Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?

BACKGROUND: To characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral state in a cry-translator. To apply the cry-translation algorithm to colic hypothesizing that these cries sound painful. METHODS: Assessment of 1000 cries in a mobile app (ChatterBabyTM). Training a cry-translation algorithm by evaluating >6000 acoustic features to predict whether infant cry was due to a pain (vaccinations, ear-piercings), fussy, or hunger states. Using the algorithm to predict the behavioral state of infants with reported colic. RESULTS: The cry-translation algorithm was 90.7% accurate for identifying pain cries, and achieved 71.5% accuracy in discriminating cries from fussiness, hunger, or pain. The ChatterBaby cry-translation algorithm overwhelmingly predicted that colic cries were most likely from pain, compared to fussy and hungry states. Colic cries had average pain ratings of 73%, significantly greater than the pain measurements found in fussiness and hunger (p < 0.001, 2-sample t test). Colic cries outranked pain cries by measures of acoustic intensity, including energy, length of voiced periods, and fundamental frequency/pitch, while fussy and hungry cries showed reduced intensity measures compared to pain and colic. CONCLUSIONS: Acoustic features of cries are consistent across a diverse infant population and can be utilized as objective markers of pain, hunger, and fussiness. The ChatterBaby algorithm detected significant acoustic similarities between colic and painful cries, suggesting that they may share a neuronal pathway.

Intrinsic Connectivity Network-Based Classification and Detection of Psychotic Symptoms in Youth With 22q11.2 Deletions.

22q11.2 Deletion syndrome (22q11DS) is a genetic disorder associated with numerous phenotypic consequences and is one of the greatest known risk factors for psychosis. We investigated intrinsic-connectivity-networks (ICNs) as potential biomarkers for patient and psychosis-risk status in 2 independent cohorts, UCLA (33 22q11DS-participants, 33 demographically matched controls), and Syracuse (28 22q11DS, 28 controls). After assessing group connectivity differences, ICNs from the UCLA cohort were used to train classifiers to distinguish cases from controls, and to predict psychosis risk status within 22q11DS; classifiers were subsequently tested on the Syracuse cohort. In both cohorts we observed significant hypoconnectivity in 22q11DS relative to controls within anterior cingulate (ACC)/precuneus, executive, default mode (DMN), posterior DMN, and salience networks. Of 12 ICN-derived classifiers tested in the Syracuse replication-cohort, the ACC/precuneus, DMN, and posterior DMN classifiers accurately distinguished between 22q11DS and controls. Within 22q11DS subjects, connectivity alterations within 4 networks predicted psychosis risk status for a given individual in both cohorts: the ACC/precuneus, DMN, left executive, and salience networks. Widespread within-network-hypoconnectivity in large-scale networks implicated in higher-order cognition may be a defining characteristic of 22q11DS during adolescence and early adulthood; furthermore, loss of coherence within these networks may be a valuable biomarker for individual prediction of psychosis-risk in 22q11DS.

Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study.

OBJECTIVES: An estimated 25% of type two diabetes mellitus (DM2) patients in the United States are undiagnosed due to inadequate screening, because it is prohibitive to administer laboratory tests to everyone. We assess whether electronic health record (EHR) phenotyping could improve DM2 screening compared to conventional models, even when records are incomplete and not recorded systematically across patients and practice locations, as is typically seen in practice. METHODS: In this cross-sectional, retrospective study, EHR data from 9948 US patients were used to develop a pre-screening tool to predict current DM2, using multivariate logistic regression and a random-forests probabilistic model for out-of-sample validation. We compared (1) a full EHR model containing commonly prescribed medications, diagnoses (as ICD9 categories), and conventional predictors, (2) a restricted EHR DX model which excluded medications, and (3) a conventional model containing basic predictors and their interactions (BMI, age, sex, smoking status, hypertension). RESULTS: Using a patient's full EHR or restricted EHR was superior to using basic covariates alone for detecting individuals with diabetes (hierarchical X(2) test, p<0.001). Migraines, depot medroxyprogesterone acetate, and cardiac dysrhythmias were associated negatively with DM2, while sexual and gender identity disorder diagnosis, viral and chlamydial infections, and herpes zoster were associated positively. Adding EHR phenotypes improved classification; the AUC for the full EHR Model, EHR DX model, and conventional model using logistic regression, were 84.9%, 83.2%, and 75.0% respectively. For random forest machine learning out-of-sample prediction, accuracy also was improved when using EHR phenotypes; the AUC values were 81.3%, 79.6%, and 74.8%, respectively. Improved AUCs reflect better performance for most thresholds that balance sensitivity and specificity. CONCLUSIONS: EHR phenotyping resulted in markedly superior detection of DM2, even in the face of missing and unsystematically recorded data, based on the ROC curves. EHR phenotypes could more efficiently identify which patients do require, and don't require, further laboratory screening. When applied to the current number of undiagnosed individuals in the United States, we predict that incorporating EHR phenotype screening would identify an additional 400,000 patients with active, untreated diabetes compared to the conventional pre-screening models.

