American Journal of Occupational Therapy, May 1, 2023
Importance: Handwriting and the fine motor control (hand and fingers) underlying it are key indic... more Importance: Handwriting and the fine motor control (hand and fingers) underlying it are key indicators of numerous motor disorders, especially among children. However, current assessment methods are expensive, slow, and subjective, leading to a lack of knowledge about the relationship between handwriting and motor control. Objective: To develop and validate the iPad precision drawing app Standardized Tracing Evaluation and Grapheme Assessment (STEGA) to enable rapid quantitative assessment of fine motor control and handwriting. Design: Cross-sectional, single-arm observational study. Setting: Academic research institution. Participants: Fifty-seven typically developing right-handed children ages 9 to 12 yr with knowledge of cursive. Outcomes and Measures: Predicted quality, measured as the correlation between handwriting letter legibility (Evaluation Tool of Children’s Handwriting–Cursive [ETCH–C]) and predicted legibility (calculated from STEGA’s 120 Hz, nine-variable data). Results: STEGA successfully predicted handwriting (r2 = .437, p < .001) using a support vector regression method. Angular error was the most important aspect of STEGA performance. STEGA was much faster to administer than the ETCH–C (M = 6.7 min, SD = 1.3, versus M = 19.7 min, SD = 5.2). Conclusions and Relevance: Assessment of motor control (and especially pen direction control) may provide a meaningful, objective way to assess handwriting. Future studies are needed to validate STEGA with a wider age range, but the initial results indicate that STEGA can provide the first rapid, quantitative, high-resolution, telehealth-capable assessment of the motor control that underpins handwriting. What This Article Adds: The ability to control pen direction may be the most important motor skill for successful handwriting. STEGA may provide the first criterion standard for the fine motor control skills that underpin handwriting, suitable for rehabilitation research and practice.
Recent genome-wide association studies (GWAS) have shown that temperament is strongly influenced ... more Recent genome-wide association studies (GWAS) have shown that temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term learning and memory. The results were replicated in three independent samples despite variable cultures and environments. The identified genes were enriched in pathways activated by behavioral conditioning in animals, including the two major molecular pathways for response to extracellular stimuli, the Ras-MEK-ERK and the PI3K-AKT-mTOR cascades. These pathways are activated by a wide variety of physiological and psychosocial stimuli that vary in positive and negative valence and in consequences for health and survival. Changes in these pathways are orchestrated to maintain cellular homeostasis despite changing conditions by modulating temperament and its circadian and seasonal rhythms. In this review we first consider traditional concepts of temperament in relation to the new genetic findings by examining the partial overlap of alternative measures of temperament. Then we propose a definition of temperament as the disposition of a person to learn how to behave, react emotionally, and form attachments automatically by associative conditioning. This definition provides necessary and sufficient criteria to distinguish temperament from other aspects of personality that become integrated with it across the life span. We describe the effects of specific stimuli on the molecular processes underlying temperament from functional, developmental, and evolutionary perspectives. Our new knowledge can improve communication among investigators, increase the power and efficacy of clinical trials, and improve the effectiveness of treatment of personality and its disorders.
Genomes of many organisms have been sequenced over the last few years. However, transforming such... more Genomes of many organisms have been sequenced over the last few years. However, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed to address part of this problem: the location of genes along a genome and their expression. We propose a multi-objective methodology to combine state-of-the-art algorithms into an aggregation scheme in order to obtain optimal methods' aggregations. The results obtained show a major improvement in sensitivity when our methodology is compared to the performance of individual methods for gene finding and gene expression problems. The methodology proposed here is an automatic method generator, and a step forward to exploit all already existing methods, by providing alternative optimal methods' aggregations to answer concrete queries for a certain biological problem with a maximized accuracy of the prediction. As more approaches are integrated for each of the presented problems, de novo accuracy can be expected to improve further. Keywords Multiobjective Á Gene finding Á Gene expression Dedicated to Professor Sandor Suhai on the occasion of his 65th birthday and published as part of the Suhai Festschrift Issue. Rocío Romero-Zaliz, Cristina Rubio-Escudero contributed equally.
