Tag: statistics

J Alzheimers Dis. 2022 Feb 28. doi: 10.3233/JAD-215596. Online ahead of print.

ABSTRACT

BACKGROUND: Mounting evidence shows that the neuropathological burdens manifest preference in affecting brain regions during the dynamic progression of Alzheimer’s disease (AD). Since the distinct brain regions are physically wired by white matter fibers, it is reasonable to hypothesize the differential spreading pattern of neuropathological burdens may underlie the wiring topology, which can be characterized using neuroimaging and network science technologies.

OBJECTIVE: To study the dynamic spreading patterns of neuropathological events in AD.

METHODS: We first examine whether hub nodes with high connectivity in the brain network (assemble of white matter wirings) are susceptible to a higher level of pathological burdens than other regions that are less involved in the process of information exchange in the network. Moreover, we propose a novel linear mixed-effect model to characterize the multi-factorial spreading process of neuropathological burdens from hub nodes to non-hub nodes, where age, sex, and APOE4 indicators are considered as confounders. We apply our statistical model to the longitudinal neuroimaging data of amyloid-PET and tau-PET, respectively.

RESULTS: Our meta-data analysis results show that 1) AD differentially affects hub nodes with a significantly higher level of pathology, and 2) the longitudinal increase of neuropathological burdens on non-hub nodes is strongly correlated with the connectome distance to hub nodes rather than the spatial proximity.

CONCLUSION: The spreading pathway of AD neuropathological burdens might start from hub regions and propagate through the white matter fibers in a prion-like manner.

PMID:35253761 | DOI:10.3233/JAD-215596

J Alzheimers Dis. 2022 Mar 2. doi: 10.3233/JAD-215667. Online ahead of print.

ABSTRACT

BACKGROUND: The differentiation of behavioral variant frontotemporal dementia (bvFTD) from early-onset Alzheimer’s disease (EOAD) by clinical criteria can be inaccurate. The volumetric quantification of clinically available magnetic resonance (MR) brain scans may facilitate early diagnosis of these neurodegenerative dementias.

OBJECTIVE: To determine if volumetric quantification of brain MR imaging can identify persons with bvFTD from EOAD.

METHODS: 3D T1 MR brain scans of 20 persons with bvFTD and 45 with EOAD were compared using Neuroreader to measure subcortical, and lobar volumes, and Volbrain for hippocampal subfields. Analyses included: 1) discriminant analysis with leave one out cross-validation; 2) input of predicted probabilities from this process into a receiver operator characteristics (ROC) analysis; and 3) Automated linear regression to identify predictive regions.

RESULTS: Both groups were comparable in age and sex with no statistically significant differences in symptom duration. bvFTD had lower volume percentiles in frontal lobes, thalamus, and putamen. EOAD had lower parietal lobe volumes. ROC analyses showed 99.3% accuracy with Neuroreader percentiles and 80.2% with subfields. The parietal lobe was the most predictive percentile. Although there were differences in hippocampal (particularly left CA2-CA3) subfields, it did not add to the discriminant analysis.

CONCLUSION: Percentiles from an MR based volumetric quantification can help differentiate between bvFTD from EOAD in routine clinical care. Use of hippocampal subfield volumes does not enhance the diagnostic separation of these two early-onset dementias.

PMID:35253765 | DOI:10.3233/JAD-215667

Clin Hemorheol Microcirc. 2022 Mar 3. doi: 10.3233/CH-211324. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aims to investigate the application value of three-dimensional arterial spin labeling (ASL) perfusion imaging in detecting cerebral hemodynamics of neonates with hypoxic-ischemic encephalopathy (HIE).

METHODS: Sixty normal full-term neonates and 60 HIE neonates were enrolled in this study and were respectively divided into three groups: the 1-3 days group, the 4-7 days group, and the 8-15 days group. The brains of these neonates were scanned with the 3D ASL sequence, and cerebral blood flow (CBF) images were obtained. The CBF values of the bilateral symmetrical brain regions and brain stem were measured on CBF images, and the values were averaged. The cerebral blood flow of HIE neonates in the 1-3 days group, the 4-7 days group, and the 8-15 days group was compared with normal neonates at matched ages, and the characteristics of cerebral hemodynamics in HIE neonates at different ages were summarized.

