Diffusion Tensor MRI Structural Connectivity and PET Amyloid Burden in Preclinical Autosomal Dominant Alzheimer Disease: The DIAN Cohort.

Background Pathologic evidence of Alzheimer disease (AD) is detectable years before onset of clinical symptoms. Imaging-based identification of structural changes of the brain in people at genetic risk for early-onset AD may provide insights into how genes influence the pathologic cascade that leads to dementia. Purpose To assess structural connectivity differences in cortical networks between cognitively normal autosomal dominant Alzheimer disease (ADAD) mutation carriers versus noncarriers and to determine the cross-sectional relationship of structural connectivity and cortical amyloid burden with estimated years to symptom onset (EYO) of dementia in carriers. Materials and Methods In this exploratory analysis of a prospective trial, all participants enrolled in the Dominantly Inherited Alzheimer Network between January 2009 and July 2014 who had normal cognition at baseline, T1-weighted MRI scans, and diffusion tensor imaging (DTI) were analyzed. Amyloid PET imaging using Pittsburgh compound B was also analyzed for mutation carriers. Areas of the cerebral cortex were parcellated into three cortical networks: the default mode network, frontoparietal control network, and ventral attention network. The structural connectivity of the three networks was calculated from DTI. General linear models were used to examine differences in structural connectivity between mutation carriers and noncarriers and the relationship between structural connectivity, amyloid burden, and EYO in mutation carriers. Correlation network analysis was performed to identify clusters of related clinical and imaging markers. Results There were 30 mutation carriers (mean age ± standard deviation, 34 years ± 10; 17 women) and 38 noncarriers (mean age, 37 years ± 10; 20 women). There was lower structural connectivity in the frontoparietal control network in mutation carriers compared with noncarriers (estimated effect of mutation-positive status, -0.0266; P = .04). Among mutation carriers, there was a correlation between EYO and white matter structural connectivity in the frontoparietal control network (estimated effect of EYO, -0.0015, P = .01). There was no significant relationship between cortical global amyloid burden and EYO among mutation carriers (P > .05). Conclusion White matter structural connectivity was lower in autosomal dominant Alzheimer disease mutation carriers compared with noncarriers and correlated with estimated years to symptom onset. Clinical trial registration no. NCT00869817 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by McEvoy in this issue.

Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer's disease.

The heterogeneity of Alzheimer's disease contributes to the high failure rate of prior clinical trials. We analyzed 5-year longitudinal outcomes and biomarker data from 562 subjects with mild cognitive impairment (MCI) from two national studies (ADNI) using a novel multilayer clustering algorithm. The algorithm identified homogenous clusters of MCI subjects with markedly different prognostic cognitive trajectories. A cluster of 240 rapid decliners had 2-fold greater atrophy and progressed to dementia at almost 5 times the rate of a cluster of 184 slow decliners. A classifier for identifying rapid decliners in one study showed high sensitivity and specificity in the second study. Characterizing subgroups of at risk subjects, with diverse prognostic outcomes, may provide novel mechanistic insights and facilitate clinical trials of drugs to delay the onset of AD.

Clusters of male and female Alzheimer's disease patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

This paper presents homogeneous clusters of patients, identified in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data population of 317 females and 342 males, described by a total of 243 biological and clinical descriptors. Clustering was performed with a novel methodology, which supports identification of patient subpopulations that are homogeneous regarding both clinical and biological descriptors. Properties of the constructed clusters clearly demonstrate the differences between female and male Alzheimer's disease patient groups. The major difference is the existence of two male subpopulations with unexpected values of intracerebral and whole brain volumes.

Homogeneous clusters of Alzheimer's disease patient population.

