Low and high-order topological disruption of functional networks in multiple system atrophy with freezing of gait: A resting-state study.

OBJECTIVE: Freezing of gait (FOG), a specific survival-threatening gait impairment, needs to be urgently explored in patients with multiple system atrophy (MSA), which is characterized by rapid progression and death within 10 years of symptom onset. The objective of this study was to explore the topological organisation of both low- and high-order functional networks in patients with MAS and FOG. METHOD: Low-order functional connectivity (LOFC) and high-order functional connectivity FC (HOFC) networks were calculated and further analysed using the graph theory approach in 24 patients with MSA without FOG, 20 patients with FOG, and 25 healthy controls. The relationship between brain activity and the severity of freezing symptoms was investigated in patients with FOG. RESULTS: Regarding global topological properties, patients with FOG exhibited alterations in the whole-brain network, dorsal attention network (DAN), frontoparietal network (FPN), and default network (DMN), compared with patients without FOG. At the node level, patients with FOG showed decreased nodal centralities in sensorimotor network (SMN), DAN, ventral attention network (VAN), FPN, limbic regions, hippocampal network and basal ganglia network (BG), and increased nodal centralities in the FPN, DMN, visual network (VIN) and, cerebellar network. The nodal centralities of the right inferior frontal sulcus, left lateral amygdala and left nucleus accumbens (NAC) were negatively correlated with the FOG severity. CONCLUSION: This study identified a disrupted topology of functional interactions at both low and high levels with extensive alterations in topological properties in MSA patients with FOG, especially those associated with damage to the FPN. These findings offer new insights into the dysfunctional mechanisms of complex networks and suggest potential neuroimaging biomarkers for FOG in patients with MSA.

Evaluation the relationship between myocardial fibrosis and left ventricular torsion measured by cardiac magnetic resonance feature-tracking in hypertrophic cardiomyopathy patients with preserved ejection fraction.

The relationship between left ventricular (LV) torsion and myocardial fibrosis (MF) in hypertrophic cardiomyopathy (HCM) patients with preserved ejection fraction was still not well understood. New developments in cardiac magnetic resonance (CMR) enable a much fuller assessment of cardiac characteristics. This study sought to assess the impact of HCM on myocardial function as assessed by LV torsion and its relationship with MF. HCM (n = 79) and healthy controls (n = 40) underwent CMR. According to whether there was late gadolinium enhancement (LGE), patients were divided into LGE+ group and LGE- group. LV torsion and torsion rate were measured by CMR feature-tracking (CMR-FT). MF was quantitatively evaluated through LGE imaging. LGE was present in 44 patients (56%). Compared with healthy controls, torsion increased in the LGE- group (P < 0.001). Compared with LGE+ group, torsion was higher in the LGE- group (P < 0.001). There was no significant difference in torsion between LGE+ group and healthy controls. Correlation analysis showed that torsion was correlated with LGE% (r = - 0.443) and LGE mass (r = - 0.435) respectively. On multivariable logistic regression analysis, LV torsion was the only feature that was independently associated with the presence of LGE (OR 0.130; 95% CI 0.040 to 0.420, P = 0.01). The best torsion value associated with MF was 1.91 (sensitivity 60.0%, specificity 77.3%, AUC = 0.733). In HCM patients with preserved ejection fraction, CMR-FT derived LV torsion analysis holds promise for myocardial fibrosis detection.

3D-CAM: a novel context-aware feature extraction framework for neurological disease classification.

In clinical practice and research, the classification and diagnosis of neurological diseases such as Parkinson's Disease (PD) and Multiple System Atrophy (MSA) have long posed a significant challenge. Currently, deep learning, as a cutting-edge technology, has demonstrated immense potential in computer-aided diagnosis of PD and MSA. However, existing methods rely heavily on manually selecting key feature slices and segmenting regions of interest. This not only increases subjectivity and complexity in the classification process but also limits the model's comprehensive analysis of global data features. To address this issue, this paper proposes a novel 3D context-aware modeling framework, named 3D-CAM. It considers 3D contextual information based on an attention mechanism. The framework, utilizing a 2D slicing-based strategy, innovatively integrates a Contextual Information Module and a Location Filtering Module. The Contextual Information Module can be applied to feature maps at any layer, effectively combining features from adjacent slices and utilizing an attention mechanism to focus on crucial features. The Location Filtering Module, on the other hand, is employed in the post-processing phase to filter significant slice segments of classification features. By employing this method in the fully automated classification of PD and MSA, an accuracy of 85.71%, a recall rate of 86.36%, and a precision of 90.48% were achieved. These results not only demonstrates potential for clinical applications, but also provides a novel perspective for medical image diagnosis, thereby offering robust support for accurate diagnosis of neurological diseases.

Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging.

BACKGROUND: The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging. METHODS: The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022. Initially, a deep learning (DL) model was developed to segment PDAC. Subsequently, radiomics features were extracted from the automatically segmented region of interest (ROI), which encompassed both the tumor region and a 3 mm surrounding area, to construct a predictive model for determining T4 stage of PDAC. The assessment of the models' performance involved the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The study encompassed a cohort of 509 PDAC patients, with a median age of 62 years (interquartile range: 55-67). The proportion of patients in T4 stage within the model was 16.9%. The model achieved an AUC of 0.849 (95% CI: 0.753-0.940), a sensitivity of 0.875, and a specificity of 0.728 in predicting T4 stage of PDAC. The performance of the model was determined to be comparable to that of two experienced abdominal radiologists (AUCs: 0.849 vs. 0.834 and 0.857). CONCLUSION: The automated AI pipeline utilizing tumor and peritumor-related radiomics features demonstrated comparable performance to that of senior abdominal radiologists in predicting T4 stage of PDAC.

Advanced Cognitive Patterns in Multiple System Atrophy Compared to Parkinson's Disease: An Individual Diffusion Tensor Imaging Study.

RATIONALE AND OBJECTIVES: Although both Multiple system atrophy (MSA) and Parkinson's disease (PD) belong to alpha-synucleinopathy, they have divergent clinical courses and prognoses. The degeneration of white matter has a considerable impact on cognitive performance, yet it is uncertain how PD and MSA affect its functioning in a similar or different manner. METHODS: In this study, a total of 116 individuals (37 PD with mild cognitive impairment (PD-MCI), 37 MSA (parkinsonian variant) with mild cognitive impairment (MSA-MCI), and 42 healthy controls) underwent diffusion tensor imaging (DTI) and cognitive assessment. Utilizing probabilistic fiber tracking, association fibers, projection fibers, and thalamic fibers were reconstructed. Subsequently, regression, support vector machine, and SHAP (Shapley Addictive exPlanations) analyzes were conducted to evaluate the association between microstructural diffusion metrics and multiple cognitive domains, thus determining the white matter predictors of MCI. RESULTS: MSA-MCI patients exhibited distinct white matter impairment extending to the middle cerebellar peduncle, corticospinal tract, and cingulum bundle. Furthermore, the fractional anisotropy (FA) and mean diffusivity (MD)values of the right anterior thalamic radiation were significantly associated with global efficiency (FA: B = 0.69, P < 0.001, VIF = 1.31; MD: B = -0.53, P = 0.02, VIF = 2.50). The diffusion metrics of white matter between PD-MCI and MSA-MCI proved to be an effective predictor of the MCI, with an accuracy of 0.73 (P < 0.01), and the most predictive factor being the MD of the anterior thalamic radiation. CONCLUSIONS: Our results demonstrated that MSA-MCI had a more noticeable deterioration in white matter, which potentially linked to various cognitive domain connections. Diffusion MRI could be a useful tool in comprehending the neurological basis of cognitive impairment in Parkinsonian disorders.

Structural and Functional Alterations of Motor-Thalamus in Different Motor Subtype of Parkinson's Disease: An Individual Study.