RESULTS: The CBF values of the basal ganglia, thalamus, and brainstem in the 1-3 days HIE group were higher than normal neonates at matched ages, and the CBF value of the frontal lobe was lower than the normal group, and the differences were statistically significant (P < 0.05). The CBF values of the basal ganglia, thalamus, corona radiata, and frontal lobe in the 4-7 days HIE group were lower than the normal group, and the differences were statistically significant (P < 0.05). There were no significant differences in CBF values of different brain regions between the 8-15 days HIE and normal groups (P > 0.05).

CONCLUSION: Early hyperperfusion of the basal ganglia and thalamus is helpful for early diagnosis and prognosis of HIE.

PMID:35253735 | DOI:10.3233/CH-211324

J Alzheimers Dis. 2022 Feb 28. doi: 10.3233/JAD-215568. Online ahead of print.

ABSTRACT

BACKGROUND: Mild cognitive impairment (MCI), which is generally regarded as the prodromal stage of Alzheimer’s disease (AD), is associated with morphological changes in brain structures, particularly the hippocampus. However, the indicators for characterizing the deformation of hippocampus in conventional methods are not precise enough and ignore the evolution information with the course of disease.

OBJECTIVE: The purpose of this study was to investigate the temporal evolution pattern of MCI and predict the conversion of MCI to AD by using the multivariate morphometry statistics (MMS) as fine features.

METHODS: First, we extracted MMS features from MRI scans of 64 MCI converters (MCIc), 81 MCI patients who remained stable (MCIs), and 90 healthy controls (HC). To make full use of the time information, the dynamic MMS (DMMS) features were defined. Then, the areas with significant differences between pairs of the three groups were analyzed using statistical methods and the atrophy/expansion were identified by comparing the metrics. In parallel, patch selection, sparse coding, dictionary learning and maximum pooling were used for the dimensionality reduction and the ensemble classifier GentleBoost was used to classify MCIc and MCIs.

RESULTS: The longitudinal analysis revealed that the atrophy of both MCIc and MCIs mainly distributed in dorsal CA1, then spread to subiculum and other regions gradually, while the atrophy area of MCIc was larger and more significant. And the introduction of longitudinal information promoted the accuracy to 91.76% for conversion prediction.

CONCLUSION: The dynamic information of hippocampus holds a huge potential for understanding the pathology of MCI.

PMID:35253759 | DOI:10.3233/JAD-215568

Front Aging Neurosci. 2022 Feb 16;14:793129. doi: 10.3389/fnagi.2022.793129. eCollection 2022.

ABSTRACT

BACKGROUND AND PURPOSE: For patients with severe spontaneous intracerebral hemorrhage on antiplatelet therapy (patients with APT-SICH), postoperative rebleeding (PR) is an important cause of poor outcomes after surgery. As impacted by coagulation disorder caused by APT, patients with APT-SICH are likely to suffer from PR. This study aimed to assess the risk of PR in patients with APT-SICH receiving emergency surgery using a novel coagulation classification.

METHODS: This prospective, multicenter cohort study consecutively selected patients with APT-SICH between September 2019 and March 2021. The preoperative coagulation factor function was recorded, and the platelet function was assessed using thrombelastography. Based on platelet and coagulation factor function, a novel four-type coagulation classification, i.e., Type I (severe coagulation disorder), Type IIa (low platelet reserve capacity), Type IIb (normal coagulation), and Type III (hypercoagulation), was presented. The primary outcome was PR, defined as the rebleeding in the operative region or new intracerebral hemorrhage correlated with the operation.

RESULTS: Of the included 197 patients with APT-SICH, PR occurred in 40 patients (20.3%). The novel coagulation classification categorized 28, 32, 122, and 15 patients into Type I, Type IIa, Type IIb, and Type III, respectively. The Type I patients had the highest incident rate of PR (39.3 per 100 persons), followed by the Type IIa patients (31.3 per 100 persons). In the PR-related analysis, the large hematoma volume (hazard ratio (HR): 1.02; 95% CI: 1.02-1.03; p < 0.001), Type I (HR: 9.72; 95% CI: 1.19-79.67; p = 0.034), and Type IIa (HR: 8.70; 95% CI: 1.09-69.61; p = 0.041) were correlated with the highest risk of PR. The coagulation classification could discriminate the PR patients from no PR (NPR) patients (p < 0.001), and it outperformed the conventional coagulation assessment (only considering platelet count and coagulation factor function) (c-statistic, 0.72 vs. 0.55).

CONCLUSION: The novel coagulation classification could discriminate the patients with APT-SICH with the highest risk of PR preoperatively. For the Type I and Type IIa patients, emergency surgery should be performed carefully.