Using Artificial Intelligence-based Methods to Address the Placebo Response in Clinical Trials.

The placebo response is a highly complex psychosocial-biological phenomenon that has challenged drug development for decades, particularly in neurological and psychiatric disease. While decades of research have aimed to understand clinical trial factors that contribute to the placebo response, a comprehensive solution to manage the placebo response in drug development has yet to emerge. Advanced data analytic techniques, such as artificial intelligence (AI), might be needed to take the next leap forward in mitigating the negative consequences of high placebo-response rates. The objective of this review was to explore the use of techniques such as AI and the sub-discipline of machine learning (ML) to address placebo response in practical ways that can positively impact drug development. This examination focused on the critical factors that should be considered in applying AI and ML to the placebo response issue, examples of how these techniques can be used, and the regulatory considerations for integrating these approaches into clinical trials.

Synaptic and Gene Regulatory Mechanisms in Schizophrenia, Autism, and 22q11.2 Copy Number Variant-Mediated Risk for Neuropsychiatric Disorders.

BACKGROUND: 22q11.2 copy number variants are among the most highly penetrant genetic risk variants for developmental neuropsychiatric disorders such as schizophrenia (SCZ) and autism spectrum disorder (ASD). However, the specific mechanisms through which they confer risk remain unclear. METHODS: Using a functional genomics approach, we integrated transcriptomic data from the developing human brain, genome-wide association findings for SCZ and ASD, protein interaction data, and gene expression signatures from SCZ and ASD postmortem cortex to 1) organize genes into the developmental cellular and molecular systems within which they operate, 2) identify neurodevelopmental processes associated with polygenic risk for SCZ and ASD across the allelic frequency spectrum, and 3) elucidate pathways and individual genes through which 22q11.2 copy number variants may confer risk for each disorder. RESULTS: Polygenic risk for SCZ and ASD converged on partially overlapping neurodevelopmental modules involved in synaptic function and transcriptional regulation, with ASD risk variants additionally enriched for modules involved in neuronal differentiation during fetal development. The 22q11.2 locus formed a large protein network during development that disproportionately affected SCZ-associated and ASD-associated neurodevelopmental modules, including loading highly onto synaptic and gene regulatory pathways. SEPT5, PI4KA, and SNAP29 genes are candidate drivers of 22q11.2 synaptic pathology relevant to SCZ and ASD, and DGCR8 and HIRA are candidate drivers of disease-relevant alterations in gene regulation. CONCLUSIONS: This approach offers a powerful framework to identify neurodevelopmental processes affected by diverse risk variants for SCZ and ASD and elucidate mechanisms through which highly penetrant, multigene copy number variants contribute to disease risk.

Hemodynamic latency is associated with reduced intelligence across the lifespan: an fMRI DCM study of aging, cerebrovascular integrity, and cognitive ability.