RATIONALE AND OBJECTIVES: This study aimed to investigate the structural and functional alterations occurring within bilateral premotor thalamus (mPMtha) in motor subtypes of Parkinson's disease (PD). MATERIALS AND METHODS: Sixty-one individuals with instability and gait difficulty (PIGD) subtype, 60 individuals with tremor-dominant (TD) subtype and 66 healthy controls (HCs) participated in the study. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) and 3D T1-weighted (3DT1) scans. Functional connectivity (FC) analysis and Voxel-based morphometry (VBM) analysis were performed to evaluate the function and volume of mPMtha. Additionally, correlations between motor performance and FC values, volumes were examined separately. Support vector machine (SVM) model based on FC values and thalamic volumes was conducted to assist in the clinical diagnosis of PD motor subtype. RESULTS: Compared to HCs and PIGD, TD subtype showed increased FC between the bilateral mPMtha and left middle occipital gyrus, left inferior parietal lobule (IPL). While PIGD subtype demonstrated decreased FC between right mPMtha and precentral gyrus (PreCG), supramarginal, IPL and superior parietal lobule. FC of bilateral mPMtha with the identified regions were significantly correlated with motor performance scores in PD patients. The SVM classification based on FC values demonstrated a high level of efficiency (AUC=0.874). The volumes of the bilateral mPMtha were indifferent among three groups. CONCLUSION: We noted distinct FC alterations of mPMtha in TD and PIGD subtypes, and these changes were correlated with motor performance. Furthermore, the machine learning based on statistically significant FC might be served as an alternative approach for automatically classifying PD motor subtypes individually.

Quantitative Dixon and intravoxel incoherent motion diffusion magnetic resonance imaging parameters in lumbar vertebrae for differentiating aplastic anemia and acute myeloid leukemia.

Objective: We sought to evaluate the use of quantitative Dixon (Q-Dixon) and intravoxel incoherent motion diffusion imaging (IVIM) for the differential diagnosis of aplastic anemia (AA) and acute myeloid leukemia (AML). Methods: Between August 2021 and October 2023, we enrolled 68 diagnosed patients, including 36 patients with AA and 32 patients with AML, as well as 26 normal controls. All patients underwent 3-Tesla magnetic resonance imaging, which included IVIM and T2*-corrected Q-Dixon imaging at the L2-4 level. The iliac crest biopsy's pathology was used as the diagnostic criterion. The interobserver measurement repeatability was evaluated using the intraclass correlation coefficient (ICC). One-way analysis of variance, Spearman analysis, and receiver operating characteristic curve analysis were used. Results: The fat fraction (FF) and perfusion fraction (f) values were statistically significantly different between the three groups (p < 0.001 and p = 0.007). The FF and f values in the AA group were higher than those in the AML group. The true apparent diffusion coefficient (D) value was substantially negatively correlated to the FF and R2* values (r = -0.601, p < 0.001; r = -0.336, p = 0.002). The f value was positively correlated with both FF and pseudo-apparent diffusion coefficient (D*) values (r = 0.376, p < 0.001; r = 0.263, p = 0.017) and negatively correlated with the D value (r = -0.320, p = 0.003). The FF and f values were negatively correlated with the degree of myelodysplasia (r = -0.597, p < 0.001; r = -0.454, p = 0.004), and the D value was positively correlated with the degree of myelodysplasia (r = 0.395, p = 0.001). For the differential diagnosis of AA and AML, the Q-Dixon model's sensitivity (93.75%) and specificity (84%) confirmed that it outperformed the IVIM model. Conclusion: Q-Dixon parameters have the potential to be used as new biomarkers to differentiate AA from AML.

Segmental disturbance of white matter microstructure in predicting mild cognitive impairment in idiopathic Parkinson's disease: An individualized study based on automated fiber quantification tractography.

PURPOSE: The neurobiological mechanisms and an early identification of MCI in idiopathic Parkinson's disease (IPD) remain unclear. To investigate the abnormalities of types of white matter (WM) fiber tracts segmentally and establish reliable indicator in IPD-MCI. METHODS: Forty IPD with normal cognition (IPD-NCI), thirty IPD-MCI, and thirty healthy controls were included. Automated fiber quantification was applied to extract the fractional anisotropy (FA) and mean diffusivity (MD) values at 100 locations along the major fibers. Partial correlation was performed between diffusion values and cognitive performance. Furthermore, machine learning analyses were conducted to determine the imaging biomarker of MCI. Permutation tests were performed to evaluate the pointwise differences under the FWE correction. RESULTS: IPD-MCI had similar but more severe and widespread WM degeneration in the association, projection, and commissural fibers compared with IPD-NCI. Meanwhile, IPD-MCI showed distinct degeneration pattern in the association fibers. The FA of the anterior segment of right inferior fronto-occipital fasciculus (IFOF) was positively correlated with MoCA (P < 0.05) and executive function (P < 0.05). The MD of the middle and posterior segment of left superior longitudinal fasciculus (SLF) was negatively correlated with MoCA P < 0.05), executive (P < 0.05), visuospatial function (P < 0.05). Furthermore, the AUC of support vector machine model was 0.80 in the validation dataset. The FA of anterior segment of right IFOF contribute the most. CONCLUSION: This study demonstrated that regional tract-specific microstructural degeneration, especially the association fibers, can be used to predict MCI in IPD. Especially, the right IFOF may be a significant imaging biomarker in predicting IPD with MCI.

Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages.

Differentiating the parkinsonian variant of multiple system atrophy (MSA-P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA-P patients and use machine learning methods to explore the diagnostic utility of these features. Resting-state fMRI data were acquired from 88 healthy controls, 76 MSA-P patients, and 53 IPD patients using a 3.0 T scanner. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA-P patients. Regarding graph metrics, early IPD and MSA-P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum-basal ganglia-cortical network, but these disconnections were mainly in the cortico-thalamo-cerebellar circuits in MSA-P patients and the basal ganglia-thalamo-cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA-P and IPD patients show similar whole-brain network topological alterations. MSA-P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum-basal ganglia-cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA-P and IPD.

Alterations of voxel-wise spontaneous activity and corresponding brain functional networks in multiple system atrophy patients with mild cognitive impairment.

Emerging evidence has indicated that cognitive impairment is an underrecognized feature of multiple system atrophy (MSA). Mild cognitive impairment (MCI) is related to a high risk of dementia. However, the mechanism underlying MCI in MSA remains controversial. In this study, we conducted the amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity (FC) analyses to detect the characteristics of local neural activity and corresponding network alterations in MSA patients with MCI (MSA-MCI). We enrolled 80 probable MSA patients classified as cognitively normal (MSA-NC, n = 36) and MSA-MCI (n = 44) and 40 healthy controls. Compared with MSA-NC, MSA-MCI exhibited decreased ALFF in the right dorsal lateral prefrontal cortex (RDLPFC) and increased ALFF in the right cerebellar lobule IX and lobule IV-V. In the secondary FC analyses, decreased FC in the left inferior parietal lobe (IPL) was observed when we set the RDLPFC as the seed region. Decreased FC in the bilateral cuneus, left precuneus, and left IPL and increased FC in the right middle temporal gyrus were shown when we set the right cerebellar lobule IX as the seed region. Furthermore, FC of DLPFC-IPL and cerebello-cerebral circuit, as well as ALFF alterations, were significantly correlated with Montreal Cognitive Assessment scores in MSA patients. We also employed whole-brain voxel-based morphometry analysis, but no gray matter atrophy was detected between the patient subgroups. Our findings indicate that altered spontaneous activity in the DLPFC and the cerebellum and disrupted DLPFC-IPL, cerebello-cerebral networks are possible biomarkers of early cognitive decline in MSA patients.

Multimodal striatal neuromarkers in distinguishing parkinsonian variant of multiple system atrophy from idiopathic Parkinson's disease.

AIMS: To develop an automatic method of classification for parkinsonian variant of multiple system atrophy (MSA-P) and Idiopathic Parkinson's disease (IPD) in early to moderately advanced stages based on multimodal striatal alterations and identify the striatal neuromarkers for distinction. METHODS: 77 IPD and 75 MSA-P patients underwent 3.0 T multimodal MRI comprising susceptibility-weighted imaging, resting-state functional magnetic resonance imaging, T1-weighted imaging, and diffusion tensor imaging. Iron-radiomic features, volumes, functional and diffusion scalars of bilateral 10 striatal subregions were calculated and provided to the support vector machine for classification RESULTS: A combination of iron-radiomic features, function, diffusion, and volumetric measures optimally distinguished IPD and MSA-P in the testing dataset (accuracy 0.911 and area under the receiver operating characteristic curves [AUC] 0.927). The diagnostic performance further improved when incorporating clinical variables into the multimodal model (accuracy 0.934 and AUC 0.953). The most crucial factor for classification was the functional activity of the left dorsolateral putamen. CONCLUSION: The machine learning algorithm applied to multimodal striatal dysfunction depicted dorsal striatum and supervening prefrontal lobe and cerebellar dysfunction through the frontostriatal and cerebello-striatal connections and facilitated accurate classification between IPD and MSA-P. The dorsolateral putamen was the most valuable neuromarker for the classification.

Identifying and validating subtypes of Parkinson's disease based on multimodal MRI data via hierarchical clustering analysis.