www.ccmjournal.org Critical Care Medicine • Volume 46 • Number 1 (Supplement) Learning Objectives... more www.ccmjournal.org Critical Care Medicine • Volume 46 • Number 1 (Supplement) Learning Objectives: ICU readmission has been associated with increased mortality, and attempts have been made to prospectively identify risk factors to reduce ICU readmission and improve outcomes. Methods: We measured hospital mortality (HM) and APACHE IVa-predicted hospital mortality (pHM) in 13,587 consecutive patients admitted to 5 ICUs in a large academic medical center over 18 months that included 1,017 (7%) patients readmitted to the ICU during this period. During the first ICU stay, patients were assigned to ‘low’ (pHM< 10%), ‘medium’ (pHM=10–50%) and ‘high’ (pHM> 50%) risk groups, and HM and hospital mortality index (‘HMI’ = HM/pHM) for nonreadmitted vs. readmitted patients were compared for each risk (pMH) group. Results: HM and HMI were 10.6% and 0.8 overall, and 10.0% vs 18.5%, and 0.8 vs 1.3 in non-readmitted vs readmitted patients, respectively. 8,734 (64%), 4,082 (30%) and 771 (6%) patients were assigned to the low, medium and high risk (pHM) groups during initial admission, respectively, with 586 (7%), 382 (9%), and 49 (6%) readmissions in each of these groups. HM was 2.7%, 17.9% and 62.5%, and HMI was 0.6, 0.8, and 0.9 in the low, medium, and high risk groups, respectively. HM for non-readmitted vs readmitted patients in the low, medium and high risk groups were 1.9% vs 13.1% (6.9 fold), 17.1% vs 25.4% (1.5 fold), and 64.8% vs 28.6% (0.4 fold), respectively. HMI for non-readmitted vs. readmitted patients were 0.5 vs 2.8 (5.6 fold), 0.8 vs 1.2 (1.5 fold) and 0.9 vs 0.4 (0.4 fold) for the low, medium and high risk groups, respectively. Conclusions: It is notable that the majority of ICU readmissions come from the initially low risk (pHM< 10%) group, and this study indicates that readmission is associated with a marked increase in risk of HM in that group. The parallel increase in HMI in the low risk group indicates that the increased HM risk is not evident a priori using APACHE. However, APACHE identifies and allows focus on this low risk population, and thus provides an opportunity to better understand the readmission phenomenon, and to develop multidimensional profiles to prospectively predict ICU readmission in this group.
The human brain's resting-state functional connectivity (rsFC) provides stable trait-like measure... more The human brain's resting-state functional connectivity (rsFC) provides stable trait-like measures of differences in the perceptual, cognitive, emotional, and social functioning of individuals. The rsFC of the prefrontal cortex is hypothesized to mediate a person's rational self-government, as is also measured by personality, so we tested whether its connectivity networks account for vulnerability to psychosis and related personality configurations. Young adults were recruited as outpatients or controls from the same communities around psychiatric clinics. Healthy controls (n = 30) and clinically stable outpatients with bipolar disorder (n = 35) or schizophrenia (n = 27) were diagnosed by structured interviews, and then were assessed with standardized protocols of the Human Connectome Project. Data-driven clustering identified five groups of patients with distinct patterns of rsFC regardless of diagnosis. These groups were distinguished by rsFC networks that regulate specific biopsychosocial aspects of psychosis: sensory hypersensitivity, negative emotional balance, impaired attentional control, avolition, and social mistrust. The rsFc group differences were validated by independent measures of white matter microstructure, personality, and clinical features not used to identify the subjects. We confirmed that each connectivity group was organized by differential collaborative interactions among six prefrontal and eight other automatically-coactivated networks. The temperament and character traits of the members of these groups strongly accounted for the differences in rsFC between groups, indicating that configurations of rsFC are internal representations of personality organization. These representations involve weakly self-regulated emotional drives of fear, irrational desire, and mistrust, which predispose to psychopathology. However, stable outpatients with different diagnoses (bipolar or schizophrenic psychoses) were highly similar in rsFC and personality. This supports a diathesis-stress model in which different complex adaptive systems regulate predisposition (which is similar in stable outpatients despite diagnosis) and stress-induced clinical dysfunction (which differs by diagnosis).
Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and... more Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30-45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value < 0.01) lipidomegenotype relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes. Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs and participants, thus representing most distinct subgroups. We identified 30 significantly enriched biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome subgroups through which the identified genetic variants can influence and regulate plasma lipid related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the studied Finnish population that may have distinct disease trajectories and therefore could be useful in precision medicine research. Atherosclerosis, the underlying pathology behind many cardiovascular diseases (CVDs), is a heterogeneous lipid accumulation and inflammation related disease with roots including genetics 1 , personality 2 , and lifestyle factors 3. Previous lipidomic analyses have revealed several ceramides and phospholipids associated with key atherosclerosis processes such as uptake and aggregation of lipoproteins, accumulation of cholesterol within macrophages, production of superoxide anions, expression of cytokines and inflammation 4-6. Similarly, genetic studies of traditional lipids such as total cholesterol (TC), HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-C), non-HDL-cholesterol and triglycerides have identified about 1000 genomic loci and improved our understanding of lipid metabolism 7-10. Some studies have reported genetic associations for subsets of lipidome 11-13 and metabolome 13-20. Only few genome-wide association studies (GWASs) of lipidome involving 141-596 lipid
Alzheimer's & Dementia: Translational Research & Clinical Interventions
IntroductionCoronavirus disease 2019 (COVID‐19) has caused >3.5 million deaths worldwide and a... more IntroductionCoronavirus disease 2019 (COVID‐19) has caused >3.5 million deaths worldwide and affected >160 million people. At least twice as many have been infected but remained asymptomatic or minimally symptomatic. COVID‐19 includes central nervous system manifestations mediated by inflammation and cerebrovascular, anoxic, and/or viral neurotoxicity mechanisms. More than one third of patients with COVID‐19 develop neurologic problems during the acute phase of the illness, including loss of sense of smell or taste, seizures, and stroke. Damage or functional changes to the brain may result in chronic sequelae. The risk of incident cognitive and neuropsychiatric complications appears independent from the severity of the original pulmonary illness. It behooves the scientific and medical community to attempt to understand the molecular and/or systemic factors linking COVID‐19 to neurologic illness, both short and long term.MethodsThis article describes what is known so far in ter...
Wheelchair propulsion interventions typically teach manual wheelchair users to perform wheelchair... more Wheelchair propulsion interventions typically teach manual wheelchair users to perform wheelchair propulsion biomechanics as recommended by the Clinical Practice Guidelines (CPG). Outcome measures for these interventions are primarily laboratory based. Discrepancies remain between manual wheelchair propulsion (MWP) in laboratory-based examinations and propulsion in the real-world. Current developments in machine learning (ML) allow for monitoring of MWP in the real world. In this study, we collected data from participants enrolled in two wheelchair propulsion interventions, then built an ML algorithm to distinguish CPG recommended MWP patterns from non-CPG-recommended patterns. Eight primary manual wheelchair users did not initially follow CPG recommendations but learned and performed CPG propulsion after the interventions. Participants each wore two inertial measurement units as they propelled their wheelchairs on a roller system, indoors overground, and outdoors. ML models were trained to classify propulsion patterns as following the CPG or not following the CPG. Video recordings were used for reference. For indoor detection, we found that a subject-independent model was able to achieve 85% accuracy. For outdoor detection, we found that the subject-independent model achieved 75.4% accuracy. These results provide further evidence that CPG and non-CPG-recommended MWP patterns can be predicted with wearable sensors using an ML algorithm.
Biomedical research has been revolutionized by highthroughput techniques and the enormous amount ... more Biomedical research has been revolutionized by highthroughput techniques and the enormous amount of data they are able to generate. In particular technology has the capacity to monitor changes in RNA abundance for thousands of genes simultaneously. The interest shown over microarray analysis methods has rapidly raised. Clustering is widely used in the analysis of microarray data to group genes of interest targeted from microarray experiments on the basis of similarity of expression patterns. In this work we apply two clustering algorithms, K-means and Expectation Maximization to particular a problem and we compare the groupings obtained on the basis of the cohesiveness of the gene products associated to the genes in each cluster. 1.
The rise of omics techniques has resulted in an explosion of molecular data in modern biomedical ... more The rise of omics techniques has resulted in an explosion of molecular data in modern biomedical research. Together with information from medical images and clinical data, the field of omics has driven the implementation of personalized medicine. Biomedical and omics datasets are complex and heterogeneous, and extracting meaningful knowledge from this vast amount of information is by far the most important challenge for bioinformatics and machine learning researchers. In this context, there is an increasing interest in the potential of deep learning (DL) methods to create predictive models and to identify complex patterns from these large datasets. This chapter provides an overview of the main applications of DL methods in biomedical research, with focus on omics data analysis and precision medicine applications. DL algorithms and the most popular architectures are introduced first. This is followed by a review of some of the main applications and problems approached by DL in omics data and medical image analysis. Finally, implementations for improving the diagnosis, treatment, and classification of complex diseases are discussed.
