High-density lipoprotein (HDL) as an indicator for alcohol use in a psychiatrically ill population.

AIMS: To examine the cross sectional and longitudinal associations between the Alcohol Use Disorders Identification Test-Concise (AUDIT-C) and differences in high-density lipoprotein (HDL) in a psychiatrically ill population. METHODS: Retrospective observational study using electronic health record data from a large healthcare system, of patients hospitalized for a mental health/substance use disorder (MH/SUD) from 1 July 2016 to 31 May 2023, who had a proximal AUDIT-C and HDL (N = 15 915) and the subset who had a repeat AUDIT-C and HDL 1 year later (N = 2915). Linear regression models examined the association between cross-sectional and longitudinal AUDIT-C scores and HDL, adjusting for demographic and clinical characteristics that affect HDL. RESULTS: Compared with AUDIT-C score = 0, HDL was higher among patients with greater AUDIT-C severity (e.g. moderate AUDIT-C score = 8.70[7.65, 9.75] mg/dl; severe AUDIT-C score = 13.02 [12.13, 13.90] mg/dL[95% confidence interval (CI)] mg/dl). The associations between cross-sectional HDL and AUDIT-C scores were similar with and without adjusting for patient demographic and clinical characteristics. HDL levels increased for patients with mild alcohol use at baseline and moderate or severe alcohol use at follow-up (15.06[2.77, 27.69] and 19.58[2.77, 36.39] mg/dL[95%CI] increase for moderate and severe, respectively). CONCLUSIONS: HDL levels correlate with AUDIT-C scores among patients with MH/SUD. Longitudinally, there were some (but not consistent) increases in HDL associated with increases in AUDIT-C. The increases were within range of typical year-to-year variation in HDL across the population independent of alcohol use, limiting the ability to use HDL as a longitudinal clinical indicator for alcohol use in routine care.

Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome.

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.

Spatiotemporal dynamics of hippocampal-cortical networks underlying the unique phenomenological properties of trauma-related intrusive memories.

Trauma-related intrusive memories (TR-IMs) possess unique phenomenological properties that contribute to adverse post-traumatic outcomes, positioning them as critical intervention targets. However, transdiagnostic treatments for TR-IMs are scarce, as their underlying mechanisms have been investigated separate from their unique phenomenological properties. Extant models of more general episodic memory highlight dynamic hippocampal-cortical interactions that vary along the anterior-posterior axis of the hippocampus (HPC) to support different cognitive-affective and sensory-perceptual features of memory. Extending this work into the unique properties of TR-IMs, we conducted a study of eighty-four trauma-exposed adults who completed daily ecological momentary assessments of TR-IM properties followed by resting-state functional magnetic resonance imaging (rs-fMRI). Spatiotemporal dynamics of anterior and posterior hippocampal (a/pHPC)-cortical networks were assessed using co-activation pattern analysis to investigate their associations with different properties of TR-IMs. Emotional intensity of TR-IMs was inversely associated with the frequency and persistence of an aHPC-default mode network co-activation pattern. Conversely, sensory features of TR-IMs were associated with more frequent co-activation of the HPC with sensory cortices and the ventral attention network, and the reliving of TR-IMs in the "here-and-now" was associated with more persistent co-activation of the pHPC and the visual cortex. Notably, no associations were found between HPC-cortical network dynamics and conventional symptom measures, including TR-IM frequency or retrospective recall, underscoring the utility of ecological assessments of memory properties in identifying their neural substrates. These findings provide novel insights into the neural correlates of the unique features of TR-IMs that are critical for the development of individualized, transdiagnostic treatments for this pervasive, difficult-to-treat symptom.

Genome-wide analysis of FRF gene family and functional identification of HvFRF9 under drought stress in barley.

