Altered intrinsic functional brain architecture in patients with functional constipation: a surface-based network study.

Background: Functional constipation (FCon) is a common functional gastrointestinal disorder (FGID). Studies have indicated a higher likelihood of psychiatric disorders, such as anxiety, depression, sleep disturbances, and impaired concentration, among patients with FCon. However, the underlying pathophysiological mechanisms responsible for these symptoms in FCon patients remain to be fully elucidated. The human brain is a complex network architecture with several fundamental organizational properties. Neurological interactions between gut symptoms and psychiatric issues may be closely associated with these complex networks. Methods: In the present study, a total of 35 patients with FCon and 40 healthy controls (HC) were recruited for a series of clinical examinations and resting-state functional magnetic imaging (RS-fMRI). We employed the surface-based analysis (SBA) approach, utilizing the Schaefer cortical parcellation template and Tikhonov regularization. Graph theoretical analysis (GTA) and functional connectivity (FC) analysis of RS-fMRI were conducted to investigate the aberrant network alterations between the two groups. Additionally, correlation analyses were performed between the network indices and clinical variables in patients with FCon. Results: At the global level, we found altered topological properties and networks in patients with FCon, mainly including the significantly increased clustering coefficient (CP), local efficiency (Eloc), and shortest path length (LP), whereas the decreased global efficiency (Eglob) compared to HC. At the regional level, patients with FCon exhibited increased nodal efficiency in the frontoparietal network (FPN). Furthermore, FC analysis demonstrated several functional alterations within and between the Yeo 7 networks, particularly including visual network (VN), limbic network (LN), default mode network (DMN), and somatosensory-motor network (SMN) in sub-network and large-scale network analysis. Correlation analysis revealed that there were no significant associations between the network metrics and clinical variables in the present study. Conclusion: These results highlight the altered topological architecture of functional brain networks associated with visual perception abilities, emotion regulation, sensorimotor processing, and attentional control, which may contribute to effectively targeted treatment modalities for patients with FCon.

Refined imaging features of culprit plaques improve the prediction of recurrence in intracranial atherosclerotic stroke within the middle cerebral artery territory.

Recurrence is a significant adverse outcome of ischemic stroke (IS), particularly in cases of intracranial arteriosclerosis (ICAS). In this study, we investigated the impact of imaging features of culprit plaque using high-resolution magnetic resonance vessel wall imaging (HR-MR-VWI) on the prediction of IS recurrence. A total of 86 patients diagnosed with ICAS-related IS within the middle cerebral artery (MCA) territory were included, of which 23.25% experienced recurrent IS within one year. Our findings revealed significant differences between the recurrence and non-recurrence groups in terms of age (p = 0.007), diabetes mellitus (p = 0.031), hyperhomocysteinemia (p = 0.021), artery-artery embolism (AAE) infarction (p = 0.019), prominent enhancement (p = 0.013), and surface irregularity of the culprit plaque (p = 0.009). Age (HR = 1.063, p = 0.005), AAE infarction (HR = 5.708, p = 0.008), and prominent enhancement of the culprit plaque (HR = 4.105, p = 0.025) were identified as independent risk factors for stroke recurrence. The areas under the receiver operating characteristic curve (AUCs) for predicting IS recurrence using clinical factors, conventional imaging findings, HR-MR-VWI plaque features, and a combination of clinical and conventional imaging models were 0.728, 0.645, 0.705, and 0.814, respectively. Notably, the combination model demonstrated superior predictive performance with an AUC of 0.870. Similarly, AUC of combination model for predicting IS recurrence in validation cohort which enrolled another 37 patients was 0.865. In conclusion, the presence of obvious enhancement in culprit plaque on HR-MR-VWI is a valuable factor in predicting IS recurrence in ICAS-related strokes within the MCA territory. Furthermore, our combination model, incorporating plaque features, exhibited improved prediction accuracy.

Aberrant intrinsic functional brain networks in patients with functional constipation.