Objective: We wished to explore Parkinson's disease (PD) subtypes by clustering analysis based on the multimodal magnetic resonance imaging (MRI) indices amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV). Then, we analyzed the differences between PD subtypes. Methods: Eighty-six PD patients and 44 healthy controls (HCs) were recruited. We extracted ALFF and GMV according to the Anatomical Automatic Labeling (AAL) partition using Data Processing and Analysis for Brain Imaging (DPABI) software. The Ward linkage method was used for hierarchical clustering analysis. DPABI was employed to compare differences in ALFF and GMV between groups. Results: Two subtypes of PD were identified. The "diffuse malignant subtype" was characterized by reduced ALFF in the visual-related cortex and extensive reduction of GMV with severe impairment in motor function and cognitive function. The "mild subtype" was characterized by increased ALFF in the frontal lobe, temporal lobe, and sensorimotor cortex, and a slight decrease in GMV with mild impairment of motor function and cognitive function. Conclusion: Hierarchical clustering analysis based on multimodal MRI indices could be employed to identify two PD subtypes. These two PD subtypes showed different neurodegenerative patterns upon imaging.

Rapid quantification of global brain volumetry and relaxometry in patients with multiple sclerosis using synthetic magnetic resonance imaging.

Background: Early pathologic studies have reported that focal areas of gray lesions in the cortex and other gray matter (GM) regions are important in multiple sclerosis (MS) patients. Quantitative magnetic resonance imaging (qMRI) can provide more specific insight into the disease process, progression, and therapeutic response of MS. The purpose of this study was to quantitatively assess the changes of global GM volumetry and relaxometry information simultaneously in MS patients using synthetic MRI. Methods: All MS patients and healthy controls (HCs) were recruited. The Expanded Disability Status Scale (EDSS) scores were obtained from all patients to evaluate the disability progression. Volumetry and relaxometry of the global brain and regional GM were obtained. The quantitative parameters between MS patients and HCs were compared using an analysis of covariance (ANCOVA). The Pearson correlation assessed the correlations between the quantitative parameters and EDSS, illness duration, education in MS patients. Results: Thirty-five MS patients and fifty-two age-matched HCs were enrolled in this prospective case-control study. The global volumetry including white matter volume (WMV), myelin volume (MYV), and brain parenchymal volume (BPV) were all significantly lower in MS patients (WMV: 613.120±65.388 vs. 579.903±68.432 mL; MYV: 151.883±22.766 vs. 192.457±27.381 mL; BPV: 1,136.771±106.126 vs. 1,276.712±107.368 mL), as well as a higher cerebral spinal fluid volume (CSFV) (241.294±81.805 vs. 177.017±39.729 mL) in MS patients than those in HCs. Similarly, brain parenchymal fraction (BPF) and myelin fraction (MYF) were significantly lower in MS patients (BPF: 82.623±5.368 vs. 87.85±2.392 mL; MYF: 11.034±1.529 vs. 13.231±1.465 mL). For regional GM volumetry, multiple regions of MS patients were significantly smaller than those of HCs (P<0.01, corrected). For regional GM relaxometry, the T1, T2, and PD values of multiple regions showed significant differences. Conclusions: These findings suggest that MS patients had global and regional brain volumetry and relaxometry alterations, and the synthetic MRI-derived parameters may be potentially used as specific quantitative markers for the clinic to improve the understanding of MS.

White matter structural network alterations in congenital bilateral profound sensorineural hearing loss children: A graph theory analysis.

Functional magnetic resonance imaging (fMRI) studies have revealed a functional reorganization in patients with sensorineural hearing loss (SNHL). The structural basement of functional changes has also been investigated recently. Graph theory analysis brings a new understanding of the structural connectome and topological features in central neural system diseases. However, little is known about the structural network connectome changes in SNHL patients, especially in children. We explored the differences in topologic organization, rich-club organization, and structural connection between children with congenital bilateral profound SNHL and normal hearing under the age of three using graph theory analysis and probabilistic tractography. Compared with the normal-hearing (NH) group, the SNHL group showed no difference in global and nodal topological parameters. Increased structural connection strength were found in the right cortico-striatal-thalamus-cortical circuity. Decreased cross-hemisphere connections were found between the right precuneus and the left auditory cortex as well as the left subcortical regions. Rich-club organization analysis found increased local connection in the SNHL group. These results revealed structural organizations after hearing deprivation in congenital bilateral profound SNHL children.

