BrainAGE, brain health, and mental disorders: A systematic review

The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.

Alterations of Functional Connectivity Dynamics in Affective and Psychotic Disorders

BACKGROUND

Psychosis and depression patients exhibit widespread neurobiological abnormalities. The analysis of dynamic functional connectivity (dFC), allows for the detection of changes in complex brain activity patterns, providing insights into common and unique processes underlying these disorders.

METHODS

In the present study, we report the analysis of dFC in a large patient sample including 127 clinical high-risk patients (CHR), 142 recent-onset psychosis (ROP) patients, 134 recent-onset depression (ROD) patients, and 256 healthy controls (HC). A sliding window-based technique was used to calculate the time-dependent FC in resting-state MRI data, followed by clustering to reveal recurrent FC states in each diagnostic group.

RESULTS

We identified five unique FC states, which could be identified in all groups with high consistency (rmean = 0.889, sd = 0.116). Analysis of dynamic parameters of these states showed a characteristic increase in the lifetime and frequency of a weakly-connected FC state in ROD patients (p < 0.0005) compared to most other groups, and a common increase in the lifetime of a FC state characterised by high sensorimotor and cingulo-opercular connectivities in all patient groups compared to the HC group (p < 0.0002). Canonical correlation analysis revealed a mode which exhibited significant correlations between dFC parameters and clinical variables (r = 0.617, p < 0.0029), which was associated with positive psychosis symptom severity and several dFC parameters.

CONCLUSIONS

Our findings indicate diagnosis-specific alterations of dFC and underline the potential of dynamic analysis to characterize disorders such as depression, psychosis and clinical risk states.

Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness

Cognitively impaired and spared patient subgroups were identified in psychosis and depression, and in clinical high-risk for psychosis (CHR). Studies suggest differences in underlying brain structural and functional characteristics. It is unclear whether cognitive subgroups are transdiagnostic phenomena in early stages of psychotic and affective disorder which can be validated on the neural level. Patients with recent-onset psychosis (ROP; N = 140; female = 54), recent-onset depression (ROD; N = 130; female = 73), CHR (N = 128; female = 61) and healthy controls (HC; N = 270; female = 165) were recruited through the multi-site study PRONIA. The transdiagnostic sample and individual study groups were clustered into subgroups based on their performance in eight cognitive domains and characterized by gray matter volume (sMRI) and resting-state functional connectivity (rsFC) using support vector machine (SVM) classification. We identified an impaired subgroup (NROP = 79, NROD = 30, NCHR = 37) showing cognitive impairment in executive functioning, working memory, processing speed and verbal learning (all p < 0.001). A spared subgroup (NROP = 61, NROD = 100, NCHR = 91) performed comparable to HC. Single-disease subgroups indicated that cognitive impairment is stronger pronounced in impaired ROP compared to impaired ROD and CHR. Subgroups in ROP and ROD showed specific symptom- and functioning-patterns. rsFC showed superior accuracy compared to sMRI in differentiating transdiagnostic subgroups from HC (BACimpaired = 58.5%; BACspared = 61.7%, both: p < 0.01). Cognitive findings were validated in the PRONIA replication sample (N = 409). Individual cognitive subgroups in ROP, ROD and CHR are more informative than transdiagnostic subgroups as they map onto individual cognitive impairment and specific functioning- and symptom-patterns which show limited overlap in sMRI and rsFC. CLINICAL TRIAL REGISTRY NAME: German Clinical Trials Register (DRKS). Clinical trial registry URL: https://www.drks.de/drks_web/ . Clinical trial registry number: DRKS00005042.

A multivariate cognitive approach to predict social functioning in recent onset psychosis in response to computerized cognitive training

Clinical and neuroimaging data has been increasingly used in recent years to disentangle heterogeneity of treatment response to cognitive training (CT) and predict which individuals may achieve the highest benefits. CT has small to medium effects on improving cognitive and social functioning in recent onset psychosis (ROP) patients, who show the most profound cognitive and social functioning deficits among psychiatric patients. We employed multivariate pattern analysis (MVPA) to investigate the potential of cognitive data to predict social functioning improvement in response to 10 h of CT in patients with ROP. A support vector machine (SVM) classifier was trained on the naturalistic data of the Personalized Prognostic Tools for Early Psychosis Management (PRONIA) study sample to predict functioning in an independent sample of 70 ROP patients using baseline cognitive data. PRONIA is a part of a FP7 EU grant program that involved 7 sites across 5 European countries, designed and conducted with the main aim of identifying (bio)markers associated with an enhanced risk of developing psychosis in order to improve early detection and prognosis. Social functioning was predicted with a balanced accuracy (BAC) of 66.4% (Sensitivity 78.8%; Specificity 54.1%; PPV 60.5%; NPV 74.1%; AUC 0.64; P = 0.01). The most frequently selected cognitive features (mean feature weights > ± 0.2) included the (1) correct number of symbol matchings within the Digit Symbol Substitution Test, (2) the number of distracting stimuli leading to an error within 300 and 200 trials in the Continuous Performance Test and (3) the dynamics of verbal fluency between 15 and 30 s within the Verbal Fluency Test, phonetic part. Next, the SVM classifier generated on the PRONIA sample was applied to the intervention sample, that obtained 54 ROP patients who were randomly assigned to a social cognitive training (SCT) or treatment as usual (TAU) group and dichotomized into good (GF-S ≥ 7) and poor (GF-S < 7) functioning patients based on their level of Global Functioning-Social (GF-S) score at follow-up (FU). By applying the initial PRONIA classifier, using out-of-sample cross-validation (OOCV) to the sample of ROP patients who have undergone the CT intervention, a BAC of 59.3% (Sensitivity 70.4%; Specificity 48.1%; PPV 57.6%; NPV 61.9%; AUC 0.63) was achieved at T0 and a BAC of 64.8% (Sensitivity 66.7%; Specificity 63.0%; PPV 64.3%; NPV 65.4%; AUC 0.66) at FU. After SCT intervention, a significant improvement in predicted social functioning values was observed in the SCT compared to TAU group (P ≤0.05; ES[Cohens' d] = 0.18). Due to a small sample size and modest variance of social functioning of the intervention sample it was not feasible to predict individual response to SCT in the current study. Our findings suggest that the use of baseline cognitive data could provide a robust individual estimate of future social functioning, while prediction of individual response to SCT using cognitive data that can be generated in the routine patient care remains to be addressed in large-scale cognitive training trials.

