Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer.

OBJECTIVES: To compare multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion (EMVI) in rectal cancer using different machine learning algorithms and to develop and validate the best diagnostic model. METHODS: We retrospectively analyzed 317 patients with rectal cancer. Of these, 114 were EMVI positive and 203 were EMVI negative. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, diffusion-weighted imaging, and enhanced T1-weighted imaging of rectal cancer, followed by the dimension reduction of the features. Logistic regression, support vector machine, Bayes, K-nearest neighbor, and random forests algorithms were trained to obtain the radiomics signatures. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each radiomics signature. The best radiomics signature was selected and combined with clinical and radiological characteristics to construct a joint model for predicting EMVI. Finally, the predictive performance of the joint model was assessed. RESULTS: The Bayes-based radiomics signature performed well in both the training set and the test set, with the AUCs of 0.744 and 0.738, sensitivities of 0.754 and 0.728, and specificities of 0.887 and 0.918, respectively. The joint model performed best in both the training set and the test set, with the AUCs of 0.839 and 0.835, sensitivities of 0.633 and 0.714, and specificities of 0.901 and 0.885, respectively. CONCLUSIONS: The joint model demonstrated the best diagnostic performance for the preoperative prediction of EMVI in patients with rectal cancer. Hence, it can be used as a key tool for clinical individualized EMVI prediction. KEY POINTS: • Radiomics features from magnetic resonance imaging can be used to predict extramural venous invasion (EMVI) in rectal cancer. • Machine learning can improve the accuracy of predicting EMVI in rectal cancer. • Radiomics can serve as a noninvasive biomarker to monitor the status of EMVI.

Radiomics nomogram based on MRI for predicting white matter hyperintensity progression in elderly adults.

BACKGROUND: White matter hyperintensity (WMH) is widely observed in aging brain and is associated with various diseases. A pragmatic and handy method in the clinic to assess and follow up white matter disease is strongly in need. PURPOSE: To develop and validate a radiomics nomogram for the prediction of WMH progression. STUDY TYPE: Retrospective. POPULATION: Brain images of 193 WMH patients from the Picture Archiving and Communication Systems (PACS) database in the A Medical Center (Zhejiang Provincial People's Hospital). MRI data of 127 WMH patients from the PACS database in the B Medical Center (Zhejiang Lishui People's Hospital) were included for external validation. All of the patients were at least 60 years old. FIELD STRENGTH/SEQUENCE: T1 -fluid attenuated inversion recovery images were acquired using a 3T scanner. ASSESSMENT: WMH was evaluated utilizing the Fazekas scale based on MRI. WMH progression was assessed with a follow-up MRI using a visual rating scale. Three neuroradiologists, who were blinded to the clinical data, assessed the images independently. Moreover, interobserver and intraobserver reproducibility were performed for the regions of interest for segmentation and feature extraction. STATISTICAL TESTS: A receiver operating characteristic (ROC) curve, the area under the curve (AUC) of the ROC was calculated, along with sensitivity and specificity. Also, a Hosmer-Lemeshow test was performed. RESULTS: The AUC of radiomics signature in the primary, internal validation cohort, external validation cohort were 0.886, 0.816, and 0.787, respectively; the specificity were 71.79%, 72.22%, and 81%, respectively; the sensitivity were 92.68%, 87.94% and 78.3%, respectively. The radiomics nomogram in the primary cohort (AUC = 0.899) and the internal validation cohort (AUC = 0.84). The Hosmer-Lemeshow test showed no significant difference between the primary cohort and the internal validation cohort (P > 0.05). The AUC of the radiomics nomogram, radiomics signature, and hyperlipidemia in all patients from the primary and internal validation cohort was 0.878, 0.848, and 0.626, respectively. DATA CONCLUSION: This multicenter study demonstrated the use of a radiomics nomogram in predicting the progression of WMH with elderly adults (an age of at least 60 years) based on conventional MRI. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:535-546.

Radiotherapy-related quality of life in patients with head and neck cancers: a meta-analysis.