FHY3 and its homologous protein FAR1 are the founding members of FRS family. They exhibited diverse and powerful physiological functions during evolution, and participated in the response to multiple abiotic stresses. FRF genes are considered to be truncated FRS family proteins. They competed with FRS for DNA binding sites to regulate gene expression. However, only few studies are available on FRF genes in plants participating in the regulation of abiotic stress. With wide adaptability and high stress-resistance, barley is an excellent candidate for the identification of stress-resistance-related genes. In this study, 22 HvFRFs were detected in barley using bioinformatic analysis from whole genome. According to evolution and conserved motif analysis, the 22 HvFRFs could be divided into subfamilies I and II. Most promoters of subfamily I members contained abscisic acid and methyl jasmonate response elements; however, a large number promoters of subfamily II contained gibberellin and salicylic acid response elements. HvFRF9, one of the members of subfamily II, exhibited a expression advantage in different tissues, and it was most significantly upregulated under drought stress. In-situ PCR revealed that HvFRF9 is mainly expressed in the root epidermal cells, as well as xylem and phloem of roots and leaves, indicating that HvFRF9 may be related to absorption and transportation of water and nutrients. The results of subcellular localization indicated that HvFRF9 was mainly expressed in the nuclei of tobacco epidermal cells and protoplast of arabidopsis. Further, transgenic arabidopsis plants with HvFRF9 overexpression were generated to verify the role of HvFRF9 in drought resistance. Under drought stress, leaf chlorosis and wilting, MDA and O2 - contents were significantly lower, meanwhile, fresh weight, root length, PRO content, and SOD, CAT and POD activities were significantly higher in HvFRF9-overexpressing arabidopsis plants than in wild-type plants. Therefore, overexpression of HvFRF9 could significantly enhance the drought resistance in arabidopsis. These results suggested that HvFRF9 may play a key role in drought resistance in barley by increasing the absorption and transportation of water and the activity of antioxidant enzymes. This study provided a theoretical basis for drought resistance in barley and provided new genes for drought resistance breeding.

Trauma-related intrusive memories and anterior hippocampus structural covariance: an ecological momentary assessment study in posttraumatic stress disorder.

Trauma-related intrusive memories (TR-IMs) are hallmark symptoms of posttraumatic stress disorder (PTSD), but their neural correlates remain partly unknown. Given its role in autobiographical memory, the hippocampus may play a critical role in TR-IM neurophysiology. The anterior and posterior hippocampi are known to have partially distinct functions, including during retrieval of autobiographical memories. This study aimed to investigate the relationship between TR-IM frequency and the anterior and posterior hippocampi morphology in PTSD. Ninety-three trauma-exposed adults completed daily ecological momentary assessments for fourteen days to capture their TR-IM frequency. Participants then underwent anatomical magnetic resonance imaging to obtain measures of anterior and posterior hippocampal volumes. Partial least squares analysis was applied to identify a structural covariance network that differentiated the anterior and posterior hippocampi. Poisson regression models examined the relationship of TR-IM frequency with anterior and posterior hippocampal volumes and the resulting structural covariance network. Results revealed no significant relationship of TR-IM frequency with hippocampal volumes. However, TR-IM frequency was significantly negatively correlated with the expression of a structural covariance pattern specifically associated with the anterior hippocampus volume. This association remained significant after accounting for the severity of PTSD symptoms other than intrusion symptoms. The network included the bilateral inferior temporal gyri, superior frontal gyri, precuneus, and fusiform gyri. These novel findings indicate that higher TR-IM frequency in individuals with PTSD is associated with lower structural covariance between the anterior hippocampus and other brain regions involved in autobiographical memory, shedding light on the neural correlates underlying this core symptom of PTSD.

What predicts psychosocial functioning in borderline personality disorder? Investigating the association with reflective functioning.