BACKGROUND: Identification of biomarkers for the Alzheimer's disease (AD) is a challenge and a very difficult task both for medical research and data analysis. METHODS: We applied a novel clustering tool with the goal to identify subpopulations of the AD patients that are homogeneous in respect of available clinical as well as in respect of biological descriptors. RESULTS: The main result is identification of three clusters of patients with significant problems with dementia. The evaluation of properties of these clusters demonstrates that brain atrophy is the main driving force of dementia. The unexpected result is that the largest subpopulation that has very significant problems with dementia has besides mild signs of brain atrophy also large ventricular, intracerebral and whole brain volumes. Due to the fact that ventricular enlargement may be a consequence of brain injuries and that a large majority of patients in this subpopulation are males, a potential hypothesis is that such medical status is a consequence of a combination of previous traumatic events and degenerative processes. CONCLUSIONS: The results may have substantial consequences for medical research and clinical trial design. The clustering methodology used in this study may be interesting also for other medical and biological domains.

The Influence of Corticosteroids on Heart Rate Variablity in Acute Cervical Spinal Cord Injury.

Heart rate variability (HRV) gives information on the sympathetic-parasympathetic autonomic balance. The aim of the study was to analyze sympathovagal balance after acute spinal cord injury (SCI), demonstrated by linear measures in time and frequency domain of HRV and to analyze the effect of corticosteroids on HRV parameters in SCI. The study included 40 tetraplegic patients with acute SCI and 40 healthy subjects as control group. In the SCI group, 29 patients received and 11 patients did not receive corticosteroid therapy. All patients underwent 24-hour Holter monitoring for evaluation of HRV. Cardiac autonomic balance was evaluated by analysis of HRV in time and frequency domain. Sympathovagal balance (LF/HF) was significantly reduced in the groups of acute SCI patients, both with and without corticosteroid therapy, as compared with controls. However, there was no statistically significant difference between the two SCI groups (1.74 (0524) with and 1.75 (0534) without corticosteroid therapy). This study showed the sympathovagal balance to be altered in the acute phase of cervical spinal cord trauma. Finally, there was no effect of corticosteroid therapy on HRV parameters in SCI patients.

A novel way of integrating rule-based knowledge into a web ontology language framework.

Web ontology language (OWL), used in combination with the Protégé visual interface, is a modern standard for development and maintenance of ontologies and a powerful tool for knowledge presentation. In this work, we describe a novel possibility to use OWL also for the conceptualization of knowledge presented by a set of rules. In this approach, rules are represented as a hierarchy of actionable classes with necessary and sufficient conditions defined by the description logic formalism. The advantages are that: the set of the rules is not an unordered set anymore, the concepts defined in descriptive ontologies can be used directly in the bodies of rules, and Protégé presents an intuitive tool for editing the set of rules. Standard ontology reasoning processes are not applicable in this framework, but experiments conducted on the rule sets have demonstrated that the reasoning problems can be successfully solved.

Control of heart rate by the autonomic nervous system in acute spinal cord injury.

Spinal cord injury may cause loss of cardiovascular reflexes mediated by sympathetic drive due to interruption in the supraspinal control of spinal sympathetic motoneurons. The aim of this study was to analyze sympathovagal balance after acute spinal cord injury demonstrated by linear measures in time and frequency domain of heart rate variability (HRV). The study included 40 tatraplegic patients after acute spinal cord injury and 40 healthy subjects as controls. Cardiac autonomic balance was evaluated by HRV analysis in time and frequency domain. The ratio of low to high frequencies (LF/HF) was statistically significantly (Mann-Whitney U = 0.0; Z = -7.7; P < 0.001) different between the group with cervical spine injuries 0.41 (0158) and control group 1.71 (1875). LH/HF was significantly reduced in the group of patients with acute trauma. This study established HRV analysis by linear methods as an objective measure of normal and abnormal function of the autonomic nervous system. In conclusion, spinal cord injury causes dysfunction of the autonomic cardiovascular regulation and leads to disturbances of the modulatory sympathetic activity on the cardiovascular system. The HRV parameters analyzed indicate decreased but still present sympathetic activity and suggest that descending and ascending fibers of the sympathetic nervous system in isolated segment are undamaged, although without supraspinal control after acute spinal cord injury.

Heart rate variability and nonlinear dynamic analysis in patients with stress-induced cardiomyopathy.

