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Because of the strong dependence on the values for the input parameters and the cluster shape, as well as the difficulties in quantifying the precipitation in constructing landslide susceptibility maps by employing existing clustering algorithms, we propose a novel method based on an Ordering Points to Identify the Clustering Structure (OPTICS) algorithm using the Hausdorff distance (OA-HD). The OA-HD algorithm distributes mapping units into many subclasses with similar characteristic values for topography and geology. To obtain more optimal subclasses, the HD was adopted to quantify precipitation. The K-medoids algorithm grouped these subclasses into five susceptibility levels according to the values of landslide density in each subclass. Applying the innovative integrated algorithms to the study area significantly improves the landslide susceptibility assessment, especially in a large study area. The method suggests new insights for better assessing landslide susceptibility in a large study area.

Hippocampal morphological change is one of the main hallmarks of Alzheimer's disease (AD). However, whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment (MCI) to AD dementia and whether these features provide any neurobiological foundation remains unclear. The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging (MRI) markers for AD. Multivariate classifier-based support vector machine (SVM) analysis provided individual-level predictions for distinguishing AD patients (n = 261) from normal controls (NCs; n = 231) with an accuracy of 88.21% and intersite cross-validation. Further analyses of a large, independent the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 1228) reinforced these findings. In MCI groups, a systemic analysis demonstrated that the identified features were significantly associated with clinical features (e.g., apolipoprotein E (APOE) genotype, polygenic risk scores, cerebrospinal fluid (CSF) Aβ, CSF Tau), and longitudinal changes in cognition ability; more importantly, the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up. These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI, and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus. The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI.

Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD‐associated functional brain alterations using one of the world's largest resting‐state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta‐analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default‐mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus (p

Mounting evidence suggests that function and connectivity of the striatum is disrupted in schizophrenia1–5. We have developed a new hypothesis-driven neuroimaging biomarker for schizophrenia identification, prognosis and subtyping based on functional striatal abnormalities (FSA). FSA scores provide a personalized index of striatal dysfunction, ranging from normal to highly pathological. Using inter-site cross-validation on functional magnetic resonance images acquired from seven independent scanners (n = 1,100), FSA distinguished individuals with schizophrenia from healthy controls with an accuracy exceeding 80% (sensitivity, 79.3%; specificity, 81.5%). In two longitudinal cohorts, inter-individual variation in baseline FSA scores was significantly associated with antipsychotic treatment response. FSA revealed a spectrum of severity in striatal dysfunction across neuropsychiatric disorders, where dysfunction was most severe in schizophrenia, milder in bipolar disorder, and indistinguishable from healthy individuals in depression, obsessive-compulsive disorder and attention-deficit hyperactivity disorder. Loci of striatal hyperactivity recapitulated the spatial distribution of dopaminergic function and the expression profiles of polygenic risk for schizophrenia. In conclusion, we have developed a new biomarker to index striatal dysfunction and established its utility in predicting antipsychotic treatment response, clinical stratification and elucidating striatal dysfunction in neuropsychiatric disorders. A new cross-validated neuroimaging biomarker that reflects striatal dysfunctioning can be used to distinguish patients with schizophrenia from healthy controls, and is associated with treatment response to antipsychotics.

Objective To predict vascular endothelial growth factor (VEGF) expression in patients with diffuse gliomas using radiomic analysis. Materials and methods Preoperative magnetic resonance images were retrospectively obtained from 239 patients with diffuse gliomas (World Health Organization grades II–IV). The patients were randomly assigned to a training group (n = 160) or a validation group (n = 79) at a 2:1 ratio. For each patient, a total of 431 radiomic features were extracted. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature selection. A machine-learning model for predicting VEGF status was then developed using the selected features and a support vector machine classifier. The predictive performance of the model was evaluated in both groups using receiver operating characteristic curve analysis, and correlations between selected features were assessed. Results Nine radiomic features were selected to generate a VEGF-associated radiomic signature of diffuse gliomas based on the mRMR algorithm. This radiomic signature consisted of two first-order statistics or related wavelet features (Entropy and Minimum) and seven textural features or related wavelet features (including Cluster Tendency and Long Run Low Gray Level Emphasis). The predictive efficiencies measured by the area under the curve were 74.1% in the training group and 70.2% in the validation group. The overall correlations between the 9 radiomic features were low in both groups. Conclusions Radiomic analysis facilitated efficient prediction of VEGF status in diffuse gliomas, suggesting that using tumor-derived radiomic features for predicting genomic information is feasible.