OBJECTIVES: The aim of this study was to investigate factors associated with functioning in participants with and without borderline personality disorder (BPD). In particular, we were interested whether mentalizing and related social cognitive capacities, as factors of internal functioning, are important in predicting psychosocial functioning, in addition to other psychopathological and sociodemographic factors. METHOD: This is a cross-sectional study with N = 53 right-handed females with and without BPD, without significant differences in age, IQ, and socioeconomic status, who completed semi-structured diagnostic and self-report measures of social cognition. Mentalizing was assessed using the Reflective Functioning Scale based on transcribed Adult Attachment Interviews. A regularized regression with the elastic net penalty was deployed to investigate whether mentalizing and social cognition predict psychosocial functioning. RESULTS: Borderline personality disorder symptom severity, sexual abuse trauma, and social and socio-economic factors ranked as the most important variables in predicting psychosocial functioning, while reflective functioning (RF) was somewhat less important in the prediction, social cognitive functioning and sociodemographic variables were least important. CONCLUSIONS: Borderline personality disorder symptom severity was most important in determining functional impairment, alongside trauma related to sexual abuse as well as social and socio-economic factors. These findings verify that BPD symptoms themselves most robustly predict functional impairment, followed by history of sexual abuse, then contextual factors (e.g. housing, financial, physical health), and then RF. These results lend marginal support to the conceptualization that mentalizing may enhance psychosocial functioning by facilitating social learning, but emphasize symptom reduction and stabilization of life context as key intervention targets.

Phenotyping Pain in Patients With Fibrous Dysplasia/McCune-Albright Syndrome.

CONTEXT: Pain is a poorly managed aspect in fibrous dysplasia/McCune-Albright syndrome (FD/MAS) because of uncertainties regarding the clinical, behavioral, and neurobiological underpinnings that contribute to pain in these patients. OBJECTIVE: Identify neuropsychological and neurobiological factors associated with pain severity in FD/MAS. DESIGN: Prospective, single-site study. PATIENTS: Twenty patients with FD/MAS and 16 age-sex matched healthy controls. INTERVENTION: Assessments of pain severity, neuropathic pain, pain catastrophizing (pain rumination, magnification, and helplessness), emotional health, and pain sensitivity with thermal quantitative sensory testing. Central nervous system (CNS) properties were measured with diffusion tensor imaging, structural magnetic resonance imaging, and functional magnetic resonance imaging. MAIN OUTCOME MEASURES: Questionnaire responses, detection thresholds and tolerances to thermal stimuli, and structural and functional CNS properties. RESULTS: Pain severity in patients with FD/MAS was associated with more neuropathic pain quality, higher levels of pain catastrophizing, and depression. Quantitative sensory testing revealed normal detection of nonnoxious stimuli in patients. Individuals with FD/MAS had higher pain tolerances relative to healthy controls. From neuroimaging studies, greater pain severity, neuropathic pain quality, and psychological status of the patient were associated with reduced structural integrity of white matter pathways (superior thalamic radiation and uncinate fasciculus), reduced gray matter thickness (pre-/paracentral gyri), and heightened responses to pain (precentral, temporal, and frontal gyri). Thus, properties of CNS circuits involved in processing sensorimotor and emotional aspects of pain were altered in FD/MAS. CONCLUSION: These results offer insights into pain mechanisms in FD/MAS, while providing a basis for implementation of comprehensive pain management treatment approaches that addresses neuropsychological aspects of pain.

Permutation Tests for Assessing Potential Non-Linear Associations between Treatment Use and Multivariate Clinical Outcomes.

In many psychometric applications, the relationship between the mean of an outcome and a quantitative covariate is too complex to be described by simple parametric functions; instead, flexible nonlinear relationships can be incorporated using penalized splines. Penalized splines can be conveniently represented as a linear mixed effects model (LMM), where the coefficients of the spline basis functions are random effects. The LMM representation of penalized splines makes the extension to multivariate outcomes relatively straightforward. In the LMM, no effect of the quantitative covariate on the outcome corresponds to the null hypothesis that a fixed effect and a variance component are both zero. Under the null, the usual asymptotic chi-square distribution of the likelihood ratio test for the variance component does not hold. Therefore, we propose three permutation tests for the likelihood ratio test statistic: one based on permuting the quantitative covariate, the other two based on permuting residuals. We compare via simulation the Type I error rate and power of the three permutation tests obtained from joint models for multiple outcomes, as well as a commonly used parametric test. The tests are illustrated using data from a stimulant use disorder psychosocial clinical trial.

Characterization of central manifestations in patients with Niemann-Pick disease type C.