PURPOSE: Patients with functional constipation (FCon) often suffer from mental and psychological problems. To explore the possible neurological interaction, we used resting-state functional magnetic imaging (RS-fMRI) to compare the alterations in intrinsic brain functional networks at multiple levels between patients with FCon and healthy controls (HC). METHODS: Twenty-eight patients with FCon and twenty-nine HC were recruited for a series of examinations and RS-fMRI. Both graph theory analysis and functional connectivity (FC) analysis were used to investigate brain functional alterations between the two groups. Correlation analyses were performed among neuropsychological scores, clinical indexes, and neuroimaging data. RESULTS: Compared with the HC, the assortativity showed significantly increased in global level in patients with FCon. In regional level, we found obviously increased nodal degree and nodal efficiency in somatosensory network (SMN), decreased nodal degree, and increased nodal efficiency in default mode network (DMN) in the FCon group. Furthermore, FC analysis demonstrated several functional alterations within and between the networks, particularly including the SMN and visual network (VN) in sub-network and large-scale network analysis. Moreover, correlation analysis indicated that nodal metrics and aberrant FC among functional brain networks were associated with emotion and scores of constipation in patients with FCon. CONCLUSION: All these findings reflect the differences in intrinsic brain functional networks between FCon and HC. Our study highlighted SMN, DMN, and VN as critical network and may be involved in the neurophysiology of FCon, which may contribute to improve personalized treatment in patients with FCon.

Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

Impaired white matter microstructure associated with severe depressive symptoms in patients with PD.

Depression is a common occurrence in patients with Parkinson's disease (PD); however, its pathophysiology is still unclear. This study assessed the association between the integrity of white matter and depressive symptoms in patients with PD. 67 patients with PD were divided into a non-depressed PD group (ndPD, n = 30) and a depressed PD group (dPD, n = 37). The dPD group was further subdivided into a mild-moderately depressed PD (mdPD, n = 22) and a severely depressed PD group (sdPD, n = 15). Tract-Based Spatial Statistics was used to compare fractional anisotropy (FA) between groups. Region-of-interest analysis was used to explore changes in diffusivity indices in the regions showing FA abnormalities. The sdPD patients exhibited significantly reduced FA in the left superior longitudinal fasciculus, uncinate fasciculus, anterior corona radiata, corticospinal tract, and bilateral inferior fronto-occipital fasciculus when compared with the ndPD patients, but the decreased FA was within a smaller area when compared with the mdPD patients. No significant difference in FA was found between the mdPD and ndPD groups. Among the dPD patients, FA values in the left superior longitudinal fasciculus negatively correlated with BDI scores. Impaired white matter integrity in the prefronto-limbic/temporal circuitry, mainly in the left hemisphere, is associated with severe, but not mild-moderate depressive symptoms in patients with PD.

Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia: A MELD study.

OBJECTIVE: Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome. METHODS: The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom. RESULTS: FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%. SIGNIFICANCE: FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy.

FLAIR and ADC Image-Based Radiomics Features as Predictive Biomarkers of Unfavorable Outcome in Patients With Acute Ischemic Stroke.

At present, it is still challenging to predict the clinical outcome of acute ischemic stroke (AIS). In this retrospective study, we explored whether radiomics features extracted from fluid-attenuated inversion recovery (FLAIR) and apparent diffusion coefficient (ADC) images can predict clinical outcome of patients with AIS. Patients with AIS were divided into a training (n = 110) and an external validation (n = 80) sets. A total of 753 radiomics features were extracted from each FLAIR and ADC image of the 190 patients. Interquartile range (IQR), Wilcoxon rank sum test, and least absolute shrinkage and selection operator (LASSO) were used to reduce the feature dimension. The six strongest radiomics features were related to an unfavorable outcome of AIS. A logistic regression analysis was employed for selection of potential predominating clinical and conventional magnetic resonance imaging (MRI) factors. Subsequently, we developed several models based on clinical and conventional MRI factors and radiomics features to predict the outcome of AIS patients. For predicting unfavorable outcome [modified Rankin scale (mRS) > 2] in the training set, the area under the receiver operating characteristic curve (AUC) of ADC radiomics model was 0.772, FLAIR radiomics model 0.731, ADC and FLAIR radiomics model 0.815, clinical model 0.791, and clinical and conventional MRI model 0.782. In the external validation set, the AUCs for the prediction with ADC radiomics model was 0.792, FLAIR radiomics model 0.707, ADC and FLAIR radiomics model 0.825, clinical model 0.763, and clinical and conventional MRI model 0.751. When adding radiomics features to the combined model, the AUCs for predicting unfavorable outcome in the training and external validation sets were 0.926 and 0.864, respectively. Our results indicate that the radiomics features extracted from FLAIR and ADC can be instrumental biomarkers to predict unfavorable clinical outcome of AIS and would additionally improve predictive performance when adding to combined model.

New Enhancement beyond Radiation Field Improves Survival Prediction in Patients with Post-Treatment High-Grade Glioma.