OBJECTIVES: To compare effects of intensity-modulated radiotherapy (IMRT) with those of conventional radiotherapy on quality of life (QoL) and severity of xerostomia in patients with head and neck cancer. MATERIAL AND METHODS: PubMed, Cochrane, and Embase databases were searched to July 1, 2019, to identify relevant studies, using the following terms: radiotherapy, head and neck cancer, quality of life, cognition, xerostomia, two-/three-dimensional conformal radiation therapy, IMRT, conformal proton beam radiation therapy, stereotactic radiosurgery, and volumetric modulated arc therapy. The outcomes of interest were QoL measured by global health status; emotional, social, and cognitive function; and severity of xerostomia. RESULTS: Seven studies with a total of 761 patients (n = 369 with IMRT; n = 392 with conventional RT) were included in this study. Median patient age was 18-65 years. IMRT group patients had better global health status (pooled standardized mean difference [SMD] = 0.80, 95% CI 0.26 to 1.35, P = 0.004) and cognitive function (pooled SMD = 0.30, 95% CI 0.06 to 0.54, P = 0.013) than the conventional RT group. Patients receiving IMRT also had significantly lower scores for xerostomia than those receiving conventional RT (pooled SMD = - 0.60, 95% CI - 0.97 to - 0.24, P = 0.001). No differences were found in emotional function (P = 0.531) and social function (P = 0.348) between the two groups. CONCLUSION: IMRT significantly improves QoL and reduces the severity of xerostomia in patients with head and neck cancer. Results of this study provide clinicians with guidelines for decisions on the use of IMRT versus conventional RT.

Benign ependymoma with extensive intracranial and spinal cerebrospinal fluid dissemination: case report and literature review.

Myxopapillary ependymoma (MPE) is a rare variant of ependymoma that is most commonly located in the cauda equina and filum terminale. We present a case of 23-year-old man diagnosed with MPE in the fourth ventricle and sacral canal area with extensive disseminated lesions along the cerebrospinal ventricular system. Additionally, a molecular pathological diagnosis was performed. The patient underwent a craniotomy and a lumbar laminectomy. In the course of 18 months of follow-up, the patient have recovered very well.

MRI-CEUS fusion-guided lymphatic mapping as a preoperative strategy for lymphedema patients undergoing lymphaticovenous anastomosis surgery.

OBJECTIVE: Contrast-enhanced ultrasound (CEUS) is useful in mapping lymphatic vessels in upper limb lymphedema; this study was aimed to evaluate its efficiency in lower limb lymphedema and investigate whether magnetic resonance lymphangiography (MRL) enhance the efficiency of CEUS. METHODS: This retrospective study enrolled 48 patients with lymphedema undergoing lymphaticovenous anastomosis (LVA) surgery who received MRL and/or CEUS in addition to conventional indocyanine green (ICG) lymphangiography. The number of anastomotic sites and the duration per site (DPS) for LVA surgery were described and compared. RESULTS: Among the 48 patients subjected to analysis, it was observed that 12 (25%), 20 (41.67%), and 16 (33.33%) of them received ICG, ICG+CEUS, and ICG+CEUS+MRL, respectively. The ICG+CEUS group demonstrated a significant increase in the number of LVAs (median, 5; range, 4-7), compared with the ICG group (median, 2; range, 1-4) (P < .001). Moreover, the ICG+CEUS+MRL group exhibited a higher number of LVAs (median, 8; range, 7-8.25) compared with both the ICG+CEUS and ICG groups (P < .001). For lower limb lymphedema, the ICG+CEUS+MRL group displayed an elevated number of LVAs (median, 8; interquartile range, 7-9) (P = .003), in contrast to the ICG group (median, 3; interquartile range, 1.75-4.25). Furthermore, the DPS in the ICG+CEUS+MRL group (median, 50.56; interquartile range, 48.13-59.29) (P = .005) exhibited a remarkable decrease when compared with the ICG group (median, 131.25; interquartile range, 86.75-198.13]). CONCLUSIONS: MRL-CEUS fusion demonstrates superior performance in the identification of lymphatic vessels for lymphedema.

Altered dynamic intrinsic brain activity of the default mode network in Alzheimer's disease: A resting-state fMRI study.