PMID:35250539 | PMC:PMC8888928 | DOI:10.3389/fnagi.2022.793129

Front Aging Neurosci. 2022 Feb 17;14:821789. doi: 10.3389/fnagi.2022.821789. eCollection 2022.

ABSTRACT

Amnestic mild cognitive impairment (aMCI) and sporadic Alzheimer’s disease (AD) are multifactorial conditions resulting from a complex crosstalk among multiple molecular and biological processes. The present study investigates the association of variants localized in genes and miRNAs with aMCI and AD, which may represent susceptibility, prognostic biomarkers or multi-target treatment options for such conditions. We included 371 patients (217 aMCI and 154 AD) and 503 healthy controls, which were genotyped for a panel of 120 single nucleotide polymorphisms (SNPs) and, subsequently, analyzed by statistical, bioinformatics and machine-learning approaches. As a result, 21 SNPs were associated with aMCI and 13 SNPs with sporadic AD. Interestingly, a set of variants shared between aMCI and AD displayed slightly higher Odd Ratios in AD with respect to aMCI, highlighting a specific risk trajectory linking aMCI to AD. Some of the associated genes and miRNAs were shown to interact within the signaling pathways of APP (Amyloid Precursor Protein), ACE2 (Angiotensin Converting Enzyme 2), miR-155 and PPARG (Peroxisome Proliferator Activated Receptor Gamma), which are known to contribute to neuroinflammation and neurodegeneration. Overall, results of this study increase insights concerning the genetic factors contributing to the neuroinflammatory and neurodegenerative mechanisms underlying aMCI and sporadic AD. They have to be exploited to develop personalized approaches based on the individual genetic make-up and multi-target treatments.

PMID:35250545 | PMC:PMC8892382 | DOI:10.3389/fnagi.2022.821789

Front Comput Neurosci. 2022 Feb 18;15:759254. doi: 10.3389/fncom.2021.759254. eCollection 2021.

ABSTRACT

In this work, we extend an influential statistical model based on the spatial classical receptive field (CRF) and non-classical receptive field (nCRF) interactions (Coen-Cagli et al., 2012) to explain the typical orientation adaptation effects observed in V1. If we assume that the temporal adaptation modifies the “state” of the model, the spatial statistical model can explain all of the orientation adaptation effects in the context of neuronal output using small and large grating observed in neurophysiological experiments in V1. The “state” of the model represents the internal parameters such as the prior and the covariance trained on a mixed dataset that totally determine the response of the model. These two parameters, respectively, reflect the probability of the orientation component and the connectivity among neurons between CRF and nCRF. Specifically, we have two key findings: First, neural adapted results using a small grating that just covers the CRF can be predicted by the change of the prior of our model. Second, the change of the prior can also predict most of the observed results using a large grating that covers both CRF and nCRF of a neuron. However, the prediction of the novel attractive adaptation using large grating covering both CRF and nCRF also necessitates the involvement of a connectivity change of the center-surround RFs. In addition, our paper contributes a new prior-based winner-take-all (WTA) working mechanism derived from the statistical-based model to explain why and how all of these orientation adaptation effects can be predicted by relying on this spatial model without modifying its structure, a novel application of the spatial model. The research results show that adaptation may link time and space by changing the “state” of the neural system according to a specific adaptor. Furthermore, different forms of stimulus used for adaptation can cause various adaptation effects, such as an a priori shift or a connectivity change, depending on the specific stimulus size.

PMID:35250523 | PMC:PMC8895385 | DOI:10.3389/fncom.2021.759254

Front Neurosci. 2022 Feb 16;15:796530. doi: 10.3389/fnins.2021.796530. eCollection 2021.

ABSTRACT

BACKGROUND: Stroke is an important cause of cognitive impairment. Rich club organization, a highly interconnected network brain core region, is closely related to cognition. We hypothesized that the disturbance of rich club organization exists in patients with post-stroke cognitive impairment (PSCI).

METHODS: We collected data on resting-state functional magnetic resonance imaging (rs-fMRI) with 21 healthy controls (HC), 16 hemorrhagic stroke (hPSCI), and 21 infarct stroke (iPSCI). 3D shape features and first-order statistics of stroke lesions were extracted using 3D slicer software. Additionally, we assessed cognitive function using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE).