The rapid expansion of oil palm is a major driver of deforestation and other associated damage to the climate and ecosystem in tropical regions, especially Southeast Asia. It is therefore necessary to precisely detect and monitor oil palm plantations to safeguard the ecosystem services and biodiversity of tropical forests. Compared with optical data, which are vulnerable to cloud cover, the Sentinel-1 dual-polarization C-band synthetic aperture radar (SAR) acquires global observations under all weather conditions and times of day and shows good performance for oil palm detection in the humid tropics. However, because accurately distinguishing mature and young oil palm trees by using optical and SAR data is difficult and considering the strong dependence on the input parameter values when detecting oil palm plantations by employing existing classification algorithms, we propose an innovative method to improve the accuracy of classifying the oil palm type (mature or young) and detecting the oil palm planting area in Sumatra by fusing Landsat-8 and Sentinel-1 images. We extract multitemporal spectral characteristics, SAR backscattering values, vegetation indices, and texture features to establish different feature combinations. Then, we use the random forest algorithm based on improved grid search optimization (IGSO-RF) and select optimal feature subsets to establish a classification model and detect oil palm plantations. Based on the IGSO-RF classifier and optimal features, our method improved the oil palm detection accuracy and obtained the best model performance (OA = 96.08% and kappa = 0.9462). Moreover, the contributions of different features to oil palm detection are different; nevertheless, the optimal feature subset performed the best and demonstrated good potential for the detection of oil palm plantations.

In history, every occurrence of a desert locust plague has brought a devastating blow to local agriculture. Analyses of the potential geographic distribution and migration paths of desert locusts can be used to better monitor and provide early warnings about desert locust outbreaks. By using environmental data from multiple remote-sensing data sources, we simulate the potential habitats of desert locusts in Africa, Asia and Europe in this study using a logistic regression model that was developed based on desert locust monitoring records. The logistic regression model showed high accuracy, with an average training area under the curve (AUC) value of 0.84 and a kappa coefficient of 0.75. Our analysis indicated that the temperature and leaf area index (LAI) play important roles in shaping the spatial distribution of desert locusts. A model analysis based on data for six environmental variables over the past 15 years predicted that the potential habitats of desert locust present a periodic movement pattern between 40°N and 30°S latitude. The area of the potential desert locust habitat reached a maximum in July, with a suitable area exceeding 2.77 × 107 km2 and located entirely between 0°N and 40°N in Asia-Europe and Africa. In December, the potential distribution of desert locusts reached its minimum area at 0.68 × 107 km2 and was located between 30°N and 30°S in Asia and Africa. According to the model estimates, desert locust-prone areas are distributed in northern Ethiopia, South Sudan, northwestern Kenya, the southern Arabian Peninsula, the border area between India and Pakistan, and the southern Indian Peninsula. In addition, desert locusts were predicted to migrate from east to west between these areas and in Africa between 10°N and 17°N. Countries in these areas should closely monitor desert locust populations and respond rapidly.