Objective: Static regional homogeneity (ReHo) based on the resting-state functional magnetic resonance imaging (rs-fMRI) has been used to study intrinsic brain activity (IBA) in Alzheimer's disease (AD). However, few studies have examined dynamic ReHo (dReHo) in AD. In this study, we used rs-fMRI and dReHo to investigate the alterations in dynamic IBA in patients with AD to uncover dynamic imaging markers of AD. Method: In total, 111 patients with AD, 29 patients with mild cognitive impairment (MCI), and 73 healthy controls (HCs) were recruited for this study ultimately. After the rs-fMRI scan, we calculated the dReHo values using the sliding window method. ANOVA and post hoc two-sample t-tests were used to detect the differences among the three groups. We used the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA) to evaluate the cognitive function of the subjects. The associations between the MMSE score, MoCA score, and dReHo were assessed by the Pearson correlation analysis. Results: Significant dReHo variability in the right middle frontal gyrus (MFG) and right posterior cingulate gyrus (PCG) was detected in the three groups through ANOVA. In post hoc analysis, the AD group exhibited significantly greater dReHo variability in the right MFG than the MCI group. Compared with the HC group, the AD group exhibited significantly increased dReHo variability in the right PCG. Furthermore, dReHo variability in the right PCG was significantly negatively correlated with the MMSE and MoCA scores of patients with AD. Conclusion: Disrupted dynamic IBA in the DMN might be an important characteristic of AD and could be a potential biomarker for the diagnosis or prognosis of AD.

An Effective Brain Imaging Biomarker for AD and aMCI: ALFF in Slow-5 Frequency Band.

BACKGROUND: As a potential brain imaging biomarker, amplitude of low frequency fluctuation (ALFF) has been used as a feature to distinguish patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) from normal controls (NC). However, it remains unclear whether the frequency-dependent pattern of ALFF alterations can effectively distinguish the different phases of the disease. METHODS: In the present study, 52 AD and 50 aMCI patients were enrolled together with 43 NC in total. The ALFF values were calculated in the following three frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) for the three different groups. Subsequently, the local functional abnormalities were employed as features to examine the effect of classification among AD, aMCI and NC using a support vector machine (SVM). RESULTS: We found that the among-group differences of ALFF in the different frequency bands were mainly located in the left hippocampus (HP), right HP, bilateral posterior cingulate cortex (PCC) and bilateral precuneus (PCu), left angular gyrus (AG) and left medial prefrontal cortex (mPFC). When the local functional abnormalities were employed as features, we identified that the ALFF in the slow-5 frequency band showed the highest accuracy to distinguish among the three groups. CONCLUSION: These findings may deepen our understanding of the pathogenesis of AD and suggest that slow-5 frequency band may be helpful to explore the pathogenesis and distinguish the phases of this disease.

Prediction of the progression from mild cognitive impairment to Alzheimer's disease using a radiomics-integrated model.

OBJECTIVE: This study aimed to build and validate a radiomics-integrated model with whole-brain magnetic resonance imaging (MRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). METHODS: 357 patients with MCI were selected from the ADNI database, which is an open-source database for AD with multicentre cooperation, of which 154 progressed to AD during the 48-month follow-up period. Subjects were divided into a training and test group. For each patient, the baseline T1WI MR images were automatically segmented into white matter, gray matter and cerebrospinal fluid (CSF), and radiomics features were extracted from each tissue. Based on the data from the training group, a radiomics signature was built using logistic regression after dimensionality reduction. The radiomics signatures, in combination with the apolipoprotein E4 (APOE4) and baseline neuropsychological scales, were used to build an integrated model using machine learning. The receiver operating characteristics (ROC) curve and data of the test group were used to evaluate the diagnostic accuracy and reliability of the model, respectively. In addition, the clinical prognostic efficacy of the model was evaluated based on the time of progression from MCI to AD. RESULTS: Stepwise logistic regression analysis showed that the APOE4, clinical dementia rating, AD assessment scale, and radiomics signature were independent predictors of MCI progression to AD. The integrated model was constructed based on independent predictors using machine learning. The ROC curve showed that the accuracy of the model in the training and the test sets was 0.814 and 0.807, with a specificity of 0.671 and 0.738, and a sensitivity of 0.822 and 0.745, respectively. In addition, the model had the most significant diagnostic efficacy in predicting MCI progression to AD within 12 months, with an AUC of 0.814, sensitivity of 0.726, and specificity of 0.798. CONCLUSION: The integrated model based on whole-brain radiomics can accurately identify and predict the high-risk population of MCI patients who may progress to AD. Radiomics biomarkers are practical in the precursory stage of such disease.

Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images.

PURPOSE: To explore the clinical feasibility of predicting the efficacy of neoadjuvant chemoradiotherapy (nCRT) for rectal cancer on the basis of texture analysis (TA) of T2-weighted imaging (T2WI). METHODS: The cohort for this prospective study comprised 136 patients with rectal cancer to be treated with nCRT, all of whom underwent three MR scans (pre-, early, and post-nCRT). Treatment efficacy was assessed on the basis of the outcomes of pathologic complete response (pCR) and non-pCR as determined by postoperative pathological examination. Extraction and analysis of texture features in T2WI of defined tumor regions were performed by AK software. Pre- and early-nCRT texture features were selected as potential predictors of outcomes by logistic regression analysis, and a prediction model for pCR was developed. A receiver operating characteristic (ROC) curve was used to assess the predictive power of texture features in pre- and early-nCRT images. RESULTS: Univariate logistic regression analysis demonstrated that the pre-nCRT features of energy, entropy, and skewness, and early-nCRT features of variance, kurtosis, energy, and entropy were independent predictors of pCR. A prediction model incorporating these predictors was constructed by multivariate logistic regression, The AUCs of pre-nCRT, early, and combined models were 0.751, 0.831, and 0.873, respectively; the sensitivities 66, 71, and 75%, respectively; and the specificities 87.22, 86.11, and 91.67%, respectively. CONCLUSIONS: TA of T2WI images can predict the efficacy of nCRT for rectal cancer, possibly providing a new marker of tumor biological response in clinical practice.

MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases.

Synchronous liver metastasis (SLM) remains a major challenge for rectal cancer. Early detection of SLM is a key factor to improve the survival rate of rectal cancer. In this radiomics study, we predicted the SLM based on the radiomics of primary rectal cancer. A total of 328 radiomics features were extracted from the T2WI images of 194 patients. The least absolute shrinkage and selection operator (LASSO) regression was used to reduce the feature dimension and to construct the radiomics signature. after LASSO, principal component analysis (PCA) was used to sort the features of the surplus characteristics, and selected the features of the total contribution of 85%. Then the prediction model was built by linear regression, and the decision curve analysis was used to judge the net benefit of LASSO and PCA. In addition, we used two independent cohorts for training (n = 135) and validation (n = 159). We found that the model based on LASSO dimensionality construction had the maximum net benefit (in the training set (AUC [95% confidence interval], 0.857 [0.787-0.912]) and in the validation set (0.834 [0.714-0.918]). The radiomics nomogram combined with clinical risk factors and LASSO features showed a good predictive performance in the training set (0.921 [0.862-0.961]) and validation set (0.912 [0.809-0.97]). Our study indicated that radiomics based on primary rectal cancer could provide a non-invasive way to predict the risk of SLM in clinical practice.

High-sensitivity neuroimaging biomarkers for the identification of amnestic mild cognitive impairment based on resting-state fMRI and a triple network model.

Many functional magnetic resonance imaging (fMRI) studies have indicated that Granger causality analysis (GCA) is a suitable method for revealing causal effects between brain regions. The purpose of the present study was to identify neuroimaging biomarkers with a high sensitivity to amnestic mild cognitive impairment (aMCI). The resting-state fMRI data of 30 patients with Alzheimer's disease (AD), 14 patients with aMCI, and 18 healthy controls (HC) were evaluated using GCA. This study focused on the "triple networks" concept, a recently proposed higher-order functioning-related brain network model that includes the default-mode network (DMN), salience network (SN), and executive control network (ECN). As expected, GCA techniques were able to reveal differences in connectivity in the three core networks among the three patient groups. The fMRI data were pre-processed using DPARSFA v2.3 and REST v1.8. Voxel-wise GCA was performed using the REST-GCA in the REST toolbox. The directed (excitatory and inhibitory) connectivity obtained from GCA could differentiate among the AD, aMCI and HC groups. This result suggests that analysing the directed connectivity of inter-hemisphere connections represents a sensitive method for revealing connectivity changes observed in patients with aMCI. Specifically, inhibitory within-DMN connectivity from the posterior cingulate cortex (PCC) to the hippocampal formation and from the thalamus to the PCC as well as excitatory within-SN connectivity from the dorsal anterior cingulate cortex (dACC) to the striatum, from the ECN to the DMN, and from the SN to the ECN demonstrated that changes in connectivity likely reflect compensatory effects in aMCI. These findings suggest that changes observed in the triple networks may be used as sensitive neuroimaging biomarkers for the early detection of aMCI.