INTRODUCTION: Vital signs (VS) are important indicators of disease severity and clinical deterior... more INTRODUCTION: Vital signs (VS) are important indicators of disease severity and clinical deterioration However, the predictive scope of VS for ICU mortality is unknown and there are no validated system for early and real-time prediction of ICU mortality from VS data alone In this study we aimed to develop and validate a Machine Learning (ML) classifier to predict ICU mortality from continuous VS data METHODS: We used de-identified patient VS data obtained from our eSearch (Philips Healthcare) database to encode 7 continuous VS time series and use of 5 VS monitoring devices Mean, standard deviation, autocorrelation, and the trend of the mean were used to encode VS time series variations and were adjusted to the entire ICU stay, and 6, 12, or 24 hours before death Our approach did not encode diagnoses but agnostically classified based on VS features Performance of the models was determined on a naive cohort and an independent sample of patients with COVID-19 RESULTS: A total of 19,266 ICU stays prior to COVID were studied including 17,339 in the training cohort, and 1,927 in the naive validation cohort with ICU mortalities of 9% An independent sample of 548 patients with COVID-19 with mortality of 22% was also used for validation For the entire stay, and 6-, 12-, and 24-hours in advance, the ML classifier achieved AUCs and PRCs of 0 97 - 0 81 and 0 78 - 0 40, respectively in the naive population obtained prior to COVID, and AUCs and PRCs of 0 92 - 0 80 and 0 81 - 0 58, respectively, for the COVID cohort Notably, a differential ranking of features was found for mortality predictions in the COVID-19 sample, as well as in 9 other specific diagnoses The effectiveness of this approach compared favorably with six other ML methods and with the DRS (Philips) mortality predictions CONCLUSIONS: A data-driven ML algorithm developed from composite vital sign data alone made ICU mortality predictions with model performance on a naive ICU test population, as well as on a COVID-19 patient population, that rivals other prediction models using more complex data domains Shapley Additive exPlanations provided interpretability and clinical validation of the ML model related to the specific features in the ICU subpopulations
Philosophical Transactions of the Royal Society B: Biological Sciences, 2018
There is fundamental doubt about whether the natural unit of measurement for temperament and pers... more There is fundamental doubt about whether the natural unit of measurement for temperament and personality corresponds to single traits or to multi-trait profiles that describe the functioning of a whole person. Biogenetic researchers of temperament usually assume they need to focus on individual traits that differ between individuals. Recent research indicates that a shift of emphasis to understand processes within the individual is crucial for identifying the natural building blocks of temperament. Evolution and development operate on adaptation of whole organisms or persons, not on individual traits or categories. Adaptive functioning generally depends on feedback among many variable processes in ways that are characteristic of complex adaptive systems, not machines with separate parts. Advanced methods of unsupervised machine learning can now be applied to genome-wide association studies and brain imaging in order to uncover the genotypic–phenotypic architecture of traits like tem...
The genetic basis for the emergence of creativity in modern humans remains a mystery despite sequ... more The genetic basis for the emergence of creativity in modern humans remains a mystery despite sequencing the genomes of chimpanzees and Neanderthals, our closest hominid relatives. Data-driven methods allowed us to uncover networks of genes distinguishing the three major systems of modern human personality and adaptability: emotional reactivity, self-control, and self-awareness. Now we have identified which of these genes are present in chimpanzees and Neanderthals. We replicated our findings in separate analyses of three high-coverage genomes of Neanderthals. We found that Neanderthals had nearly the same genes for emotional reactivity as chimpanzees, and they were intermediate between modern humans and chimpanzees in their numbers of genes for both self-control and self-awareness. 95% of the 267 genes we found only in modern humans were not protein-coding, including many long-non-coding RNAs in the self-awareness network. These genes may have arisen by positive selection for the characteristics of human well-being and behavioral modernity, including creativity, prosocial behavior, and healthy longevity. The genes that cluster in association with those found only in modern humans are over-expressed in brain regions involved in human self-awareness and creativity, including late-myelinating and phylogenetically recent regions of neocortex for autobiographical memory in frontal, parietal, and temporal regions, as well as related components of cortico-thalamo-ponto-cerebellar-cortical and cortico-striato-cortical loops. We conclude that modern humans have more than 200 unique non-protein-coding genes regulating co-expression of many more proteincoding genes in coordinated networks that underlie their capacities for self-awareness, creativity, prosocial behavior, and healthy longevity, which are not found in chimpanzees or Neanderthals.
