Improved prediction of glioma-related aphasia by diffusion MRI metrics, machine learning, and automated fiber bundle segmentation.

White matter impairments caused by gliomas can lead to functional disorders. In this study, we predicted aphasia in patients with gliomas infiltrating the language network using machine learning methods. We included 78 patients with left-hemispheric perisylvian gliomas. Aphasia was graded preoperatively using the Aachen aphasia test (AAT). Subsequently, we created bundle segmentations based on automatically generated tract orientation mappings using TractSeg. To prepare the input for the support vector machine (SVM), we first preselected aphasia-related fiber bundles based on the associations between relative tract volumes and AAT subtests. In addition, diffusion magnetic resonance imaging (dMRI)-based metrics [axial diffusivity (AD), apparent diffusion coefficient (ADC), fractional anisotropy (FA), and radial diffusivity (RD)] were extracted within the fiber bundles' masks with their mean, standard deviation, kurtosis, and skewness values. Our model consisted of random forest-based feature selection followed by an SVM. The best model performance achieved 81% accuracy (specificity = 85%, sensitivity = 73%, and AUC = 85%) using dMRI-based features, demographics, tumor WHO grade, tumor location, and relative tract volumes. The most effective features resulted from the arcuate fasciculus (AF), middle longitudinal fasciculus (MLF), and inferior fronto-occipital fasciculus (IFOF). The most effective dMRI-based metrics were FA, ADC, and AD. We achieved a prediction of aphasia using dMRI-based features and demonstrated that AF, IFOF, and MLF were the most important fiber bundles for predicting aphasia in this cohort.

Zfp580 inactivation as a new therapeutic target to enhance recovery after stroke in mice.

Paracrine cerebral Interleukin 6 (Il6) is relevant for stroke recovery, but systemic Il6 elevation may worsen outcome. Hence, paracrine Il6 response modulation within the neurovascular unit has emerged as an attractive therapeutic approach. Lithium modulates Il6 responses and improves stroke outcome. However, lithium may cause serious adverse effects. Here, we report that Zincfinger protein 580 (Zfp580) mediates the effects of lithium on Il6 signaling. In contrast to lithium, Zfp580 inactivation had no neurotoxic effects, and Zfp580 knock out mice showed no phenotypic changes in cognitive and motor function behavioral tests. We discovered that lithium and hypoxia disinhibited Il6 via Zfp580 suppression and post-translational modification by small ubiquitin-like modifier (SUMO). After transient middle cerebral artery occlusion, loss of Zfp580 reduced paracrine Il6 and increased Il6 trans-signaling. Aside from modulating Il6 signaling, Zfp580 loss improved endothelial resilience to ischemia, was highly neuroprotective resulting in smaller infarcts and enhanced use-dependent neuroplasticity, all of which led to improved functional outcome. In conclusion, inactivation of Zfp580 exerts positive effects on multiple key mechanisms without exhibiting relevant adverse side effects, making it potentially a more specific and effective treatment target for stroke recovery than lithium. To fully assess its potential, Zfp580 inhibitors must be developed.

Role of interhemispheric connectivity in recovery from postoperative supplementary motor area syndrome in glioma patients.

OBJECTIVE: Surgical resection of gliomas involving the supplementary motor area (SMA) frequently results in SMA syndrome, a symptom complex characterized by transient akinesia and mutism. Because the factors influencing patient functional outcomes after surgery remain elusive, the authors investigated network-based predictors in a multicentric cohort of glioma patients. METHODS: The participants were 50 patients treated for glioma located in the SMA at one of the three centers participating in the study. Postoperative functional outcomes (motor deficits, mutism) and duration of symptoms were assessed during hospitalization. Long-term outcome was assessed 3 months after surgery. MRI-based lesion-symptom mapping was performed to estimate the severity of gray matter damage and white matter disconnection. RESULTS: The median duration of acute symptoms was 3 days (range 1-42 days). Long-term deficits involving fine motor movements and speech were found at follow-up in 27 patients (54%). Disconnection of the central callosal fibers was associated with prolonged acute symptoms (p < 0.05). Postoperative mutism was significantly related to disconnection severity of the left frontopontine tract, frontal aslant tract, cingulum, and corticostriatal tract (p < 0.05). Disconnection of midposterior callosal fibers and lesion loads within the left medial Brodmann area 4 were associated with long-term motor deficits (p < 0.05). CONCLUSIONS: This study provides evidence for the pathophysiology and predictive factors of postoperative SMA syndrome by demonstrating the relation of the disconnection of callosal fibers with prolonged symptom duration (central segment) and long-term motor deficits (midposterior segment). These data may be useful for presurgical risk assessment and adequate consultation for patients prior to undergoing resection of glioma located within the SMA region.