Normative Modeling of Brain Morphometry in Clinical High Risk for Psychosis

Importance

The lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in individuals at psychosis risk may be nested within the range observed in healthy individuals.

Objective

To quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder.

Design, Setting, and Participants

This case-control study used clinical-, IQ-, and neuroimaging software (FreeSurfer)-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1340 individuals with CHR-P and 1237 healthy individuals pooled from 29 international sites participating in the Enhancing Neuroimaging Genetics Through Meta-analysis (ENIGMA) Clinical High Risk for Psychosis Working Group. Healthy individuals and individuals with CHR-P were matched on age and sex within each recruitment site. Data were analyzed between September 1, 2021, and November 30, 2022.

Main Outcomes and Measures

For each regional morphometric measure, deviation scores were computed as z scores indexing the degree of deviation from their normative means from a healthy reference population. Average deviation scores (ADS) were also calculated for regional CT, SA, and SV measures and globally across all measures. Regression analyses quantified the association of deviation scores with clinical severity and cognition, and 2-proportion z tests identified case-control differences in the proportion of individuals with infranormal (z < -1.96) or supranormal (z > 1.96) scores.

Results

Among 1340 individuals with CHR-P, 709 (52.91%) were male, and the mean (SD) age was 20.75 (4.74) years. Among 1237 healthy individuals, 684 (55.30%) were male, and the mean (SD) age was 22.32 (4.95) years. Individuals with CHR-P and healthy individuals overlapped in the distributions of the observed values, regional z scores, and all ADS values. For any given region, the proportion of individuals with CHR-P who had infranormal or supranormal values was low (up to 153 individuals [<11.42%]) and similar to that of healthy individuals (<115 individuals [<9.30%]). Individuals with CHR-P who converted to a psychotic disorder had a higher percentage of infranormal values in temporal regions compared with those who did not convert (7.01% vs 1.38%) and healthy individuals (5.10% vs 0.89%). In the CHR-P group, only the ADS SA was associated with positive symptoms (β = -0.08; 95% CI, -0.13 to -0.02; P = .02 for false discovery rate) and IQ (β = 0.09; 95% CI, 0.02-0.15; P = .02 for false discovery rate).

Conclusions and Relevance

In this case-control study, findings suggest that macroscale neuromorphometric measures may not provide an adequate explanation of psychosis risk.

Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).

Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study

BACKGROUND

Formal thought disorder (FThD) is a core feature of psychosis, and its severity and long-term persistence relates to poor clinical outcomes. However, advances in developing early recognition and management tools for FThD are hindered by a lack of insight into the brain-level predictors of FThD states and progression at the individual level.

METHODS

Two hundred thirty-three individuals with recent-onset psychosis were drawn from the multisite European Prognostic Tools for Early Psychosis Management study. Support vector machine classifiers were trained within a cross-validation framework to separate two FThD symptom-based subgroups (high vs. low FThD severity), using cross-sectional whole-brain multiband fractional amplitude of low frequency fluctuations, gray matter volume and white matter volume data. Moreover, we trained machine learning models on these neuroimaging readouts to predict the persistence of high FThD subgroup membership from baseline to 1-year follow-up.

RESULTS

Cross-sectionally, multivariate patterns of gray matter volume within the salience, dorsal attention, visual, and ventral attention networks separated the FThD severity subgroups (balanced accuracy [BAC] = 60.8%). Longitudinally, distributed activations/deactivations within all fractional amplitude of low frequency fluctuation sub-bands (BACslow-5 = 73.2%, BACslow-4 = 72.9%, BACslow-3 = 68.0%), gray matter volume patterns overlapping with the cross-sectional ones (BAC = 62.7%), and smaller frontal white matter volume (BAC = 73.1%) predicted the persistence of high FThD severity from baseline to follow-up, with a combined multimodal balanced accuracy of BAC = 77%.

CONCLUSIONS

We report the first evidence of brain structural and functional patterns predictive of FThD severity and persistence in early psychosis. These findings open up avenues for the development of neuroimaging-based diagnostic, prognostic, and treatment options for the early recognition and management of FThD and associated poor outcomes.

Reduced neural connectivity in the caudate anterior head predicts hallucination severity in schizophrenia

BACKGROUND

Caudate functional abnormalities have been identified as one critical neural substrate underlying sensory gating impairments that lead to auditory phantom hallucinations in both patients with schizophrenia (SZ) and tinnitus, characterized by the perception of internally generated sounds in the absence of external environmental auditory stimuli. In this study, we tested the hypothesis as to whether functional connectivity abnormalities in distinct caudate subdivisions implicated in sensory gating and auditory phantom percepts in tinnitus, which are currently being localized for neuromodulation targeting using deep brain stimulation techniques, would be associated with auditory phantom hallucination severity in SZ.

METHODS

Twenty five SZ and twenty eight demographically-matched healthy control (HC) participants, completed this fMRI resting-state study and clinical assessments.

RESULTS

Between-group seed-to-voxel analyses revealed only one region, the caudate anterior head, which showed reduced functional connectivity with the thalamus that survived whole-brain multiple comparison corrections. Importantly, connectivity between the caudate anterior head with thalamus negatively correlated with hallucination severity.

CONCLUSIONS

In the present study, we deliver the first evidence of caudate subdivision specificity for the neural pathophysiology underlying hallucinations in schizophrenia within a sensory gating framework that has been developed for auditory phantoms in patients with tinnitus. Our findings provide transdiagnostic convergent evidence for the role of the caudate in the gating of auditory phantom hallucinations, observed across patients with SZ and tinnitus by specifying the anterior caudate division is key to mediation of hallucinations, and creating a path towards personalized treatment approaches to arrest auditory phantom hallucinations from reaching perceptual awareness.