RESULTS: Normalized rich club coefficients were higher in hPSCI and iPSCI than HC at low-degree k-levels (k = 1-8 in iPSCI, k = 2-8 in hPSCI). Feeder and local connections were significantly decreased in PSCI patients versus HC, mainly distributed in salience network (SN), default-mode network (DMN), cerebellum network (CN), and orbitofrontal cortex (ORB), especially involving the right and left caudate with changed nodal efficiency. The feeder and local connections of significantly between-group difference were positively related to MMSE and MoCA scores, primarily distributed in the sensorimotor network (SMN) and visual network (VN) in hPSCI, SN, and DMN in iPSCI. Additionally, decreased local connections and low-degree ϕ_norm(k) were correlated to 3D shape features and first-order statistics of stroke lesions.

CONCLUSION: This study reveals the disrupted low-degree level rich club organization and relatively preserved functional core network in PSCI patients. Decreased feeder and local connections in cognition-related networks (DMN, SN, CN, and ORB), particularly involving the caudate nucleus, may offer insight into pathological mechanism of PSCI patients. The shape and signal features of stroke lesions may provide an essential clue for the damage of functional connectivity and the whole brain networks.

PMID:35250435 | PMC:PMC8890030 | DOI:10.3389/fnins.2021.796530

Bull Earthq Eng. 2022;20(3):1519-1565. doi: 10.1007/s10518-021-01303-w. Epub 2022 Jan 10.

ABSTRACT

The creation of building exposure models for seismic risk assessment is frequently challenging due to the lack of availability of detailed information on building structures. Different strategies have been developed in recent years to overcome this, including the use of census data, remote sensing imagery and volunteered graphic information (VGI). This paper presents the development of a building-by-building exposure model based exclusively on openly available datasets, including both VGI and census statistics, which are defined at different levels of spatial resolution and for different moments in time. The initial model stemming purely from building-level data is enriched with statistics aggregated at the neighbourhood and city level by means of a Monte Carlo simulation that enables the generation of full realisations of damage estimates when using the exposure model in the context of an earthquake scenario calculation. Though applicable to any other region of interest where analogous datasets are available, the workflow and approach followed are explained by focusing on the case of the German city of Cologne, for which a scenario earthquake is defined and the potential damage is calculated. The resulting exposure model and damage estimates are presented, and it is shown that the latter are broadly consistent with damage data from the 1978 Albstadt earthquake, notwithstanding the differences in the scenario. Through this real-world application we demonstrate the potential of VGI and open data to be used for exposure modelling for natural risk assessment, when combined with suitable knowledge on building fragility and accounting for the inherent uncertainties.

PMID:35250417 | PMC:PMC8887924 | DOI:10.1007/s10518-021-01303-w

Comp Clin Path. 2022 Feb 28:1-4. doi: 10.1007/s00580-022-03334-4. Online ahead of print.

ABSTRACT

Lupus is an autoimmune disease that has various manifestations in various organs. One of the manifestations of lupus is lupus nephritis (LN), which often causes kidney failure and death. Cytokines play an essential role in the pathogenesis of LN and might be helpful for LN biomarkers. This study aimed to evaluate urine TNF-like weak inducer of apoptosis (TWEAK) for detecting LN since this is not an invasive procedure and is more cost-effective. The gold standard procedure for diagnosing LN needs a biopsy of the kidney. However, the procedure is invasive, high cost, and takes time. Thus, a biomarker from urine is needed for early diagnosis of LN. This research conducted was cross-sectional. The total participants were 57, consisting of 29 lupus nephritis and 28 lupus without nephritis. TWEAK levels were determined by ELISA method; urine protein, urine erythrocyte, and leukocyte were examined by a urine autoanalyzer. Statistical analysis using Mann-Whitney, Spearman correlation, Kruskal-Wallis, ROC curve analysis, and a 2 × 2 contingency table. This study showed a significant difference in TWEAK levels between lupus nephritis and lupus without nephritis (p < 0.05), but no significant difference between TWEAK level and renal domain scores of SLEDAI. There were significant correlations between TWEAK level and urine erythrocyte and urine protein, but there was no significant correlation with urine leukocytes. The sensitivity and specificity of TWEAK for determining LN were 72.4% and 72.5%, respectively, with AUC 0.77. TWEAK had a good diagnostic test for detecting lupus nephritis and substantially correlated with urine erythrocyte and urine protein.

PMID:35250424 | PMC:PMC8884517 | DOI:10.1007/s00580-022-03334-4