A data-driven approach to categorizing early life adversity exposure in the ABCD Study.

BACKGROUND: Adversity occurring during development is associated with detrimental health and quality of life outcomes, not just following exposure but throughout the lifespan. Despite increased research, there exists both overlapping and distinct definitions of early life adversity exposure captured by over 30 different empirically validated tools. A data-driven approach to defining and cataloging exposure is needed to better understand associated outcomes and advance the field. METHODS: We utilized baseline data on 11,566 youth enrolled in the ABCD Study to catalog youth and caregiver-reported early life adversity exposure captured across 14 different measures. We employed an exploratory factor analysis to identify the factor domains of early life adversity exposure and conducted a series of regression analyses to examine its association with problematic behavioral outcomes. RESULTS: The exploratory factor analysis yielded a 6-factor solution corresponding to the following distinct domains: 1) physical and sexual violence; 2) parental psychopathology; 3) neighborhood threat; 4) prenatal substance exposure; 5) scarcity; and 6) household dysfunction. The prevalence of exposure among 9-and 10-year-old youth was largely driven by the incidence of parental psychopathology. Sociodemographic characteristics significantly differed between youth with adversity exposure and controls, depicting a higher incidence of exposure among racial and ethnic minoritized youth, and among those identifying with low socioeconomic status. Adversity exposure was significantly associated with greater problematic behaviors and largely driven by the incidence of parental psychopathology, household dysfunction and neighborhood threat. Certain types of early life adversity exposure were more significantly associated with internalizing as opposed to externalizing problematic behaviors. CONCLUSIONS: We recommend a data-driven approach to define and catalog early life adversity exposure and suggest the incorporation of more versus less data to capture the nuances of exposure, e.g., type, age of onset, frequency, duration. The broad categorizations of early life adversity exposure into two domains, such as abuse and neglect, or threat and deprivation, fail to account for the routine co-occurrence of exposures and the duality of some forms of adversity. The development and use of a data-driven definition of early life adversity exposure is a crucial step to lessening barriers to evidence-based treatments and interventions for youth.

Consistency checks to improve measurement with the Hamilton Rating Scale for Anxiety (HAM-A).

BACKGROUND: Mitigating rating inconsistency can improve measurement fidelity and detection of treatment response. METHODS: The International Society for CNS Clinical Trials and Methodology convened an expert Working Group that developed consistency checks for ratings of the Hamilton Anxiety Rating Scale (HAM-A) and Clinical Global Impression of Severity of anxiety (CGIS) that are widely used in studies of mood and anxiety disorders. Flags were applied to 40,349 HAM-A administrations from 15 clinical trials and to Monte Carlo-simulated data as a proxy for applying flags under conditions of inconsistency. RESULTS: Thirty-three flags were derived these included logical consistency checks and statistical outlier-response pattern checks. Twenty-percent of the HAM-A administrations had at least one logical scoring inconsistency flag, 4 % had two or more. Twenty-six percent of the administrations had at least one statistical outlier flag and 11 % had two or more. Overall, 35 % of administrations had at least one flag of any type, 19 % had one and 16 % had 2 or more. Most of administrations in the Monte Carlo- simulated data raised multiple flags. LIMITATIONS: Flagged ratings may represent less-common presentations of administrations done correctly. Conclusions-Application of flags to clinical ratings may aid in detecting imprecise measurement. Flags can be used for monitoring of raters during an ongoing trial and as part of post-trial evaluation. Appling flags may improve reliability and validity of trial data.

An Observed Assessment of Sensory Responsivity in Autism Spectrum Disorders: Associations with Diagnosis, Age, and Parent Report.

Sensory features are common and impairing in autism spectrum disorder (ASD), but there are few observational sensory assessments that are valid across ages. We used the Sensory Processing 3-Dimensional (SP3-D) observed Assessment and parent-reported Inventory to examine sensory responsivity in 41 ASD and 33 typically-developing (TD) youth across 7-17 years. ASD youth had higher and more variable observed and reported sensory responsivity symptoms compared to TD, but the two measures were not correlated. Observed sensory over-responsivity (SOR) and sensory craving (SC) decreased with age in ASD, though SOR remained higher in ASD versus TD through adolescence. Results suggest that in ASD, the SP3-D Assessment can identify SOR through adolescence, and that there is value in integrating multiple sensory measures.

Machine learning approach for obstructive sleep apnea screening using brain diffusion tensor imaging.