Complexity-based analyses may quantify abnormalities in heart rate variability (HRV). The aim of this study was to investigate the clinical and prognostic significances of dynamic HRV changes in patients with stress-induced cardiomyopathy Takotsubo syndrome (TS) by means of linear and nonlinear analysis. Patients with TS were included in study after complete noninvasive and invasive cardiovascular diagnostic evaluation and compared to an age and gender matched control group of healthy subjects. Series of R-R interval and of ST-T interval values were obtained from 24-h ECG recordings after digital sampling. HRV analysis was performed by 'range rescaled analysis' to determine the Hurst exponent, by detrended fluctuation analysis to quantify fractal long-range correlation properties, and by approximate entropy to assess time-series predictability. Short- and long-term fractal-scaling exponents were significantly higher in patients with TS in acute phases, opposite to lower approximate entropy and Hurst exponent, but all variables normalized in a few weeks. Dynamic HRV analysis allows assessing changes in complexity features of HRV in TS patients during the acute stage, and to monitor recovery after treatment, thus complementing traditional ECG and clinically analysis.

CSM-SD: methodology for contrast set mining through subgroup discovery.

This paper addresses a data analysis task, known as contrast set mining, whose goal is to find differences between contrasting groups. As a methodological novelty, it is shown that this task can be effectively solved by transforming it to a more common and well-understood subgroup discovery task. The transformation is studied in two learning settings, a one-versus-all and a pairwise contrast set mining setting, uncovering the conditions for each of the two choices. Moreover, the paper shows that the explanatory potential of discovered contrast sets can be improved by offering additional contrast set descriptors, called the supporting factors. The proposed methodology has been applied to uncover distinguishing characteristics of two groups of brain stroke patients, both with rapidly developing loss of brain function due to ischemia:those with ischemia caused by thrombosis and by embolism, respectively.

Semantic web ontology utilization for heart failure expert system design.

In this work we present the usage of semantic web knowledge representation formalism in combination with general purpose reasoning for building a medical expert system. The properties of the approach have been studied on the example of the knowledge base construction for decision support tasks in the heart failure domain. The work consisted of descriptive knowledge presentation in the ontological form and its integration with the heart failure procedural knowledge. In this setting instance checking in description logic represents the main process of the expert system reasoning.

[Arterial hypertension in Croatia. Results of EH-UH study]. / Arterijska hipertenzija u Hrvatskoj. Rezultati EH-UH studije.

Arterial hypertension (AH) is the most important independent risk factor of cardiovascular diseases. The prevalence of AH is higher than it was several decades before, and in Europe it is approximately 40%. A higher prevalence of AH has been reported in Europe than in the United States and Canada. According to the results of epidemiology of arterial hypertension in Croatia study (EH-UH), the prevalence of AH in Croatia is 37.5%. Women are more aware, they were treated more often, and blood pressure control was more frequently achieved than in men. However, body mass index and socioeconomic factors have a greater influence on blood pressure values and prevalence of AH in women. A low level of health education and inappropriate treatment are the most important reasons for the poor blood pressure control observed. Measures of primary prevention should be performed continuously and adjusted to special population subgroups. As an increase of obesity and hypertension was observed in school children, these measures should start at that age.

HEARTFAID: A knowledge based platform of services for supporting medical-clinical management of heart failure within elderly population.

HEARTFAID is a research and development project aimed at devising, developing and validating an innovative knowledge based platform of services, able to improve early diagnosis and to make more effective the medical-clinical management of heart diseases within elderly population. Chronic Heart Failure is one of the most remarkable health problems for prevalence and morbidity, especially in the developed western countries, with a strong impact in terms of social and economic effects. All these aspects are typically emphasized within the elderly population, with very frequent hospital admissions and a significant increase of medical costs. Recent studies and experiences have demonstrated that accurate heart failure management programs, based on a suitable integration of inpatient and outpatient clinical procedures, might prevent and reduce hospital admissions, improving clinical status and reducing costs. HEARTFAID aims at defining efficient and effective health care delivery organization and management models for the "optimal" management of the care in the field of cardiovascular diseases. The HEARTFAID innovative computerized system will improve the processes of diagnosis, prognosis and therapy provision, providing the following services: * electronic health record for easy and ubiquitous access to heterogeneous patients data;* integrated services for healthcare professionals, including patient telemonitoring, signal and image processing, alert and alarm system;* clinical decision support in the heart failure domain, based on pattern recognition in historical data, knowledge discovery analysis and inferences on patients' clinical data.The formalization of the pre-existing clinical knowledge and the discovery of new elicited knowledge represent the core of the HEARTFAID platform.