Normative modeling of brain morphometry in Clinical High-Risk for Psychosis

Importance

The lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in the majority of individuals at psychosis risk may be nested within the range observed in healthy individuals.

Objective

To quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high-risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder.

Design Setting and Participants

Clinical, IQ and FreeSurfer-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1,340 CHR-P individuals [47.09% female; mean age: 20.75 (4.74) years] and 1,237 healthy individuals [44.70% female; mean age: 22.32 (4.95) years] from 29 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group.

Main Outcomes and Measures

For each regional morphometric measure, z-scores were computed that index the degree of deviation from the normative means of that measure in a healthy reference population (N=37,407). Average deviation scores (ADS) for CT, SA, SV, and globally across all measures (G) were generated by averaging the respective regional z-scores. Regression analyses were used to quantify the association of deviation scores with clinical severity and cognition and two-proportion z-tests to identify case-control differences in the proportion of individuals with infranormal (z<-1.96) or supranormal (z>1.96) scores.

Results

CHR-P and healthy individuals overlapped in the distributions of the observed values, regional z-scores, and all ADS vales. The proportion of CHR-P individuals with infranormal or supranormal values in any metric was low (<12%) and similar to that of healthy individuals. CHR-P individuals who converted to psychosis compared to those who did not convert had a higher percentage of infranormal values in temporal regions (5-7% vs 0.9-1.4%). In the CHR-P group, only the ADSSA showed significant but weak associations (|β|<0.09; PFDR<0.05) with positive symptoms and IQ.

Conclusions and Relevance

The study findings challenge the usefulness of macroscale neuromorphometric measures as diagnostic biomarkers of psychosis risk and suggest that such measures do not provide an adequate explanation for psychosis risk.

Sex differences in predictors and regional patterns of brain age gap estimates

The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.

Construct validity for computational linguistic metrics in individuals at clinical risk for psychosis: Associations with clinical ratings

Language deficits are prevalent in psychotic illness, including its risk states, and are related to marked impairment in functioning. It is therefore important to characterize language impairment in the psychosis spectrum in order to develop potential preventive interventions. Natural language processing (NLP) metrics of semantic coherence and syntactic complexity have been used to discriminate schizophrenia patients from healthy controls (HC) and predict psychosis onset in individuals at clinical high-risk (CHR) for psychosis. To date, no studies have yet examined the construct validity of key NLP features with respect to clinical ratings of thought disorder in a CHR cohort. Herein we test the association of key NLP metrics of coherence and complexity with ratings of positive and negative thought disorder, respectively, in 60 CHR individuals, using Andreasen's Scale of Assessment of Thought, Language and Communication (TLC) Scale to measure of positive and negative thought disorder. As hypothesized, in CHR individuals, the NLP metric of semantic coherence was significantly correlated with positive thought disorder severity and the NLP metrics of complexity (sentence length and determiner use) were correlated with negative thought disorder severity. The finding of construct validity supports the premise that NLP analytics, at least in respect to core features of reduction of coherence and complexity, are capturing clinically relevant language disturbances in risk states for psychosis. Further psychometric study is required, in respect to reliability and other forms of validity.

Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages

Importance

Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures.

Objective

To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages.

Design, Setting, and Participants

A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depression (ROD), and healthy controls were recruited between February 1, 2014, to July 1, 2019. Data were analyzed between January 2020 and January 2022.

Main Outcomes and Measures

A nonnegative matrix factorization technique separately decomposed clinical (287 variables) and parcellated brain structural volume (204 gray, white, and cerebrospinal fluid regions) data across CHR-P, ROP, ROD, and healthy controls study groups. Stability criteria determined cluster number using nested cross-validation. Validation targets were compared across subgroup solutions (premorbid, longitudinal, and schizophrenia polygenic risk scores). Multiclass supervised machine learning produced a transferable solution to the validation sample.

Results

There were a total of 749 individuals in the discovery group and 610 individuals in the validation group. Individuals included those with CHR-P (n = 287), ROP (n = 323), ROD (n = 285), and healthy controls (n = 464), The mean (SD) age was 25.1 (5.9) years, and 702 (51.7%) were female. A clinical 4-dimensional solution separated individuals based on positive symptoms, negative symptoms, depression, and functioning, demonstrating associations with all validation targets. Brain clustering revealed a subgroup with distributed brain volume reductions associated with negative symptoms, reduced performance IQ, and increased schizophrenia polygenic risk scores. Multilevel results distinguished between normative and illness-related brain differences. Subgroup results were largely validated in the external sample.

Conclusions and Relevance

The results of this longitudinal cohort study provide stratifications beyond the expression of positive symptoms that cut across illness stages and diagnoses. Clinical results suggest the importance of negative symptoms, depression, and functioning. Brain results suggest substantial overlap across illness stages and normative variation, which may highlight a vulnerability signature independent from specific presentations. Premorbid, longitudinal, and genetic risk validation suggested clinical importance of the subgroups to preventive treatments.

Lower functional hippocampal connectivity in healthy adults is jointly associated with higher levels of leptin and insulin resistance

Background

Metabolic dysregulation is currently considered a major risk factor for hippocampal pathology. The aim of the present study was to characterize the influence of key metabolic drivers on functional connectivity of the hippocampus in healthy adults.

Methods

Insulin resistance was directly quantified by measuring steady-state plasma glucose (SSPG) concentration during the insulin suppression test and fasting levels of insulin, glucose, leptin, and cortisol, and measurements of body mass index and waist circumference were obtained in a sample of healthy cognitively intact adults (n = 104). Resting-state neuroimaging data were also acquired for the quantification of hippocampal functional cohesiveness and integration with the major resting-state networks (RSNs). Data-driven analysis using unsupervised machine learning (k-means clustering) was then employed to identify clusters of individuals based on their metabolic and functional connectivity profiles.

