Magnetic Resonance Imaging Data Research Articles

Structure-function interrelationships and associated neurotransmitter profiles in drug-naïve benign childhood epilepsy with central-temporal spikes patients.

To explore the interrelationships between structural and functional changes as well as the potential neurotransmitter profile alterations in drug-naïve benign childhood epilepsy with central-temporal spikes (BECTS) patients. Structural magnetic resonance imaging (sMRI) and resting-state functional MRI data from 20 drug-naïve BECTS patients and 33 healthy controls (HCs) were acquired. Parallel independent component analysis (P-ICA) was used to identify covarying components among gray matter volume (GMV) maps and fractional amplitude of low-frequency fluctuations (fALFF) maps. Furthermore, we explored the spatial correlations between GMV/fALFF changes derived from P-ICA and neurotransmitter maps in JuSpace toolbox. A significantly positive correlation (p < 0.001) was identified between one structural component (GMV_IC6) and one functional component (fALFF_IC4), which showed significant group differences between drug-naïve BECTS patients and HCs (GMV_IC6: p < 0.01; fALFF_IC4: p < 0.001). GMV_IC6 showed increased GMV in the frontal lobe, temporal lobe, thalamus, and precentral gyrus as well as fALFF_IC4 had enhanced fALFF in the cerebellum in drug-naïve BECTS patients compared to HCs. Moreover, significant correlations between GMV alterations in GMV_IC6 and the serotonin (5HT1a: p < 0.001; 5HT2a: p < 0.001), norepinephrine (NAT: p < 0.001) and glutamate systems (mGluR5: p < 0.001) as well as between fALFF alterations in fALFF_IC4 and the norepinephrine system (NAT: p < 0.001) were detected. The current findings suggest co-altered structural/functional components that reflect the correlation of language and motor networks as well as associated with the serotonergic, noradrenergic, and glutamatergic neurotransmitter systems. The relationship between anatomical brain structure and intrinsic neural activity was evaluated using a multimodal fusion analysis and neurotransmitters which might provide an important window into the multimodal neural and underlying molecular mechanisms of benign childhood epilepsy with central-temporal spikes. Structure-function relationships in drug-naïve benign childhood epilepsy with central-temporal spikes (BECTS) patients were explored. The interrelated structure-function components were found and correlated with the serotonin, norepinephrine, and glutamate systems. Co-altered structural/functional components reflect the correlation of language and motor networks and correlate with the specific neurotransmitter systems.

Goal-directed attention transforms both working and long-term memory representations in the human parietal cortex.

The abundance of distractors in the world poses a major challenge to our brain's limited processing capacity, but little is known about how selective attention modulates stimulus representations in the brain to reduce interference and support durable target memory. Here, we collected functional magnetic resonance imaging (fMRI) data in a selective attention task in which target and distractor pictures of different visual categories were simultaneously presented. Participants were asked to selectively process the target according to the effective cue, either before the encoding period (i.e., perceptual attention) or the maintenance period (i.e., reflective attention). On the next day, participants were asked to perform a memory recognition task in the scanner in which the targets, distractors, and novel items were presented in a pseudorandom order. Behavioral results showed that perceptual attention was better at enhancing target memory and reducing distractor memory than reflective attention, although the overall memory capacity (memory for both target and distractor) was comparable. Using multiple-voxel pattern analysis of the neural data, we found more robust target representation and weaker distractor representation in working memory for perceptual attention than for reflective attention. Interestingly, perceptual attention partially shifted the regions involved in maintaining the target representation from the visual cortex to the parietal cortex. Furthermore, the targets and distractors simultaneously presented in the perceptual attention condition showed reduced pattern similarity in the parietal cortex during retrieval compared to items not presented together. This neural pattern repulsion positively correlated with individuals' recognition of both targets and distractors. These results emphasize the critical role of selective attention in transforming memory representations to reduce interference and improve long-term memory performance.

Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks

Background:Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods. Method:Here, we used a unique dataset compilation comprising 7837 T1-weighted (T1w) MR images from 191 different OpenNeuro datasets in combination with advanced deep learning methods to build a fast, high-precision brain extraction tool called deepbet. Results:deepbet sets a novel state-of-the-art performance during cross-dataset validation with a median Dice score (DSC) of 99.0 on unseen datasets, outperforming the current best performing deep learning (DSC=97.9) and classic (DSC=96.5) methods. While current methods are more sensitive to outliers, deepbet achieves a Dice score of >97.4 across all 7837 images from 191 different datasets. This robustness was additionally tested in 5 external datasets, which included challenging clinical MR images. During visual exploration of each method’s output which resulted in the lowest Dice score, major errors could be found for all of the tested tools except deepbet. Finally, deepbet uses a compute efficient variant of the UNet architecture, which accelerates brain extraction by a factor of ≈10 compared to current methods, enabling the processing of one image in ≈2 s on low level hardware. Conclusions:In conclusion, deepbet demonstrates superior performance and reliability in brain extraction across a wide range of T1w MR images of adults, outperforming existing top tools. Its high minimal Dice score and minimal objective errors, even in challenging conditions, validate deepbet as a highly dependable tool for accurate brain extraction. deepbet can be conveniently installed via “pip install deepbet” and is publicly accessible at https://github.com/wwu-mmll/deepbet.

Risk of type 2 diabetes, MASLD and cardiovascular disease in people living with polycystic ovary syndrome.

Summary Background Polycystic ovary syndrome (PCOS) is associated with adverse clinical outcomes which may differ according to PCOS phenotype. Methods Using UK Biobank data, we compared the incidence of type 2 diabetes (T2D), metabolic dysfunction associated steatotic liver disease (MASLD), cardiovascular disease (CVD), hormone-dependent cancers, and dementia between PCOS participants, and age- and BMI-matched controls. We also compared multi-organ (liver, cardiac and brain) magnetic resonance imaging (MRI) data and examined the impact of PCOS phenotype (hyperandrogenic and normoandrogenic) on these outcomes. Results We included 1008 women with PCOS (defined by diagnostic codes, self-reported diagnoses, or clinical/biochemical features of hyperandrogenism and a/oligomenorrhoea), and 5017 matched controls (5:1 ratio); median age, 61 years, body mass index, 28.4 kg/m². Adjusted Cox proportional hazard modelling demonstrated PCOS participants had greater incident T2D (HR 1.47; 95% CI, 1.11-1.95) and all-cause CVD (1.76; 1.35-2.30). No between-group differences existed for cancers or dementia. Liver MRI confirmed more PCOS participants had hepatic steatosis (proton density fat fraction &amp;gt;5.5%: 35.9 vs. 23.9% (p=0.02)), and higher fibroinflammation (corrected T1 (cT1) (721.4 vs. 701.5ms (p=&amp;lt;0.01)), vs. controls. No between-group difference existed for cardiac (bi-ventricular/atrial structure and function) or brain (grey and white matter volumes) imaging. Normoandrogenic (but not hyperandrogenic) PCOS participants had greater incident all-cause CVD (1.82; 1.29-2.56) while hyperandrogenic (but not normoandrogenic) PCOS participants were more likely to have hepatic steatosis (8.96 vs. 6.04 vs. 5.23% (p=0.03)) with greater fibro-inflammation (776.3 vs. 707.7 vs. 701.9 ms (p=&amp;lt;0.01)). Conclusions Cardiometabolic disease may be increased in PCOS patients with a disease phenotype-specific pattern.

An Integrated Multi-omics prediction model for stroke recurrence based on [formula omitted] transformer layer and dynamic weighting mechanism

Background and objectiveStroke is a disease with high mortality and disability. Importantly, the fatality rate demonstrates a significant increase among patients afflicted by recurrent strokes compared to those experiencing their initial stroke episode. Currently, the existing research encounters three primary challenges. The first is the lack of a reliable, multi-omics image dataset related to stroke recurrence. The second is how to establish a high-performance feature extraction model and eliminate noise from continuous magnetic resonance imaging (MRI) data. The third is how to integration multi-omics data and dynamically weighted for different omics data. MethodsWe systematically compiled MRI and conventional detection data from a cohort comprising 737 stroke patients and established PSTSZC, a multi-omics dataset for predicting stroke recurrence. We introduced the first-ever Integrated Multi-omics Prediction Model for Stroke Recurrence, MPSR, which is based on ResNet, Lnet-transformer, LSTM and dynamically weighted DNN. The MPSR model comprises two principal modules, the Feature Extraction Module, and the Integrated Multi-Omics Prediction Module. In the Feature Extraction module, we proposed a novel Lnet regularization layer, which effectively addresses noise issues in MRI data. In the Integrated Multi-omics Prediction Module, we propose a dynamic weighted mechanism based on evaluators, which mitigates the noise impact brought about by low-performance omics. ResultsWe compared seven single-omics models and six state-of-the-art multi-omics stroke recurrence models. The experimental results demonstrate that the MPSR model exhibited superior performance. The accuracy, AUROC, specificity, and sensitivity of the MPSR model can reach 0.96, 0.97, 1, and 0.94, respectively, which is higher than the results of contrast model. ConclusionMPSR is the first available high-performance multi-omics prediction model for stroke recurrence. We assert that the MPSR model holds the potential to function as a valuable tool in assisting clinicians in accurately diagnosing individuals with a predisposition to stroke recurrence.