The imaging signs which can accurately predict survival prognosis after standard treatment of high-grade glioma (HGG) are highly desirable. This study aims to explore the role of new enhancement beyond radiation field (NERF) in the survival prediction in patients with post-treatment HGG. The present study included 142 pathologically confirmed HGG patients who had received standard treatment. NERF, as well as other conventional MR findings and clinical variables, were included in univariate and multivariate analyses for evaluating their impactions on progression-free survival (PFS) and overall survival (OS). Univariate analysis showed that histological grade (p=0.008) and NERF (p=0.001) were the prognostic variables for poor PFS, whereas histological grade (p=0.017), NERF (p=0.001), and new subventricular zone enhancement (nSVZE) (p=0.001) were prognostic variables for poor OS. The multivariate analysis showed that NERF (HR 3.93; 95% CI 1.93-8.01; p=0.001) and nSVZE (HR 3.92; 95% CI 1.95-7.89; p=0.001) were the prognostic variables for poor OS. However, only nSVZE was (HR 3.29; 95% CI 2.04-5.28; p=0.001) the prognostic variable for poor PFS. When combining the NERF with the clinical and other MR variables, the highest AUC (0.924) and specificity (0.899) for predicting poor OS were achieved. The location of new developed enhancements relevant to high dose radiation field appears to be the main determinant of their prognostic value. Our results suggest that the new enhancement beyond radiation field can improve the survival prediction in patients with HGG after standard treatment.

Change in CAIDE Dementia Risk Score and Neuroimaging Biomarkers During a 2-Year Multidomain Lifestyle Randomized Controlled Trial: Results of a Post-Hoc Subgroup Analysis.

The CAIDE (Cardiovascular Risk Factors, Aging and Dementia) Risk Score is a validated tool estimating dementia risk. It was previously associated with imaging biomarkers. However, associations between dementia risk scores (including CAIDE) and dementia-related biomarkers have not been studied in the context of an intervention. This study investigated associations between change in CAIDE score and change in neuroimaging biomarkers (brain magnetic resonance imaging [MRI] and Pittsburgh Compound B-positron emission tomography [PiB-PET] measures) during the 2-year Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) (post-hoc analyses). FINGER targeted at-risk older adults, aged 60-77 years, from the general population. Participants were randomized to either multidomain intervention (diet, exercise, cognitive training, and vascular risk management) or control group (general health advice). Neuroimaging (MRI and PiB-PET) data from baseline and 2-year visits were used. A toal of 112 participants had repeated brain MRI measures (hippocampal, total gray matter, and white matter lesion volumes, and Alzheimer's disease signature cortical thickness). Repeated PiB-PET scans were available for 39 participants. Reduction in CAIDE score (indicating lower dementia risk) during the intervention was associated with less decline in hippocampus volume in the intervention group, but not the control group (Randomization group × CAIDE change interaction ß coefficient = -0.40, p = .02). Associations for other neuroimaging measures were not significant. The intervention may have benefits on hippocampal volume in individuals who succeed in improving their overall risk level as indicated by a reduction in CAIDE score. This exploratory finding requires further testing and validation in larger studies.

Obesity and Brain Vulnerability in Normal and Abnormal Aging: A Multimodal MRI Study.

BACKGROUND: How the relationship between obesity and MRI-defined neural properties varies across distinct stages of cognitive impairment due to Alzheimer's disease is unclear. OBJECTIVE: We used multimodal neuroimaging to clarify this relationship. METHODS: Scans were acquired from 47 patients clinically diagnosed with mild Alzheimer's disease dementia, 68 patients with mild cognitive impairment, and 57 cognitively healthy individuals. Voxel-wise associations were run between maps of gray matter volume, white matter integrity, and cerebral blood flow, and global/visceral obesity. RESULTS: Negative associations were found in cognitively healthy individuals between obesity and white matter integrity and cerebral blood flow of temporo-parietal regions. In mild cognitive impairment, negative associations emerged in frontal, temporal, and brainstem regions. In mild dementia, a positive association was found between obesity and gray matter volume around the right temporoparietal junction. CONCLUSION: Obesity might contribute toward neural tissue vulnerability in cognitively healthy individuals and mild cognitive impairment, while a healthy weight in mild Alzheimer's disease dementia could help preserve brain structure in the presence of age and disease-related weight loss.

Role of Dynamic Susceptibility Contrast Perfusion MRI in Glioma Progression Evaluation.