Tractography-based navigated TMS language mapping protocol.

Introduction: This study explores the feasibility of implementing a tractography-based navigated transcranial magnetic stimulation (nTMS) language mapping protocol targeting cortical terminations of the arcuate fasciculus (AF). We compared the results and distribution of errors from the new protocol to an established perisylvian nTMS protocol that stimulated without any specific targeting over the entire perisylvian cortex. Methods: Sixty right-handed patients with language-eloquent brain tumors were examined in this study with one half of the cohort receiving the tractographybased protocol and the other half receiving the perisylvian protocol. Probabilistic tractography using MRtrix3 was performed for patients in the tractography-based group to identify the AF's cortical endpoints. nTMS mappings were performed and resulting language errors were classified into five psycholinguistic groups. Results: Tractography and nTMS were successfully performed in all patients. The tractogram-based group showed a significantly higher median overall ER than the perisylvian group (3.8% vs. 2.9% p <.05). The median ER without hesitation errors in the tractogram-based group was also significantly higher than the perisylvian group (2.0% vs. 1.4%, p <.05). The ERs by error type showed no significant differences between protocols except in the no response ER, with a higher median ER in the tractogram-based group (0.4% vs. 0%, p <.05). Analysis of ERs based on the Corina cortical parcellation system showed especially high nTMS ERs over the posterior middle temporal gyrus (pMTG) in the perisylvian protocol and high ERs over the middle and ventral postcentral gyrus (vPoG), the opercular inferior frontal gyrus (opIFG) and the ventral precentral gyrus (vPrG) in the tractography-based protocol. Discussion: By considering the white matter anatomy and performing nTMS on the cortical endpoints of the AF, the efficacy of nTMS in disrupting patients' object naming abilities was increased. The newly introduced method showed proof of concept and resulted in AF-specific ERs and noninvasive cortical language maps, which could be applied to additional fiber bundles related to the language network in future nTMS studies.

Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract.

Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 ± 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts' profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model's performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.

Network analysis shows decreased ipsilesional structural connectivity in glioma patients.

Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network.

Does stereoscopic imaging improve the memorization of medical imaging by neurosurgeons? Experience of a single institution.

Stereoscopic imaging has increasingly been used in anatomical teaching and neurosurgery. The aim of our study was to analyze the potential utility of stereoscopic imaging as a tool for memorizing neurosurgical patient cases compared to conventional monoscopic visualization. A total of 16 residents and 6 consultants from the Department of Neurosurgery at Charité - Universitätsmedizin Berlin were recruited for the study. They were divided into two equally experienced groups. A comparative analysis of both imaging modalities was conducted in which four different cases were assessed by the participants. Following the image assessment, two questionnaires, one analyzing the subjective judgment using the 5-point Likert Scale and the other assessing the memorization and anatomical accuracy, were completed by all participants. Both groups had the same median year of experience (5) and stereoacuity (≤ 75 s of arc). The analysis of the first questionnaire demonstrated significant subjective superiority of the monoscopic imaging in evaluation of the pathology (median: monoscopic: 4; stereoscopic: 3; p = 0.020) and in handling of the system (median: monoscopic: 5; stereoscopic: 2; p < 0.001). The second questionnaire showed that the anatomical characterization of the pathologies was comparable between both visualization methods. Most participants rated the stereoscopic visualization as worse compared to the monoscopic visualization, probably due to a lack of familiarity with the newer technique. Stereoscopic imaging, however, was not objectively inferior to traditional monoscopic imaging for anatomical comprehension. Further methodological developments and incorporation in routine clinical workflows will most likely enhance the usability and acceptance of stereoscopic visualization.

Lesion-symptom mapping of language impairments in patients suffering from left perisylvian gliomas.