Changes in neurovascular coupling are associated with both Alzheimer's disease and vascular dementia in later life, but this may be confounded by cerebrovascular risk. We hypothesized that hemodynamic latency would be associated with reduced cognitive functioning across the lifespan, holding constant demographic and cerebrovascular risk. In 387 adults aged 18-85 (mean = 48.82), dynamic causal modeling was used to estimate the hemodynamic response function in the left and right V1 and V3-ventral regions of the visual cortex in response to a simple checkerboard block design stimulus with minimal cognitive demands. The hemodynamic latency (transit time) in the visual cortex was used to predict general cognitive ability (Full-Scale IQ), controlling for demographic variables (age, race, education, socioeconomic status) and cerebrovascular risk factors (hypertension, alcohol use, smoking, high cholesterol, BMI, type 2 diabetes, cardiac disorders). Increased hemodynamic latency in the visual cortex predicted reduced cognitive function (p < 0.05), holding constant demographic and cerebrovascular risk. Increased alcohol use was associated with reduced overall cognitive function (Full Scale IQ 2.8 pts, p < 0.05), while cardiac disorders (Full Scale IQ 3.3 IQ pts; p < 0.05), high cholesterol (Full Scale IQ 3.9 pts; p < 0.05), and years of education (2 IQ pts/year; p < 0.001) were associated with higher general cognitive ability. Increased hemodynamic latency was associated with reduced executive functioning (p < 0.05) as well as reductions in verbal concept formation (p < 0.05) and the ability to synthesize and analyze abstract visual information (p < 0.01). Hemodynamic latency is associated with reduced cognitive ability across the lifespan, independently of other demographic and cerebrovascular risk factors. Vascular health may predict cognitive ability long before the onset of dementias.

Intraindividual variability in neuropsychological performance predicts cognitive decline and death in HIV.

OBJECTIVE: HIV-associated neurocognitive disorder (HAND) occurs in a significant percentage of HIV-infected (HIV+) adults. Increased intraindividual variability (IIV) in cognitive function may be an early marker of emerging neurocognitive disorder, which suggests that IIV may be a sensitive measure of neurologic compromise in HIV. In the current study, we hypothesize that increased IIV may predict impending morbidity, including future cognitive decline and death. METHOD: In 708 HIV+ participants followed longitudinally for up to 14 years, we assessed the role of dispersion in forecasting death and cognitive decline. Incident neurocognitive impairment was predicted in a mixed-effects ordinal logistic regression model using age, gender, baseline mean cognitive functioning, CD4+, time followed, years of education, and dispersion at the previous visit. Death before the next visit was predicted in a binomial mixed-effects regression model using age, gender, baseline mean cognitive functioning, CD4+, time followed, years of education, and dispersion. RESULTS: Point-in-time dispersion and change in dispersion between visits predict future cognitive decline and death in HIV+ individuals. Individuals with greater dispersion at a visit or who had larger changes in dispersion between visits were more likely to demonstrate greater neurocognitive impairment at the subsequent visit. Greater IIV was also associated with an increased risk of death prior to the subsequent visit, even after controlling for HAND severity and global cognitive functioning. CONCLUSIONS: We conclude that the IIV in cognitive functioning may be more predictive of future disease consequence than mean level of cognitive functioning. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Neuroimaging of Traumatic Brain Injury.

The purpose of this article is to review conventional and advanced neuroimaging techniques performed in the setting of traumatic brain injury (TBI). The primary goal for the treatment of patients with suspected TBI is to prevent secondary injury. In the setting of a moderate to severe TBI, the most appropriate initial neuroimaging examination is a noncontrast head computed tomography (CT), which can reveal life-threatening injuries and direct emergent neurosurgical intervention. We will focus much of the article on advanced neuroimaging techniques including perfusion imaging and diffusion tensor imaging and discuss their potentials and challenges. We believe that advanced neuroimaging techniques may improve the accuracy of diagnosis of TBI and improve management of TBI.

Bifactor Modeling of the Positive and Negative Syndrome Scale: Generalized Psychosis Spans Schizoaffective, Bipolar, and Schizophrenia Diagnoses.