Results

K-means clustering identified two clusters of increasing metabolic deviance evidenced by cluster differences in the plasma levels of leptin (40.36 (29.97) vs. 27.59 (25.58) μg/L) and the degree of insulin resistance (SSPG concentration: 161.63 (65.27) vs. 125.72 (66.81) mg/dL). Individuals in the cluster with higher metabolic deviance showed lower functional cohesiveness within each hippocampus and lower integration of posterior and anterior components of the left and right hippocampus with the major RSNs. The two clusters did not differ in general intellectual ability or episodic memory.

Conclusions

We identified two clusters of individuals differentiated by abnormalities in insulin resistance, leptin levels, and hippocampal connectivity, with one of the clusters showing greater deviance. These findings support the link between metabolic dysregulation and hippocampal function even in nonclinical samples.

Evidence of discontinuity between psychosis-risk and non-clinical samples in the neuroanatomical correlates of social function

Objective

Social dysfunction is a major feature of clinical-high-risk states for psychosis (CHR-P). Prior research has identified a neuroanatomical pattern associated with impaired social function outcome in CHR-P. The aim of the current study was to test whether social dysfunction in CHR-P is neurobiologically distinct or in a continuum with the lower end of the normal distribution of individual differences in social functioning.

Methods

We used a machine learning classifier to test for the presence of a previously validated brain structural pattern associated with impaired social outcome in CHR-P (CHR-outcome-neurosignature) in the neuroimaging profiles of individuals from two non-clinical samples (total n = 1763) and examined its association with social function, psychopathology and cognition.

Results

Although the CHR-outcome-neurosignature could be detected in a subset of the non-clinical samples, it was not associated was adverse social outcomes or higher psychopathology levels. However, participants whose neuroanatomical profiles were highly aligned with the CHR-outcome-neurosignature manifested subtle disadvantage in fluid (P_FDR = 0.004) and crystallized intelligence (P_FDR = 0.01), cognitive flexibility (P_FDR = 0.02), inhibitory control (P_FDR = 0.01), working memory (P_FDR = 0.0005), and processing speed (P_FDR = 0.04).

Conclusions

We provide evidence of divergence in brain structural underpinnings of social dysfunction derived from a psychosis-risk enriched population when applied to non-clinical samples. This approach appears promising in identifying brain mechanisms bound to psychosis through comparisons of patient populations to non-clinical samples with the same neuroanatomical profiles.

A qualitative study on identity in individuals at clinical high risk for psychosis: " … Why does it have to be one thing?"

OBJECTIVE

Qualitative research can shed light on the subjective experiences of individuals at clinical high risk (CHR) for psychosis, complement quantitative research, broaden our understanding of experiencing CHR, and inform intervention development. The aim of this study was to explore life experiences of individuals at CHR through qualitative research.

METHOD

Participants were 37 individuals at CHR (20 male, 17 female) aged 16-34 (M_age = 23.32 ± 5.26), and 16 healthy controls (HCs; 7 male, 9 female) aged 18-34 (M_age = 25.37 ± 4.05). Qualitative data were obtained through open-ended interviews (30-45 min). No a priori hypotheses were made, and thematic analyses were used to develop themes.

RESULTS

Four major themes and one subtheme related to identity were identified through the iterative thematic analysis: defining a self-concept (with a subtheme of creativity), identity development/formation, feeling different from others, and change from a former self. Over 80% of the CHR cohort spontaneously discussed topics related to their identity, compared to 38% of HCs. HCs only reported content within the defining a self-concept theme, while the CHR group reported content within all themes.

CONCLUSIONS AND IMPLICATIONS FOR PRACTICE

The present study demonstrates that identity formation is a major process for youth in general and that psychosis experiences can make this process more challenging. CHR participants spontaneously brought up multiple themes related to identity in open-ended interviews, suggesting the relevance of this topic in this population. Clinicians should continue to probe identity-related concerns on an individual basis and research should focus on integrating this framework into the conceptualization and treatment of CHR. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Accelerated Global and Local Brain Aging Differentiate Cognitively Impaired From Cognitively Spared Patients With Schizophrenia

BACKGROUND

Accelerated aging has been proposed as a mechanism underlying the clinical and cognitive presentation of schizophrenia. The current study extends the field by examining both global and regional patterns of brain aging in schizophrenia, as inferred from brain structural data, and their association with cognitive and psychotic symptoms.

METHODS

Global and local brain-age-gap-estimates (G-brainAGE and L-brainAGE) were computed using a U-Net Model from T₁-weighted structural neuroimaging data from 84 patients (aged 16-35 years) with early-stage schizophrenia (illness duration <5 years) and 1,169 healthy individuals (aged 16-37 years). Multidomain cognitive data from the patient sample were submitted to Heterogeneity through Discriminative Analysis (HYDRA) to identify cognitive clusters.

RESULTS

HYDRA classified patients into a cognitively impaired cluster (n = 69) and a cognitively spared cluster (n = 15). Compared to healthy individuals, G-brainAGE was significantly higher in the cognitively impaired cluster (+11.08 years) who also showed widespread elevation in L-brainAGE, with the highest deviance observed in frontal and temporal regions. The cognitively spared cluster showed a moderate increase in G-brainAGE (+8.94 years), and higher L-brainAGE localized in the anterior cingulate cortex. Psychotic symptom severity in both clusters showed a positive but non-significant association with G-brainAGE.

DISCUSSION

Accelerated aging in schizophrenia can be detected at the early disease stages and appears more closely associated with cognitive dysfunction rather than clinical symptoms. Future studies replicating our findings in multi-site cohorts with larger numbers of participants are warranted.