PURPOSE: Niemann-Pick disease type C (NPC) is a rare lysosomal storage disease characterized by progressive neurodegeneration and neuropsychiatric symptoms. This study investigated pathophysiological mechanisms underlying motor deficits, particularly speech production, and cognitive impairment. METHODS: We prospectively phenotyped 8 adults with NPC and age-sex-matched healthy controls using a comprehensive assessment battery, encompassing clinical presentation, plasma biomarkers, hand-motor skills, speech production, cognitive tasks, and (micro-)structural and functional central nervous system properties through magnetic resonance imaging. RESULTS: Patients with NPC demonstrated deficits in fine-motor skills, speech production timing and coordination, and cognitive performance. Magnetic resonance imaging revealed reduced cortical thickness and volume in cerebellar subdivisions (lobule VI and crus I), cortical (frontal, temporal, and cingulate gyri) and subcortical (thalamus and basal ganglia) regions, and increased choroid plexus volumes in NPC. White matter fractional anisotropy was reduced in specific pathways (intracerebellar input and Purkinje tracts), whereas diffusion tensor imaging graph theory analysis identified altered structural connectivity. Patients with NPC exhibited altered activity in sensorimotor and cognitive processing hubs during resting-state and speech production. Canonical component analysis highlighted the role of cerebellar-cerebral circuitry in NPC and its integration with behavioral performance and disease severity. CONCLUSION: This deep phenotyping approach offers a comprehensive systems neuroscience understanding of NPC motor and cognitive impairments, identifying potential central nervous system biomarkers.

Online psychoeducation and digital assessments as a first step of treatment for borderline personality disorder: A protocol for a pilot randomized controlled trial.

BACKGROUND: Treatment trials for borderline personality disorder (BPD) have consistently demonstrated that approaches that are diagnostically tailored are superior to those which are not. Currently, gold standard treatments for BPD are highly intensive, lengthy, and specialized, leading to a critical gap between the supply and demand of effective, evidence-based treatment for patients who receive a diagnosis of BPD. Psychoeducation, which is a common component of most treatments known to be effective, is a low-cost, low-burden intervention proven to relieve symptoms. The present study builds on psychoeducation research, assessing online video prescriptions as a means of disseminating information patients need to know about their diagnosis and care. METHODS: This article presents the study protocol for a safety, feasibility, and preliminary efficacy trial of psychoeducational video prescriptions and online assessment with feedback for newly diagnosed individuals with BPD. We aim to recruit 100 adults recently diagnosed with BPD to be randomly assigned to receive videos about BPD or videos about non-BPD mental health topics that are matched in length in the first step of the study. All participants will complete daily surveys about their emotions, interpersonal interactions, and behaviors, as well as self-report assessments and cognitive tests at 4 different time points. Half of the participants in the intervention group will receive feedback on their symptom ratings and cognitive test performance to assess whether there is incremental value in tailoring this online set of interventions with individualized feedback unique to each participant. This study aims to assess the effects of BPD-focused psychoeducational videos with and without personalized feedback, on BPD and depressive symptom severity as well as core mechanisms of the disorder such as loneliness, rejection sensitivity, cognitive control difficulties, and self-clarity. Results will inform efforts to progress to a larger, more definitive trial. TRIAL REGISTRATION: Clinical trials registration: The protocol is registered with ClinicalTrials.gov NCT05358925.

Brain-based graph-theoretical predictive modeling to map the trajectory of transdiagnostic symptoms of anhedonia, impulsivity, and hypomania from the human functional connectome.

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM's mean square error (MSE) to that of a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region for information spread) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders, highlighting transdiagnostic generalization. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level. ClinicalTrials.gov identifier: NCT01976975.

Methods for handling missing binary data in substance use disorder trials.

Missing data are a ubiquitous problem in longitudinal substance use disorder (SUD) clinical trials. In particular, the rates of missingness are often high and study participants often intermittently skip their scheduled outcome assessments, leading to so-called "non-monotone" missing data patterns. Moreover, when the primary outcome is a measure of substance use, study investigators often have strong prior beliefs based on their clinical experience that those participants with missing data are more likely to be using substances at those occasions, i.e., data are missing not at random (MNAR). Although approaches for handling missing data are well-developed when the missing data patterns are monotone, arising primarily from study participants withdrawing from the trial prematurely, fewer methods are available for non-monotone missingness. In this paper we review some conventional, as well as more novel, methods for handling non-monotone missingness in SUD trials when the repeatedly measured outcome variable is binary (e.g., denoting presence/absence of substance use). We compare and contrast the different approaches using data from a longitudinal clinical trial of four psychosocial treatments from the Collaborative Cocaine Treatment Study. We conclude by making some recommendations to the SUD research community concerning how more principled methods for handling missing data can be incorporated in the analysis and reporting of trial results.