Objective: We aimed to identify a radiomic signature to be used as a noninvasive biomarker of prognosis in patients with lower-grade gliomas (LGGs) and to reveal underlying biological processes through comprehensive radiogenomic investigation. Methods: We extracted 55 radiomic features from T2-weighted images of 233 patients with LGGs (training cohort: n = 85; validation cohort: n = 148). Univariate Cox regression and linear risk score formula were applied to generate a radiomic-based signature. Gene ontology analysis of highly expressed genes in the high-risk score group was conducted to establish a radiogenomic map. A nomogram was constructed for individualized survival prediction. Results: The six-feature radiomic signature stratified patients in the training cohort into low- or high-risk groups for overall survival (P = 0.0018). This result was successfully verified in the validation cohort (P = 0.0396). Radiogenomic analysis revealed that the prognostic radiomic signature was associated with hypoxia, angiogenesis, apoptosis, and cell proliferation. The nomogram resulted in high prognostic accuracy (C-index: 0.92, C-index: 0.70) and favorable calibration for individualized survival prediction in the training and validation cohorts. Conclusions: Our results suggest a great potential for the use of radiomic signature as a biological surrogate in providing prognostic information for patients with LGGs.

Background In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to explore the internal mediation pathway among genetic factor, brain features and cognitive scores in a large Chinese dataset. Methods Gray matter (GM) volume, fractional amplitude of low-frequency fluctuations (fALFF), and 4522 schizophrenia-susceptible single nucleotide polymorphisms (SNP) from 905 Chinese subjects were jointly analyzed, to investigate the multimodal association. Based on the identified imaging-genetic pattern, correlations with cognition and mediation analysis were then conducted to reveal the potential mediation pathways. Findings One linked imaging-genetic pattern was identified to be group discriminative, which was also associated with working memory performance. Particularly, GM reduction in thalamus, putamen and bilateral temporal gyrus in schizophrenia was associated with fALFF decrease in medial prefrontal cortex, both were also associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including CSMD1, CNTNAP2, DCC, GABBR2 etc. This linked pattern was also replicated in an independent cohort (166 subjects), which although showed certain age and clinical differences with the discovery cohort. A further mediation analysis suggested that GM alterations significantly mediated the association from SNP to fALFF, while fALFF mediated the association from SNP and GM to working memory performance. Interpretation This study has not only verified the impaired imaging-genetic association in schizophrenia, but also initially revealed a potential genetic-brain-cognition mediation pathway, indicating that polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus could further affect cognition in schizophrenia.

Objective The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature. Methods In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS. Results There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P, Highlights • We developed a non-invasive model for the prediction of PFS in patients with lower-grade gliomas. • We further revealed the biological processes underlying the radiomic signature by using comprehensive radiogenomic analysis. • PFS of lower-grade gliomas could be predicted effectively based on the radiomics model.

Cognitive impairment and psychiatric symptoms are common in patients with Cushing's disease (CD) owing to elevated levels of glucocorticoids. Molecular neuroimaging methods may help to detect changes in the brain of patients with CD. The aim of this study was to investigate the characteristics of brain metabolism and its association with serum cortisol level in CD. We compared brain metabolism, as measured using [18F]-fluorodeoxyglucose positron emission tomography (FDG PET), between 92 patients with CD and 118 normal subjects on a voxel-wise basis. Pearson correlation was performed to evaluate the association between cerebral FDG uptake and serum cortisol level in patients with CD. We demonstrated that certain brain regions in patients with CD showed significantly increased FDG uptake, including the basal ganglia, anteromedial temporal lobe, thalamus, precentral cortex, and cerebellum. The clusters that demonstrated significantly decreased uptake were mainly located in the medial and lateral frontal cortex, superior and inferior parietal lobule, medial occipital cortex, and insular cortex. The metabolic rate of the majority of these regions was found to be significantly correlated with the serum cortisol level. Our findings may help to explain the underlying mechanisms of cognitive impairment and psychiatric symptoms in patients exposed to excessive glucocorticoids and evaluate the efficacy of treatments during follow-up., Highlights • Hypercortisolism leads to metabolic changes in specific brain regions of CD patients. • These brain regions involve in the regulation of cortisol and the symptoms of CD. • The metabolism of 6 specific brain regions is correlated with cortisol level.