Brain tumors cause local structural impairments of the cerebral network. Moreover, brain tumors can also affect functional brain networks more distant from the lesion. In this study, we analyzed the impact of glioma WHO grade II-IV tumors on grey and white matter in relation to impaired language function. In a retrospective analysis of 60 patients, 14 aphasic and 46 non-aphasic, voxel-based lesion-symptom mapping (VLSM) was used to identify tumor induced lesions in grey (GM) and white matter (WM) related to patients' performance in subtests of the Aachen Aphasia Test (AAT). Significant clusters were analyzed for atlas-based grey and white matter involvements in relation to different linguistic modalities. VLSM analysis indicated significant contribution of a posterior perisylvian cluster covering WM and GM to AAT performance averaged across subtests. When considering individual AAT subtests, a substantial overlap between significant clusters for analysis of the token test, picture naming and language comprehension results could be observed. The WM-cluster intersections reflect the overall importance of the perisylvian area in language function, similarly to GM participations. Especially the constant high percentages of Heschl's gyrus, superior temporal gyrus, inferior longitudinal and middle longitudinal fascicles, but also arcuate and inferior fronto-occipital fascicles highlight the importance of the posterior perisylvian area for language function.

Towards a tractography-based risk stratification model for language area associated gliomas.

OBJECTIVES: Injury to major white matter pathways during language-area associated glioma surgery often leads to permanent loss of neurological function. The aim was to establish standardized tractography of language pathways as a predictor of language outcome in clinical neurosurgery. METHODS: We prospectively analyzed 50 surgical cases of patients with left perisylvian, diffuse gliomas. Standardized preoperative Diffusion-Tensor-Imaging (DTI)-based tractography of the 5 main language tracts (Arcuate Fasciculus [AF], Frontal Aslant Tract [FAT], Inferior Fronto-Occipital Fasciculus [IFOF], Inferior Longitudinal Fasciculus [ILF], Uncinate Fasciculus [UF]) and spatial analysis of tumor and tracts was performed. Postoperative imaging and the resulting resection map were analyzed for potential surgical injury of tracts. The language status was assessed preoperatively, postoperatively and after 3 months using the Aachen Aphasia Test and Berlin Aphasia Score. Correlation analyses, two-step cluster analysis and binary logistic regression were used to analyze associations of tractography results with language outcome after surgery. RESULTS: In 14 out of 50 patients (28%), new aphasic symptoms were detected 3 months after surgery. The preoperative infiltration of the AF was associated with functional worsening (cc = 0.314; p = 0.019). Cluster analysis of tract injury profiles revealed two areas particularly related to aphasia: the temporo-parieto-occipital junction (TPO; temporo-parietal AF, middle IFOF, middle ILF) and the temporal stem/peri-insular white matter (middle IFOF, anterior ILF, temporal UF, temporal AF). Injury to these areas (TPO: OR: 23.04; CI: 4.11 - 129.06; temporal stem: OR: 21.96; CI: 2.93 - 164.41) was associated with a higher-risk of persisting aphasia. CONCLUSIONS: Tractography of language pathways can help to determine the individual aphasia risk profile pre-surgically. The TPO and temporal stem/peri-insular white matter were confirmed as functional nodes particularly sensitive to surgical injuries.

Support vector machine based aphasia classification of transcranial magnetic stimulation language mapping in brain tumor patients.