Multiple imputation for non-monotone missing not at random data using the no self-censoring model.

Although approaches for handling missing data from longitudinal studies are well-developed when the patterns of missingness are monotone, fewer methods are available for non-monotone missingness. Moreover, the conventional missing at random assumption-a natural benchmark for monotone missingness-does not model realistic beliefs about the non-monotone missingness processes (Robins and Gill, 1997). This has provided the impetus for alternative non-monotone missing not at random mechanisms. The "no self-censoring" model is such a mechanism and assumes the probability an outcome variable is missing is independent of its value when conditioning on all other possibly missing outcome variables and their missingness indicators. As an alternative to "weighting" methods that become computationally demanding with increasing number of outcome variables, we propose a multiple imputation approach under no self-censoring. We focus on the case of binary outcomes and present results of simulation and asymptotic studies to investigate the performance of the proposed imputation approach. We describe a related approach to sensitivity analysis to departure from no self-censoring. We discuss the relationship between missing at random and no self-censoring and prove that one is not a special case of the other. Finally, we discuss extensions to non-binary data settings. The proposed methods are illustrated with application to a substance use disorder clinical trial.

Predicting Fetal Alcohol Spectrum Disorders Using Machine Learning Techniques: Multisite Retrospective Cohort Study.

BACKGROUND: Fetal alcohol syndrome (FAS) is a lifelong developmental disability that occurs among individuals with prenatal alcohol exposure (PAE). With improved prediction models, FAS can be diagnosed or treated early, if not completely prevented. OBJECTIVE: In this study, we sought to compare different machine learning algorithms and their FAS predictive performance among women who consumed alcohol during pregnancy. We also aimed to identify which variables (eg, timing of exposure to alcohol during pregnancy and type of alcohol consumed) were most influential in generating an accurate model. METHODS: Data from the collaborative initiative on fetal alcohol spectrum disorders from 2007 to 2017 were used to gather information about 595 women who consumed alcohol during pregnancy at 5 hospital sites around the United States. To obtain information about PAE, questionnaires or in-person interviews, as well as reviews of medical, legal, or social service records were used to gather information about alcohol consumption. Four different machine learning algorithms (logistic regression, XGBoost, light gradient-boosting machine, and CatBoost) were trained to predict the prevalence of FAS at birth, and model performance was measured by analyzing the area under the receiver operating characteristics curve (AUROC). Of the total cases, 80% were randomly selected for training, while 20% remained as test data sets for predicting FAS. Feature importance was also analyzed using Shapley values for the best-performing algorithm. RESULTS: Overall, there were 20 cases of FAS within a total population of 595 individuals with PAE. Most of the drinking occurred in the first trimester only (n=491) or throughout all 3 trimesters (n=95); however, there were also reports of drinking in the first and second trimesters only (n=8), and 1 case of drinking in the third trimester only (n=1). The CatBoost method delivered the best performance in terms of AUROC (0.92) and area under the precision-recall curve (AUPRC 0.51), followed by the logistic regression method (AUROC 0.90; AUPRC 0.59), the light gradient-boosting machine (AUROC 0.89; AUPRC 0.52), and XGBoost (AUROC 0.86; AURPC 0.45). Shapley values in the CatBoost model revealed that 12 variables were considered important in FAS prediction, with drinking throughout all 3 trimesters of pregnancy, maternal age, race, and type of alcoholic beverage consumed (eg, beer, wine, or liquor) scoring highly in overall feature importance. For most predictive measures, the best performance was obtained by the CatBoost algorithm, with an AUROC of 0.92, precision of 0.50, specificity of 0.29, F1 score of 0.29, and accuracy of 0.96. CONCLUSIONS: Machine learning algorithms were able to identify FAS risk with a prediction performance higher than that of previous models among pregnant drinkers. For small training sets, which are common with FAS, boosting mechanisms like CatBoost may help alleviate certain problems associated with data imbalances and difficulties in optimization or generalization.