MRGazer: decoding eye gaze points from functional magnetic resonance imaging in individual space.

Eye-tracking research has proven valuable in understanding numerous cognitive functions. Recently, Frey et al. provided an exciting deep learning method for learning eye movements from functional magnetic resonance imaging (fMRI) data. It employed the multi-step co-registration of fMRI into the group template to obtain eyeball signal, and thus required additional templates and was time consuming. To resolve this issue, in this paper, we propose a framework named MRGazer for predicting eye gaze points from fMRI in individual space. The MRGazer consists of an eyeball extraction module and a residual network-based eye gaze prediction module. Compared to the previous method, the proposed framework skips the fMRI co-registration step, simplifies the processing protocol, and achieves end-to-end eye gaze regression. The proposed method achieved superior performance in eye fixation regression (Euclidean error, EE=2.04°) than the co-registration-based method (EE=2.89°)， and delivered objective results within a shorter time (~0.02 second/volume) than prior method (~0.3 second/volume). The code is available at https://github.com/ustc-bmec/MRGazer.

A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data

BackgroundInferring directional connectivity of brain regions from functional magnetic resonance imaging (fMRI) data has been shown to provide additional insights into predicting mental disorders such as schizophrenia. However, existing research has focused on the magnitude data from complex-valued fMRI data without considering the informative phase data, thus ignoring potentially important information.MethodsWe propose a new complex-valued transfer entropy (CTE) method to measure causal links among brain regions in complex-valued fMRI data. We use the transfer entropy to model a general non-linear magnitude–magnitude and phase–phase directed connectivity and utilize partial transfer entropy to measure the complementary phase and magnitude effects on magnitude–phase and phase–magnitude causality. We also define the significance of the causality based on a statistical test and the shuffling strategy of the two complex-valued signals.ResultsSimulated results verified higher accuracy of CTE than four causal analysis methods, including a simplified complex-valued approach and three real-valued approaches. Using experimental fMRI data from schizophrenia and controls, CTE yields results consistent with previous findings but with more significant group differences. The proposed method detects new directed connectivity related to the right frontal parietal regions and achieves 10.2–20.9% higher SVM classification accuracy when inferring directed connectivity using anatomical automatic labeling (AAL) regions as features.ConclusionThe proposed CTE provides a new general method for fully detecting highly predictive directed connectivity from complex-valued fMRI data, with magnitude-only fMRI data as a specific case.

Neuroprotective Effects of Inhaled Xenon Gas on Brain Structural Gray Matter Changes After Out-of-Hospital Cardiac Arrest Evaluated by Morphometric Analysis: A Substudy of the Randomized Xe-Hypotheca Trial.

