Pattern recognition in data as a diagnosis tool

Medical data often appear in the form of numerical matrices or sequences. We develop mathematical tools for automatic screening of such data in two medical contexts: diagnosis of systemic lupus erythematosus (SLE) patients and identification of cardiac abnormalities. The idea is first to implement adequate data normalizations and then identify suitable hyperparameters and distances to classify relevant patterns. To this purpose, we discuss the applicability of Plackett-Luce models for rankings to hyperparameter and distance selection. Our tests suggest that, while Hamming distances seem to be well adapted to the study of patterns in matrices representing data from laboratory tests, dynamic time warping distances provide robust tools for the study of cardiac signals. The techniques developed here may set a basis for automatic screening of medical information based on pattern comparison.

Peripheral Insulin Resistance Is Unrelated to Cognition in Highly Educated, Cognitively Normal Adults

Objectives Peripheral insulin resistance (IR) and impaired glucose metabolism increase the risk for cognitive decline. However, data looking at peripheral IR's relationship with cognition in cognitively normal adults is limited. This study aimed to assess the relationship between peripheral IR and tests of executive function, attention, and processing speed in cognitively normal older adults using a novel IR measure. Methods Baseline data from 81 cognitively normal older adults participating in a nutrition intervention study (Nutrition Interventions for Cognitive Enhancement study; NICE study) were analyzed. Fasting blood draws were attained, and peripheral IR was measured using Quest Diagnostics’ Cardio IQ Insulin Resistance Panel (Test Code: 36,509). The NIH Toolbox Cognition Battery was conducted by a trained psychometrician. Executive function was measured by the Flanker Inhibitory Control and Dimensional Change Card Sort tests. The Flanker test also measures attention. Processing Speed was measured by the Pattern Comparison Processing Speed test. We constructed ordinary least squares regression models to assess IR's relationships with the individual cognitive tests, including age, education, and gender as covariates. Statistical analyses were performed using R (v. 3.6.2; R Foundation, Vienna, Austria). Statistical significance was set at p < 0.05. Results Participants were 83% female and had a mean age of 72 ± 4.9 years. The mean IR score was 29.9 ± 29.83. There was no statistically significant relationships with IR and any of the cognitive tests: Flanker Inhibitory Control (β = -0.03, P = 0.12), Dimensional Change Card Sort (β = -0.03, P = 0.16), Pattern Comparison Processing Speed (β = -0.10, P = 0.15). Conclusions There were no statistically significant relationships between IR scores and performance on the different cognitive tests. Although the tests were not significantly correlated with IR scores, directionality of the relationships indicated trend for higher IR being related to poorer scores. Consequently, maintaining insulin sensitivity with healthy lifestyle choices may be important for healthy brain aging. Future analyses with a larger sample size will be more informative for understanding the relationship between IR and various cognitive tests at baseline and over time. Funding Sources National Institutes on Aging.

A Spatial Understanding of Well-Being in the City of Toronto

Social Capital is an emergent field of study that has the potential of applications in a wide variety of fields, from public health to economics, but most widely for human well-being. This study is a spatial and statistical analysis of the relation between commonly accepted indicators of wellbeing, commonly accepted indicators of social capital, and socio-economic factors within Toronto, as well as their respective spatial patterns. This research discovered that within Toronto there does not exist a clear link between wellbeing and social capital indicators, both in terms of statistical regression analysis and spatial pattern comparison. The results suggest that, at least within the context of Toronto, much of the research surrounding the effects of high social capital on wellbeing are not applicable, at least using the methods demonstrated in this study.

A Spatial Understanding of Well-Being in the City of Toronto

Social Capital is an emergent field of study that has the potential of applications in a wide variety of fields, from public health to economics, but most widely for human well-being. This study is a spatial and statistical analysis of the relation between commonly accepted indicators of wellbeing, commonly accepted indicators of social capital, and socio-economic factors within Toronto, as well as their respective spatial patterns. This research discovered that within Toronto there does not exist a clear link between wellbeing and social capital indicators, both in terms of statistical regression analysis and spatial pattern comparison. The results suggest that, at least within the context of Toronto, much of the research surrounding the effects of high social capital on wellbeing are not applicable, at least using the methods demonstrated in this study.

