Dongrong Xu

Abstract-Virtual Reality (VR) has been widely applied in healthcare domain. Virtual Reality Mirror Therapy (VRMT) is one such clinical application for phantom limb pain management and post-stroke hemiparesis rehabilitation. However, the prevailing design of VRMT tasks typically follows the schema of traditional mirror therapy paradigm that prescribes strictly symmetric bilateral movements along the body's midline. While symmetry is not a requirement by the mirror neuron therapy where mirror therapies derive, this constraint imposes unnecessary limitations to both the development of diversified game-based rehabilitation tasks and portability of VR equipment and hardware. As a result, average patients using a conventional VR system may often find it difficult to perform certain exercises, such as arm abductions, or not possible to run a VRMT paradigm on affordable VR equipment for home-based rehabilitation. We therefore propose AsymmVRMT, an asymmetric strategy for mirrored affected limb mapping, and adopted it in our VRMT paradigm. We conducted a randomized controlled trial (RCT) on post-stroke hemiparetic patients, and examined their clinical outcome measures (FM-UE, BI & WMFT) along with their user experience data (SUS & VEQ). Results showed that patients demonstrated stronger adaptability and more positive perceptions toward using the AsymmVRMT model, while the therapeutic outcomes were as good as those using conventional VRMT. AsymmVRMT therefore appears to be an effective alternative for human-computer interaction in VR that may offer higher flexibility and diversity to address the hardware-related constraints in rehabilitation system development, facilitating convenient VR-based rehabilitation for patients, both at the hospital and at home.

Brain functional connectivity evolves dynamically during brain development, aging, illness, and cognitive activities. Traditional methods rely on static network snapshots, which do not capture the dynamics of the brain. We propose two new indices: Dynamic Network Similarity (DNS) to measure both temporal and structural dynamic similarity and Dynamic Network Evolution Similarity (DNES) to specifically measure the temporal evolution of dynamic networks. Performance was tested using simulated dynamic networks controlled by four variables (Δφ, λ, α, and β) concerning evolution variations in phase, relative amplitude, noise power, and the span of connectivity strength, respectively. Furthermore, real-world fMRI data from 25 stroke patients pre/post transcranial direct current stimulation (tDCS) rehabilitation were used to test the indices. Patients were randomly sub-grouped into tDCS1 and tDCS2. DNS and DNES thus compared those who received the same therapy (ST: tDCS1 versus tDCS2) and those who received different therapies (DT: tDCS1 versus sham-tDCS). The results showed that DNS was sensitive to all dynamic features, and DNES was primarily sensitive to Δφ and λ. Both indices were able to detect overall difference and capture significantly higher similarity in the ST groups than in the DT groups. Briefly, DNS and DNES appear to be effective tools for studying dynamically evolving brain networks, and may serve as alternatives to traditional static methods. They are particularly useful for analyzing longitudinal neuroimaging data in contexts such as neurodevelopment, aging, and recovery from illness.

Purpose The present study aimed to evaluate magnetic resonance (MR) thermometry using proton resonance frequency shift (PRFS) during laser-induced thermotherapy (LITT), and to compare the results of using different sequences at a field strength of 7-Tesla to identify the optimal for use in ablation so that the surrounding healthy tissues may be protected from damaging in real time. Materials and methods LITT was applied to agarose gel phantoms and ex-vivo porcine brains. We reconstructed both magnitude and phase images to perform MR thermometry based on PRFS methods. We tested four different sequences: a gradient-echo (GRE), a segmented gradient-echo echoplanar imaging (EPI-GRE), a fast-low angle shot (FLASH), and a true fast imaging with steady precession (TRUFI). Temperature was monitored and verified using a fiber-optic thermometry device. Results All sequences showed good linear correlations (R = 0.97–0.99) between the measured temperature and the calculated MR-thermometry measurements. The phantom/porcine brain experiments revealed the temperature precisions at 1.53/0.69 °C (GRE), 0.61/0.43 °C (EPI-GRE), 1.64/1.32 °C (FLASH), and 0.58/1.52 °C (TRUFI), respectively. Furthermore, we performed a Bland–Altman analysis and the temperature accuracies were found to be − 1.32/− 0.60 °C (GRE), 0.42/− 0.33 °C (EPI-GRE), − 1.28/− 0.98 °C (FLASH), and 0.14/0.46 °C (TRUFI) in the phantom/porcine brain experiments, respectively. Conclusions Our experiments recommend that EPI-GRE sequence be the best of the all sequences for MR temperature imaging with PRFS in the LITT on 7 T magnetic resonance imaging (MRI) systems because of its relatively higher precision and accuracy.

Improved magnetic resonance myelin water imaging using multi-channel denoising convolutional neural networks (MCDnCNN) Guojun Xu, Yongquan He, Qiurong Yu, Hongjian He, Zhiyong Zhao, Mingxia Fan, Jianqi Li, Dongrong Xu

Using magnetic resonance diffusion tensor imaging data from 45 patients with major depressive disorder (MDD) and 41 healthy controls (HCs), network indices based on a 246-region Brainnetcome Atlas were investigated in the two groups, and in the MDD subgroups that were subgrouped based on their duration of the disease. Correlation between the network indices and the duration of illness was also examined. Differences were observed between the MDD_S subgroup (short disease duration) and the HC group, but not between the MDD and HC groups. Compared with the HCs, the clustering coefficient (CC) values of MDD_S were higher in precentral gyrus, and caudal lingual gyrus; the CC of MDD_L subgroup (long disease duration) was higher in postcentral gyrus and dorsal granular insula in the right hemisphere. Network resilience analyses showed that the MDD_S group was higher than the HC group, representing relatively more randomized networks in the diseased brains. The correlation analyses showed that the caudal lingual gyrus in the right hemisphere and the rostral lingual gyrus in the left hemisphere were particularly correlated with disease duration. The analyses showed that duration of the illness appears to have an impact on the networking patterns. Networking abnormalities in MDD patients could be blurred or hidden by the heterogeneity of the MDD clinical subgroups. Brain plasticity may introduce a recovery effect to the abnormal network patterns seen in patients with a relative short term of the illness, as the abnormalities may disappear in MDD_L.

Objective: A few former studies suggested that there are partial overlaps in abnormal brain structure and cognitive function between hypochondriasis (HS) and schizophrenia (SZ). But their differences in brain activity and cognitive function were unclear.Methods: Twenty-one HS patients, 23 SZ patients, and 24 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI) with the regional homogeneity analysis (ReHo), subsequently exploring the relationship between ReHo value and cognitive functions. The support vector machines (SVM) were used on effectiveness evaluation of ReHo for differentiating HS from SZ.Results: Compared with HC, HS showed significantly increased ReHo values in right middle temporal gyrus (MTG), left inferior parietal lobe (IPL), and right fusiform gyrus (FG), while SZ showed increased ReHo in left insula, decreased ReHo values in right paracentral lobule. Additionally, HS showed significantly higher ReHo values in FG, MTG, and left paracentral lobule, but lower in insula than SZ. The higher ReHo values in insula were associated with worse performance in MATRICS consensus cognitive battery (MCCB) in HS group. SVM analysis showed a combination of the ReHo values in insula and FG was able to satisfactorily distinguish the HS and SZ patients.Conclusion: Our results suggested that the altered default mode network (DMN), of which abnormal spontaneous neural activity occurs in multiple brain regions, might play a key role in the pathogenesis of HS, and the resting-state alterations of insula are closely related to cognitive dysfunction in HS. Furthermore, the combination of the ReHo in FG and insula was a relatively ideal indicator to distinguish HS from SZ.