American Journal of Occupational Therapy, May 1, 2023
Importance: Handwriting and the fine motor control (hand and fingers) underlying it are key indic... more Importance: Handwriting and the fine motor control (hand and fingers) underlying it are key indicators of numerous motor disorders, especially among children. However, current assessment methods are expensive, slow, and subjective, leading to a lack of knowledge about the relationship between handwriting and motor control. Objective: To develop and validate the iPad precision drawing app Standardized Tracing Evaluation and Grapheme Assessment (STEGA) to enable rapid quantitative assessment of fine motor control and handwriting. Design: Cross-sectional, single-arm observational study. Setting: Academic research institution. Participants: Fifty-seven typically developing right-handed children ages 9 to 12 yr with knowledge of cursive. Outcomes and Measures: Predicted quality, measured as the correlation between handwriting letter legibility (Evaluation Tool of Children’s Handwriting–Cursive [ETCH–C]) and predicted legibility (calculated from STEGA’s 120 Hz, nine-variable data). Results: STEGA successfully predicted handwriting (r2 = .437, p &lt; .001) using a support vector regression method. Angular error was the most important aspect of STEGA performance. STEGA was much faster to administer than the ETCH–C (M = 6.7 min, SD = 1.3, versus M = 19.7 min, SD = 5.2). Conclusions and Relevance: Assessment of motor control (and especially pen direction control) may provide a meaningful, objective way to assess handwriting. Future studies are needed to validate STEGA with a wider age range, but the initial results indicate that STEGA can provide the first rapid, quantitative, high-resolution, telehealth-capable assessment of the motor control that underpins handwriting. What This Article Adds: The ability to control pen direction may be the most important motor skill for successful handwriting. STEGA may provide the first criterion standard for the fine motor control skills that underpin handwriting, suitable for rehabilitation research and practice.
Recent genome-wide association studies (GWAS) have shown that temperament is strongly influenced ... more Recent genome-wide association studies (GWAS) have shown that temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term learning and memory. The results were replicated in three independent samples despite variable cultures and environments. The identified genes were enriched in pathways activated by behavioral conditioning in animals, including the two major molecular pathways for response to extracellular stimuli, the Ras-MEK-ERK and the PI3K-AKT-mTOR cascades. These pathways are activated by a wide variety of physiological and psychosocial stimuli that vary in positive and negative valence and in consequences for health and survival. Changes in these pathways are orchestrated to maintain cellular homeostasis despite changing conditions by modulating temperament and its circadian and seasonal rhythms. In this review we first consider traditional concepts of temperament in relation to the new genetic findings by examining the partial overlap of alternative measures of temperament. Then we propose a definition of temperament as the disposition of a person to learn how to behave, react emotionally, and form attachments automatically by associative conditioning. This definition provides necessary and sufficient criteria to distinguish temperament from other aspects of personality that become integrated with it across the life span. We describe the effects of specific stimuli on the molecular processes underlying temperament from functional, developmental, and evolutionary perspectives. Our new knowledge can improve communication among investigators, increase the power and efficacy of clinical trials, and improve the effectiveness of treatment of personality and its disorders.
Genomes of many organisms have been sequenced over the last few years. However, transforming such... more Genomes of many organisms have been sequenced over the last few years. However, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed to address part of this problem: the location of genes along a genome and their expression. We propose a multi-objective methodology to combine state-of-the-art algorithms into an aggregation scheme in order to obtain optimal methods' aggregations. The results obtained show a major improvement in sensitivity when our methodology is compared to the performance of individual methods for gene finding and gene expression problems. The methodology proposed here is an automatic method generator, and a step forward to exploit all already existing methods, by providing alternative optimal methods' aggregations to answer concrete queries for a certain biological problem with a maximized accuracy of the prediction. As more approaches are integrated for each of the presented problems, de novo accuracy can be expected to improve further. Keywords Multiobjective Á Gene finding Á Gene expression Dedicated to Professor Sandor Suhai on the occasion of his 65th birthday and published as part of the Suhai Festschrift Issue. Rocío Romero-Zaliz, Cristina Rubio-Escudero contributed equally.