Repetitive TMS (rTMS) allows for non-invasive and transient disruption of local neuronal functioning. We used machine learning approaches to assess whether brain tumor patients can be accurately classified into aphasic and non-aphasic groups using their rTMS language mapping results as input features. Given that each tumor affects the subject-specific language networks differently, resulting in heterogenous rTMS functional mappings, we propose the use of machine learning strategies to classify potential patterns of rTMS language mapping results. We retrospectively included 90 patients with left perisylvian world health organization (WHO) grade II-IV gliomas that underwent presurgical navigated rTMS language mapping. Within our cohort, 29 of 90 (32.2%) patients suffered from at least mild aphasia as shown in the Aachen Aphasia Test based Berlin Aphasia Score (BAS). After spatial normalization to MNI 152 of all rTMS spots, we calculated the error rate (ER) in each stimulated cortical area (28 regions of interest, ROI) by automated anatomical labeling parcellation (AAL3) and IIT. We used a support vector machine (SVM) to classify significant areas in relation to aphasia. After feeding the ROIs into the SVM model, it revealed that in addition to age (w = 2.98), the ERs of the left supramarginal gyrus (w = 3.64), left inferior parietal gyrus (w = 2.28) and right pars triangularis (w = 1.34) contributed more than other features to the model. The model's sensitivity was 86.2%, the specificity was 82.0%, the overall accuracy was 85.5% and the AUC was 89.3%. Our results demonstrate an increased vulnerability of right inferior pars triangularis to rTMS in aphasic patients due to left perisylvian gliomas. This finding points towards a functional relevant involvement of the right pars triangularis in response to aphasia. The tumor location feature, specified by calculating overlaps with white and grey matter atlases, did not affect the SVM model. The left supramarginal gyrus as a feature improved our SVM model the most. Additionally, our results could point towards a decreasing potential for neuroplasticity with age.

Detecting Corticospinal Tract Impairment in Tumor Patients With Fiber Density and Tensor-Based Metrics.

Tumors infiltrating the motor system lead to significant disability, often caused by corticospinal tract injury. The delineation of the healthy-pathological white matter (WM) interface area, for which diffusion magnetic resonance imaging (dMRI) has shown promising potential, may improve treatment outcome. However, up to 90% of white matter (WM) voxels include multiple fiber populations, which cannot be correctly described with traditional metrics such as fractional anisotropy (FA) or apparent diffusion coefficient (ADC). Here, we used a novel fixel-based along-tract analysis consisting of constrained spherical deconvolution (CSD)-based probabilistic tractography and fixel-based apparent fiber density (FD), capable of identifying fiber orientation specific microstructural metrics. We addressed this novel methodology's capability to detect corticospinal tract impairment. We measured and compared tractogram-related FD and traditional microstructural metrics bihemispherically in 65 patients with WHO grade III and IV gliomas infiltrating the motor system. The cortical tractogram seeds were based on motor maps derived by transcranial magnetic stimulation. We extracted 100 equally distributed cross-sections along each streamline of corticospinal tract (CST) for along-tract statistical analysis. Cross-sections were then analyzed to detect differences between healthy and pathological hemispheres. All metrics showed significant differences between healthy and pathologic hemispheres over the entire tract and between peritumoral segments. Peritumoral values were lower for FA and FD, but higher for ADC within the entire cohort. FD was more specific to tumor-induced changes in CST than ADC or FA, whereas ADC and FA showed higher sensitivity. The bihemispheric along-tract analysis provides an approach to detect subject-specific structural changes in healthy and pathological WM. In the current clinical dataset, the more complex FD metrics did not outperform FA and ADC in terms of describing corticospinal tract impairment.

Metastatic dissemination patterns of different primary tumors to the spine and other bones.

Metastatic spine disease (MSD) is a severe event in cancer patients. Experimental data indicate that bone metastasis is mostly mediated by blood flow-dependent, passive arrest of circulating tumor cells to the bone metastatic niche (BMN). Here, we have set out to test these experimental observations in a clinical, human setting to improve our understanding of MSD. 507 patients, treated on spinal metastases in our institution from 2005 to 2015 were retrospectively evaluated. We identified 259 patients with accessible staging reports of the skeleton before and at initial diagnosis of MSD. Data analysis comprised localizations of bone metastases, underlying malignancy and time to development of MSD. Dissemination pattern of bone metastasis was correlated with red bone marrow (RBM) content of the respective bone as a measure of blood flow. Spinal metastases occurred most frequently in lung cancer (21%), prostate cancer (19%), and breast cancer (12%). At the diagnosis of MSD, majority of patients have multiple extra-spinal bone metastases (2/3). The distribution of metastases to extra-spinal bones and to the spine is mostly proportional to the RBM content of the involved bone. Corresponding to the high RBM content, thoracic spine, pelvic bones and ribs represent a predilection site for bone metastasis. We confirm a distinct preference of cancer types to metastasize to bones. When it comes to bone metastases all primaries show uniform distribution pattern, which supports the hypothesis of a predominantly blood flow-dependent distribution of tumor cells and passive arrest to the BMN rather than a spine-specific homing mechanism.