[Application value of texture analysis of magnetic resonance images in prediction of neoadjuvant chemoradiotherapy efficacy for rectal cancer].

OBJECTIVE: To explore the application value of texture analysis of magnetic resonance images (MRI) in predicting the efficacy of neoadjuvant chemoradiotherapy(nCRT) for rectal cancer. METHODS: A total of 34 rectal cancer patients who were hospitalized at Zhejiang Provincial People's Hospital from February 2015 to April 2017 were prospectively enrolled and received 3.0T MRI examination at pre-nCRT (1 day before nCRT), early stage (at 10-day after nCRT) and middle stage (at 20-day after nCRT). INCLUSION CRITERIA: distance from tumor lower margin to anal edge was less than 12 cm under rectoscope; rectal cancer was confirmed by preoperative pathology; clinical stage was T3 or above; lymph node metastasis existed but without distant metastasis; functions of liver, kidney and heart present no contraindications of operation. EXCLUSION CRITERIA: unfinished nCRT, surgery and three examinations of MRI; image motion artifacts; lack of postoperative pathological results. All the patients underwent rectal cancer long-term three-dimensional radiotherapy and chemotherapy combined with nCRT (oxaliplatin plus capecitabine). The tumor regression grading (TRG) was divided into TRG 0 to 4 grade after nCRT, and TRG 4 was classified as pathological complete remission (pCR); TRG 2 to 3 was classified as partial remission (PR); the rest was no remission (NR). Extraction and analysis of texture features in T2-weighted MR-defined tumor region were performed using Omni Kinetics texture software. The texture values of each time point were statistically analyzed, and the differences of texture values and change differences between pCR and PR+NR, and NR and pCR+PR were compared respectively. Statistically significant texture values were screened and were used in receiver operating characteristic (ROC) curve to assess the prediction of the efficacy of nCRT. RESULTS: Of 34 patients, 21 were males and 13 were females with median age of 49.3 years. Nineteen (55.9%) patients were low rectal adenocarcinoma and 15 (44.1%) patients were middle rectal adenocarcinoma. Nine (26.5%) cases belonged to pCR, 13 (38.2%) belonged to PR, and 12 (35.3%) belonged to NR. Before nCRT, the entropy of tumor area in pCR patients was significantly higher than that in PR+NR patients (7.164±0.272 vs. 6.823±0.309, t=2.925, P=0.006). At the middle stage of nCRT, as compared with PR+NR patients for the texture features of tumor region, the variance (1566±281 vs. 2883±867, t=-4.435, P=0.000) and entropy(5.436±0.934 vs. 6.803±0.577, t=-4.118,P=0.002) of pCR patients were significantly lower; kurtosis(4.800±1.288 vs. 3.206±1.211, t=3.333, P=0.002) and energy (0.016±0.005 vs. 0.010±0.004, t=3.240, P=0.003) of pCR patients were significantly higher. As compared to pCR+PR patients, the kurtosis(2.461±0.931 vs. 4.264±1.205, t=-4.493, P=0.000) and energy (0.011±0.004 vs. 0.014±0.004, t=-3.453, P=0.000) of the NR patients were significantly lower. As for texture change values between early stage and middle stage, the entropy difference was significant between pCR and PR+NR, NR and pCR+PR (1.344±0.819 vs. 0.489±0.319, t=3.047, P=0.014; 0.446±0.213 vs. 0.917±0.677, t=-3.638, P=0.001, respectively). As for texture change values between pre-nCRT and middle stage, variance and entropy differences between pCR and PR+NR (1759±1226 vs. 977±842, t=2.113, P=0.042; 1.728±0.918 vs. 0.524±0.355, t=3.832, P=0.004), and the change values of entropy between NR and pCR+PR (0.475±0.349 vs. 1.044±0.860, t=-2.722, P=0.011) were statistically significant. The above indicators were included in the ROC curve. The results revealed that at the middle stage, entropy value >5.983 indicated the best efficacy for the diagnosis of pCR, with the area under the ROC curve (AUC) of 0.885, the sensitivity of 100%, and the specificity of 66.7%; the energy <0.010 indicated the best AUC for diagnosis of NR was 0.902, with the sensitivity of 91.7% and specificity of 81.8%. CONCLUSIONS: Texture analysis based on T2 weighted images can predict the efficacy of nCRT for rectal cancer. The middle stage of nCRT is the best time of prediction. The entropy and energy of this period are texture parameters having higher predictive ability.

Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that causes the decline of some cognitive impairments. The present study aimed to identify the corpus callosum (CC) radiomic features related to the diagnosis of AD and build and evaluate a classification model. Methods: Radiomics analysis was applied to the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images of 78 patients with AD and 44 healthy controls (HC). The CC, in each subject, was segmented manually and 385 features were obtained after calculation. Then, the feature selection were carried out. The logistic regression model was constructed and evaluated according to identified features. Thus, the model can be used for distinguishing the AD from HC subjects. Results: Eleven features were selected from the three-dimensional T1-weighted MPRAGE images using the LASSO model, following which, the logistic regression model was constructed. The area under the receiver operating characteristic curve values (AUC), sensitivity, specificity, accuracy, precision, and positive and negative predictive values were 0.720, 0.792, 0.500, 0.684, 0.731, 0.731, and 0.583, respectively. Conclusion: The results demonstrated the potential of CC texture features as a biomarker for the diagnosis of AD. This is the first study showing that the radiomics model based on machine learning was a valuable method for the diagnosis of AD.

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment.

Impaired functional connectivity in the Default Mode Network (DMN) may be involved in the progression of Alzheimer's Disease (AD). The Posterior Cingulate Cortex (PCC) is a potential imaging marker for monitoring the progression of AD. Previous studies did not focus on the functional connectivity between the PCC and nodes in regions outside the DMN, but our study is an effort to explore these overlooked functional connections. For collecting data, we used functional Magnetic Resonance Imaging (fMRI) and Granger Causality Analysis (GCA). fMRI provides a non-invasive method for studying the dynamic interactions between the different brain regions. GCA is a statistical hypothesis test for determining whether one-time series is useful in forecasting another. In simple terms, it is judged by comparing the "Known all the information on the last moment, the distribution of the probability of X at this time" and the "Known all the information on the last moment except Y, the distribution of the probability of X at this time", to determine whether there is a causal relationship between Y and X. This definition is based on the complete information source and stationary chronological sequence. The main step of this analysis is to use X and Y to establish the regression equation and draw a causal relationship by a hypothetical test. Since GCA can measure causal effects, we used it to investigate the anisotropy of the functional connectivity and explore the hub function of the PCC. Here, we screened 116 participants for MRI scanning, and after preprocessing the data obtained from neuroimaging, we used GCA to derive the causal relationship of each node. Finally, we concluded that the directed connection is significantly different between the Mild Cognitive Impairment (MCI) and AD groups, both from the PCC to the whole brain and from the whole brain to the PCC.

Directed Functional Connectivity of Posterior Cingulate Cortex and Whole Brain in Alzheimer's Disease and Mild Cognitive Impairment.