Diabetes, obesity-associated comorbidities and NIH-Toolbox neurocognitive performance

Background/Objectives: Severe obesity is associated with cognitive deficits in adults without current or past neurological brain disturbances. This study examined the relationship between specific metabolic and vascular risk factors and cognitive performance on a computerized neurocognitive assessment battery in adults with BMI > 35. Subjects/Methods: 123 adults with Class II or III obesity, ages 20-75, were enrolled in a study of the cognitive and brain effects of reduced BMI and improved diabetes mellitus (DM) following bariatric surgery. Baseline clinical/cognitive assessments were conducted with the NIH Toolbox (NIH-TB) cognitive module prior to surgery, and in severely obese controls recruited from the community. Global, Fluid and Crystallized indices were derived from performance across nine tasks. Hierarchical regression analyses examined six obesity-associated clinical factors (BMI, HbA1c, and DM, hypertension, sleep apnea, and osteoarthritis diagnoses) relative to NIH-TB performance. Results: Fluid Cognition deficits were observed, greatest on attention-executive and cognitive processing speed tasks (Flanker and Pattern Comparison). DM diagnosis was most strongly associated with weaker cognitive performance (Global and Fluid Cognition), and with poorer performance on the Flanker, Pattern Comparison, Picture Sequencing, Verbal Learning, and Symbol Coding tasks. Elevated HbA1c was associated with weaker Card Sorting and Symbol Coding performance, hypertension with poorer Fluid Cognition, and osteoarthritis with lower List Sorting performance. Elevated BMI was only associated with Flanker performance, though DM was more strongly associated with this measure. Conclusion: Deficits of fluid cognitive functions (attention-executive, processing speed) exist among adults with Class II and III obesity. DM was most consistently associated with weaker NIH-TB performance. BMI was not as strongly associated with NIH-TB performance, perhaps reflecting the elevated BMI of the entire sample. That cognitive deficits were linked to specific obesity-associated comorbidities support the validity and potential clinical utility of the NIH-TB for the assessment and management of adults with severe obesity.

Intergenerational Transmission of Cortical Sulcal Patterns from Mothers to their Children

Intergenerational effects are described as the genetic, epigenetic, as well as pre- and postnatal environmental influence parents have on their offspring’s behavior, cognition, and brain. During fetal brain development, the primary cortical sulci emerge with a distinctive folding pattern that are under strong genetic influence and show little change of this pattern throughout postnatal brain development. We examined intergenerational transmission of cortical sulcal patterns by comparing primary sulcal patterns between children (N = 16, age 5.5 ± 0.81 years, 8 males) and their biological mothers (N = 15, age 39.72 ± 4.68 years) as well as between children and unrelated adult females. Our graph-based sulcal pattern comparison method detected stronger sulcal pattern similarity for child–mother pairs than child-unrelated pairs, where higher similarity between child–mother pairs was observed mostly for the right lobar regions. Our results also show that child–mother versus child-unrelated pairs differ for daughters and sons with a trend toward significance, particularly for the left hemisphere lobar regions. This is the first study to reveal significant intergenerational transmission of cortical sulcal patterns, and our results have important implications for the study of the heritability of complex behaviors, brain-based disorders, the identification of biomarkers, and targets for interventions.

Bioelectrical pattern discrimination of Miconia plants by spectral analysis and machine learning

We have conducted an in loco investigation into the species Miconia albicans (SW.) Triana and Miconia chamissois Naudin (Melastomataceae), distributed in different phytophysiognomies of three Cerrado fragments in the State of São Paulo, Brazil, to characterize their oscillatory bioelectrical signals and to find out whether these signals have distinct spectral density. The experiments provided a sample bank of bioelectrical amplitudes, which were analyzed in the time and frequency domain. On the basis of the power spectral density (PSD) and machine learning techniques, analyses in the frequency domain suggested that each species has a characteristic biological pattern. Comparison between the oscillatory behavior of the species clearly showed that they have bioelectrical features, that collecting data is feasible, that Miconia display a bioelectrical pattern, and that environmental factors influence this pattern. From the point of view of experimental Botany, new questions and concepts must be formulated to advance understanding of the interactions between the communicative nature of plants and the environment. The results of this on-site technique represent a new methodology to acquire non-invasive information that might be associated with physiological, chemical, and ecological aspects of plants.HighlightIn loco characterization of the bioelectrical signals of two Miconia species in the time and frequency domain suggests that the species have distinct biological patterns.