www.ccmjournal.org Critical Care Medicine • Volume 46 • Number 1 (Supplement) Learning Objectives... more www.ccmjournal.org Critical Care Medicine • Volume 46 • Number 1 (Supplement) Learning Objectives: ICU readmission has been associated with increased mortality, and attempts have been made to prospectively identify risk factors to reduce ICU readmission and improve outcomes. Methods: We measured hospital mortality (HM) and APACHE IVa-predicted hospital mortality (pHM) in 13,587 consecutive patients admitted to 5 ICUs in a large academic medical center over 18 months that included 1,017 (7%) patients readmitted to the ICU during this period. During the first ICU stay, patients were assigned to ‘low’ (pHM< 10%), ‘medium’ (pHM=10–50%) and ‘high’ (pHM> 50%) risk groups, and HM and hospital mortality index (‘HMI’ = HM/pHM) for nonreadmitted vs. readmitted patients were compared for each risk (pMH) group. Results: HM and HMI were 10.6% and 0.8 overall, and 10.0% vs 18.5%, and 0.8 vs 1.3 in non-readmitted vs readmitted patients, respectively. 8,734 (64%), 4,082 (30%) and 771 (6%) patients were assigned to the low, medium and high risk (pHM) groups during initial admission, respectively, with 586 (7%), 382 (9%), and 49 (6%) readmissions in each of these groups. HM was 2.7%, 17.9% and 62.5%, and HMI was 0.6, 0.8, and 0.9 in the low, medium, and high risk groups, respectively. HM for non-readmitted vs readmitted patients in the low, medium and high risk groups were 1.9% vs 13.1% (6.9 fold), 17.1% vs 25.4% (1.5 fold), and 64.8% vs 28.6% (0.4 fold), respectively. HMI for non-readmitted vs. readmitted patients were 0.5 vs 2.8 (5.6 fold), 0.8 vs 1.2 (1.5 fold) and 0.9 vs 0.4 (0.4 fold) for the low, medium and high risk groups, respectively. Conclusions: It is notable that the majority of ICU readmissions come from the initially low risk (pHM< 10%) group, and this study indicates that readmission is associated with a marked increase in risk of HM in that group. The parallel increase in HMI in the low risk group indicates that the increased HM risk is not evident a priori using APACHE. However, APACHE identifies and allows focus on this low risk population, and thus provides an opportunity to better understand the readmission phenomenon, and to develop multidimensional profiles to prospectively predict ICU readmission in this group.
The human brain's resting-state functional connectivity (rsFC) provides stable trait-like measure... more The human brain's resting-state functional connectivity (rsFC) provides stable trait-like measures of differences in the perceptual, cognitive, emotional, and social functioning of individuals. The rsFC of the prefrontal cortex is hypothesized to mediate a person's rational self-government, as is also measured by personality, so we tested whether its connectivity networks account for vulnerability to psychosis and related personality configurations. Young adults were recruited as outpatients or controls from the same communities around psychiatric clinics. Healthy controls (n = 30) and clinically stable outpatients with bipolar disorder (n = 35) or schizophrenia (n = 27) were diagnosed by structured interviews, and then were assessed with standardized protocols of the Human Connectome Project. Data-driven clustering identified five groups of patients with distinct patterns of rsFC regardless of diagnosis. These groups were distinguished by rsFC networks that regulate specific biopsychosocial aspects of psychosis: sensory hypersensitivity, negative emotional balance, impaired attentional control, avolition, and social mistrust. The rsFc group differences were validated by independent measures of white matter microstructure, personality, and clinical features not used to identify the subjects. We confirmed that each connectivity group was organized by differential collaborative interactions among six prefrontal and eight other automatically-coactivated networks. The temperament and character traits of the members of these groups strongly accounted for the differences in rsFC between groups, indicating that configurations of rsFC are internal representations of personality organization. These representations involve weakly self-regulated emotional drives of fear, irrational desire, and mistrust, which predispose to psychopathology. However, stable outpatients with different diagnoses (bipolar or schizophrenic psychoses) were highly similar in rsFC and personality. This supports a diathesis-stress model in which different complex adaptive systems regulate predisposition (which is similar in stable outpatients despite diagnosis) and stress-induced clinical dysfunction (which differs by diagnosis).
Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and... more Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30-45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value < 0.01) lipidomegenotype relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes. Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs and participants, thus representing most distinct subgroups. We identified 30 significantly enriched biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome subgroups through which the identified genetic variants can influence and regulate plasma lipid related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the studied Finnish population that may have distinct disease trajectories and therefore could be useful in precision medicine research. Atherosclerosis, the underlying pathology behind many cardiovascular diseases (CVDs), is a heterogeneous lipid accumulation and inflammation related disease with roots including genetics 1 , personality 2 , and lifestyle factors 3. Previous lipidomic analyses have revealed several ceramides and phospholipids associated with key atherosclerosis processes such as uptake and aggregation of lipoproteins, accumulation of cholesterol within macrophages, production of superoxide anions, expression of cytokines and inflammation 4-6. Similarly, genetic studies of traditional lipids such as total cholesterol (TC), HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-C), non-HDL-cholesterol and triglycerides have identified about 1000 genomic loci and improved our understanding of lipid metabolism 7-10. Some studies have reported genetic associations for subsets of lipidome 11-13 and metabolome 13-20. Only few genome-wide association studies (GWASs) of lipidome involving 141-596 lipid
Alzheimer's & Dementia: Translational Research & Clinical Interventions
IntroductionCoronavirus disease 2019 (COVID‐19) has caused >3.5 million deaths worldwide and a... more IntroductionCoronavirus disease 2019 (COVID‐19) has caused >3.5 million deaths worldwide and affected >160 million people. At least twice as many have been infected but remained asymptomatic or minimally symptomatic. COVID‐19 includes central nervous system manifestations mediated by inflammation and cerebrovascular, anoxic, and/or viral neurotoxicity mechanisms. More than one third of patients with COVID‐19 develop neurologic problems during the acute phase of the illness, including loss of sense of smell or taste, seizures, and stroke. Damage or functional changes to the brain may result in chronic sequelae. The risk of incident cognitive and neuropsychiatric complications appears independent from the severity of the original pulmonary illness. It behooves the scientific and medical community to attempt to understand the molecular and/or systemic factors linking COVID‐19 to neurologic illness, both short and long term.MethodsThis article describes what is known so far in ter...
Wheelchair propulsion interventions typically teach manual wheelchair users to perform wheelchair... more Wheelchair propulsion interventions typically teach manual wheelchair users to perform wheelchair propulsion biomechanics as recommended by the Clinical Practice Guidelines (CPG). Outcome measures for these interventions are primarily laboratory based. Discrepancies remain between manual wheelchair propulsion (MWP) in laboratory-based examinations and propulsion in the real-world. Current developments in machine learning (ML) allow for monitoring of MWP in the real world. In this study, we collected data from participants enrolled in two wheelchair propulsion interventions, then built an ML algorithm to distinguish CPG recommended MWP patterns from non-CPG-recommended patterns. Eight primary manual wheelchair users did not initially follow CPG recommendations but learned and performed CPG propulsion after the interventions. Participants each wore two inertial measurement units as they propelled their wheelchairs on a roller system, indoors overground, and outdoors. ML models were trained to classify propulsion patterns as following the CPG or not following the CPG. Video recordings were used for reference. For indoor detection, we found that a subject-independent model was able to achieve 85% accuracy. For outdoor detection, we found that the subject-independent model achieved 75.4% accuracy. These results provide further evidence that CPG and non-CPG-recommended MWP patterns can be predicted with wearable sensors using an ML algorithm.
Biomedical research has been revolutionized by highthroughput techniques and the enormous amount ... more Biomedical research has been revolutionized by highthroughput techniques and the enormous amount of data they are able to generate. In particular technology has the capacity to monitor changes in RNA abundance for thousands of genes simultaneously. The interest shown over microarray analysis methods has rapidly raised. Clustering is widely used in the analysis of microarray data to group genes of interest targeted from microarray experiments on the basis of similarity of expression patterns. In this work we apply two clustering algorithms, K-means and Expectation Maximization to particular a problem and we compare the groupings obtained on the basis of the cohesiveness of the gene products associated to the genes in each cluster. 1.
The rise of omics techniques has resulted in an explosion of molecular data in modern biomedical ... more The rise of omics techniques has resulted in an explosion of molecular data in modern biomedical research. Together with information from medical images and clinical data, the field of omics has driven the implementation of personalized medicine. Biomedical and omics datasets are complex and heterogeneous, and extracting meaningful knowledge from this vast amount of information is by far the most important challenge for bioinformatics and machine learning researchers. In this context, there is an increasing interest in the potential of deep learning (DL) methods to create predictive models and to identify complex patterns from these large datasets. This chapter provides an overview of the main applications of DL methods in biomedical research, with focus on omics data analysis and precision medicine applications. DL algorithms and the most popular architectures are introduced first. This is followed by a review of some of the main applications and problems approached by DL in omics data and medical image analysis. Finally, implementations for improving the diagnosis, treatment, and classification of complex diseases are discussed.