We have earlier reported that inhaled xenon combined with hypothermia attenuates brain white matter injury in comatose survivors of out-of-hospital cardiac arrest (OHCA). A predefined secondary objective was to assess the effect of inhaled xenon on the structural changes in gray matter in comatose survivors after OHCA. Patients were randomly assigned to receive either inhaled xenon combined with target temperature management (33°C) for 24h (n = 55, xenon group) or target temperature management alone (n = 55, control group). A change of brain gray matter volume was assessed with a voxel-based morphometry evaluation of high-resolution structural brain magnetic resonance imaging (MRI) data with Statistical Parametric Mapping. Patients were scheduled to undergo the first MRI between 36 and 52h and a second MRI 10days after OHCA. Of the 110 randomly assigned patients in the Xe-Hypotheca trial, 66 patients completed both MRI scans. After all imaging-based exclusions, 21 patients in the control group and 24 patients in the xenon group had both scan 1 and scan 2 available for analyses with scans that fulfilled the quality criteria. Compared with the xenon group, the control group had a significant decrease in brain gray matter volume in several clusters in the second scan compared with the first. In a between-group analysis, significant reductions were found in the right amygdala/entorhinal cortex (p = 0.025), left amygdala (p = 0.043), left middle temporal gyrus (p = 0.042), left inferior temporal gyrus (p = 0.008), left parahippocampal gyrus (p = 0.042), left temporal pole (p = 0.042), and left cerebellar cortex (p = 0.005). In the remaining gray matter areas, there were no significant changes between the groups. In comatose survivors of OHCA, inhaled xenon combined with targeted temperature management preserved gray matter better than hypothermia alone. ClinicalTrials.gov: NCT00879892.

The soft palate enables extreme feeding and explosive breathing in the fin whale (Balaenoptera physalus)

Abstract The evolution of lunge-feeding in rorqual whales was associated with the evolution of several unique morphological features that include non-synovial ligamentous temporomandibular joints, a tongue that can invert and extend backwards to the umbilicus, walls of the oral cavity that can dramatically expand, and muscles and nerves that are stretchy. Also, among the acquired features was an enlargement of the rostral end of the soft palate into an oral plug that occludes the opening between the oral cavity and pharynx and prevents water incursion into the pharynx during the engulfment phase of a feeding lunge. During this engulfment phase of a lunge, the volume of water entering the oral cavity can exceed the volume of the whale itself. Here, using dissection of fetuses and adults and a Magnetic Resonance Imaging (MRI) data set of a fetus, we examine the detailed anatomy of the soft palate in fin whales. We describe several innovative features relative to other mammals, including changes in the attachment and positions of the major extrinsic muscles of the palate, alterations in the morphology of the pterygoid processes related to the palate and pharynx, and the presence of distinct muscle layers in the part of the palate caudal to the oral plug. Based on the anatomy, we present a model for how the soft palate is positioned at rest, and how it functions during feeding, breathing, and swallowing.

Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.

Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.

Occipital-temporal cortical tuning to semantic and affective features of natural images predicts associated behavioral responses

In everyday life, people need to respond appropriately to many types of emotional stimuli. Here, we investigate whether human occipital-temporal cortex (OTC) shows co-representation of the semantic category and affective content of visual stimuli. We also explore whether OTC transformation of semantic and affective features extracts information of value for guiding behavior. Participants viewed 1620 emotional natural images while functional magnetic resonance imaging data were acquired. Using voxel-wise modeling we show widespread tuning to semantic and affective image features across OTC. The top three principal components underlying OTC voxel-wise responses to image features encoded stimulus animacy, stimulus arousal and interactions of animacy with stimulus valence and arousal. At low to moderate dimensionality, OTC tuning patterns predicted behavioral responses linked to each image better than regressors directly based on image features. This is consistent with OTC representing stimulus semantic category and affective content in a manner suited to guiding behavior.

Occipital-temporal cortical tuning to semantic and affective features of natural images predicts associated behavioral responses

In everyday life, people need to respond appropriately to many types of emotional stimuli. Here, we investigate whether human occipital-temporal cortex (OTC) shows co-representation of the semantic category and affective content of visual stimuli. We also explore whether OTC transformation of semantic and affective features extracts information of value for guiding behavior. Participants viewed 1620 emotional natural images while functional magnetic resonance imaging data were acquired. Using voxel-wise modeling we show widespread tuning to semantic and affective image features across OTC. The top three principal components underlying OTC voxel-wise responses to image features encoded stimulus animacy, stimulus arousal and interactions of animacy with stimulus valence and arousal. At low to moderate dimensionality, OTC tuning patterns predicted behavioral responses linked to each image better than regressors directly based on image features. This is consistent with OTC representing stimulus semantic category and affective content in a manner suited to guiding behavior.

