Chuh-Hyoun Na

Background: Patients with brain tumors involving the SMA often develop postoperatively a mostly transient contralateral akinesia (‘SMA-syndrome'), which may primarily be a cognitive disorder of executive functions. However, the neurophysiological correlates are unclear. Considering the SMA as a gateway between motor and cognitive control functions, we explored structural SMA-connectivity (SC), and functional connectivity (FC) of the sensorimotor (SMN), and of cognitive networks in SMA-tumor patients. Methods: 13 SMA-tumor patients and 13 matched healthy controls underwent diffusion-tensor-imaging (DTI) and resting-state-fMRI preoperatively. Intra- (Frontal Aslant Tract) and interhemispheric SC of the SMA (SMA-SMA, SMA-M1) was compared between groups. FC-networks were established by group Independent Component Analysis (ICA), and subjected to a second-level ROI-to-ROI (region-of-interest) analysis to investigate between-group differences. Results: DTI revealed microstructural white-matter disintegration and smaller tract volumes of SMA-M1 connectivity ( p < 0.005), and by trend of the FAT ( p < 0.06), with differing SMA-M1 fiber-length in patients ( p < 0.026). SMA-SMA SC was not reduced, but showed greater variance in patients. While FC was significantly decreased within different cognitive networks, FC was increased within the SMN, as well as between the SMA and the contralesional frontoparietal network ( p < 0.046) in patients. Conclusions: In this exploratory study, we found sensorimotor- as well as large-scale cognitive network alterations prior to therapeutic intervention, suggesting tumor-associated SC disintegration to be accompanied by in- and decreases of FC within and across different networks. Future studies incorporating neuropsychological-testing are required to characterize pretherapeutic connectivity alterations with regard to their behavioral significance, and to determine whether they may impact the postoperative functional outcome.

Background: Neurological gait disorders are mainly classified based on clinical observation, and therefore difficult to objectify or quantify. Movement analysis systems provide objective parameters, which may increase diagnostic accuracy and may aid in monitoring the disease course. Despite the increasing wealth of kinematic movement and balance parameter data, the discriminative value for the differentiation of neurological gait disorders is still unclear. We hypothesized that kinematic motion and balance parameter metrics would be differently altered across neurological gait disorders when compared to healthy controls. Methods: Thirty one patients (9 normal pressure hydrocephalus < NPH > , 16 cervical myelopathy < CM > , 6 lumbar stenosis < LST >) and 14 healthy participants were investigated preoperatively in an outpatient setting using an inertial measurement system (MyoMotion) during 3 different walking tasks (normal walking, dual-task walking with simultaneous backward counting, fast walking). In addition, the natural postural sway of participants was measured by pedobarography, with the eyes opened and closed. The range of motion (ROM) in different joint angles, stride time, as well as sway were compared between different groups (between-subject factor), and different task conditions (within-subject factor) by a mixed model ANOVA. Results: Kinematic metrics and balance parameters were differently altered across different gait disorders compared to healthy controls. Overall, NPH patients significantly differed from controls in all movement parameters except for stride time, while they differed in balance parameters only with regard to AP movement. LST patients had significantly reduced ROMs of the shoulders, hips, and ankles, with significantly altered balance parameters regarding AP movement and passed center-of-pressure (COP) distance. CM patients differed from controls only in the ROM of the hip and ankle, but were affected in nearly all balance parameters, except for force distribution. Conclusion: The application of inertial measurement systems and pedobarography is feasible in an outpatient setting in patients with different neurological gait disorders. Rather than defining singular discriminative values, kinematic gait and balance metrics may provide characteristic profiles of movement parameter alterations in the sense of specific ´gait signatures´ for different pathologies, which could improve diagnostic accuracy by defining objective and quantifiable measures for the discrimination of different neurological gait disorders. Trial registration: The study was retrospectively registered on the 27th of March 2023 in the ‘Deutsches Register für Klinische Studien’ under the number DRKS00031555.