Measuring Sentence Information via Surprisal: Theoretical and Clinical Implications in Nonfluent Aphasia.

OBJECTIVE: Nonfluent aphasia is characterized by simplified sentence structures and word-level abnormalities, including reduced use of verbs and function words. The predominant belief about the disease mechanism is that a core deficit in syntax processing causes both structural and word-level abnormalities. Here, we propose an alternative view based on information theory to explain the symptoms of nonfluent aphasia. We hypothesize that the word-level features of nonfluency constitute a distinct compensatory process to augment the information content of sentences to the level of healthy speakers. We refer to this process as lexical condensation. METHODS: We use a computational approach based on language models to measure sentence information through surprisal, a metric calculated by the average probability of occurrence of words in a sentence, given their preceding context. We apply this method to the language of patients with nonfluent primary progressive aphasia (nfvPPA; n = 36) and healthy controls (n = 133) as they describe a picture. RESULTS: We found that nfvPPA patients produced sentences with the same sentence surprisal as healthy controls by using richer words in their structurally impoverished sentences. Furthermore, higher surprisal in nfvPPA sentences correlated with the canonical features of agrammatism: a lower function-to-all-word ratio, a lower verb-to-noun ratio, a higher heavy-to-all-verb ratio, and a higher ratio of verbs in -ing forms. INTERPRETATION: Using surprisal enables testing an alternative account of nonfluent aphasia that regards its word-level features as adaptive, rather than defective, symptoms, a finding that would call for revisions in the therapeutic approach to nonfluent language production. ANN NEUROL 2023;94:647-657.

Less is more in language production: an information-theoretic analysis of agrammatism in primary progressive aphasia.

Agrammatism is a disorder of language production characterized by short, simplified sentences, the omission of function words, an increased use of nouns over verbs and a higher use of heavy verbs. Despite observing these phenomena for decades, the accounts of agrammatism have not converged. Here, we propose and test the hypothesis that the lexical profile of agrammatism results from a process that opts for words with a lower frequency of occurrence to increase lexical information. Furthermore, we hypothesize that this process is a compensatory response to patients' core deficit in producing long, complex sentences. In this cross-sectional study, we analysed speech samples of patients with primary progressive aphasia (n = 100) and healthy speakers (n = 65) as they described a picture. The patient cohort included 34 individuals with the non-fluent variant, 41 with the logopenic variant and 25 with the semantic variant of primary progressive aphasia. We first analysed a large corpus of spoken language and found that the word types preferred by patients with agrammatism tend to have lower frequencies of occurrence than less preferred words. We then conducted a computational simulation to examine the impact of word frequency on lexical information as measured by entropy. We found that strings of words that exclude highly frequent words have a more uniform word distribution, thereby increasing lexical entropy. To test whether the lexical profile of agrammatism results from their inability to produce long sentences, we asked healthy speakers to produce short sentences during the picture description task. We found that, under this constrained condition, a similar lexical profile of agrammatism emerged in the short sentences of healthy individuals, including fewer function words, more nouns than verbs and more heavy verbs than light verbs. This lexical profile of short sentences resulted in their lower average word frequency than unconstrained sentences. We extended this finding by showing that, in general, shorter sentences get packaged with lower-frequency words as a basic property of efficient language production, evident in the language of healthy speakers and all primary progressive aphasia variants.

Emotional memories are enhanced when reactivated in slow wave sleep, but impaired when reactivated in REM.