Disrupted Brain Network Measures in Parkinson’s Disease Patients with Severe Hyposmia and Cognitively Normal Ability

Neuroscience has revolved around brain structural changes, functional activity, and connectivity alteration in Parkinson’s Disease (PD); however, how the network topology organization becomes altered is still unclear, specifically in Parkinson’s patients with severe hyposmia. In this study, we have examined the functional network topological alteration in patients affected by Parkinson’s Disease with normal cognitive ability (ODN), Parkinson’s Disease with severe hyposmia (ODP), and healthy controls (HCs) using resting-state functional magnetic resonance imaging (rsfMRI) data. We have analyzed brain topological organization using popular graph measures such as network segregation (clustering coefficient, modularity), network integration (participation coefficient, path length), small-worldness, efficiency, centrality, and assortativity. Then, we used a feature ranking approach based on the diagonal adaptation of neighborhood component analysis, aiming to determine a graph measure that is sensitive enough to distinguish between these three different groups. We noted significantly lower segregation and local efficiency and small-worldness in ODP compared to ODN and HCs. On the contrary, we did not find differences in network integration in ODP compared to ODN and HCs, which indicates that the brain network becomes fragmented in ODP. At the brain network level, a progressive increase in the DMN (Default Mode Network) was observed from healthy controls to ODN to ODP, and a continuous decrease in the cingulo-opercular network was observed from healthy controls to ODN to ODP. Further, the feature ranking approach has shown that the whole-brain clustering coefficient and small-worldness are sensitive measures to classify ODP vs. ODN, as well as HCs. Looking at the brain regional network segregation, we have found that the cerebellum and limbic, fronto-parietal, and occipital lobes have higher ODP reductions than ODN and HCs. Our results suggest network topological measures, specifically whole-brain segregation and small-worldness decreases. At the network level, an increase in DMN and a decrease in the cingulo-opercular network could be used as biomarkers to characterize ODN and ODP.

Disrupted Brain Network Measures in Parkinson’s Disease Patients with Severe Hyposmia and Cognitively Normal Ability

Neuroscience has revolved around brain structural changes, functional activity, and connectivity alteration in Parkinson’s Disease (PD); however, how the network topology organization becomes altered is still unclear, specifically in Parkinson’s patients with severe hyposmia. In this study, we have examined the functional network topological alteration in patients affected by Parkinson’s Disease with normal cognitive ability (ODN), Parkinson’s Disease with severe hyposmia (ODP), and healthy controls (HCs) using resting-state functional magnetic resonance imaging (rsfMRI) data. We have analyzed brain topological organization using popular graph measures such as network segregation (clustering coefficient, modularity), network integration (participation coefficient, path length), small-worldness, efficiency, centrality, and assortativity. Then, we used a feature ranking approach based on the diagonal adaptation of neighborhood component analysis, aiming to determine a graph measure that is sensitive enough to distinguish between these three different groups. We noted significantly lower segregation and local efficiency and small-worldness in ODP compared to ODN and HCs. On the contrary, we did not find differences in network integration in ODP compared to ODN and HCs, which indicates that the brain network becomes fragmented in ODP. At the brain network level, a progressive increase in the DMN (Default Mode Network) was observed from healthy controls to ODN to ODP, and a continuous decrease in the cingulo-opercular network was observed from healthy controls to ODN to ODP. Further, the feature ranking approach has shown that the whole-brain clustering coefficient and small-worldness are sensitive measures to classify ODP vs. ODN, as well as HCs. Looking at the brain regional network segregation, we have found that the cerebellum and limbic, fronto-parietal, and occipital lobes have higher ODP reductions than ODN and HCs. Our results suggest network topological measures, specifically whole-brain segregation and small-worldness decreases. At the network level, an increase in DMN and a decrease in the cingulo-opercular network could be used as biomarkers to characterize ODN and ODP.