Objective: Common genetic variation spans schizophrenia, schizoaffective and bipolar disorders, but historically, these syndromes have been distinguished categorically. A symptom dimension shared across these syndromes, if such a general factor exists, might provide a clearer target for understanding and treating mental illnesses that share core biological bases. Method: We tested the hypothesis that a bifactor model of the Positive and Negative Syndrome Scale (PANSS), containing 1 general factor and 5 specific factors (positive, negative, disorganized, excited, anxiety), explains the cross-diagnostic structure of symptoms better than the traditional 5-factor model, and examined the extent to which a general factor reflects the overall severity of symptoms spanning diagnoses in 5094 total patients with a diagnosis of schizophrenia, schizoaffective, and bipolar disorder. Results: The bifactor model provided superior fit across diagnoses, and was closer to the "true" model, compared to the traditional 5-factor model (Vuong test; P < .001). The general factor included high loadings on 28 of the 30 PANSS items, omitting symptoms associated with the excitement and anxiety/depression domains. The general factor had highest total loadings on symptoms that are often associated with the positive and disorganization syndromes, but there were also substantial loadings on the negative syndrome thus leading to the interpretation of this factor as reflecting generalized psychosis. Conclusions: A bifactor model derived from the PANSS can provide a stronger framework for measuring cross-diagnostic psychopathology than a 5-factor model, and includes a generalized psychosis dimension shared at least across schizophrenia, schizoaffective, and bipolar disorder.

Measuring pathology using the PANSS across diagnoses: Inconsistency of the positive symptom domain across schizophrenia, schizoaffective, and bipolar disorder.

Although the Positive and Negative Syndrome Scale (PANSS) was developed for use in schizophrenia (SZ), antipsychotic drug trials use the PANSS to measure symptom change also for bipolar (BP) and schizoaffective (SA) disorder, extending beyond its original indications. If the dimensions measured by the PANSS are different across diagnoses, then the same score change for the same drug condition may have different meanings depending on which group is being studied. Here, we evaluated whether the factor structure in the PANSS was consistent across schizophrenia (n = 3647), bipolar disorder (n = 858), and schizoaffective disorder (n = 592). Along with congruency coefficients, Hancock's H, and Jaccard indices, we used target rotations and statistical tests of invariance based on confirmatory factor models. We found the five symptom dimensions measured by the 30-item PANSS did not generalize well to schizoaffective and bipolar disorders. A model based on an 18-item version of the PANSS generalized better across SZ and BP groups, but significant problems remained in generalizing some of the factors to the SA sample. Schizophrenia and bipolar disorder showed greater similarity in factor structure than did schizophrenia and schizoaffective disorder. The Anxiety/Depression factor was the most consistent across disorders, while the Positive factor was the least consistent.

The Relationship of Cognitive Performance and the Theta-Alpha Power Ratio Is Age-Dependent: An EEG Study of Short Term Memory and Reasoning during Task and Resting-State in Healthy Young and Old Adults.

Objective: The Theta-Alpha ratio (TAR) is known to differ based upon age and cognitive ability, with pathological electroencephalography (EEG) patterns routinely found within neurodegenerative disorders of older adults. We hypothesized that cognitive ability would predict EEG metrics differently within healthy young and old adults, and that healthy old adults not showing age-expected EEG activity may be more likely to demonstrate cognitive deficits relative to old adults showing these expected changes. Methods: In 216 EEG blocks collected in 16 young and 20 old adults during rest (eyes open, eyes closed) and cognitive tasks (short-term memory [STM]; matrix reasoning [RM; Raven's matrices]), models assessed the contributing roles of cognitive ability, age, and task in predicting the TAR. A general linear mixed-effects regression model was used to model this relationship, including interaction effects to test whether increased cognitive ability predicted TAR differently for young and old adults at rest and during cognitive tasks. Results: The relationship between cognitive ability and the TAR across all blocks showed age-dependency, and cognitive performance at the CZ midline location predicted the TAR measure when accounting for the effect of age (p < 0.05, chi-square test of nested models). Age significantly interacted with STM performance in predicting the TAR (p < 0.05); increases in STM were associated with increased TAR in young adults, but not in old adults. RM showed similar interaction effects with aging and TAR (p < 0.10). Conclusion: EEG correlates of cognitive ability are age-dependent. Adults who did not show age-related EEG changes were more likely to exhibit cognitive deficits than those who showed age-related changes. This suggests that healthy aging should produce moderate changes in Alpha and TAR measures, and the absence of such changes signals impaired cognitive functioning.