Hippocampal volume reduction is associated with direct measure of insulin resistance in adults

Hippocampal integrity is highly susceptible to metabolic dysfunction, yet its mechanisms are not well defined. We studied 126 healthy individuals aged 23-61 years. Insulin resistance (IR) was quantified by measuring steady-state plasma glucose (SSPG) concentration during the insulin suppression test. Body mass index (BMI), adiposity, fasting insulin, glucose, leptin as well as structural neuroimaing with automatic hippocampal subfield segmentation were performed. Data analysis using unsupervised machine learning (k-means clustering) identified two subgroups reflecting a pattern of more pronounced hippocampal volume reduction being concurrently associated with greater adiposity and insulin resistance; the hippocampal volume reductions were uniform across subfields. Individuals in the most deviant subgroup were predominantly women (79 versus 42 %) with higher BMI [27.9 (2.5) versus 30.5 (4.6) kg/m²], IR (SSPG concentration, [156 (61) versus 123 (70) mg/dL] and leptinemia [21.7 (17.0) versus 44.5 (30.4) μg/L]. The use of person-based modeling in healthy individuals suggests that adiposity, insulin resistance and compromised structural hippocampal integrity behave as a composite phenotype; female sex emerged as risk factor for this phenotype.

A case report and first-person account of an individual at risk for psychosis who improved during the COVID-19 pandemic

Background

The COVID-19 pandemic is expected to increase stress and mental health symptoms. We present the case of a young man at risk for psychosis who has paradoxically shown symptomatic and functional improvement as a result of circumstances produced by COVID-19. These changes were unexpected given the client's persistent mental health struggles in the year leading up to the COVID-19 outbreak in the United States and the expectation of an exacerbation of psychotic-like symptoms.

Methods

This report is based on clinical assessments and the client's first-person account during the height of the pandemic when stay at home orders were in place.

Results

During the pandemic, the client demonstrated increased agency by taking on more responsibility at home and obtaining part-time employment. He showed improvement in his mood and relationships with others, had less symptom-related distress, and significantly reduced his cannabis use. The client interpreted these improvements in terms of changing his mindset toward more adaptive thoughts and engaging in healthy coping skills such as praying, reading, and healthy eating.

Conclusions

This case highlights the importance of fostering agency in clients during a time of crisis and ensuring that clinicians be aware of potential biases about mental health symptom exacerbation.

Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

Cognitive gains following cognitive training interventions are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single-subject level. We employed whole-brain multivariate pattern analysis with support vector machine (SVM) modeling to identify gray matter (GM) patterns that predicted higher vs. lower functioning after 40 h of ABCT at the single-subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an out-of-sample cross-validation analysis to unseen SCZ patients from an independent validation sample who underwent 50 h of ABCT. The whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). In particular, greater baseline GM volumes in regions within superior temporal gyrus, thalamus, anterior cingulate, and cerebellum predicted improved functioning at the single-subject level following ABCT in SCZ participants. The present findings provide a structural MRI fingerprint associated with preserved GM volumes at a single baseline timepoint, which predicted improved functioning following an ABCT intervention, and serve as a model for how to facilitate precision clinical therapies for SCZ based on imaging data, operating at the single-subject level.

Association of Structural Magnetic Resonance Imaging Measures With Psychosis Onset in Individuals at Clinical High Risk for Developing Psychosis: An ENIGMA Working Group Mega-analysis

Importance

The ENIGMA clinical high risk (CHR) for psychosis initiative, the largest pooled neuroimaging sample of individuals at CHR to date, aims to discover robust neurobiological markers of psychosis risk.

Objective

To investigate baseline structural neuroimaging differences between individuals at CHR and healthy controls as well as between participants at CHR who later developed a psychotic disorder (CHR-PS+) and those who did not (CHR-PS-).

Design, Setting, and Participants

In this case-control study, baseline T1-weighted magnetic resonance imaging (MRI) data were pooled from 31 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. CHR status was assessed using the Comprehensive Assessment of At-Risk Mental States or Structured Interview for Prodromal Syndromes. MRI scans were processed using harmonized protocols and analyzed within a mega-analysis and meta-analysis framework from January to October 2020.

Main Outcomes and Measures

Measures of regional cortical thickness (CT), surface area, and subcortical volumes were extracted from T1-weighted MRI scans. Independent variables were group (CHR group vs control group) and conversion status (CHR-PS+ group vs CHR-PS- group vs control group).

Results

Of the 3169 included participants, 1428 (45.1%) were female, and the mean (SD; range) age was 21.1 (4.9; 9.5-39.9) years. This study included 1792 individuals at CHR and 1377 healthy controls. Using longitudinal clinical information, 253 in the CHR-PS+ group, 1234 in the CHR-PS- group, and 305 at CHR without follow-up data were identified. Compared with healthy controls, individuals at CHR exhibited widespread lower CT measures (mean [range] Cohen d = -0.13 [-0.17 to -0.09]), but not surface area or subcortical volume. Lower CT measures in the fusiform, superior temporal, and paracentral regions were associated with psychosis conversion (mean Cohen d = -0.22; 95% CI, -0.35 to 0.10). Among healthy controls, compared with those in the CHR-PS+ group, age showed a stronger negative association with left fusiform CT measures (F = 9.8; P < .001; q < .001) and left paracentral CT measures (F = 5.9; P = .005; q = .02). Effect sizes representing lower CT associated with psychosis conversion resembled patterns of CT differences observed in ENIGMA studies of schizophrenia (ρ = 0.35; 95% CI, 0.12 to 0.55; P = .004) and individuals with 22q11.2 microdeletion syndrome and a psychotic disorder diagnosis (ρ = 0.43; 95% CI, 0.20 to 0.61; P = .001).

Conclusions and Relevance

This study provides evidence for widespread subtle, lower CT measures in individuals at CHR. The pattern of CT measure differences in those in the CHR-PS+ group was similar to those reported in other large-scale investigations of psychosis. Additionally, a subset of these regions displayed abnormal age associations. Widespread disruptions in CT coupled with abnormal age associations in those at CHR may point to disruptions in postnatal brain developmental processes.