Patients with obstructive sleep apnea (OSA) show autonomic, mood, cognitive, and breathing dysfunctions that are linked to increased morbidity and mortality, which can be improved with early screening and intervention. The gold standard and other available methods for OSA diagnosis are complex, require whole-night data, and have significant wait periods that potentially delay intervention. Our aim was to examine whether using faster and less complicated machine learning models, including support vector machine (SVM) and random forest (RF), with brain diffusion tensor imaging (DTI) data can classify OSA from healthy controls. We collected two DTI series from 59 patients with OSA [age: 50.2 ± 9.9 years; body mass index (BMI): 31.5 ± 5.6 kg/m2 ; apnea-hypopnea index (AHI): 34.1 ± 21.2 events/h 23 female] and 96 controls (age: 51.8 ± 9.7 years; BMI: 26.2 ± 4.1 kg/m2 ; 51 female) using a 3.0-T magnetic resonance imaging scanner. Using DTI data, mean diffusivity maps were calculated from each series, realigned and averaged, normalised to a common space, and used to conduct cross-validation for model training and selection and to predict OSA. The RF model showed 0.73 OSA and controls classification accuracy and 0.85 area under the curve (AUC) value on the receiver-operator curve. Cross-validation showed the RF model with comparable fitting over SVM for OSA and control data (SVM; accuracy, 0.77; AUC, 0.84). The RF ML model performs similar to SVM, indicating the comparable statistical fitness to DTI data. The findings indicate that RF model has similar AUC and accuracy over SVM, and either model can be used as a faster OSA screening tool for subjects having brain DTI data.

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.

Consistency checks to improve measurement with the Hamilton Rating Scale for Depression (HAM-D).

BACKGROUND: Symptom manifestations in mood disorders can be subtle. Cumulatively, small imprecisions in measurement can limit our ability to measure treatment response accurately. Logical and statistical consistency checks between item responses (i.e., cross-sectionally) and across administrations (i.e., longitudinally) can contribute to improving measurement fidelity. METHODS: The International Society for CNS Clinical Trials and Methodology convened an expert Working Group that assembled flags indicating consistency/inconsistency ratings for the Hamilton Rating Scale for Depression (HAM-D17), a widely-used rating scale in studies of depression. Proposed flags were applied to assessments derived from the NEWMEDS data repository of 95,468 HAM-D administrations from 32 registration trials of antidepressant medications and to Monte Carlo-simulated data as a proxy for applying flags under conditions of known inconsistency. RESULTS: Two types of flags were derived: logical consistency checks and statistical outlier-response pattern checks. Almost thirty percent of the HAMD administrations had at least one logical scoring inconsistency flag. Seven percent had flags judged to suggest that a thorough review of rating is warranted. Almost 22% of the administrations had at least one statistical outlier flag and 7.9% had more than one. Most of the administrations in the Monte Carlo- simulated data raised multiple flags. LIMITATIONS: Flagged ratings may represent less-common presentations of administrations done correctly. CONCLUSIONS: Application of flags to clinical ratings may aid in detecting imprecise measurement. Reviewing and addressing these flags may improve reliability and validity of clinical trial data.

Consistency checks to improve measurement with the Personal and Social Performance Scale (PSP).

International Society for CNS Clinical Trials and Methodology convened an expert Working Group that assembled consistency/inconsistency flags for the Personal and Social Performance Scale (PSP). One hundred and forty seven flags were identified, 16 flag errors in deriving the PSP decile (i.e., total) score from the four individual domain scores, 74 flag inconsistencies between domain scores relative to Positive and Negative Symptom Scale (PANSS) item ratings and 57 flag inconsistencies between PSP decile score and PANSS items ratings. The flags were applied to assessments from randomized clinical trial data of antipsychotics in schizophrenia from almost 18,000 ratings. Twenty-two flags were raised in at least 5 of 1000 ratings. Nearly 20% of the PSP ratings had at least one inconsistency flag raised. Application of flags to clinical ratings may improve the reliability of ratings and validity of trials.

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.

Training and Certifying Users of the National Institutes of Health Stroke Scale.

Background and Purpose- The National Institutes of Health Stroke Scale, designed and validated for use in clinical stroke trials, is now required for all patients with stroke at hospital admission. Recertification is required annually but no data support this frequency; the effect of mandatory training before recertification is unknown. Methods- To clarify optimal recertification frequency and training effect, we assessed users' mastery of the National Institutes of Health Stroke Scale over several years using correct scores (accuracy) on each scale item of the 15-point scale. We also constructed 9 technical errors that could result from misunderstanding the scoring rules. We measured accuracy and the frequency of these technical errors over time. Using multivariable regression, we assessed the effect of time, repeat testing, and profession on user mastery. Results- The final dataset included 1.3×106 examinations. Data were consistent among all 3 online vendors that provide training and certification. Test accuracy showed no significant changes over time. Technical error rates were remarkably low, ranging from 0.48 to 1.36 per 90 test items. Within 2 vendors (that do not require training), the technical error rates increased negligibly over time (P<0.05). In data from a third vendor, mandatory training before recertification improved (reduced) technical errors but not accuracy. Conclusions- The data suggest that mastery of National Institutes of Health Stroke Scale scoring rules is stable over time, and the recertification interval should be lengthened. Mandatory retraining may be needed after unsuccessful recertifications, but not routinely otherwise.

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.

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.

Inter-hemispheric Asymmetry Patterns in the ADHD Brain: A Neuroimaging Replication Study.