Brain age revisited: Investigating the state vs. trait hypotheses of EEG-derived brain-age dynamics with deep learning

Abstract The brain’s biological age has been considered as a promising candidate for a neurologically significant biomarker. However, recent results based on longitudinal magnetic resonance imaging (MRI) data have raised questions on its interpretation. A central question is whether an increased biological age of the brain is indicative of brain pathology and if changes in brain age correlate with diagnosed pathology (state hypothesis). Alternatively, could the discrepancy in brain age be a stable characteristic unique to each individual (trait hypothesis)? To address this question, we present a comprehensive study on brain aging based on clinical Electroencephalography (EEG), which is complementary to previous MRI-based investigations. We apply a state-of-the-art temporal convolutional network (TCN) to the task of age regression. We train on recordings of the Temple University Hospital EEG Corpus (TUEG) explicitly labeled as non-pathological and evaluate on recordings of subjects with non-pathological as well as pathological recordings, both with examinations at a single point in time TUH Abnormal EEG Corpus (TUAB) and repeated examinations over time. Therefore, we created four novel subsets of TUEG that include subjects with multiple recordings: repeated non-pathological (RNP): all labeled non-pathological; repeated pathological (RP): all labeled pathological; transition non-patholoigical pathological (TNPP): at least one recording labeled non-pathological followed by at least one recording labeled pathological; and transition pathological non-pathological (TPNP): similar to TNPP but with opposing transition (first pathological and then non-pathological). The results show that our TCN reaches state-of-the-art performance in age decoding on non-pathological subjects of TUAB with a mean absolute error of 6.6 years and an R2 score of 0.73. Our extensive analyses demonstrate that the model underestimates the age of non-pathological and pathological subjects, the latter significantly (-1 and -5 years, paired t-test, p = 0.18 and p = 6.6e−3). Furthermore, there exist significant differences in average brain age gap between non-pathological and pathological subjects both with single examinations (TUAB) and repeated examinations (RNP vs. RP) (-4 and -7.48 years, permutation test, p = 1.63e−2 and p = 1e−5). We find mixed results regarding the significance of pathology classification based on the brain age gap biomarker. While it is indicative of pathological EEG in datasets TUAB and RNP versus RP (61.12% and 60.80% BACC, permutation test, p = 1.32e−3 and p = 1e−5), it is not indicative in TNPP and TPNP (44.74% and 47.79% BACC, permutation test, p = 0.086 and p = 0.483). Additionally, all of these classification scores are clearly inferior to the ones obtained from direct EEG pathology classification at 86% BACC and higher. Furthermore, we could not find evidence that a change of EEG pathology status within subjects relates to a significant change in brain age gap in datasets TNPP and TPNP (0.46 and 1.35 years, permutation test, p = 0.825 and p = 0.43; and Wilcoxon-Mann-Whitney and Brunner-Munzel test, p = 0.13). Our findings, thus, support the trait rather than the state hypothesis for brain age estimates derived from EEG. In summary, our findings indicate that the neural underpinnings of brain age changes are likely more multifaceted than previously thought, and that taking this into account will benefit the interpretation of empirically observed brain age dynamics.

Evidence that robot-assisted gait training modulates neuroplasticity after stroke: An fMRI pilot study based on graph theory analysis

ObjectivesTo investigate alterations of whole-brain network after stroke and therapeutic mechanisms of robot-assisted gait training (RAGT). Methods21 stroke patients and 20 healthy subjects were enrolled, with the stroke patients randomized to either control group (n = 11) or robot group (n = 10), and resting-state functional magnetic resonance imaging data were collected. The global network metrics were obtained using graph theory analysis and compared between stroke patients and healthy subjects, and the effect of the RAGT on the whole-brain networks was explored. ResultsCompared to healthy subjects, area under the curve (AUC) for small-worldness (σ), clustering coefficient (Cp), global efficiency (Eg) and mean local efficiency (Eloc) were significantly lower in stroke patients, whereas AUC for characteristic path length (Lp) were significantly higher. Compared with the control group, patients in robot group showed significant improvement in lower limb motor function, balance function and walking function after intervention, with a significant reduction in the AUC of Cp. Moreover, the improvement of walking function was positively correlated with the changes of AUC of σ and Eg, and negatively correlated with the changes of AUC of Cp. ConclusionsSmall-worldness and network efficiency were significantly reduced after stroke, whereas RAGT decreased characteristic path length and promoted normalization of the whole-brain network, and this change was associated with improvement in walking function. Our findings reveal the mechanism by which RAGT regulates network reorganization and neuroplasticity after stroke.

