatedly for 30–60 min. Estimated GMV was decreased in motor regions during FTT compared with rest. Motor-related BOLD signal changes did not overlap nor correlate with GMV changes. Nearly simultaneous BOLD signals cannot fully explain task-induced changes in T1-weighted images. These sensitive and behavior-related GMV changes pose serious questions to reproducibility across studies, and morphological investigations during skill learning can also open new avenues on how to study rapid brain plasticity.

Background: Dynamic magnetic resonance imaging (dMRI) plays an important role in cardiac perfusion and functional clinical exams. However, further applications are limited by the speed of data acquisition. Objective: A low-rank plus sparse decomposition approach is often introduced for reconstructing dynamic magnetic resonance imaging (dMRI) from highly under-sampling K-space data. In this paper, the reconstruction problem of DMR is transformed into a low-rank tensor plus sparse tensor recovery problem. Methods: A sequentially truncated higher-order singular value decomposition method is proposed to quickly approximate the low-rank tensor space structure and learn sparse components by adding a tensor kernel norm to the low-rank tensor and a l1 norm to the sparse tensor to constrain the two parts at the same time. The optimization problem is solved by using the iterative soft-thresholding algorithm; therefore, under the premise of ensuring the accuracy of the data, the amount of computation can be effectively reduced. Results: Compared with the state-of-the-art methods, the experimental results show that the proposed method can achieve better performance in terms of reconstruction speed and reconstruction quality on 3D and 4D dMRI datasets. Conclusion: The multidimensional MRI time series is represented by the tensor tool and decomposed into low rank tensor terms and sparse tensor terms. The low rank spatial structure is captured by the adaptive ST-HOSVD for fast approximation and the sparse component is constrained efficiently with a sparsity transform and l1 norm. The optimization problem is solved by an iterative soft-thresholding algorithm. Through extensive 3D and 4D dMRI experiments, it is demonstrated that our method can achieve superior reconstruction performance and efficiency compared with the other three state-of-theart methods reported in the literature.

Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN ar-chitecture for the detection of abnormal cervical cells in cytology images from cancer screening test. We extended the Faster RCNN-FPN model by infusing deformable convolution layers into the feature pyramid network (FPN) to improve scalability. Furthermore, we introduced a global contextual aware module alongside the Region Proposal Network (RPN) to enhance the spatial correlation between the background and the foreground. Extensive experimentations with the proposed deformable and global context aware (DGCA) RCNN were carried out using the cer-vical image dataset of “Digital Human Body" Vision Challenge from the Alibaba Cloud TianChi Company. Performance evaluation based on the mean average precision (mAP) and receiver operating characteristic (ROC) curve has demonstrated considerable advantages of the proposed framework. Particularly, when combined with tagging of the negative image samples using tra-ditional computer-vision techniques, 6-9% increase in mAP has been achieved. The proposed DGCA-RCNN model has potential to become a clinically useful AI tool for automated detection of cervical cancer cells in whole slide images of Pap smear.

Measuring brain morphology with non-invasive structural magnetic resonance imaging is common practice, and can be used to investigate neuroplasticity. Brain morphology changes have been reported over the course of weeks, days, and hours in both animals and humans. If such short-term changes occur even faster, rapid morphological changes while being scanned could have important implications. In a randomized within-subject study on 47 healthy individuals, two high-resolution T1-weighted anatomical images were acquired (á 263 s) per individual. The images were acquired during passive viewing of pictures or a fixation cross. Two common pipelines for analyzing brain images were used: voxel-based morphometry on gray matter (GM) volume and surface-based cortical thickness. We found that the measures of both GM volume and cortical thickness showed increases in the visual cortex while viewing pictures relative to a fixation cross. The increase was distributed across the two hemispheres and significant at a corrected level. Thus, brain morphology enlargements were detected in less than 263 s. Neuroplasticity is a far more dynamic process than previously shown, suggesting that individuals’ current mental state affects indices of brain morphology. This needs to be taken into account in future morphology studies and in everyday clinical practice.