Accurately and quickly differentiating true progression from pseudoprogression in glioma patients is still a challenge. This study aims to explore if dynamic susceptibility contrast- (DSC-) MRI can improve the evaluation of glioma progression. We enrolled 65 glioma patients with suspected gadolinium-enhancing lesion. Longitudinal MRI follow-up (mean 590 days, range: 210-2670 days) or re-operation (n = 3) was used to confirm true progression (n = 51) and pseudoprogression (n = 14). We assessed the diagnostic performance of each MRI variable and the different combinations. Our results showed that the relative cerebral blood volume (rCBV) in the true progression group (1.094, 95%CI: 1.135-1.636) was significantly higher than that of the pseudoprogression group (0.541 ± 0.154) (p < 0.001). Among the 18 patients who had serial DSC-MRI, the rCBV of the progression group (0.480, 95%CI: 0.173-0.810) differed significantly from pseudoprogression (-0.083, 95%CI: -1.138-0.620) group (p=0.015). With an rCBV threshold of 0.743, the sensitivity and specificity for discriminating true progression from pseudoprogression were 76.5% and 92.9%, respectively. The Cho/Cr and Cho/NAA ratios of the true progression group (2.520, 95%CI: 2.331-2.773; 2.414 ± 0.665, respectively) were higher than those of the pseudoprogression group (1.719 ± 0.664; 1.499 ± 0.500, respectively) ((p=0.001), (p < 0.001), respectively). The areas under ROC curve (AUCs) of enhancement pattern, MRS, and DSC-MRI for the differentiation were 0.782, 0.881, and 0.912, respectively. Interestingly, when combined enhancement pattern, MRS, and DSC-MRI variables, the AUC was 0.965 and achieved sensitivity 90.2% and specificity 100.0%. Our results suggest that DSC-MRI can significantly improve the diagnostic performance for identifying glioma progression. DSC-MRI combined with conventional MRI may promptly distinguish true gliomas progression from pseudoprogression when the suspected gadolinium-enhancing lesion was found, without the need for a long-term follow-up.

Improved Activation and Hemodynamic Response Function of Olfactory fMRI Using Simultaneous Multislice with Reduced TR Acquisition.

OBJECTIVES: The respiration could decrease the time synchronization between odor stimulation and data acquisition, consequently deteriorating the functional activation and hemodynamic response function (HRF) in olfactory functional magnetic resonance imaging (fMRI) with a conventional repetition time (TR). In this study, we aimed to investigate whether simultaneous multislice (SMS) technology with reduced TR could improve the blood oxygen level-dependent (BOLD) activation and optimize HRF modeling in olfactory fMRI. METHODS: Sixteen young healthy subjects with normal olfaction underwent olfactory fMRI on a 3T MRI scanner using a 64 channel head coil. FMRI data were acquired using SMS acceleration at three different TRs: 3000 ms, 1000 ms, and 500 ms. Both metrics of BOLD activation (activated voxels, mean, and maximum t-scores) and the HRF modeling (response height and time to peak) were calculated in the bilateral amygdalae, hippocampi, and insulae. RESULTS: The 500 ms and 1000 ms TRs both significantly improved the number of activated voxels, mean, and maximum t-score in the amygdalae and insulae, compared with a 3000 ms TR (all P < 0.05). But the increase of these metrics in the hippocampi did not reach a statistical significance (all P > 0.05). No significant difference in any BOLD activation metrics between TR 500 ms and 1000 ms was observed in all regions of interest (ROIs) (all P > 0.05). The HRF curves showed that higher response height and shorter time to peak in all ROIs were obtained at 500 ms and 1000 ms TRs compared to 3000 ms TR. TR 500 ms had a more significant effect on response height than TR 1000 ms in the amygdalae (P = 0.017), and there was no significant difference in time to peak between TR 500 ms and 1000 ms in all ROIs (all P > 0.05). CONCLUSIONS: The fast image acquisition technique of SMS with reduced TR could significantly improve the functional activation and HRF curve in olfactory fMRI.

White Matter Hyperintensities Are No Major Confounder for Alzheimer's Disease Cerebrospinal Fluid Biomarkers.

