Altered large-scale individual-based morphological brain network in spinocerebellar ataxia type 3.

BACKGROUND: Accumulating evidences indicate regional gray matter (GM) morphology atrophy in spinocerebellar ataxia type 3 (SCA3); however, whether large-scale morphological brain networks (MBNs) undergo widespread reorganization in these patients remains unclear. OBJECTIVE: To investigate the topological organization of large-scale individual-based MBNs in SCA3 patients. METHODS: The individual-based MBNs were constructed based on the inter-regional morphological similarity of GM regions. Graph theoretical analysis was taken to assess GM structural connectivity in 76 symptomatic SCA3, 24 pre-symptomatic SCA3, and 54 healthy normal controls (NCs). Topological parameters of the resulting graphs and network-based statistics analysis were compared among symptomatic SCA3, pre-symptomatic SCA3, and NCs groups. The inner association between network properties and clinical variables was further analyzed. RESULTS: Compared to NCs and pre-symptomatic SCA3 patients, symptomatic SCA3 indicated significantly decreased integration and segregation, a shift to "weaker small-worldness", characterized by decreased Cp , lower Eloc, and Eglob (all p < 0.005). Regarding nodal properties, symptomatic SCA3 exhibited significantly decreased nodal profiles in the central executive network (CEN)-related left inferior frontal gyrus, limbic regions involving the bilateral amygdala, left hippocampus, and bilateral pallidum, thalamus; and increased nodal degree, efficiency in bilateral caudate (all pFDR <0.05). Meanwhile, clinical variables were correlated with altered nodal profiles (pFDR ≤0.029). SCA3-related subnetwork was closely interrelated with dorsolateral cortico-striatal circuitry extending to orbitofrontal-striatal circuits and dorsal visual systems (lingual gyrus-striatal). CONCLUSION: Symptomatic SCA3 patients undergo an extensive and significant reorganization in large-scale individual-based MBNs, probably due to disrupted prefrontal cortico-striato-thalamo-cortical loops, limbic-striatum circuitry, and enhanced connectivity in the neostriatum. This study highlights the crucial role of abnormal morphological connectivity alterations beyond the pattern of brain atrophy, which might pave the way for therapeutic development in the future.

Analysis and hierarchical clustering of infratentorial morphological MRI identifies SCAs phenogroups.

BACKGROUND: Clinical decision-making in spinocerebellar ataxia spectrum diseases (SCAs) has mainly been based on genetic tests, not considering the SCAs' imaging and clinical heterogenicity. OBJECTIVE: To identify SCAs phenogroups by analysis and hierarchical clustering of infratentorial morphological MRI for unveiling pathophysiological differences among common SCA subtypes. METHODS: We prospectively enrolled 119 (62 women; mean age 37 years) genetically diagnosed SCAs (SCA1 n = 21, SCA2 n = 10, symptomatic SCA3 n = 59, presymptomatic SCA3 n = 22, SCA6 n = 7) and 35 healthy controls (HCs). All patients underwent MRI and detailed neurological and neuropsychology examinations. The width of each cerebellar peduncle (CP) and anteroposterior diameter of the spinal cord and pontine were measured. Twenty-five SCAs patients (15 women; mean age 35 years) were followed for at least a year (17 (15, 24) months), whose MRI and the Scale for the Assessment and Rating of Ataxia (SARA) were collected. RESULTS: Infratentorial morphological MRI measurements could significantly discriminate SCAs from HCs, even among SCA subtypes. Two mutually exclusive and clinically distinct phenogroups were identified. Despite similar (CAG)n, phenogroup 1 (n = 66, 55.5%) presented more atrophied infratentorial brain structures and more severe clinical symptoms with older age and earlier age of onset when compared with phenogroup 2. More importantly, all SCA2, most of SCA1 (76%), and symptomatic SCA3 (68%) were classified into phenogroup 1, whereas all SCA6 and all presymptomatic SCA3 were in phenogroup 2. The right middle CP had the highest diagnostic value in predicting phenogroup 2 (AUC = 0.99; P < 0.01) with high specificity (95%). Consistent with the significantly increased SARA (7.5 vs 10, P = 0.021), the bilateral inferior CP, spinal cord, and pontine tegmentum were more atrophy during the follow-up (P < 0.05). CONCLUSION: SCAs were with significant infratentorial brain atrophy than HCs. We identified two different SCAs phenogroups associated with substantial differences in infratentorial brain atrophy, clinical presentation, and may reflect the underlying molecular profiles to some extent, paving the way for a more personalized diagnostic and treatment approach.