Interactions among different brain regions are usually examined through functional connectivity (FC) analysis, which is exclusively based on measuring pairwise correlations in activities. However, interactions beyond the pairwise level, that is, higher-order interactions (HOIs), are vital in understanding the behavior of many complex systems. So far, whether HOIs exist among brain regions and how they can affect the brain's activities remains largely elusive. To address these issues, here, we analyzed blood oxygenation level-dependent (BOLD) signals recorded from six typical macroscopic functional networks of the brain in 100 human subjects (46 males and 54 females) during the resting state. Through examining the binarized BOLD signals, we found that HOIs within and across individual networks were both very weak regardless of the network size, topology, degree of spatial proximity, spatial scales, and whether the global signal was regressed. To investigate the potential mechanisms underlying the weak HOIs, we analyzed the dynamics of a network model and also found that HOIs were generally weak within a wide range of key parameters provided that the overall dynamic feature of the model was similar to the empirical data and it was operating close to a linear fluctuation regime. Our results suggest that weak HOI may be a general property of brain's macroscopic functional networks, which implies the dominance of pairwise interactions in shaping brain activities at such a scale and warrants the validity of widely used pairwise-based FC approaches.SIGNIFICANCE STATEMENTTo explain how activities of different brain areas are coordinated through interactions is essential to revealing the mechanisms underlying various brain functions. Traditionally, such an interaction structure is commonly studied using pairwise-based functional network analyses. It is unclear whether the interactions beyond the pairwise level (higher-order interactions or HOIs) play any role in this process. Here, we show that HOIs are generally weak in macroscopic brain networks. We also suggest a possible dynamical mechanism that may underlie this phenomenon. These results provide plausible explanation for the effectiveness of widely used pairwise-based approaches in analyzing brain networks. More importantly, it reveals a previously unknown, simple organization of the brain's macroscopic functional systems.

Background: Hippocampal morphological change is one of the main hallmarks of Alzheimer's disease (AD). The primary aim of this study is to explore whether radiomics feature is a robust biomarker for AD and further explore the biological basis of those features.Methods: Hippocampal radiomic features were extracted for classification and prediction using a support vector machine (SVM) with 1943 subjects (multi-site).Results: Multivariate classifier-based SVM analysis provided individual-level predictions for distinguishing AD patients (N=261) from normal controls (NCs; N=231) with accuracy=88.21% with inter-site cross-validation. Further analyses of a large, independent ADNI dataset (N=1228) reinforced these findings. In mild cognitive impairment (MCI) groups, a systemic analysis demonstrated that the identified features were significantly associated with clinical features (e.g., apolipoprotein E (APOE) genotype, polygenic risk scores, cerebrospinal fluid (CSF) Aβ, CSF Tau), and longitudinal changes in cognition ability; more importantly, the radiomic features have a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up.Conclusion: These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical applications in AD/MCI.

BackgroundSchizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated.AimsTo investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia.MethodGenomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant.ResultsThe imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus–medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia.ConclusionsThese findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.

Additional file 3. The feature extraction method used in the current study.

Chronic exposure to elevated levels of glucocorticoids can exert a neurotoxic effect in patients, possibly manifesting as molecular imaging alterations in patients. The aim of this study was to investigate the potential association between brain metabolism and elevated hormone level using 18F-fluorodeoxyglucose positron emission tomography. We retrospectively enrolled 92 consecutive patients with confirmed diagnosis of Cushing's disease. A voxel-based analysis was performed to investigate the association between cerebral 18F-fluorodeoxyglucose uptake and serum cortisol level. Relatively impaired metabolism of specific brain regions correlated with serum cortisol level was found. Specifically, notable correlations were found in the hippocampus, amygdala, and cerebellum, regions considered to be involved in the regulation and central action of glucocorticoids. Moreover, some hormone-associated regions were found in the frontal and occipital cortex, possibly mediating the cognitive changes seen in Cushing's disease. Our findings link patterns of perturbed brain metabolism relates to individual hormone level, thus presenting a substrate for cognitive disturbances seen in Cushing's disease patients, as well as in other conditions with abnormal cortisol levels., Highlights • Specific brain regions correlated with serum cortisol level was found. • Some of these brain regions validate previous findings. • Some other brain regions could be new targets for further studies. • These findings may help to evaluate the brain functions and efficacy of treatments.