Disparity between General Symptom Relief and Remission Criteria in the Positive and Negative Syndrome Scale (PANSS): A Post-treatment Bifactor Item Response Theory Model.

Objective: Total scale scores derived by summing ratings from the 30-item PANSS are commonly used in clinical trial research to measure overall symptom severity, and percentage reductions in the total scores are sometimes used to document the efficacy of treatment. Acknowledging that some patients may have substantial changes in PANSS total scores but still be sufficiently symptomatic to warrant diagnosis, ratings on a subset of 8 items, referred to here as the "Remission set," are sometimes used to determine if patients' symptoms no longer satisfy diagnostic criteria. An unanswered question remains: is the goal of treatment better conceptualized as reduction in overall symptom severity, or reduction in symptoms below the threshold for diagnosis? We evaluated the psychometric properties of PANSS total scores, to assess whether having low symptom severity post-treatment is equivalent to attaining Remission. Design: We applied a bifactor item response theory (IRT) model to post-treatment PANSS ratings of 3,647 subjects diagnosed with schizophrenia assessed at the termination of 11 clinical trials. The bifactor model specified one general dimension to reflect overall symptom severity, and five domain-specific dimensions. We assessed how PANSS item discrimination and information parameters varied across the range of overall symptom severity (θ), with a special focus on low levels of symptoms (i.e., θ<-1), which we refer to as "Relief" from symptoms. A score of θ=-1 corresponds to an expected PANSS item score of 1.83, a rating between "Absent" and "Minimal" for a PANSS symptom. Results: The application of the bifactor IRT model revealed: (1) 88% of total score variation was attributable to variation in general symptom severity, and only 8% reflected secondary domain factors. This implies that a general factor may provide a good indicator of symptom severity, and that interpretation is not overly complicated by multidimensionality; (2) Post-treatment, 534 individuals (about 15% of the whole sample) scored in the "Relief" range of general symptom severity, but more than twice that number (n = 1351) satisfied Remission criteria (37%). 2 in 3 Remitted patients had scores that were not in a low symptom range (corresponding to Absent or Minimal item scores); (3) PANSS items vary greatly in their ability to measure the general symptom severity dimension; while many items are highly discriminating and relatively "pure" indicators of general symptom severity (delusions, conceptual disorganization), others are better indicators of specific dimensions (blunted affect, depression). The utility of a given PANSS item for assessing a patient depended on the illness level of the patient. Conclusion: Satisfying conventional Remission criteria was not strongly associated with low levels of symptoms. The items providing the most information for patients in the symptom Relief range were Delusions, Preoccupation, Suspiciousness Persecution, Unusual Thought Content, Conceptual Disorganization, Stereotyped Thinking, Active Social Avoidance, and Lack of Judgment and Insight. Lower scores on these items (item scores ≤2) were strongly associated with having a low latent trait θ or experiencing overall symptom relief. The inter-rater agreement between Remission and Relief subjects suggested that these criteria identified different subsets of patients. Alternative subsets of items may offer better indicators of general symptom severity and provide better discrimination (and lower standard errors) for scaling individuals and judging symptom relief, where the "best" subset of items ultimately depends on the illness range and treatment phase being evaluated.

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

BACKGROUND: Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. NEW METHOD: The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. RESULTS AND COMPARISON WITH EXISTING METHOD: The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (p<0.001) for predicting the task being performed within each scan using artifact-cleaned components. The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy compared to the ICA and sparse coding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p<0.001). Lower classification accuracy occurred when the extracted spatial maps contained more CSF regions (p<0.001). CONCLUSION: The success of sparse coding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations.