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.

A pairwise strategy for imputing predictive features when combining multiple datasets.

MOTIVATION: In the training of predictive models using high-dimensional genomic data, multiple studies' worth of data are often combined to increase sample size and improve generalizability. A drawback of this approach is that there may be different sets of features measured in each study due to variations in expression measurement platform or technology. It is often common practice to work only with the intersection of features measured in common across all studies, which results in the blind discarding of potentially useful feature information that is measured in individual or subsets of studies. RESULTS: We characterize the loss in predictive performance incurred by using only the intersection of feature information available across all studies when training predictors using gene expression data from microarray and sequencing datasets. We study the properties of linear and polynomial regression for imputing discarded features and demonstrate improvements in the external performance of prediction functions through simulation and in gene expression data collected on breast cancer patients. To improve this process, we propose a pairwise strategy that applies any imputation algorithm to two studies at a time and averages imputed features across pairs. We demonstrate that the pairwise strategy is preferable to first merging all datasets together and imputing any resulting missing features. Finally, we provide insights on which subsets of intersected and study-specific features should be used so that missing-feature imputation best promotes cross-study replicability. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/YujieWuu/Pairwise_imputation. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

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.

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.

The natural course of binge-eating disorder: findings from a prospective, community-based study of adults.

BACKGROUND: Epidemiological data offer conflicting views of the natural course of binge-eating disorder (BED), with large retrospective studies suggesting a protracted course and small prospective studies suggesting a briefer duration. We thus examined changes in BED diagnostic status in a prospective, community-based study that was larger and more representative with respect to sex, age of onset, and body mass index (BMI) than prior multi-year prospective studies. METHODS: Probands and relatives with current DSM-IV BED (n = 156) from a family study of BED ('baseline') were selected for follow-up at 2.5 and 5 years. Probands were required to have BMI > 25 (women) or >27 (men). Diagnostic interviews and questionnaires were administered at all timepoints. RESULTS: Of participants with follow-up data (n = 137), 78.1% were female, and 11.7% and 88.3% reported identifying as Black and White, respectively. At baseline, their mean age was 47.2 years, and mean BMI was 36.1. At 2.5 (and 5) years, 61.3% (45.7%), 23.4% (32.6%), and 15.3% (21.7%) of assessed participants exhibited full, sub-threshold, and no BED, respectively. No participants displayed anorexia or bulimia nervosa at follow-up timepoints. Median time to remission (i.e. no BED) exceeded 60 months, and median time to relapse (i.e. sub-threshold or full BED) after remission was 30 months. Two classes of machine learning methods did not consistently outperform random guessing at predicting time to remission from baseline demographic and clinical variables. CONCLUSIONS: Among community-based adults with higher BMI, BED improves with time, but full remission often takes many years, and relapse is common.

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.

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.

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.

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.

A statistical model for describing and simulating microbial community profiles.

Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSA's model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbe-microbe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. "taxa") or between features and "phenotypes" to be simulated for method benchmarking. Here, we demonstrate SparseDOSSA's performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSA's utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at http://huttenhower.sph.harvard.edu/sparsedossa2.

Multivariable association discovery in population-scale meta-omics studies.

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.

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.

Ecological Momentary Assessments of Trauma-Related Intrusive Memories: Potential Clinical Utility.

As the global prevalence of trauma rises, there is a growing need for accessible and scalable treatments for trauma-related disorders like posttraumatic stress disorder (PTSD). Trauma-related intrusive memories (TR-IMs) are a central PTSD symptom and a target of exposure-based therapies, gold-standard treatments that are effective but resource-intensive. This study examined whether a brief ecological momentary assessment (EMA) protocol assessing the phenomenology of TR-IMs could reduce intrusion symptoms in trauma-exposed adults. Participants (N=131) experiencing at least 2 TR-IMs per week related to a DSM-5 criterion A trauma completed a 2-week EMA protocol during which they reported on TR-IM properties three times per day, and on posttraumatic stress symptoms at the end of each day. Longitudinal symptom measurements were entered into linear mixed-effects models to test the effect of Time on TR-IMs. Over the 2-week EMA protocol, intrusion symptom severity (cluster B scores) significantly declined (t = -2.78, p = 0.006), while other symptom cluster scores did not significantly change. Follow-up analyses demonstrated that this effect was specific to TR-IMs (t = -4.02, p < 0.001), and was not moderated by survey completion rate, total PTSD symptom severity, or ongoing treatment. Our findings indicate that implementing an EMA protocol assessing intrusive memories could be an effective trauma intervention. Despite study limitations like its quasi-experimental design and absence of a control group, the specificity of findings to intrusive memories argues against a mere regression to the mean. Overall, an EMA approach could provide a cost-effective and scalable treatment option targeting intrusive memory symptoms.

Reported Personality Traits and Histories of Childhood Maltreatment in Borderline Personality Disorder and Bipolar 2 Disorder: A Comparative Study.

This study compared borderline personality disorder (BPD) and bipolar 2 disorder (BP 2 disorder) with respect to reported childhood trauma and Five-Factor personality traits using the Childhood Trauma Questionnaire (CTQ) and the NEO Five-Factor Inventory (NEO-FFI). Participants were 50 men and women, aged 18-45, with DSM-5-diagnosed BPD and 50 men and women in the same age group with DSM-5-diagnosed BP 2 disorder. Participants could not meet criteria for both BPD and BP 2 disorder. Borderline participants had significantly higher scores on the neuroticism subscale and significantly lower scores on the agreeableness subscale of the NEO-FFI. After correction for multiple comparisons, there were no between-group differences on CTQ scores. Study results suggest that BPD and BP 2 disorder differ primarily with respect to underlying temperament/genetic architecture and that environmental factors have only a limited role in the differential etiologies of the two disorders.