Background: We hypothesize that the anterior insula is important for maintenance of awareness. Here, we explored the functional connectivity alterations of the anterior insula with changes in the consciousness level or over time in patients with disorders of consciousness (DOC) and determined potential correlation with clinical outcomes.Methods: We examined 20 participants (9 patients with DOC and 11 healthy controls). Each patient underwent resting-state functional magnetic resonance imaging (rs-fMRI) and a standardized Coma Recovery Scale-Revised (CRS-R) assessment on the same day. We categorized the patients according to the prognosis: those who emerged from a minimally conscious state (recovery group, n = 4) and those who remained in the unconscious state (unrecovery group, n = 5). Two rs-fMRI scans were obtained from all patients, and the second scan of patients in the recovery group was obtained after they regained consciousness. We performed seed-based fMRI analysis and selected the left ventral agranular insula (vAI) and dorsal agranular insula (dAI) as the regions of interest. Correlations with CRS-R were determined with the Spearman's correlation coefficient.Results: Compared with healthy controls, the functional connectivity between dAI and gyrus rectus of patients who recovered was significantly increased (p < 0.001, cluster-wise family-wise error rate [FWER] < 0.05). The second rs-fMRI scan of patients who remained with DOC showed a significant decreased functional connectivity between the dAI to contralateral insula, pallidum, bilateral inferior parietal lobule (IPL), precentral gyrus, and middle cingulate cortex (p < 0.001, cluster-wise FWER < 0.05) as well as the functional connectivity between vAI to caudate and cingulum contrast to controls (p < 0.001, cluster-wise FWER < 0.05). Finally, the functional connectivity strength of dAI-temporal pole (Spearman r = 0.491, p < 0.05) and dAI-IPL (Spearman r = 0.579, p < 0.05) were positively correlated with CRS-R scores in all DOC patients. The connectivity of dAI-IPL was also positively correlated with clinical scores in the recovery group (Spearman r = 0.807, p < 0.05).Conclusions: Our findings indicate that the recovery of consciousness is associated with an increased connectivity of the dAI to IPL and temporal pole. This possibly highlights the role of the insula in human consciousness. Moreover, longitudinal variations in dAI-IPL and dAI-temporal pole connectivity may be potential hallmarks in the outcome prediction of DOC patients.

Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fractional anisotropy (FA) from diffusion MRI, and functional network connectivity (FNC) resulted from group independent component analysis, were jointly analyzed by a data-driven multivariate fusion method. Results suggest that a widely distributed network disruption appears in SZ patients, with synchronous changes in both functional and structural regions, especially the basal ganglia network, salience network (SAN), and the frontoparietal network. Such a multimodal coalteration was also replicated in another independent Chinese sample (40 SZs, 66 HCs). Our results on auditory verbal hallucination (AVH) also provide evidence for the hypothesis that prefrontal hypoactivation and temporal hyperactivation in SZ may lead to failure of executive control and inhibition, which is relevant to AVH. In addition, impaired working memory performance was found associated with GM reduction and FA decrease in SZ in prefrontal and superior temporal area, in both discovery and replication datasets. In summary, by leveraging multiple imaging and clinical information into one framework to observe brain in multiple views, we can integrate multiple inferences about SZ from large-scale population and offer unique perspectives regarding the missing links between the brain function and structure that may not be achieved by separate unimodal analyses.