Attention-deficit/ hyperactivity disorder (ADHD) is the most common neurodevelopment disorder in children, and many genetic markers have been linked to the behavioral phenotypes of this highly heritable disease. The neuroimaging correlates are similarly complex, with multiple combinations of structural and functional alterations associated with the disease presentations of hyperactivity and inattentiveness. Thus far, neuroimaging studies have provided mixed results in ADHD patients, particularly with respect to the laterality of findings. It is possible that hemispheric asymmetry differences may help reconcile the variability of these findings. We recently reported that inter-hemispheric asymmetry differences were more sensitive descriptors for identifying differences between ADHD and typically developing (TD) brains (n=849) across volumetric, morphometric, and white matter neuroimaging metrics. Here, we examined the replicability of these findings across a new data set (n=202) of TD and ADHD subjects at the time of diagnosis (medication naive) and after a six week course of either stimulant drugs, non-stimulant medications, or combination therapy. Our findings replicated our earlier work across a number of volumetric and white matter measures confirming that asymmetry is more robust at detecting differences between TD and ADHD brains. However, the effects of medication failed to produce significant alterations across either lateralized or symmetry measures. We suggest that the delay in brain volume maturation observed in ADHD youths may be hemisphere dependent. Future work may investigate the extent to which these inter-hemispheric asymmetry differences are causal or compensatory in nature.

Therapeutic hypothermia and Type II errors: Do not throw out the baby with the ice water.

After initial enthusiasm for mild therapeutic hypothermia (TH) treatment after brain injuries, including global cerebral ischemia after cardiac arrest, subsequent trials suggested similar benefit using only targeted temperature management (TTM), with fewer side effects. Globally, effective treatment of brain ischemia with TH has declined. Recent data suggest, however, that TH to 33°C may be superior to TTM. We review the background and rationale underlying TH and TTM. We present previously published data from our own laboratory that confirms TH to 33°C provides superior brain cytoprotection, compared to 35°C or 37°C, over a range of delays to treatment and several durations of TH. We illustrate that the treatment effect size of either or 35 is superior to 37, but the effect size difference between 33 and 35, although significant, is small. We estimate that to demonstrate the superiority of TTM over TH, a clinical trial would need between 3,000 and 9,000 patients depending on the desired treatment effect size. Our review and our own data suggest that TH to 33°C is superior to TTM to 36°C, but an extremely large clinical trial would be needed to demonstrate the difference.

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.

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).

Understanding Friendship Sex Heterophily and Relational Characteristics to Optimize the Selection of Peer Models for Children with Autism Spectrum Disorder.

This study used social network analysis to evaluate whether sex heterophily, the degree to which peers are different in sex, between 126 children with autism (ages 5-12 years) and their peers affected social network connectivity. Results indicate that: (1) the quantity and sex of friends were more important in predicting social network connectivity than the relational characteristics of the friends (friendship nominations and social network salience/popularity); and (2) sex heterophily is an important factor in predicting social network connectivity. For males with autism, having friends of the same sex was associated with better social network connectivity; this was not true for females with autism. These findings have important implications for the selection of peer models for elementary-aged children with autism.

Hemispheric brain asymmetry differences in youths with attention-deficit/hyperactivity disorder.

Introduction: Attention-deficit hyperactive disorder (ADHD) is the most common neurodevelopmental disorder in children. Diagnosis is currently based on behavioral criteria, but magnetic resonance imaging (MRI) of the brain is increasingly used in ADHD research. To date however, MRI studies have provided mixed results in ADHD patients, particularly with respect to the laterality of findings. Methods: We studied 849 children and adolescents (ages 6-21 y.o.) diagnosed with ADHD (n = 341) and age-matched typically developing (TD) controls with structural brain MRI. We calculated volumetric measures from 34 cortical and 14 non-cortical brain regions per hemisphere, and detailed shape morphometry of subcortical nuclei. Diffusion tensor imaging (DTI) data were collected for a subset of 104 subjects; from these, we calculated mean diffusivity and fractional anisotropy of white matter tracts. Group comparisons were made for within-hemisphere (right/left) and between hemisphere asymmetry indices (AI) for each measure. Results: DTI mean diffusivity AI group differences were significant in cingulum, inferior and superior longitudinal fasciculus, and cortico-spinal tracts (p < 0.001) with the effect of stimulant treatment tending to reduce these patterns of asymmetry differences. Gray matter volumes were more asymmetric in medication free ADHD individuals compared to TD in twelve cortical regions and two non-cortical volumes studied (p < 0.05). Morphometric analyses revealed that caudate, hippocampus, thalamus, and amygdala were more asymmetric (p < 0.0001) in ADHD individuals compared to TD, and that asymmetry differences were more significant than lateralized comparisons. Conclusions: Brain asymmetry measures allow each individual to serve as their own control, diminishing variability between individuals and when pooling data across sites. Asymmetry group differences were more significant than lateralized comparisons between ADHD and TD subjects across morphometric, volumetric, and DTI comparisons.

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.

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.

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.