Freezing of Gait in Multiple System Atrophy.

Background and Purpose: Freezing of gait (FOG) is a common gait disturbance phenomenon in multiple system atrophy (MSA) patients. The current investigation assessed the incidence FOG in a cross-sectional clinical study, and clinical correlations associated with it. Methods: Ninety-nine MSA patients from three hospitals in China were consecutively enrolled in the study. Eight patients were subsequently excluded from the analysis due to incomplete information. The prevalence of FOG symptoms in the MSA cohort was determined, and clinical manifestations in MSA patients with and without FOG were assessed. Results: Of 91 MSA patients, 60 (65.93%) exhibited FOG. The incidence of FOG increased with disease duration and motor severity and was correlated with modified Hoehn and Yahr (H-Y) stages [odds ratio (OR), 0.54; 95% confidence interval (CI), 0.33-3.92], longer disease duration (OR, 0.54, 95% CI, 0.37-0.78), higher Unified Multiple System Atrophy Rating Scale (UMSARS) score (OR, 0.96, 95% CI, 0.93-0.99), MSA-cerebellum subtype (OR, 2.99, 95% CI, 1.22-7.33), levodopa-equivalent dose (LDED) (OR, 0.998, 95% CI, 0.997-1.00), and higher Scale for the Assessment and Rating of Ataxia (SARA) score (OR, 0.80, 95% CI, 0.72-0.89) (logistic regression). Motor dysfunction was significantly positively associated with lower quality of life scores (p < 0.01). Conclusion: FOG is a common symptom in MSA patients and it is correlated with poor quality of life, disease progression and severity, levodopa-equivalent dose, and cerebellum impairment.

Unilaterally disrupted structural and functional connectivity of the fronto-limbic system in idiopathic hypogonadotropic hypogonadism.

OBJECTIVE: Idiopathic hypogonadotropic hypogonadism (IHH) is rare and can either be associated with normal or defective olfactory sensation, classified as normosmic IHH (nIHH) or Kallmann syndrome (KS). We do not yet understand the central processing pathways in the olfactory system. We aimed to compare the resting-state structural and functional connectivity (FC) of olfactory neural pathways in patients with IHH. We hypotheses that alterations of structural connectivity and FC may exist in the olfactory cortex pathways in IHH patients. DESIGN: STRUCTURAL AND FUNCTIONAL CONNECTIVITY DATA RESULTS BETWEEN TWO GROUPS WERE ANALYZED: Patients: Twenty-five IHH patients (13 nIHH patients and 12 KS patients) were recruited from the Department of Endocrinology and were assessed. A total of 25 age-matched healthy male controls were recruited from the community. MEASUREMENTS: All subjects underwent diffusion tensor imaging and functional magnetic resonance imaging (fMRI) scans. Structural and functional connectivity data analyses were then performed. Pearson's correlation analyses were performed to investigate the correlations between the fractional anisotropy (FA) value and FC strength, showing significant differences among the three groups separately. RESULTS: Compared with the HC group, FA value in the right uncinate fasciculus (UF) decreased significantly in the IHH group. The olfactory cortex FC values of the right gyrus rectus, orbitofrontal cortex (OFC) and right middle temporal gyrus in the IHH group were decreased compared with those in the HC group. Moreover, there were significant negative correlations between right UF FA and olfactory cortex-FC to both the gyrus rectus and OFC within the HC group (p < .05). CONCLUSION: Our findings suggest that alterations of structural and FC support the presence of neurobiological disruptions in IHH patients, particularly a specific structural-functional asymmetry disruption may exist in the olfactory cortex pathways in IHH patients.

Differentiation Between Lipomas and Atypical Lipomatous Tumors of the Extremities Using Radiomics.