BACKGROUND: The cerebrospinal fluid (CSF) biomarkers amyloid-ß 1-42 (Aß42), total and phosphorylated tau (t-tau, p-tau) are increasingly used to assist in the clinical diagnosis of Alzheimer's disease (AD). However, CSF biomarker levels can be affected by confounding factors. OBJECTIVE: To investigate the association of white matter hyperintensities (WMHs) present in the brain with AD CSF biomarker levels. METHODS: We included CSF biomarker and magnetic resonance imaging (MRI) data of 172 subjects (52 controls, 72 mild cognitive impairment (MCI), and 48 AD patients) from 9 European Memory Clinics. A computer aided detection system for standardized automated segmentation of WMHs was used on MRI scans to determine WMH volumes. Association of WMH volume with AD CSF biomarkers was determined using linear regression analysis. RESULTS: A small, negative association of CSF Aß42, but not p-tau and t-tau, levels with WMH volume was observed in the AD (r2 = 0.084, p = 0.046), but not the MCI and control groups, which was slightly increased when including the distance of WMHs to the ventricles in the analysis (r2 = 0.105, p = 0.025). Three global patterns of WMH distribution, either with 1) a low, 2) a peak close to the ventricles, or 3) a high, broadly-distributed WMH volume could be observed in brains of subjects in each diagnostic group. CONCLUSION: Despite an association of WMH volume with CSF Aß42 levels in AD patients, the occurrence of WMHs is not accompanied by excess release of cellular proteins in the CSF, suggesting that WMHs are no major confounder for AD CSF biomarker assessment.

White Matter Changes on Diffusion Tensor Imaging in the FINGER Randomized Controlled Trial.

BACKGROUND: Early pathological changes in white matter microstructure can be studied using the diffusion tensor imaging (DTI). It is not only important to study these subtle pathological changes leading to cognitive decline, but also to ascertain how an intervention would impact the white matter microstructure and cognition in persons at-risk of dementia. OBJECTIVES: To study the impact of a multidomain lifestyle intervention on white matter and cognitive changes during the 2-year Finnish Geriatric Intervention Study to prevent Cognitive Impairment and Disability (FINGER), a randomized controlled trial in at-risk older individuals (age 60-77 years) from the general population. METHODS: This exploratory study consisted of a subsample of 60 FINGER participants. Participants were randomized to either a multidomain intervention (diet, exercise, cognitive training, and vascular risk management, n = 34) or control group (general health advice, n = 26). All underwent baseline and 2-year brain DTI. Changes in fractional anisotropy (FA), diffusivity along domain (F1) and non-domain (F2) diffusion orientations, mean diffusivity (MD), axial diffusivity (AxD), radial diffusivity (RD), and their correlations with cognitive changes during the 2-year multidomain intervention were analyzed. RESULTS: FA decreased, and cognition improved more in the intervention group compared to the control group (p < 0.05), with no significant intergroup differences for changes in F1, F2, MD, AxD, or RD. The cognitive changes were significantly positively related to FA change, and negatively related to RD change in the control group, but not in the intervention group. CONCLUSION: The 2-year multidomain FINGER intervention may modulate white matter microstructural alterations.

Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline.

The importance of early interventions in Alzheimer's disease (AD) emphasizes the need to accurately and efficiently identify at-risk individuals. Although many dementia prediction models have been developed, there are fewer studies focusing on detection of brain pathology. We developed a model for identification of amyloid-PET positivity using data on demographics, vascular factors, cognition, APOE genotype, and structural MRI, including regional brain volumes, cortical thickness and a visual medial temporal lobe atrophy (MTA) rating. We also analyzed the relative importance of different factors when added to the overall model. The model used baseline data from the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) exploratory PET sub-study. Participants were at risk for dementia, but without dementia or cognitive impairment. Their mean age was 71 years. Participants underwent a brain 3T MRI and PiB-PET imaging. PiB images were visually determined as positive or negative. Cognition was measured using a modified version of the Neuropsychological Test Battery. Body mass index (BMI) and hypertension were used as cardiovascular risk factors in the model. Demographic factors included age, gender and years of education. The model was built using the Disease State Index (DSI) machine learning algorithm. Of the 48 participants, 20 (42%) were rated as Aß positive. Compared with the Aß negative group, the Aß positive group had a higher proportion of APOE ε4 carriers (53 vs. 14%), lower executive functioning, lower brain volumes, and higher visual MTA rating. AUC [95% CI] for the complete model was 0.78 [0.65-0.91]. MRI was the most effective factor, especially brain volumes and visual MTA rating but not cortical thickness. APOE was nearly as effective as MRI in improving detection of amyloid positivity. The model with the best performance (AUC 0.82 [0.71-0.93]) was achieved by combining APOE and MRI. Our findings suggest that combining demographic data, vascular risk factors, cognitive performance, APOE genotype, and brain MRI measures can help identify Aß positivity. Detecting amyloid positivity could reduce invasive and costly assessments during the screening process in clinical trials.