To investigate the impact of adult age on the brain functional connectivity, whole-brain resting-state functional magnetic resonance imaging (R-fMRI) data were acquired on a 3T clinical MRI scanner in a cohort of 227, right-handed, native Swedish-speaking, healthy adult volunteers (N=227, aged 18-74 years old, male/female=99/128). The dataset is mainly consisted of a younger (18-30 years old n=124, males/females=51/73) and elderly adult (n=76, 60-76 years old, males/females=35/41) subgroups. The dataset was analyzed using a new data-driven analysis (QDA) framework. With QDA two types of threshold-free voxel-wise resting-state functional connectivity (RFC) metrics were derived: the connectivity strength index (CSI) and connectivity density index (CDI), which can be utilized to assess the brain changes in functional connectivity associated with adult age. The dataset can also be useful as a reference to identify abnormal changes in brain functional connectivity resulted from neurodegenerative or neuropsychiatric disorders.

PurposeThe objective of this study is to introduce a new quantitative data-driven analysis (QDA) framework for the analysis of resting-state fMRI (R-fMRI) and use it to investigate the effect of adult age on resting-state functional connectivity (RFC).MethodsWhole-brain R-fMRI measurements were conducted on a 3T clinical MRI scanner in 227 healthy adult volunteers (N=227, aged 18-74 years old, male/female=99/128). With the proposed QDA framework we derived two types of voxel-wise RFC metrics: the connectivity strength index (CSI) and connectivity density index (CDI) utilizing the convolutions of the cross-correlation (CC) histogram with different kernels. Furthermore, we assessed the negative and positive portions of these metrics separately.ResultsWith the QDA framework we found age-related declines of RFC metrics in the superior and middle frontal gyrus (MFG), posterior cingulate cortex (PCC), right insula and inferior parietal lobule (IPL) of the default mode network (DMN), which resembles previously reported results using other types of RFC data processing methods. Importantly, our new findings complement previously undocumented results in the following aspects: 1) the PCC and right insula are anti-correlated and tend to manifest simultaneously declines of both the negative and positive connectivity strength with subjects’ age; 2) separate assessment of the negative and positive RFC metrics provides enhanced sensitivity to the aging effect; 3) the sensorimotor network depicts enhanced negative connectivity strength with the adult age.ConclusionThe proposed QDA framework can produce threshold-free, voxel-wise analysis of R-fMRI data the RFC metrics. The detected adult age effect is largely consistent with previously reported studies using different R-fMRI analysis approaches. Moreover, the separate assessment of the negative and positive contributions to the RFC metrics can enhance the RFC sensitivity and clarify some of the mixed results in the literature regarding to the DMN and sensorimotor network involvement in adult aging.HighlightsA quantitative data-driven analysis (QDA) framework was proposed to analysis resting-state fMRI data.Threshold-free resting-state functional connectivity (RFC) metrics were derived to assess brain changes with adult age.Separate assessment of the positive and negative correlations improve sensitivity of the RFC metrics.The posterior cingulate and right insula cortices are anti-correlated and tend to manifest declines in both the negative and positive connectivity strength with adult age.Negative connectivity strength enhances with adult age in sensorimotor network.

Myalgic encephalomyelitis, ME, previously also known as chronic fatigue syndrome (CFS) is a heterogeneous, debilitating syndrome of unknown etiology responsible for long-lasting disability in millions of patients worldwide. The most well-known symptom of ME is post-exertional malaise, but many patients also experience autonomic dysregulation, cranial nerve dysfunction and signs of immune system activation. Many patients also report a sudden onset of disease following an infection. The brainstem is a suspected focal point in ME pathogenesis and patients with structural impairment to the brainstem often show ME-like symptoms. The brainstem is also where the vagus nerve originates, a critical neuro-immune interface and mediator of the inflammatory reflex which regulate systemic inflammation. Here we report the results of a randomized, placebo-controlled trial using intranasal mechanical stimulation (INMEST) targeting the vagus nuclei, and higher centers in the brain of ME-patients and induce a sustainable, ∼30% reduction in overall symptom scores after eight weeks of treatment. By performing longitudinal, systems-level monitoring of the blood immune system in these patients, we uncover chronic immune activation in ME, as well as immunological correlates of improvement that center around the IL-17 axis, gut-homing immune cells and reduced inflammation. The mechanisms of symptom relief remains to be determined, but transcriptional analyses suggest an upregulation of disease tolerance mechanisms. We wish for these results to bring some hope to patients suffering from ME and inspire researchers to help test our new hypothesis that ME is a condition caused by a failure of inducing disease tolerance upon infection and persistent immune activation.