BACKGROUND: The differentiation of soft tissue lipomas from atypical lipoma tumors (ALTs) of the extremities is important because of the distinction of the cytogenetic profiles and the treatment decisions. PURPOSE: To investigate a radiomics method to differentiate between lipomas and ALTs of the extremities. STUDY TYPE: Retrospective. POPULATION: Imaging data of 122 patients including 90 cases of lipomas and 32 cases of ALTs. FIELD STRENGTH/SEQUENCE: Axial T1-weighted imaging and fat suppressed T2-weighted imaging at 3.0T MRI. ASSESSMENT: Analysis of variance and the least absolute shrinkage and selection operator methods were used for feature selection and the random forest method was used to build three radiomics models based on T1WI, FS T2WI, and their combination (T1&T2WI). Three independent radiologists classified the tumors based on the subjective assessments. STATISTICAL TESTS: The area under the curve (AUC) of the receiver operating characteristic curve, accuracy, F1-score, specificity, and sensitivity were employed. The differences of the classifiers and discriminating ability of the radiologists and the radiomics model were compared by Delong test. A P value <0.05 was considered significant. Kappa test was used to determine the inter-reader agreements between the radiologists. RESULT: The AUCs were 0.952 (95% confidence interval [CI]: 0.785-0.998), 0.944 (95% CI: 0.774-0.997), and 0.968 (95% CI: 0.809-1) for T1WI, FS T2WI, and T1&T2WI models in testing sets respectively. Delong test showed there were no significant difference between the different radiomics models (P > 0.05). The AUCs of the radiologists were 0.893 (95% CI: 0.824-0.942), 0.831 (95% CI: 0.752-0.893), and 0.893 (95% CI: 0.824-0.94), respectively. There were significant difference between radiomics model and radiologists' model in the training and entire cohorts (P < 0.05) while there were no significant difference in the testing sets (P > 0.05). DATA CONCLUSION: Radiomics has the potential to distinguish between lipomas and ALTs of the extremities and their discrimination ability is no weaker than the senor radiologists. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Decreased Functional Connectivity of Vermis-Ventral Prefrontal Cortex in Bipolar Disorder.

Objectives: To investigate changes in functional connectivity between the vermis and cerebral regions in the resting state among subjects with bipolar disorder (BD). Methods: Thirty participants with BD and 28 healthy controls (HC) underwent the resting state functional magnetic resonance imaging (fMRI). Resting-state functional connectivity (rsFC) of the anterior and posterior vermis was examined. For each participant, rsFC maps of the anterior and posterior vermis were computed and compared across the two groups. Results: rsFC between the whole vermis and ventral prefrontal cortex (VPFC) was significantly lower in the BD groups compared to the HC group, and rsFC between the anterior vermis and the middle cingulate cortex was likewise significantly decreased in the BD group. Limitations: 83.3% of the BD participants were taking medication at the time of the study. Our findings may in part be attributed to treatment differences because we did not examine the effects of medication on rsFC. Further, the mixed BD subtypes in our current study may have confounding effects influencing the results. Conclusions: These rsFC differences of vermis-VPFC between groups may contribute to the BD mood regulation.

Use of machine learning method on automatic classification of motor subtype of Parkinson's disease based on multilevel indices of rs-fMRI.

OBJECTIVE: This study aimed to develop an automatic classifier to distinguish different motor subtypes of Parkinson's disease (PD) based on multilevel indices of resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: Ninety-six PD patients, which included thirty-nine postural instability and gait difficulty (PIGD) subtype and fifty-seven tremor-dominant (TD) subtype, were enrolled and allocated to training and validation datasets with a ratio of 7:3. A total of five types of index, consisting of mean regional homogeneity (mReHo), mean amplitude of low-frequency fluctuation (mALFF), degree of centrality (DC), voxel-mirrored homotopic connectivity (VMHC), and functional connectivity (FC), were extracted. The features were then selected using a two-sample t-test, the least absolute shrinkage and selection operator (LASSO), and Spearman's rank correlation coefficient. Finally, support vector machine (SVM) models based on the separate index and multilevel indices were built, and the performance of models was assessed via the area under the receiver operating characteristic curve (AUC). Feature importance was evaluated using Shapley additive explanation (SHAP) values. RESULTS: The optimal SVM model was obtained based on multilevel rs-fMRI indices, with an AUC of 0.934 in the training dataset and an AUC of 0.917 in the validation dataset. The AUCs of the models based on the separate index were ranged from 0.783 to 0.858 for the training dataset and from 0.713 to 0.792 for the validation dataset. SHAP analysis revealed that functional activity and connectivity in frontal lobe and cerebellum were important features for differentiating PD subtypes. CONCLUSIONS: Our findings demonstrated multilevel rs-fMRI indices could provide more comprehensive information on brain functionalteration. Furthermore, the machine learning method based on multilevel rs-fMRI indices might be served as an alternative approach for automatically classifying clinical subtypes in PD at the individual level.