INTRODUCTION: Vital signs (VS) are important indicators of disease severity and clinical deterior... more INTRODUCTION: Vital signs (VS) are important indicators of disease severity and clinical deterioration However, the predictive scope of VS for ICU mortality is unknown and there are no validated system for early and real-time prediction of ICU mortality from VS data alone In this study we aimed to develop and validate a Machine Learning (ML) classifier to predict ICU mortality from continuous VS data METHODS: We used de-identified patient VS data obtained from our eSearch (Philips Healthcare) database to encode 7 continuous VS time series and use of 5 VS monitoring devices Mean, standard deviation, autocorrelation, and the trend of the mean were used to encode VS time series variations and were adjusted to the entire ICU stay, and 6, 12, or 24 hours before death Our approach did not encode diagnoses but agnostically classified based on VS features Performance of the models was determined on a naive cohort and an independent sample of patients with COVID-19 RESULTS: A total of 19,266 ICU stays prior to COVID were studied including 17,339 in the training cohort, and 1,927 in the naive validation cohort with ICU mortalities of 9% An independent sample of 548 patients with COVID-19 with mortality of 22% was also used for validation For the entire stay, and 6-, 12-, and 24-hours in advance, the ML classifier achieved AUCs and PRCs of 0 97 - 0 81 and 0 78 - 0 40, respectively in the naive population obtained prior to COVID, and AUCs and PRCs of 0 92 - 0 80 and 0 81 - 0 58, respectively, for the COVID cohort Notably, a differential ranking of features was found for mortality predictions in the COVID-19 sample, as well as in 9 other specific diagnoses The effectiveness of this approach compared favorably with six other ML methods and with the DRS (Philips) mortality predictions CONCLUSIONS: A data-driven ML algorithm developed from composite vital sign data alone made ICU mortality predictions with model performance on a naive ICU test population, as well as on a COVID-19 patient population, that rivals other prediction models using more complex data domains Shapley Additive exPlanations provided interpretability and clinical validation of the ML model related to the specific features in the ICU subpopulations
Philosophical Transactions of the Royal Society B: Biological Sciences, 2018
There is fundamental doubt about whether the natural unit of measurement for temperament and pers... more There is fundamental doubt about whether the natural unit of measurement for temperament and personality corresponds to single traits or to multi-trait profiles that describe the functioning of a whole person. Biogenetic researchers of temperament usually assume they need to focus on individual traits that differ between individuals. Recent research indicates that a shift of emphasis to understand processes within the individual is crucial for identifying the natural building blocks of temperament. Evolution and development operate on adaptation of whole organisms or persons, not on individual traits or categories. Adaptive functioning generally depends on feedback among many variable processes in ways that are characteristic of complex adaptive systems, not machines with separate parts. Advanced methods of unsupervised machine learning can now be applied to genome-wide association studies and brain imaging in order to uncover the genotypic–phenotypic architecture of traits like tem...
The genetic basis for the emergence of creativity in modern humans remains a mystery despite sequ... more The genetic basis for the emergence of creativity in modern humans remains a mystery despite sequencing the genomes of chimpanzees and Neanderthals, our closest hominid relatives. Data-driven methods allowed us to uncover networks of genes distinguishing the three major systems of modern human personality and adaptability: emotional reactivity, self-control, and self-awareness. Now we have identified which of these genes are present in chimpanzees and Neanderthals. We replicated our findings in separate analyses of three high-coverage genomes of Neanderthals. We found that Neanderthals had nearly the same genes for emotional reactivity as chimpanzees, and they were intermediate between modern humans and chimpanzees in their numbers of genes for both self-control and self-awareness. 95% of the 267 genes we found only in modern humans were not protein-coding, including many long-non-coding RNAs in the self-awareness network. These genes may have arisen by positive selection for the characteristics of human well-being and behavioral modernity, including creativity, prosocial behavior, and healthy longevity. The genes that cluster in association with those found only in modern humans are over-expressed in brain regions involved in human self-awareness and creativity, including late-myelinating and phylogenetically recent regions of neocortex for autobiographical memory in frontal, parietal, and temporal regions, as well as related components of cortico-thalamo-ponto-cerebellar-cortical and cortico-striato-cortical loops. We conclude that modern humans have more than 200 unique non-protein-coding genes regulating co-expression of many more proteincoding genes in coordinated networks that underlie their capacities for self-awareness, creativity, prosocial behavior, and healthy longevity, which are not found in chimpanzees or Neanderthals.
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Papers by Igor Zwir