BackgroundDevelopmental dyslexia (DD) is a neurodevelopmental learning disorder with high heritability. A number of candidate susceptibility genes have been identified, some of which are linked to the function of the cilium, an organelle regulating left-right asymmetry development in the embryo. Furthermore, it has been suggested that disrupted left-right asymmetry of the brain may play a role in neurodevelopmental disorders such as DD. However, it is unknown whether there is a common genetic cause to DD and laterality defects or ciliopathies.Case presentationHere, we studied two individuals with co-occurringsitus inversus(SI) and DD using whole genome sequencing to identify genetic variants of importance for DD and SI. Individual 1 had primary ciliary dyskinesia (PCD), a rare, autosomal recessive disorder with oto-sino-pulmonary phenotype and SI. We identified two rare nonsynonymous variants in the dynein axonemal heavy chain 5 gene (DNAH5): a previously reported variant c.7502G > C; p.(R2501P), and a novel variant c.12043 T > G; p.(Y4015D). Both variants are predicted to be damaging. Ultrastructural analysis of the cilia revealed a lack of outer dynein arms and normal inner dynein arms. MRI of the brain revealed no significant abnormalities. Individual 2 had non-syndromic SI and DD. In individual 2, one rare variant (c.9110A > G;p.(H3037R)) in the dynein axonemal heavy chain 11 gene (DNAH11),coding for another component of the outer dynein arm, was identified.ConclusionsWe identified the likely genetic cause of SI and PCD in one individual, and a possibly significant heterozygosity in the other, both involving dynein genes. Given the present evidence, it is unclear if the identified variants also predispose to DD and further studies into the association between laterality, ciliopathies and DD are needed.

Background Amyotrophic Lateral Sclerosis (ALS) is characterized by a spectrum of phenotypes, but a few study addressed the presence of parkinsonian (PK) symptoms. Aim of our study was to investigate the occurrence of PK features in a prospective population-based cohort of ALS patients, determining their demographic, clinical, neuropsychological and genetic characteristics, and identifying their morphological and functional imaging correlates. Methods A consecutive series of ALS patients were enrolled and prospectively followed for two years. Patients were classified according to the presence (ALS-PK) or absence (ALS) of PK signs, and they underwent neuropsychological testing, genetic analysis for the main ALS and PD genes, brain MRI and 18F-FDG-PET. ALS-PK patients underwent 123I-ioflupane SPECT Results Out of 114 eligible patients, 101 (64 men; mean age at onset 65.1 years) were recruited. Thirty-one patients (30.7%) were classified as ALS-PK. Compared to ALS patients, ALS-PK patients were more frequently male, but did not differ for any other clinical, demographic or neuropsychological factor. 123I-ioflupane SPECT was normal in all but two ALS-PK patients. At 18F-FDG-PET, ALSPK patients showed a relative hypometabolism in left cerebellum and a relatively more preserved metabolism in right insula and frontal regions; MRI fractional anisotropy was reduced in the sagittal stratum and increased in the retrolenticular part of the internal capsule. Conclusions In our study about 30% of ALS patients showed PK signs. Neuroimaging data indicate that PK signs are due to the involvement of brain circuitries other than classical nigrostriatal ones, strengthening the hypothesis of ALS as complex multisystem disease.