Early neutrophil count relates to infarct size and fatal outcome after large hemispheric infarction.

AIMS: To investigate the relationship between peripheral leukocyte dynamics and the outcome of large hemispheric infarction (LHI) patients. METHODS: Patients with acute LHI admitted to the neuro-intensive care unit of Xuanwu Hospital from 2013 to 2017 were prospectively enrolled and followed up for 6 months after LHI. RESULTS: A total of 84 LHI patients were included, 38 patients suffered brain herniation and 20 patients died from stroke. Compared to patients with benign course, LHI patients with fatal outcome showed larger infarcts and more severe brain edema (P < .01), as well as increased WBC and neutrophil counts throughout the first week after stroke (P < .05). Correlation analysis revealed that neutrophil counts on D2 after LHI positively correlated with infarct and edema volumes measured from CT/MRI (R2  = 0.22 and R2  = 0.15, P < .01) and negatively correlated with Glasgow Coma Scale (ρ = -0.234, P < .05). Patients with D2 neutrophils > 7.14 × 109 /L had higher risk of brain herniation [odds ratio (OR) = 7.5, 95% CI: 2.0-28.1, P = .001], and patients with D2 neutrophils > 7.79 × 109 /L had a higher risk of death (OR = 5.8, 95% CI: 1.2-27.0, P = .015). CONCLUSION: Early peripheral neutrophil count after stroke relates to infarct size and the fatal outcome of LHI patients, which might help guiding acute LHI management such as reduction of intracranial pressure and potential antiinflammatory therapy in the future.

Cerebrospinal Fluid and MRI Biomarkers in Neurodegenerative Diseases: A Retrospective Memory Clinic-Based Study.

BACKGROUND: Cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI) biomarkers of neurodegenerative diseases are relatively sensitive and specific in highly curated research cohorts, but proper validation for clinical use is mostly missing. OBJECTIVE: We studied these biomarkers in a novel memory clinic cohort with a variety of different neurodegenerative diseases. METHODS: This study consisted of 191 patients with subjective or objective cognitive impairment who underwent neurological, CSF biomarker (Aß42, p-tau, and tau) and T1-weighted MRI examinations at Kuopio University Hospital. We assessed CSF and imaging biomarkers, including structural MRI focused on volumetric and cortical thickness analyses, across groups stratified based on different clinical diagnoses, including Alzheimer's disease (AD), frontotemporal dementia, dementia with Lewy bodies, Parkinson's disease, vascular dementia, and mild cognitive impairment (MCI), and subjects with no evidence of neurodegenerative disease underlying the cognitive symptoms. Imaging biomarkers were also studied by profiling subjects according to the novel amyloid, tau, and, neurodegeneration (AT(N)) classification. RESULTS: Numerous imaging variables differed by clinical diagnosis, including hippocampal, amygdalar and inferior lateral ventricular volumes and entorhinal, lingual, inferior parietal and isthmus cingulate cortical thicknesses, at a false discovery rate (FDR)-corrected threshold for significance (analysis of covariance; p < 0.005). In volumetric comparisons by AT(N) profile, hippocampal volume significantly differed (p < 0.001) between patients with normal AD biomarkers and patients with amyloid pathology. CONCLUSION: Our analysis suggests that CSF and MRI biomarkers function well also in clinical practice across multiple clinical diagnostic groups in addition to AD, MCI, and cognitively normal groups.

Dural Venous Sinuses: What We Need to Know.

BACKGROUND: The dural venous sinuses (DVS), in general, are frequently asymmetrical and display far more anatomical variations than arterial systems. A comprehensive study of the anatomy and variants of the DVS can help surgeons in the preoperative evaluation and management as well as minimizing possible complications in the following treatment. METHODS: The current review was designed to provide a general overview of the normal anatomy and notable variants of the cerebral venous system as surveyed from the available literature. The pros and cons of different multimodal imaging methods for investigating DVS are also outlined. Finally, cases of various pathological entities are illustrated from our clinical practice. CONCLUSION: There are many anatomical variations and lesions involving the DVS. MRI examination can provide essential information both on anatomical variation and morphological or functional change of the offending DVS in most circumstances. Multimodal non-invasive venography protocols may become a feasible alternative to the classical digital subtraction angiography and would improve the diagnostic accuracy in future studies.