Structural alterations of spinocerebellar ataxias type 3: from pre-symptomatic to symptomatic stage.

OBJECTIVES: To investigate and characterize the structural alterations of the brain in SCA3, and their correlations with the scale for the assessment and rating of ataxia (SARA) and normal brain ATXN3 expression. METHODS: We performed multimodal analyses in 52 SCA3 (15 pre-symptomatic) and healthy controls (HCs) (n = 35) to assess the abnormalities of gray and white matter (WM) of the cerebrum, brainstem, and cerebellum via FreeSurfer, SUIT, and TBSS, and their associations with disease severity. Twenty SCA3 patients (5 pre- and 15 symptomatic) were followed for at least a year. Besides, we uncovered the normal pattern of brain ATXN3 spatial distribution. RESULTS: Pre-symptomatic patients showed only WM damage, mainly in the cerebellar peduncles, compared to HCs. In the advanced stage, the WM damage followed a caudal-rostral pattern. Meanwhile, continuous nonlinear structure damage was characterized by brainstem volumetric reduction and relatively symmetric cerebellar and basal ganglia atrophy but spared the cerebral cortex, partially explained by the ATXN3 overexpression. The bilateral pallidum, brainstem, and cerebellar peduncles demonstrated a very large effect size. Besides, all these alterations were significantly correlated with SARA; the pons (r = -0.65) and superior cerebellar peduncle (r = -0.68) volume demonstrated a higher correlation than the cerebellum with SARA. The longitudinal study further uncovered progressive atrophy of pons in symptomatic SCA3. CONCLUSIONS: Significant WM damage starts before the ataxia onset. The bilateral pallidum, brainstem, and cerebellar peduncles are the most vulnerable targets. The volume of pons appears to be the most promising imaging biomarker for a longitudinal study. TRIAL REGISTRATION: ClinicalTrial ID: ChiCTR2100045857 ( http://www.chictr.org.cn/edit.aspx?pid=55652&htm=4 ) KEY POINTS: • Pre- SCA3 showed WM damage mainly in cerebellar peduncles. Continuous brain damage was characterized by brainstem, widespread, and relatively symmetric cerebellar and basal ganglia atrophy. • Volumetric abnormalities were most evident in the bilateral pallidum, brainstem, and cerebellar peduncles in SCA3. • The volume of pons might identify the disease progression longitudinally.

Pituicytoma: Four cases with unusual imaging features and a literature review.

OBJECTIVE: Pituicytomas (PTs) are rare and benign neoplasms. The variable imaging and clinical features of PTs, which overlap with other sellar pathologies, can make preoperative diagnosis challenging. In the interest of a more comprehensive understanding of the diagnostic aspects of PTs, it is necessary to report and synthesize the variable imaging and clinical features of PTs. METHODS: We retrospectively included and analysed four pathologically proven PTs with unusual imaging and/or clinical features. Additionally, we reviewed the literature on PT between 2007 and 2019 in the PubMed database to provide context for the individual patient data described herein. RESULTS: Our series included three female and one male adult patient (mean age: 44.75, age range: 20-56 y). Based on clinical symptoms, we noticed that case 1 had Cushing's syndrome, case 2 had increased prolactin, case 3 had extremity enlargement but with a normal level of human growth factor, and case 4 presented with tinnitus and dizziness. On radiograph, inconsistent with the main imaging findings of PTs in the literature, there was one case in the pituitary anterior lobe, three cases with hypointensity on T2-weighted images, two patients with reduced homogeneous contrast enhancement, and one case demonstrating invasion potential. In addition, one of our patients underwent PET-CT examination, and the lesion had a slight increase in glucose uptake and no significant decrease in ammonia uptake. Postoperative follow-up monitoring revealed no tumour recurrence. CONCLUSION: Our cases highlight the unusual imaging manifestations of PTs. Recognizing these imaging features plays an important role in the preoperative diagnosis, treatment, and postsurgery monitoring of PTs.

Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability.

OBJECTIVES: Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data. METHODS: Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed. RESULTS: The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (p < 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the IDH-1 mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81). CONCLUSIONS: Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma. KEY POINTS: • The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods. • DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma. • The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.

Whole brain volume and cortical thickness abnormalities in Wilson's disease: a clinical correlation study.

Wilson's disease (WD) is an inherited autosomal recessive disorder of copper metabolism, and its neurological and neuropsychiatric manifestations are associated with copper accumulation in brain. A few neuroimaging studies have shown that gray matter atrophy in WD affects both subcortical structures and cortex. This study aims to quantitatively evaluate the morphometric brain abnormalities in patients with WD in terms of whole brain volume and cortical thickness and their associations with clinical severity of WD. Thirty patients clinically diagnosed as WD with neurological manifestations and 25 healthy controls (HC) were recruited. 3D T1-weighted images were segmented into 276 whole-brain regions of interest (ROIs) and 68 cortical ROIs. WD-vs-HC group comparisons were then conducted for each ROI. The associations between those morphometric measurements and the Global Assessment Scale (GAS) score for WD were analyzed. Compared with HC, significant WD-related volumetric decreases were found in the bilateral subcortical nuclei (putamen, globus pallidus, caudate nucleus, substantia nigra, red nucleus and thalamus), diffuse white matter and several gray matter regions. WD patients showed reduced cortical thickness in the left precentral gyrus and the left insula. Further, the volumes of the right globus pallidus, bilateral putamen, right external capsule and left superior longitudinal fasciculus were negatively correlated with GAS. Our results indicated that significant WD-related morphometric abnormalities were quantified in terms of whole-brain volumes and cortical thicknesses, some of which correlated significantly to the clinical severity of WD. Those morphometrics may provide a potentially effective biomarker of WD.

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.

OBJECTIVES: To assess the diagnostic accuracy of machine learning (ML) in predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma and to identify potential covariates that could influence the diagnostic performance of ML. METHODS: A systematic search of PubMed, Web of Science, and the Cochrane library up to 1 August 2019 was conducted to collect all the articles investigating the diagnostic performance of ML for prediction of IDH mutation in glioma. The search strategy combined synonyms for 'machine learning', 'glioma', and 'IDH'. Pooled sensitivity, specificity, and their 95% confidence intervals (CIs) were calculated, and the area under the receiver operating characteristic curve (AUC) was obtained. RESULTS: Nine original articles assessing a total of 996 patients with glioma were included. Among these studies, five divided the participants into training and validation sets, while the remaining four studies only had a training set. The AUC of ML for predicting IDH mutation in the training and validation sets was 93% (95% CI 91-95%) and 89% (95% CI 86-92%), respectively. The pooled sensitivity and specificity were, respectively, 87% (95% CI 82-91%) and 88% (95% CI 83-92%) in the training set and 87% (95% CI 76-93%) and 90% (95% CI 72-97%) in the validation set. In subgroup analyses in the training set, the combined use of clinical and imaging features with ML yielded higher sensitivity (90% vs. 83%) and specificity (90% vs. 82%) than the use of imaging features alone. In addition, ML performed better for high-grade gliomas than for low-grade gliomas, and ML that used conventional MRI sequences demonstrated higher specificity for predicting IDH mutation than ML using conventional and advanced MRI sequences. CONCLUSIONS: ML demonstrated an excellent diagnostic performance in predicting IDH mutation of glioma. Clinical information, MRI sequences, and glioma grade were the main factors influencing diagnostic specificity. KEY POINTS: • Machine learning demonstrated an excellent diagnostic performance for prediction of IDH mutation in glioma (the pooled sensitivity and specificity were 88% and 87%, respectively). • Machine learning that used conventional MRI sequences demonstrated higher specificity in predicting IDH mutation than that based on conventional and advanced MRI sequences (89% vs. 85%). • Integration of clinical and imaging features in machine learning yielded a higher sensitivity (90% vs. 83%) and specificity (90% vs. 82%) than that achieved by using imaging features alone.