Lesion characteristics are associated with bowel, bladder, and overall independence following cervical spinal cord injury

Context/Objective There is a growing global interest in quantifying spinal cord lesions and spared neural tissue using magnetic resonance imaging (MRI) in individuals with spinal cord injury (SCI). The primary objective of this study was to assess the relationships between spinal cord lesion characteristics assessed on MRI and bowel, bladder, and overall independence following SCI. Design Retrospective, exploratory study. Participants 93 individuals with cervical SCI who were enrolled in a local United States Model Systems SCI database from 2010 to 2017. Methods Clinical and MRI data were obtained for potential participants, and MRIs of eligible participants were analyzed. Explanatory variables, captured on MRIs, included intramedullary lesion length (IMLL), midsagittal ventral tissue bridge width (VTBW), midsagittal dorsal tissue bridge width (DTBW), and axial damage ratio (ADR). Outcome Measures Bowel and bladder management scale of the Functional Independence Measure (FIM) and FIM total motor score. Results When accounting for all four variables, only ADR was significantly associated with bowel independence (OR = 0.970, 95% CI: 0.942–0.997, P = 0.030), and both ADR and IMLL were strongly associated with bladder independence (OR = 0.967, 95% CI: 0.936–0.999, P = 0.046 and OR = 0.948, 95% CI: 0.919–0.978, P = 0.0007, respectively). 32% of the variation in overall independence scores were explained by all four predictive variables, but only ADR was significantly associated with overall independence after accounting for all other predictive variables (β = −0.469, 95% CI: −0.719, −0.218, P = 0.0004). Conclusions Our results suggest that the MRI-measured extent of spinal cord lesion may be predictive of bowel, bladder, and overall independence following cervical SCI.

Linking functional and structural brain organisation with behaviour in healthy adults.

Multimodal data integration approaches, such as Linked Independent Component Analysis (LICA), increase sensitivity to brain-behaviour relationships and allow us to probe the relationship between modalities. Here we focus on inter-regional functional and structural organisation to determine if organisational patterns persist across modalities and if investigating multi-modality organisations provides increased sensitivity to brain-behaviour associations. We utilised multimodal magnetic resonance imaging (MRI; T1w, resting-state functional [fMRI] and diffusion weighted [DWI]) and behavioural data from the Human Connectome Project (HCP, n=676; 51% female). Unimodal features were extracted to produce individual grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps from the T1w, DWI and fMRI data, respectively. DWI and fMRI analyses were restricted to subcortical regions for computational reasons. LICA was then used to integrate features, generating 100 novel independent components. Associations between these components and demographic/behavioural (n=308) variables were examined. 15 components were significantly associated with various demographic/behavioural measures. 2 components were strongly related to various measures of intoxication, driven by DWI and resemble components previously identified. Another component was driven by striatal functional data and related to working memory. A small number of components showed shared variance between structure and function but none of these displayed any significant behavioural associations. Our working memory findings provide support for the use of fMRI connectopic mapping in future research of working memory. Given the lack of behaviourally relevant shared variance between functional and structural organisation, as indexed here, we question the utility of integrating connectopic maps and tractography data.

Exploring the Relationship between Subcutaneous Fat Index and Neck Pain in Patients with Spinal Cervical Spondylosis: A Retrospective Cohort Study

Retrospective Cohort Study. This study measures the subcutaneous fat index (SFI) of the cervical spine in patients with spinal cervical spondylosis using cervical magnetic resonance imaging (MRI) and explores its relationship with neck pain in patients with spinal cervical spondylosis. In this single-center retrospective study, 298 patients hospitalized for spinal cervical spondylosis between January and June 2021 were initially considered. After applying inclusion and exclusion criteria, 93 patients were enrolled. The cervical MRI data for these patients were analyzed using A-Site software. The SFI was measured at the median sagittal plane on T2-weighted images. Patients were categorized into two groups based on their admission complaints: those with cervical pain and those without it. Differences between these groups were then statistically analyzed. The mean SFIs with standard deviations for the neck and non-neck pain groups were 36.4%±7.7% and 27.0%±7.9%, respectively, with a significant difference (P<0.0001). The SFI was consistently higher across all neck segments in the neck pain group compared to the non-neck pain group (P<0.05). The two groups had no statistically significant difference in the body mass index (BMI). The SFI provides a more precise assessment of muscle and fat distribution in the posterior cervical region than BMI and is generally higher in patients with spinal cervical spondylosis who experience neck pain. These findings suggest the importance of early functional exercises post-surgery for potentially improving surgical outcomes in this patient population.

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization

Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.