A Neural Biomarker for Hallucinations: Medial Prefrontal Aberrations in Neural Connectivity Predict Self-Agency Deficits and Hallucination Severity in Schizophrenia

Prior studies have shown that the medial prefrontal cortex (mPFC) represents one neural substrate that mediates judgments of self-agency (i.e., the awareness that 'I am the originator of my actions'). Patients with schizophrenia (SZ) manifest cardinal self-agency deficits that contribute to debilitating psychotic symptoms (e.g. hallucinations) and distort reality monitoring. This is the first study in which we examine across 2 SZ samples, the mPFC site that underlies self-agency deficits during an explicit reality-monitoring task (i.e., while subjects distinguish self-generated information from externally-derived information) in one SZ sample, and link intrinsic functional connectivity (iFC) during rest within this a priori task-evoked self-agency seed with hallucination symptoms in a different SZ sample. In particular, we examined the iFC between the mPFC site that underlies self-agency deficits with all other brain regions in SZ using resting-state functional magnetic resonance imaging (fMRI). Resting-state fMRI data were collected from 32 SZ and 28 age, gender, and education-matched healthy control (HC) subjects. Functional connectivity maps were computed for each subject and compared between the HC and SZ groups. Within-group and between-group analyses revealed that aberrant iFC in this a priori-defined mPFC 'self-agency seed' predicted hallucination severity. The present findings reveal that the neural aberrations in this mPFC site represents one cardinal biomarker that underlies explicit self-agency deficits during a reality-monitoring task in one SZ sample that generalized to aberrant iFC differences in a different SZ sample and predicted worsening psychotic hallucinatory experiences. This region may represent a key neurobiological target for treatment avenues to improve hallucinatory symptoms.

A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis

Two decades of studies suggest that computerized cognitive training (CCT) has an effect on cognitive improvement and the restoration of brain activity. Nevertheless, individual response to CCT remains heterogenous, and the predictive potential of neuroimaging in gauging response to CCT remains unknown. We employed multivariate pattern analysis (MVPA) on whole-brain resting-state functional connectivity (rsFC) to (neuro)monitor clinical outcome defined as psychosis-likeness change after 10-hours of CCT in recent onset psychosis (ROP) patients. Additionally, we investigated if sensory processing (SP) change during CCT is associated with individual psychosis-likeness change and cognitive gains after CCT. 26 ROP patients were divided into maintainers and improvers based on their SP change during CCT. A support vector machine (SVM) classifier separating 56 healthy controls (HC) from 35 ROP patients using rsFC (balanced accuracy of 65.5%, P < 0.01) was built in an independent sample to create a naturalistic model representing the HC-ROP hyperplane. This model was out-of-sample cross-validated in the ROP patients from the CCT trial to assess associations between rsFC pattern change, cognitive gains and SP during CCT. Patients with intact SP threshold at baseline showed improved attention despite psychosis status on the SVM hyperplane at follow-up (p < 0.05). Contrarily, the attentional gains occurred in the ROP patients who showed impaired SP at baseline only if rsfMRI diagnosis status shifted to the healthy-like side of the SVM continuum. Our results reveal the utility of MVPA for elucidating treatment response neuromarkers based on rsFC-SP change and pave the road to more personalized interventions.

Personalized Estimates of Brain Structural Variability in Individuals With Early Psychosis

BACKGROUND

Early psychosis in first-episode psychosis (FEP) and clinical high-risk (CHR) individuals has been associated with alterations in mean regional measures of brain morphology. Examination of variability in brain morphology could assist in quantifying the degree of brain structural heterogeneity in clinical relative to healthy control (HC) samples.

METHODS

Structural magnetic resonance imaging data were obtained from CHR (n = 71), FEP (n = 72), and HC individuals (n = 55). Regional brain variability in cortical thickness (CT), surface area (SA), and subcortical volume (SV) was assessed with the coefficient of variation (CV). Furthermore, the person-based similarity index (PBSI) was employed to quantify the similarity of CT, SA, and SV profile of each individual to others within the same diagnostic group. Normative modeling of the PBSI-CT, PBSI-SA, and PBSI-SV was used to identify CHR and FEP individuals whose scores deviated markedly from those of the healthy individuals.

RESULTS

There was no effect of diagnosis on the CV for any regional measure (P > .38). CHR and FEP individuals differed significantly from the HC group in terms of PBSI-CT (P < .0001), PBSI-SA (P < .0001), and PBSI-SV (P = .01). In the clinical groups, normative modeling identified 32 (22%) individuals with deviant PBSI-CT, 12 (8.4%) with deviant PBSI-SA, and 21 (15%) with deviant PBSI-SV; differences of small effect size indicated that individuals with deviant PBSI scores had lower IQ and higher psychopathology.

CONCLUSIONS

Examination of brain structural variability in early psychosis indicated heterogeneity at the level of individual profiles and encourages further large-scale examination to identify individuals that deviate markedly from normative reference data.

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

IMPORTANCE

Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear.

OBJECTIVES

To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system.

DESIGN, SETTING, AND PARTICIPANTS

This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020.

MAIN OUTCOMES AND MEASURES

Accuracy and generalizability of prognostic systems.

RESULTS

A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results.

CONCLUSIONS AND RELEVANCE

These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.

Linking language features to clinical symptoms and multimodal imaging in individuals at clinical high risk for psychosis

BACKGROUND

Abnormalities in the semantic and syntactic organization of speech have been reported in individuals at clinical high-risk (CHR) for psychosis. The current study seeks to examine whether such abnormalities are associated with changes in brain structure and functional connectivity in CHR individuals.

METHODS

Automated natural language processing analysis was applied to speech samples obtained from 46 CHR and 22 healthy individuals. Brain structural and resting-state functional imaging data were also acquired from all participants. Sparse canonical correlation analysis (sCCA) was used to ascertain patterns of covariation between linguistic features, clinical symptoms, and measures of brain morphometry and functional connectivity related to the language network.