Necrosis is a hallmark feature of glioblastoma (GBM). This study investigated the prognostic role of necrotic patterns in GBM using fractal dimension (FD) and lacunarity analyses of magnetic resonance imaging (MRI) data and evaluated the role of lacunarity in the biological processes leading to necrosis. We retrospectively reviewed clinical and MRI data of 95 patients with GBM. FD and lacunarity of the necrosis on MRI were calculated by fractal analysis and subjected to survival analysis. We also performed gene ontology analysis in 32 patients with available RNA-seq data. Univariate analysis revealed that FD 0.46 significantly correlated with poor progression-free survival (p = 0.006 and p = 0.012, respectively) and overall survival (p = 0.008 and p = 0.005, respectively). Multivariate analysis revealed that both parameters were independent factors for unfavorable progression-free survival (p = 0.001 and p = 0.015, respectively) and overall survival (p = 0.002 and p = 0.007, respectively). Gene ontology analysis revealed that genes positively correlated with lacunarity were involved in the suppression of apoptosis and necrosis-associated biological processes. We demonstrate that the fractal parameters of necrosis in GBM can predict patient survival and are associated with the biological processes of tumor necrosis.

Background P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images. Methods Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a training (n = 180) or validation (n = 92) set. A total of 431 radiomic features were extracted from each patient. The lest absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomic signature construction. Subsequently, a machine-learning model to predict p53 status was established using the selected features and a Support Vector Machine classifier. The predictive performance of all individual features and the model was calculated using receiver operating characteristic curves in both the training and validation sets. Results The p53-related radiomic signature was built using the LASSO algorithm; this procedure consisted of four first-order statistics or related wavelet features (including Maximum, Median, Minimum, and Uniformity), a shape and size-based feature (Spherical Disproportion), and ten textural features or related wavelet features (including Correlation, Run Percentage, and Sum Entropy). The prediction accuracies based on the area under the curve were 89.6% in the training set and 76.3% in the validation set, which were better than individual features. Conclusions These results demonstrate that MR image texture features are predictive of p53 mutation status in lower-grade gliomas. Thus, our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis., Highlights • We established a p53-related radiomic signature in lower-grade gliomas based on LASSO algorithm. • We developed a machine-learning model using the radiomic signature and a support vector machine. • P53 mutation status of lower-grade gliomas was predicted effectively based on our machine-learning model.

Alzheimer’s disease (AD) patients and those with high-risk mild cognitive impairment are increasingly considered to have dysfunction syndromes. Large-scale network studies based on neuroimaging techniques may provide additional insight into AD pathophysiology. The aim of the present study is to evaluate the impaired network functional connectivity with the disease progression. For this purpose, we explored altered functional connectivities based on previously well-defined brain areas that comprise the five key functional systems [the default mode network (DMN), dorsal attention network (DAN), control network (CON), salience network (SAL), sensorimotor network (SMN)] in 35 with AD and 27 with mild cognitive impairment (MCI) subjects, compared with 27 normal cognitive subjects. Based on three levels of analysis, we found that intra- and inter-network connectivity were impaired in AD. Importantly, the interaction between the sensorimotor and attention functions was first attacked at the MCI stage and then extended to the key functional systems in the AD individuals. Lower cognitive ability (lower MMSE scores) was significantly associated with greater reductions in intra- and inter-network connectivity across all patient groups. These profiles indicate that aberrant intra- and inter-network dysfunctions might be potential biomarkers or predictors of AD progression and provide new insight into AD pathophysiology.

The purpose of our study was to investigate whether the whole-brain functional connectivity pattern exhibits disease severity-related alterations in patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Resting-state functional magnetic resonance imaging data were acquired in 27 MCI subjects, 35 AD patients, and 27 age- and gender-matched subjects with normal cognition (NC). Interregional functional connectivity was assessed based on a predefined template which parcellated the brain into 90 regions. Altered whole-brain functional connectivity patterns were identified via connectivity comparisons between the AD and NC subjects. Finally, the relationship between functional connectivity strength and cognitive ability according to the mini-mental state examination (MMSE) was evaluated in the MCI and AD groups. Compared with the NC group, the AD group exhibited decreased functional connectivities throughout the brain. The most significantly affected regions included several important nodes of the default mode network and the temporal lobe. Moreover, changes in functional connectivity strength exhibited significant associations with disease severity-related alterations in the AD and MCI groups. The present study provides novel evidence and will facilitate meta-analysis of whole-brain analyses in AD and MCI, which will be critical to better understand the neural basis of AD.