[Treatment and control of hypertension in Croatia. The BEL-AH study]. / Lijecenje i kontrola arterijske hipertenzije u hrvatskoj. BEL-AH istrazivanje.

Poor control of blood pressure (BP) is one of the main reasons for high cardiovascular mortality and morbidity. The aim of this study was to analyse control of BP in outpatient settings in four biggest towns in Croatia. The study included 412 medical doctors (GP) and 7031 middle-aged patients (62.9 +/- 11.5 years). Mean BP in treated patients was 162.9 +/- 16.8/95.6 +/- 9.9 mmHg. There were no statistically significant differences in systolic (p = 0.173) and diastolic (p = 0.561) BP between men and women. In this group of patients only 8% achieved target BP values. In contrast, and surprisingly, 44.5% of medical doctors and 72% of patients were satisfied with obtained BP control. Higher percentage of male patients vs. female were satisfied with blood pressure control (81.9%:66.9%). BMI < 25 was registered in 22.9% of hypertensive patients, and there was statistically significant difference in BMI between men and women (chi2 = 56.769, p < 0.001). In this study we found a statistically significant difference of hypertension in regard to BMI in both sexes (chi2 = 46.339, p < 0.001; chi(z)2 = 45.992, p = 0.024). BMI was in correlation with severity of hypertension as well as with obtained treatment result. BMI was in correlation with the number of prescribed drugs. According to this, patients with BMI < 25 were prescribed less drugs than those with BMI > 30 (1.4:1,6 p = 0.001). BP control in Croatia is, according to this study, very poor. The main reason for such situation is, beside obesity which determines the stage of hypertension and BP control in both sexes, insufficiently developed conscience in patients and doctors about the importance of stronger blood pressure control. The results indicate the necessity for the more intensive education of the population.

Induction of comprehensible models for gene expression datasets by subgroup discovery methodology.

Finding disease markers (classifiers) from gene expression data by machine learning algorithms is characterized by a high risk of overfitting the data due the abundance of attributes (simultaneously measured gene expression values) and shortage of available examples (observations). To avoid this pitfall and achieve predictor robustness, state-of-the-art approaches construct complex classifiers that combine relatively weak contributions of up to thousands of genes (attributes) to classify a disease. The complexity of such classifiers limits their transparency and consequently the biological insights they can provide. The goal of this study is to apply to this domain the methodology of constructing simple yet robust logic-based classifiers amenable to direct expert interpretation. On two well-known, publicly available gene expression classification problems, the paper shows the feasibility of this approach, employing a recently developed subgroup discovery methodology. Some of the discovered classifiers allow for novel biological interpretations.

Evaluation of artificial intelligence based models for chemical biodegradability prediction.

This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.

Active subgroup mining: a case study in coronary heart disease risk group detection.

This paper presents an approach to active mining of patient records aimed at discovering patient groups at high risk for coronary heart disease (CHD). The approach proposes active expert involvement in the following steps of the knowledge discovery process: data gathering, cleaning and transformation, subgroup discovery, statistical characterization of induced subgroups, their interpretation, and the evaluation of results. As in the discovery and characterization of risk subgroups, the main risk factors are made explicit, the proposed methodology has high potential for patient screening and early detection of patient groups at risk for CHD.

Data mining server--on-line knowledge induction tool.

The aim of this paper is to present an on-line data mining tool and illustrate its use on example of real medical data. Data from the Laboratory for in-vitro Thyroid diagnostics at the Sisters of Charity University Hospital in Zagreb were used. Preparation of the data set and one session of knowledge induction is described.