RESULTS

In CHR individuals, we found a significant mode of covariation between linguistic and clinical features (r = 0.73; p = 0.003), with negative symptoms and bizarre thinking covarying mostly with measures of syntactic complexity. In the entire sample, separate sCCAs identified a single mode of covariation linking linguistic features with brain morphometry (r = 0.65; p = 0.05) and resting-state network connectivity (r = 0.63; p = 0.01). In both models, semantic and syntactic features covaried with brain structural and functional connectivity measures of the language network. However, the contribution of diagnosis to both models was negligible.

CONCLUSIONS

Syntactic complexity appeared sensitive to prodromal symptoms in CHR individuals while the patterns of brain-language covariation seemed preserved. Further studies in larger samples are required to establish the reproducibility of these findings.

Modeling Social Sensory Processing During Social Computerized Cognitive Training for Psychosis Spectrum: The Resting-State Approach

Background: Greater impairments in early sensory processing predict response to auditory computerized cognitive training (CCT) in patients with recent-onset psychosis (ROP). Little is known about neuroimaging predictors of response to social CCT, an experimental treatment that was recently shown to induce cognitive improvements in patients with psychosis. Here, we investigated whether ROP patients show interindividual differences in sensory processing change and whether different patterns of SPC are (1) related to the differential response to treatment, as indexed by gains in social cognitive neuropsychological tests and (2) associated with unique resting-state functional connectivity (rsFC). Methods: Twenty-six ROP patients completed 10 h of CCT over the period of 4-6 weeks. Subject-specific improvement in one CCT exercise targeting early sensory processing-a speeded facial Emotion Matching Task (EMT)-was studied as potential proxy for target engagement. Based on the median split of SPC from the EMT, two patient groups were created. Resting-state activity was collected at baseline, and bold time series were extracted from two major default mode network (DMN) hubs: left medial prefrontal cortex (mPFC) and left posterior cingulate cortex (PCC). Seed rsFC analysis was performed using standardized Pearson correlation matrices, generated between the average time course for each seed and each voxel in the brain. Results: Based on SPC, we distinguished improvers-i.e., participants who showed impaired performance at baseline and reached the EMT psychophysical threshold during CCT-from maintainers-i.e., those who showed intact EMT performance at baseline and sustained the EMT psychophysical threshold throughout CCT. Compared to maintainers, improvers showed an increase of rsFC at rest between PCC and left superior and medial frontal regions and the cerebellum. Compared to improvers, maintainers showed increased rsFC at baseline between PCC and superior temporal and insular regions bilaterally. Conclusions: In ROP patients with an increase of connectivity at rest in the default mode network, social CCT is still able to induce sensory processing changes that however do not translate into social cognitive gains. Future studies should investigate if impairments in short-term synaptic plasticity are responsible for this lack of response and can be remediated by pharmacological augmentation during CCT.

Multi-outcome meta-analysis (MOMA) of cognitive remediation in schizophrenia: Revisiting the relevance of human coaching and elucidating interplay between multiple outcomes

Cognitive remediation (CR) is nowadays mainly administered in a computerized fashion, yet frequently supplemented by human guidance. The effects of CR on cognitive, functional and clinical outcomes are consistently reported, yet the response is heterogeneous. In order to resolve this heterogeneity, we employed a multi-outcome meta-analytic approach, examined effects of CR on each outcome category separately and estimated directed effects between three outcome categories. We extracted treatment effects from 67 studies that trained patients with schizophrenia (total n = 4067) using either 1) computerized CR modality alone or 2) in combination with supplementary human guidance (SHG). All three outcome domains were significantly improved by CR with small to moderate effect sizes when assessing outcomes across all studies. The comparison between CR administered with SHG revealed largest effects on the cognitive subdomains of working and verbal memory. Structural equation modeling in the single-study data suggests that cognitive gains trigger restoration of psychosocial functioning which in turn facilitates improvement in clinical symptoms.

Limitations of liability insurance

In the unfortunate event that you are sued for malpractice, the following suggestions could maximize the benefits of your professional liability insurance policy: Do not contact the claimant, because any statements you make can be used against you in a suit. Provide the insurer with as much information as possible. Monitor the case regularly by contacting the insurance adjuster assigned to the case and request copies of all documents generated. Communicate your feelings with the company about defending or settling the case. If you completely violated a standard of care such as giving an IV push medication to the wrong patient because of failure to check the patient's name bracelet, try to settle this case out of court. A jury would not likely be sympathetic to a nurse who injured a patient in this manner. Review the entire medical record and discuss your analysis with your attorney. Remember, as a policyholder, you have the right to demand that your claim be handled by a competent, professional lawyer. Unlike other professionals, attorneys are not required to have national certification. Defendants have the right to ask assigned attorneys about their malpractice experience. Does the attorney have a working knowledge of the procedures and technical jargon concerning the case? How many similar cases has the attorney handled? You have a right to these answers.

Limitations of liability insurance

In the unfortunate event that you are sued for malpractice, the following suggestions could maximize the benefits of your professional liability insurance policy: Do not contact the claimant, because any statements you make can be used against you in a suit. Provide the insurer with as much information as possible. Monitor the case regularly by contacting the insurance adjuster assigned to the case and request copies of all documents generated. Communicate your feelings with the company about defending or settling the case. If you completely violated a standard of care such as giving an IV push medication to the wrong patient because of failure to check the patient's name bracelet, try to settle this case out of court. A jury would not likely be sympathetic to a nurse who injured a patient in this manner. Review the entire medical record and discuss your analysis with your attorney. Remember, as a policyholder, you have the right to demand that your claim be handled by a competent, professional lawyer. Unlike other professionals, attorneys are not required to have national certification. Defendants have the right to ask assigned attorneys about their malpractice experience. Does the attorney have a working knowledge of the procedures and technical jargon concerning the case? How many similar cases has the attorney handled? You have a right to these answers.