Sleep supports memory consolidation. However, it is not completely clear how different sleep stages contribute to this process. While rapid eye movement sleep (REM) has traditionally been implicated in the processing of emotionally charged material, recent studies indicate a role for slow wave sleep (SWS) in strengthening emotional memories. Here, to directly examine which sleep stage is primarily involved in emotional memory consolidation, we used targeted memory reactivation (TMR) in REM and SWS during a daytime nap. Contrary to our hypothesis, reactivation of emotional stimuli during REM led to impaired memory. Consistent with this, REM% was correlated with worse recall in the group that took a nap without TMR. Meanwhile, cueing benefit in SWS was strongly correlated with the product of times spent in REM and SWS (SWS-REM product), and reactivation significantly enhanced memory in those with high SWS-REM product. Surprisingly, SWS-REM product was associated with better memory for reactivated items and poorer memory for non-reactivated items, suggesting that sleep both preserved and eliminated emotional memories, depending on whether they were reactivated. Notably, the emotional valence of cued items modulated both sleep spindles and delta/theta power. Finally, we found that emotional memories benefited from TMR more than did neutral ones. Our results suggest that emotional memories decay during REM, unless they are reactivated during prior SWS. Furthermore, we show that active forgetting complements memory consolidation, and both take place across SWS and REM. In addition, our findings expand upon recent evidence indicating a link between sleep spindles and emotional processing.

Brain Structure Relations With Psychopathology Trajectories in the ABCD Study.

OBJECTIVE: A general psychopathology (p) factor captures shared variation across mental disorders. Structural neural alterations have been associated with the p factor concurrently, but less is known about whether these alterations relate to within-person change in the p factor over time, especially during preadolescence, a period of neurodevelopmental changes. METHOD: This study examined whether baseline brain structure was prospectively related to the trajectory of the p factor and specific forms of psychopathology over 2 years in 9,220 preadolescents (aged 9-10 at baseline) from the Adolescent Brain Cognitive Development Study (ABCD). Longitudinal multilevel models were conducted to determine whether baseline brain structure (volume, surface area, thickness) was associated with between-person differences and within-person change in the p factor (from a higher-order confirmatory factor model) and internalizing, externalizing, neurodevelopmental, somatization, and detachment factor scores (from a correlated factors model) over 3 study waves. RESULTS: Smaller global volume and surface area, but not thickness, were associated with higher between-person levels of the p factor scores, which persisted over time. None of the brain structure measures were related to within-person change in the p factor scores. Lower baseline cortical thickness was associated with steeper decreases in internalizing psychopathology, which was driven by lower thickness within sensorimotor and temporal regions. CONCLUSION: These novel results identify specific brain structure features that might contribute to transdiagnostic psychopathology development in preadolescence. Children with smaller total brain volume and surface area may be vulnerable to persistent general psychopathology during preadolescence. Cortical thinning reflective of pruning and myelination in sensorimotor and temporal brain regions specifically may protect against increases in internalizing, but not general psychopathology, during preadolescence.

Predicting states of elevated negative affect in adolescents from smartphone sensors: a novel personalized machine learning approach.

BACKGROUND: Adolescence is characterized by profound change, including increases in negative emotions. Approximately 84% of American adolescents own a smartphone, which can continuously and unobtrusively track variables potentially predictive of heightened negative emotions (e.g. activity levels, location, pattern of phone usage). The extent to which built-in smartphone sensors can reliably predict states of elevated negative affect in adolescents is an open question. METHODS: Adolescent participants (n = 22; ages 13-18) with low to high levels of depressive symptoms were followed for 15 weeks using a combination of ecological momentary assessments (EMAs) and continuously collected passive smartphone sensor data. EMAs probed negative emotional states (i.e. anger, sadness and anxiety) 2-3 times per day every other week throughout the study (total: 1145 EMA measurements). Smartphone accelerometer, location and device state data were collected to derive 14 discrete estimates of behavior, including activity level, percentage of time spent at home, sleep onset and duration, and phone usage. RESULTS: A personalized ensemble machine learning model derived from smartphone sensor data outperformed other statistical approaches (e.g. linear mixed model) and predicted states of elevated anger and anxiety with acceptable discrimination ability (area under the curve (AUC) = 74% and 71%, respectively), but demonstrated more modest discrimination ability for predicting states of high sadness (AUC = 66%). CONCLUSIONS: To the extent that smartphone data could provide reasonably accurate real-time predictions of states of high negative affect in teens, brief 'just-in-time' interventions could be immediately deployed via smartphone notifications or mental health apps to alleviate these states.