Intrinsic functional connectivity of right dorsolateral prefrontal cortex and hippocampus subregions relates to emotional and sensory-perceptual properties of intrusive trauma memories.

BACKGROUND: Trauma-related intrusive memories (TR-IMs), unwanted and vivid, are core symptoms of posttraumatic stress disorder (PTSD). Prior research links voluntary TR-IM suppression to inhibitory control of the right dorsolateral prefrontal cortex (dlPFC) over the hippocampus (HPC). However, the potential relevance of tonic resting-state inhibition has not been examined, nor has the functional differentiation of the anterior and posterior hippocampus (aHPC/HPC). This study examined relationships of TR-IM frequency and properties with resting-state negative coupling between the right dlPFC and right aHPC/pHPC in trauma-exposed individuals with PTSD symptoms. METHODS: Participants (N=109; 88 female) completed two weeks of ecological momentary assessments capturing TR-IM frequency and properties (intrusiveness, emotional intensity, vividness, visual properties, and reliving). Using 3T resting-state magnetic resonance imaging, participant-specific 4-mm spheres were placed at the right dlPFC voxel most anticorrelated with the right aHPC and pHPC. Quasi-Poisson and linear mixed-effects models assessed relationships of TR-IM frequency and properties with right dlPFC-right aHPC and pHPC anticorrelation. RESULTS: TR-IM emotional intensity was positively associated with right dlPFC-aHPC connectivity, while vividness and visual properties were linked to right dlPFC-pHPC connectivity. No significant associations were found between TR-IM frequency, intrusiveness, or reliving, and anticorrelation with either HPC subregion. CONCLUSIONS: This study provides novel insights into the neural correlates of TR-IMs, highlighting the relevance of intrinsic negative coupling between the right dlPFC and aHPC/pHPC to their emotional impact and perceptual properties. Further research on inhibitory mechanisms in this circuit could improve understanding of component processes of intrusive reexperiencing, a severe and treatment-refractory PTSD symptom.

NMDA and GABA Receptor-Mediated Plasticity Induced by 10-Hz Repetitive Transcranial Magnetic Stimulation.

Although 10-Hz repetitive transcranial magnetic stimulation (rTMS) is an FDA-approved treatment for depression, we have yet to fully understand the mechanism through which rTMS induces therapeutic and durable changes in the brain. Two competing theories have emerged suggesting that 10-Hz rTMS induces N-methyl-D-aspartate receptor (NMDAR)-dependent long-term potentiation (LTP), or alternatively, removal of inhibitory gamma-aminobutyric acid receptors (GABARs). We examined these two proposed mechanisms of action in the human motor cortex in a double-blind, randomized, four-arm crossover study in healthy subjects. We tested motor-evoked potentials (MEPs) before and after 10-Hz rTMS in the presence of four drugs separated by 1-week each: placebo, NMDAR partial agonist d-cycloserine (DCS 100mg), DCS 100mg + NMDAR partial antagonist dextromethorphan (DMO 150mg; designed to "knock down" DCS-mediated facilitation), and GABAR agonist lorazepam (LZP 2.5mg). NMDAR agonism by DCS enhanced rTMS-induced cortical excitability more than placebo. This enhancement was blocked by combining DCS with NMDAR antagonist, DMO. If GABARs are removed by rTMS, GABAR agonism via LZP should lack its inhibitory effect yielding higher post/pre MEPs. However, MEPs were reduced after rTMS indicating stability of GABAR numbers. These data suggest that 10-Hz rTMS facilitation in the healthy motor cortex may enact change in the brain through NMDAR-mediated LTP-like mechanisms rather than through GABAergic reduction.

A Prospective Study of the Relationship of COVID-19 Vaccination to Menstrual Cycle Characteristics in Adolescent Girls.

PURPOSE: The current study aimed to evaluate the impact of the COVID-19 booster vaccine on menstrual cycle characteristics in adolescent girls (aged 13-20) compared to those who did not receive a booster vaccine. METHODS: This prospective study measured menstrual cycle length for three cycles prior to and four cycles after vaccination (booster group), seven cycles without vaccination (control group). Menstrual flow, menstrual pain, and menstrual symptoms were assessed at baseline and monthly for 3 months. Stress was assessed at baseline using the PROMIS Pediatric Psychological Stress Experiences scale. Generalized linear mixed effects models were used to examine the changes in menstrual characteristics. RESULTS: 65 adolescent girls (47 booster; 18 control) were recruited via social media and from ongoing studies in the United States. Girls in the booster group experienced shorter postbooster cycles by an average 5.35 days (p = .03) compared to prebooster cycle lengths, specifically in the second postbooster cycle, while the control group did not show any changes in cycle length pre-to postbooster. Participants who received the booster in the follicular phase had shorter mean postbooster cycle length (p = .0157) compared to their prebooster cycle length. Higher stress was associated with shorter cycles (p = .03) and increased menstrual symptoms (p = <.001), regardless of group. There were no differences in menstrual flow, menstrual pain, or menstrual symptoms in either group. DISCUSSION: The COVID-19 booster vaccine was associated with shorter cycles in adolescent girls. These data demonstrate the need for further investigation regarding potential mechanisms of these observed changes.

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.

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.