Purpose Characterize the static and dynamic functional connectivity for subjects with juvenile myoclonic epilepsy (JME) using a quantitative data-driven analysis approach. Methods Whole-brain resting-state functional MRI data were acquired on a 3 T whole-body clinical MRI scanner from 18 subjects clinically diagnosed with JME and 25 healthy control subjects. 2-min sliding-window approach was incorporated in the quantitative data-driven data analysis framework to assess both the dynamic and static functional connectivity in the resting brains. Two-sample t-tests were performed voxel-wise to detect the differences in functional connectivity metrics based on connectivity strength and density. Results The static functional connectivity metrics based on quantitative data-driven analysis of the entire 10-min acquisition window of resting-state functional MRI data revealed significantly enhanced functional connectivity in JME patients in bilateral dorsolateral prefrontal cortex, dorsal striatum, precentral and middle temporal gyri. The dynamic functional connectivity metrics derived by incorporating a 2-min sliding window into quantitative data-driven analysis demonstrated significant hyper dynamic functional connectivity in the dorsolateral prefrontal cortex, middle temporal gyrus and dorsal striatum. Connectivity strength metrics (both static and dynamic) can detect more extensive functional connectivity abnormalities in the resting-state functional networks (RFNs) and depict also larger overlap between static and dynamic functional connectivity results. Conclusion Incorporating a 2-min sliding window into quantitative data-driven analysis of resting-state functional MRI data can reveal additional information on the temporally fluctuating RFNs of the human brain, which indicate that RFNs involving dorsolateral prefrontal cortex have temporal varying hyper dynamic characteristics in JME patients. Assessing dynamic along with static functional connectivity may provide further insights into the abnormal function connectivity underlying the pathological brain functioning in JME., Highlights • JME patients had hyper-connectivity in dorsolateral prefrontal cortex, dorsal striatum, and middle temporal gyri. • Sliding window analysis captured more brain regions with enhanced functional connectivity. • The dynamic functional connectivity produces both larger and more distinct brain regions of hyper-connectivity. • Dynamic analysis also produces more statistically significant group analysis results.

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is an effective technique to reduce MRI data acquisition time. There is currently growing interest in using Alternating Direction Method of Multiplier (ADMM) for CS-MRI reconstruction. In this paper, we propose a flexible plug-and-play framework to incorporate the block matching 3D (BM3D) denoising algorithm as prior into the plug-and-play ADMM reconstruction procedure for CS-MRI reconstruction, termed BM3D Plug-and-play ADMM (BPA) method. We investigated the performance of the proposed BPA method for the construction of highly under-sampled MRI data of two different sampling masks. Compared with other widely used CS-MRI reconstruction methods, such as, PANO, BM3D-IT, BM3D-MRI and BM3D-AMP-MRI, the proposed framework can reconstruct highly under-sampled CS-MRI data with improved gains in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).

Accumulating evidence suggests that engagement in leisure activities is associated with favorable trajectories of cognitive aging, but little is known about brain changes related to both activities and cognition. White matter microstructure shows experience-dependent plasticity and declines in aging. Therefore, we investigated the role of change in white matter microstructure in the activities-cognition link. We used repeated assessments of engagement, perceptual speed, and white matter microstructure (probed with diffusion tensor imaging) in a population-based sample of individuals over 80 years without dementia (n = 442, Mage = 85.1; n = 70 for diffusion tensor imaging; 2 occasions 3 years apart). Using multivariate latent change modeling, we observed positive correlations among changes in predominantly social activities, white matter microstructure, and perceptual speed. Interindividual differences in change in white matter microstructure statistically accounted for the association between change in leisure activities and change in perceptual speed. However, as analyses are based on observational data from 2 measurement occasions, causality remains unclear.

Hierarchical clustering is a useful data-driven approach to classify complex data and has been used to analyze resting-state functional magnetic resonance imaging (fMRI) data and derive functional networks of the human brain at very large scale, such as the entire visual or sensory-motor cortex. In this study, we developed a voxel-wise, whole-brain hierarchical clustering framework to perform multi-stage analysis of group-averaged resting-state fMRI data in different levels of detail. With the framework we analyzed particularly the somatosensory motor and visual systems in fine details and constructed the corresponding sub-dendrograms, which corroborate consistently with the known modular organizations from previous clinical and experimental studies. The framework provides a useful tool for data-driven analysis of resting-state fMRI data to gain insight into the hierarchical organization and degree of functional modulation among the sub-units.

This study set out to investigate the behavioral correlates of changes in resting-state functional connectivity before and after performing a 20 minute continuous psychomotor vigilance task (PVT) for patients with chronic post-concussion syndrome. Ten patients in chronic phase after mild traumatic brain injury (mTBI) with persisting symptoms of fatigue and ten matched healthy controls participated in the study. We assessed the participants' fatigue levels and conducted resting-state fMRI before and after a sustained PVT. We evaluated the changes in brain functional connectivity indices in relation to the subject's fatigue behavior using a quantitative data-driven analysis approach. We found that the PVT invoked significant mental fatigue and specific functional connectivity changes in mTBI patients. Furthermore, we found a significant linear correlation between self-reported fatigue and functional connectivity in the thalamus and middle frontal cortex. Our findings indicate that resting-state fMRI measurements may be a useful indicator of performance potential and a marker of fatigue level in the neural attentional system. De 2 första författarna delar förstaförfattarskapet.