BACKGROUND: Impaired functional connectivity in the default mode network (DMN) is supposedly involved in Alzheimer's disease (AD) progression. The posterior cingulate cortex (PCC) might be an imaging marker for monitoring AD progression. OBJECTIVE: To investigate the alterations in the directed functional connectivity between the PCC and whole brain in patients with AD, patients with mild cognitive impairment (MCI), and healthy controls. METHODS: A total of 116 enrolled participants were divided into three groups: AD (n=32), MCI (n=26), and controls (n=58). Using resting-state functional magnetic resonance imaging (rs-fMRI), the directed functional connectivity was studied using Granger causality analysis (GCA). RESULTS: Almost all of the directed functional connections with significant differences were unidirectional. Compared with the NC group, the AD group showed enhanced directed connectivity from the whole brain to the PCC mainly for regions outside the DMN, and reduced connectivity from the PCC to the whole brain mainly for regions inside the DMN. Compared with the NC group, the MCI group showed enhanced directed connectivity from the PCC to the whole brain for the bilateral precuneus and postcentralgyrus, and reduced connectivity from the whole brain to the PCC for regions outside the DMN. Compared with the MCI group, the abnormal directed connectivity in the AD group was predominantly in the left hemisphere, possibly suggesting asymmetric characteristics. CONCLUSION: In patients with AD, the PCC in the DMN shows disorders in receiving and transmitting information, and these abnormalities are directional.

PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI.

The main goal of brain tumor surgery is to maximize tumor resection while minimizing the risk of irreversible postoperative functional sequelae. Eloquent functional areas should be delineated preoperatively, particularly for patients with tumors near eloquent areas. Functional magnetic resonance imaging (fMRI) is a noninvasive technique that demonstrates great promise for presurgical planning. However, specialized data processing toolkits for presurgical planning remain lacking. Based on several functions in open-source software such as Statistical Parametric Mapping (SPM), Resting-State fMRI Data Analysis Toolkit (REST), Data Processing Assistant for Resting-State fMRI (DPARSF) and Multiple Independent Component Analysis (MICA), here, we introduce an open-source MATLAB toolbox named PreSurgMapp. This toolbox can reveal eloquent areas using comprehensive methods and various complementary fMRI modalities. For example, PreSurgMapp supports both model-based (general linear model, GLM, and seed correlation) and data-driven (independent component analysis, ICA) methods and processes both task-based and resting-state fMRI data. PreSurgMapp is designed for highly automatic and individualized functional mapping with a user-friendly graphical user interface (GUI) for time-saving pipeline processing. For example, sensorimotor and language-related components can be automatically identified without human input interference using an effective, accurate component identification algorithm using discriminability index. All the results generated can be further evaluated and compared by neuro-radiologists or neurosurgeons. This software has substantial value for clinical neuro-radiology and neuro-oncology, including application to patients with low- and high-grade brain tumors and those with epilepsy foci in the dominant language hemisphere who are planning to undergo a temporal lobectomy.

Low-Frequency Fluctuations of the Resting Brain: High Magnitude Does Not Equal High Reliability.

The amplitude of low-frequency fluctuation (ALFF) measures low-frequency oscillations of the blood-oxygen-level-dependent signal, characterizing local spontaneous activity during the resting state. ALFF is a commonly used measure for resting-state functional magnetic resonance imaging (rs-fMRI) in numerous basic and clinical neuroscience studies. Using a test-retest rs-fMRI dataset consisting of 21 healthy subjects and three repetitive scans, we found that several key brain regions with high ALFF intensities (or magnitude) had poor reliability. Such regions included the posterior cingulate cortex, the medial prefrontal cortex in the default mode network, parts of the right and left thalami, and the primary visual and motor cortices. The above finding was robust with regard to different sample sizes (number of subjects), different scanning parameters (repetition time) and variations of test-retest intervals (i.e., intra-scan, intra-session, and inter-session reliability), as well as with different scanners. Moreover, the qualitative, map-wise results were validated further with a region-of-interest-based quantitative analysis using "canonical" coordinates as reported previously. Therefore, we suggest that the reliability assessments be incorporated in future ALFF studies, especially for the brain regions with a large ALFF magnitude as listed in our paper. Splitting single data into several segments and assessing within-scan "test-retest" reliability is an acceptable alternative if no "real" test-retest datasets are available. Such evaluations might become more necessary if the data are collected with clinical scanners whose performance is not as good as those that are used for scientific research purposes and are better maintained because the lower signal-to-noise ratio may further dampen ALFF reliability.