Diffusion-MRI (dMRI) measures molecular diffusion, which allows to characterize microstructural properties of the human brain. Gliomas strongly alter these microstructural properties. Delineation of brain tumors currently mainly relies on conventional MRI-techniques, which are, however, known to underestimate tumor volumes in diffusely infiltrating glioma. We hypothesized that dMRI is well suited for tumor delineation, and developed two different deep-learning approaches. The first diffusion-anomaly detection architecture is a denoising autoencoder, the second consists of a reconstruction and a discrimination network. Each model was exclusively trained on non-annotated dMRI of healthy subjects, and then applied on glioma patients’ data. To validate these models, a state-of-the-art supervised tumor segmentation network was modified to generate groundtruth tumor volumes based on structural MRI. Compared to groundtruth segmentations, a dice score of 0.67 ± 0.2 was obtained. Further inspecting mismatches between diffusion-anomalous regions and groundtruth segmentations revealed, that these colocalized with lesions delineated only later on in structural MRI follow-up data, which were not visible at the initial time of recording. Anomaly-detection methods are suitable for tumor delineation in dMRI acquisitions, and may further enhance brain-imaging analysis by detection of occult tumor infiltration in glioma patients, which could improve prognostication of disease evolution and tumor treatment strategies.

Diffusion-MRI measures molecular diffusion, which allows to characterize microstructural properties of the human brain. Gliomas strongly alter these microstructural properties. Delineation of brain tumors currently mainly rely on conventional MRI-techniques, which are known to underestimate tumor volumes in diffusely infiltrating glioma. We hypothesized that diffusion-MRI is well suited for tumor delineation and developed two different deep learning approaches. The first diffusion anomaly detection architecture is a denoising autoencoder, the second consists of a reconstruction and a discrimination network. Each model was exclusively trained on non-annotated diffusion-MRI of healthy subjects and then applied on glioma patients´ data. To validate these models, a state-of-the-art supervised tumor segmentation network was modified to generate groundtruth tumor volumes based on structural-MRI. Compared to groundtruth segmentations, a dice score of 0.67 ± 0.2 was obtained. Further inspecting mismatches between diffusion-anomalous regions and groundtruth segmentations revealed, that these colocalized with lesions delineated only later on in structural-MRI follow-up scans, but which were not visible at the time of recording. Anomaly- detection methods are suitable for tumor delineation in diffusion-MRI acquisitions and may further enhance brain imaging analysis by detection of occult tumor infiltration in glioma patients, which could improve prognostication of disease evolution and tumor treatment strategies.

Previous studies suggest that the topological properties of structural and functional neural networks in glioma patients are altered beyond the tumor location. These alterations are due to the dynamic interactions with large-scale neural circuits. Understanding and describing these interactions may be an important step towards deciphering glioma disease evolution. In this study, we analyze structural and functional brain networks in terms of determining the correlation between network robustness and topological features regarding the default-mode network (DMN), comparing prognostically differing patient groups to healthy controls. We determine the driver nodes of these networks, which are receptive to outside signals, and the critical nodes as the most important elements for controllability since their removal will dramatically affect network controllability. Our results suggest that network controllability and robustness of the DMN is decreased in glioma patients. We found losses of driver and critical nodes in patients, especially in the prognostically less favorable IDH wildtype (IDHwt) patients, which might reflect lesion-induced network disintegration. On the other hand, topological shifts of driver and critical nodes, and even increases in the number of critical nodes, were observed mainly in IDH mutated (IDHmut) patients, which might relate to varying degrees of network plasticity accompanying the chronic disease course in some of the patients, depending on tumor growth dynamics. We hereby implement a novel approach for further exploring disease evolution in brain cancer under the aspects of neural network controllability and robustness in glioma patients.

Metacognition has only scarcely been investigated in brain tumor patients. It is unclear if and how the tumor-lesioned brain might be able to maintain an adequate sense-of-self. As cortical midline structures (CMS) are regarded as essential for self-referential mental activity, we investigated resting-state fMRI connectivity (FC) of CMS to the default-mode network (DMN) and to the whole brain, comparing glioma patients and matched controls. Subjects furthermore performed a trait judgement (TJ), a trait recall task (TR), and neuropsychological testing. In the TJ, adjectives had to be ascribed as self- or non-self-describing, assessing the self-serving effect (SSE), a normally observed bias for positive traits. In the TR, the mnemic neglect effect (MNE), a memory advantage for positive traits, was tested. The groups were compared and partial correlations between FC and test metrics were analyzed. Although patients were significantly impaired in terms of verbal memory, groups did not differ in the SSE or the MNE results, showing preserved metacognitive abilities in patients. FC of CMS to the DMN was maintained, but was significantly decreased to whole brain in the patients. FC of the dorsomedial prefrontal cortex (DMPFC) to whole brain was correlated with the MNE in patients. Preserving the DMPFC in therapeutic interventions might be relevant for maintaining self-related verbal information processing in the memory domain in glioma patients.