The internal parking characteristic and parking mode and layout influence factors are analyzed to solve the parking problems in the old residential quarter, and then based on which the principle and layout of the parking mode and layout of parking are researched, and then the forms of parking mode and layout of parking in the old residential quarter are proposed to solve the parking problems in the old residential quarter

Pseudo-continuous arterial spin labeling (ASL) can provide best signal-to-noise ratio efficiency with a sufficiently long tag at high fields such as 7 T, but it is very sensitive to off-resonance fields at the tagging location. Here, a robust Prescan procedure is demonstrated to estimate the pseudo-continuous ASL radiofrequency phase and gradients parameters required to compensate the off-resonance effects at each vessel location. The Prescan is completed in 1-2 min and is based on acquisition of label/control pair-wise ASL data as a function of the radiofrequency phase increment applied to the pseudo-continuous ASL train. It is shown that this approach can be used to acquire high quality whole-brain pseudo-continuous ASL perfusion data of the human brain at 7 T.

UML, as an international standard, is not suitable for static modeling of the program procedure. While Procedure Blueprint is a visual process modeling language and is suitable for modeling the procedure, which can supplement the UML deficiencies during the process of modeling the program static structure. In this paper, taking XMI as a common data structure and a data exchange bridge, proposed the general transformation idea, the transformation rules and the transformation algorithm from activity diagram to the procedure blueprint. This has laid the foundations for achieving the model transformation at last.

SummaryThe question of how we experience ownership of an entire body distinct from the external world is a fundamental problem in psychology and neuroscience [1–6]. Earlier studies suggest that integration of visual, tactile, and proprioceptive information in multisensory areas [7–11] mediates self-attribution of single limbs. However, it is still unknown how ownership of individual body parts translates into the unitary experience of owning a whole body. Here, we used a “body-swap” illusion [12], in which people experienced an artificial body to be their own, in combination with functional magnetic resonance imaging to reveal a coupling between the experience of full-body ownership and neural responses in bilateral ventral premotor and left intraparietal cortices, and left putamen. Importantly, activity in the ventral premotor cortex reflected the construction of ownership of a whole body from the parts, because it was stronger when the stimulated body part was attached to a body, was present irrespective of whether the illusion was triggered by stimulation of the hand or the abdomen, and displayed multivoxel patterns carrying information about full-body ownership. These findings suggest that the unitary experience of owning an entire body is produced by neuronal populations that integrate multisensory information across body segments.

When volume coils are used for (1)H imaging of the human head at 7 T, wavelength effects in tissue cause a variation in intensity, that is typically brighter at the center of the head and darker in the periphery. Much of this image nonuniformity can be attributed to variation in the effective transmit B(1) field, which falls by ∼ 50% to the left and right of center at mid-elevation in the brain. Because most of this B(1) loss occurs in the periphery of the brain, we have explored use of actively controlled, off-resonant loop elements to locally enhance the transmit B(1) field in these regions. When tuned to frequencies above the NMR frequency, these elements provide strong local enhancement of the B(1) field of the transmit coil. Because they are tuned off-resonance, some volume coil detuning results, but resistive loading of the coil mode remains dominated by the sample. By digitally controlling their frequency offsets, the field enhancement of each inner element can be placed under active control. Using an array of eight digitally controlled elements placed around a custom-built head phantom, we demonstrate the feasibility of improving the B(1) homogeneity of a transmit/receive volume coil without the need for multiple radio frequency transmit channels.

The aim of this review is to examine the recent literature on using advanced magnetic resonance imaging (MRI) techniques for finding neuroimaging biomarkers that are sensitive to the detection of risks for Alzheimer's disease (AD). Since structural MRI techniques, such as brain structural volumetry and voxel-based morphometry (VBM), have been widely used for AD studies and extensively reviewed, we will only briefly touch on the topics of volumetry and morphometry. The focus of the current review is about the more recent developments in the search for AD neuroimaging biomarkers with functional MRI (fMRI), resting-state functional connectivity MRI (fcMRI), diffusion tensor imaging (DTI), arterial spin-labeling (ASL), and magnetic resonance spectroscopy (MRS).