Low back pain in the competitive tennis player

The etiologies of low back pain and the biomechanics and pathology of the lumbar spine as they relate to tennis stroke mechanics have been reviewed, and a treatment protocol has been presented. A recent survey of the Men's Professional Tennis Tour is the only article found that discusses low back pain in tennis players; the orthopaedic and sports medicine literature is otherwise devoid of any relevant studies. Because this one survey indicates that 38 per cent of 143 tennis players missed at least one tournament because of low back problems, it seems obvious that an epidemiologic study on low back pain in racquet sports is vital to a more thorough understanding of the problem.

Intensive care in small hospitals

The scene: a small ICU in a 120-bed community hospital with no resident house staff. Emergency coverage is assigned to the emergency room physician. A patient in the ICU has cardiac arrest. His physician is called but cannot come to the hospital immediately, and the emergency room physician is attending a seriously injured patient. In the critical interim, paramedical professionals must shoulder the heavy responsibilities for decisions and interventions.

Effect of mechanical loading on breathing patterns in women

First-breath ventilatory responses to graded inspiratory elastic and resistive loads were obtained from 80 women unfamiliar with respiratory experimentation. For each load 1) responses from different subjects ranged from a weak tidal volume defense coupled with an increased breathing frequency to a strong tidal volume defense coupled with a decreased frequency; 2) strong tidal volume defenders employed longer inspirations than did weak tidal volume defenders; and 3) individual respiratory frequency responses were mediated by changes in inspiratory and/or expiratory timing. Thus the group response was qualitatively the same as that reported for 80 men. Quantitatively, however, mean inspiratory airflow responses of women exceeded those of men by an amount attributable to women's higher intrinsic respiratory elastance. Tidal volume responses, on the other hand, did not differ significantly, suggesting that men and women produce different neural adjustments to loads. In support of this hypothesis, analysis of respiratory timing responses revealed that 1) men actively prolonged inspiration more than women during resistive loading; and 2) women actively shortened inspiration more than men during elastic loading. These findings indicate that the load-compensating behavior exhibited by men and women is similar but not identical.

Ventilatory adjustments during sustained mechanical loading in conscious humans

Ventilatory responses to inspiratory elastic and resistive loads of 67 men were analyzed. During the 1st, 5th, and 10th consecutively loaded breaths 1) individual responses ranged from a rapid-shallow to a slow-deep breathing pattern; 2) strong tidal volume (VT) defenders employed longer inspirations than did weak VT defenders; and 3) individual frequency responses were mediated by changes in inspiratory and/or expiratory timing. Thus the group response was qualitatively similar on the 1st, 5th, and 10th loaded breaths. Quantitatively, however, the group's mean minute ventilation increased throughout each episode owing to progressively larger tidal volumes coupled with equal breathing frequencies. During elastic loading this amplified VT defense was achieved by stronger inspirations with no systematic changes in timing, whereas during resistive loading it was achieved both by stronger and longer inspirations. Inspiring 5% CO2 induced a degree of hypercapnia exceeding that accompanying mechanical loading and yet elicited a comparatively modest enhancement of respiratory output. These findings suggest that in conscious humans 1) repeated mechanical loading activates neural load-compensating mechanisms; 2) the range of these neural adjustments varies with both load size and type; and 3) the stimulus to initiate this behavior is largely nonchemical.

Effect of thoracic deafferentation on load-compensating mechanisms in humans

First-breath responses to graded elastic (delta E) and resistive (delta R) loads in 29 cervical cord-injured and 80 normal men were compared with those predicted assuming identical respiratory muscle pressure (Pmus) wave forms in the unloaded and loaded states. The cord-injured group's mean actual and predicted inspiratory duration (TI) responses were equal, indicating that the average duration of their phrenic discharge remained constant during both delta E and delta R. Their mean expiratory duration (TE) responses fell short of predictions with delta E but exceeded predictions with delta R, signifying that a neural mechanism hastened inspiration following delta E but postponed it following delta R. TE/(pred TE) values (an index of phrenic timing) from different loads were proportional to concomitant TI responses but not to indices of diaphragmatic force [peak Pmus, (VT/TI)/(pred VT/TI)], movement (VT, VT/TI), or duration of contraction [TI/(pred TI)]. These findings suggest that oropharyngeal and/or pulmonary receptors signaling airflow duration, rather than diaphragmatic length or tension receptors, adjust phrenic motoneuron timing during mechanical loading. The cord-injured group produced a normal tidal volume (VT) defense coupled with a depressed frequency (f) response to delta E and, conversely, a weak VT defense coupled with a normal f response to delta R. Since both groups' VT/TI responses surpassed predictions equally, the cord-injured group's deficit was attributable entirely to impaired regulation of TE during delta E and TI during delta R. Thus chest wall receptors modulate duration and timing, but probably not average intensity, of the phrenic output during loading. Nearly one-third of the normal, but virtually none of the cord-injured, samples prolonged TI against large delta R and shortened TI agaist large delta E, suggesting that thoracic deafferentation impairs human ability to distinguish large delta R from delta E. These latter findings support the hypothesis that chest wall afferents subserve perception of respiratory force or position in humans.

A proposed specification for acrylic bone cement

A proposed specification covering handling characteristics and physical and chemical properties of bone cement composed primarily of methyl methacrylate has been prepared on the basis of data from the authors' studies and from various other sources. Under handling characteristics, requirements included relate to dough, handling and setting time, proper plasticity for insertion and temperature rise on setting. Mechanical properties specified include compressive strength and indentation and recovery characteristics. Maximum limits are proposed for water sorption and solubility. Suggested packaging requirements are also included.

A characterization of polymethylmethacrylate bone cement

Polymethylmethacrylate cement is characterized in terms of chemical composition, handling characteristics, and physical properties; the dough time, setting time, handling time, and temperature rise were found to be most affected by environmental temperature and kneading of the dough mass. As the set material ages, the residual monomer content gradually decreases and the strength increases. A volume shrinkage of up to 5 per cent was observed. The porosity, which is increased by rapid mixing, may reach 10 per cent. The mechanical properties of the cement approximate those of polymethylmethacrylate denture base material. Some variability encountered in the handling qualities of the cement could be attributed only to the variability of different batches.