Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome.

22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.

Neuroimaging profiling identifies distinct brain maturational subtypes of youth with mood and anxiety disorders.

Mood and anxiety disorders typically begin in adolescence and have overlapping clinical features but marked inter-individual variation in clinical presentation. The use of multimodal neuroimaging data may offer novel insights into the underlying brain mechanisms. We applied Heterogeneity Through Discriminative Analysis (HYDRA) to measures of regional brain morphometry, neurite density, and intracortical myelination to identify subtypes of youth, aged 9-10 years, with mood and anxiety disorders (N = 1931) compared to typically developing youth (N = 2823). We identified three subtypes that were robust to permutation testing and sample composition. Subtype 1 evidenced a pattern of imbalanced cortical-subcortical maturation compared to the typically developing group, with subcortical regions lagging behind prefrontal cortical thinning and myelination and greater cortical surface expansion globally. Subtype 2 displayed a pattern of delayed cortical maturation indicated by higher cortical thickness and lower cortical surface area expansion and myelination compared to the typically developing group. Subtype 3 showed evidence of atypical brain maturation involving globally lower cortical thickness and surface coupled with higher myelination and neural density. Subtype 1 had superior cognitive function in contrast to the other two subtypes that underperformed compared to the typically developing group. Higher levels of parental psychopathology, family conflict, and social adversity were common to all subtypes, with subtype 3 having the highest burden of adverse exposures. These analyses comprehensively characterize pre-adolescent mood and anxiety disorders, the biopsychosocial context in which they arise, and lay the foundation for the examination of the longitudinal evolution of the subtypes identified as the study sample transitions through adolescence.

Brain age and cognitive functioning in first-episode bipolar disorder.

BACKGROUND: There is significant heterogeneity in cognitive function in patients with bipolar I disorder (BDI); however, there is a dearth of research into biological mechanisms that might underlie cognitive heterogeneity, especially at disease onset. To this end, this study investigated the association between accelerated or delayed age-related brain structural changes and cognition in early-stage BDI. METHODS: First episode patients with BDI (n = 80) underwent cognitive assessment to yield demographically normed composite global and domain-specific scores in verbal memory, non-verbal memory, working memory, processing speed, attention, and executive functioning. Structural magnetic resonance imaging data were also collected from all participants and subjected to machine learning to compute the brain-predicted age difference (brainPAD), calculated by subtracting chronological age from age predicted by neuroimaging data (positive brainPAD values indicating age-related acceleration in brain structural changes and negative values indicating delay). Patients were divided into tertiles based on brainPAD values, and cognitive performance compared amongst tertiles with ANCOVA. RESULTS: Patients in the lowest (delayed) tertile of brainPAD values (brainPAD range -17.9 to -6.5 years) had significantly lower global cognitive scores (p = 0.025) compared to patients in the age-congruent tertile (brainPAD range -5.3 to 2.4 yrs), and significantly lower verbal memory scores (p = 0.001) compared to the age-congruent and accelerated (brainPAD range 2.8 to 16.1 yrs) tertiles. CONCLUSION: These results provide evidence linking cognitive dysfunction in the early stage of BDI to apparent delay in typical age-related brain changes. Further studies are required to assess how age-related brain changes and cognitive functioning evolve over time.

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.

Sex effects on cortical morphological networks in healthy young adults.

Understanding sex-related differences across the human cerebral cortex is an important step in elucidating the basis of psychological, behavioural and clinical differences between the sexes. Prior structural neuroimaging studies primarily focused on regional sex differences using univariate analyses. Here we focus on sex differences in cortical morphological networks (CMNs) derived using multivariate modelling of regional cortical measures of volume and surface from high-quality structural MRI scans from healthy participants in the Human Connectome Project (HCP) (n = 1,063) and the Southwest University Longitudinal Imaging Multimodal (SLIM) study (n = 549). The functional relevance of the CMNs was inferred using the NeuroSynth decoding function. Sex differences were widespread but not uniform. In general, females had higher volume, thickness and cortical folding in networks that involve prefrontal (both ventral and dorsal regions including the anterior cingulate) and parietal regions while males had higher volume, thickness and cortical folding in networks that primarily include temporal and posterior cortical regions. CMN loading coefficients were used as input features to linear discriminant analyses that were performed separately in the HCP and SLIM; sex was predicted with a high degree of accuracy (81%-85%) across datasets. The availability of behavioral data in the HCP enabled us to show that male-biased surface-based CMNs were associated with externalizing behaviors. These results extend previous literature on regional sex-differences by identifying CMNs that can reliably predict sex, are relevant to the expression of psychopathology and provide the foundation for the future investigation of their functional significance in clinical populations.

Effect of combined yoga and transcranial direct current stimulation intervention on working memory and mindfulness.

Transcranial direct stimulation, a non-invasive neurostimulation technique for modulating cortical excitability, and yoga have both respectively been shown to positively affect cognition. While preliminary research has shown that combined transcranial direct stimulation and meditation may have synergistic effects on mood and cognition, this was the first study to explore the combination of transcranial direct stimulation and yoga. Twenty-two healthy volunteers with a regular yoga practice were randomized to receive either active transcranial direct stimulation (anodal left, cathodal right dorsolateral prefrontal cortex) followed by yoga intervention or sham transcranial direct stimulation followed by yoga intervention a double-blind, cross-over design over two separate intervention days. Outcome measures included working memory performance, measured with the n-back task and mindfulness state, measured with the Toronto Mindfulness Scale, and were conducted offline, with pre-post assessments. Twenty participants completed both days of the intervention. Active transcranial direct stimulation did not have a significant effect on working memory or levels of mindfulness. There was a significant placebo effect, with better performance on day 1 of the intervention, irrespective of whether participants received active or sham transcranial direct stimulation. There was no significant difference between active versus sham transcranial direct stimulation concerning working memory performance and mindfulness, which may be accounted by the small sample size, the transient nature of the intervention, the fact that yoga and transcranial direct stimulation concerning were not conducted simultaneously, and the specific site of stimulation.

Parcellation of the human hippocampus based on gray matter volume covariance: Replicable results on healthy young adults.

The hippocampus is a key brain region that participates in a range of cognitive and affective functions, and is involved in the etiopathogenesis of numerous neuropsychiatric disorders. The structural complexity and functional diversity of the hippocampus suggest the existence of structural and functional subdivisions within this structure. For the first time, we parcellated the human hippocampus with two independent data sets, each of which consisted of 198 T1-weighted structural magnetic resonance imaging (sMRI) images of healthy young subjects. The method was based on gray matter volume (GMV) covariance, which was quantified by a bivariate voxel-to-voxel linear correlation approach, as well as a multivariate masked independent component analysis approach. We subsequently interrogated the relationship between the GMV covariance patterns and the functional connectivity patterns of the hippocampal subregions using sMRI and resting-state functional MRI (fMRI) data from the same participants. Seven distinct GMV covariance-based subregions were identified for bilateral hippocampi, with robust reproducibility across the two data sets. We further demonstrated that the structural covariance patterns of the hippocampal subregions had a correspondence with the intrinsic functional connectivity patterns of these subregions. Together, our results provide a topographical configuration of the hippocampus with converging structural and functional support. The resulting subregions may improve our understanding of the hippocampal connectivity and functions at a subregional level, which provides useful parcellations and masks for future neuroscience and clinical research on the structural and/or functional connectivity of the hippocampus.

A two-step super-Gaussian independent component analysis approach for fMRI data.

Independent component analysis (ICA) has been widely applied to functional magnetic resonance imaging (fMRI) data analysis. Although ICA assumes that the sources underlying data are statistically independent, it usually ignores sources' additional properties, such as sparsity. In this study, we propose a two-step super-GaussianICA (2SGICA) method that incorporates the sparse prior of the sources into the ICA model. 2SGICA uses the super-Gaussian ICA (SGICA) algorithm that is based on a simplified Lewicki-Sejnowski's model to obtain the initial source estimate in the first step. Using a kernel estimator technique, the source density is acquired and fitted to the Laplacian function based on the initial source estimates. The fitted Laplacian prior is used for each source at the second SGICA step. Moreover, the automatic target generation process for initial value generation is used in 2SGICA to guarantee the stability of the algorithm. An adaptive step size selection criterion is also implemented in the proposed algorithm. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of 2SGICA and made a performance comparison between InfomaxICA, FastICA, mean field ICA (MFICA) with Laplacian prior, sparse online dictionary learning (ODL), SGICA and 2SGICA. Both simulated and real fMRI experiments showed that the 2SGICA was most robust to noises, and had the best spatial detection power and the time course estimation among the six methods.

Nel-like Molecule Type 1 Combined with Gold Nanoparticles Modulates Macrophage Polarization, Osteoclastogenesis, and Oral Microbiota in Periodontitis.

The disruption of host-microbe homeostasis and uncontrolled inflammatory response have been considered as vital causes for developing periodontitis, subsequently leading to an imbalance between the bone and immune system and the collapse of bone homeostasis. Consequently, strategies to modulate the immune response and bone metabolization have become a promising approach to prevent and treat periodontitis. In this study, we investigated the cooperative effects of Nel-like molecule type 1 (Nell-1) and gold nanoparticles (AuNPs) on macrophage polarization, osteoclast differentiation, and the corresponding functions in an experimental model of periodontitis in rats. Nell-1-combined AuNPs in in vitro studies were found to reduce the production of inflammatory factors (TNF-α, p < 0.0001; IL-6, p = 0.0012), modulate the ratio of M2/M1 macrophages by inducing macrophage polarization into the M2 phenotype, and inhibit cell fusion, maturation, and activity of osteoclasts. Furthermore, the local application of Nell-1-combined AuNPs in in vivo studies resulted in alleviation of damages to the periodontal and bone tissues, modulation of macrophage polarization and the activity of osteoclasts, and alteration of the periodontal microbiota, in which the relative abundance of the probiotic Bifidobacterium increased (p < 0.05). These findings reveal that Nell-1-combined AuNPs could be a promising drug candidate for the prevention and treatment of periodontitis. However, Nell-1-combined AuNPs did not show organ toxicity or impair the integrity of intestinal epithelium but alter the gut microbiota, leading to the dysbiosis of gut microbiota. The adverse impact of changes in gut microbiota needs to be further investigated. Nonetheless, this study provides a novel perspective and direction for the biological safety assessment of biomaterials in oral clinical applications.

Neural epidermal growth factor-like 1 protein (Nell-1) alleviates periodontal tissue destruction in periodontitis by regulating the ratio of M2/M1 macrophage phenotypes.

BACKGROUND: Periodontitis is a common oral disease with high prevalence worldwide. Neural epidermal growth factor-like 1 protein (Nell-1) has recently been reported to have anti-inflammation effects and may be a drug candidate for osteoarthritis. However, its immunotherapeutic effects in periodontitis remain unknown. Therefore, this study aimed to investigate the effects of Nell-1 on periodontitis in terms of macrophage polarization and analyze its possible underlying mechanism. METHODS: A rat ligation-induced experimental periodontitis model was established and locally injected with Nell-1 (n = 6/group). Periodontal tissue destruction and macrophage polarization in vivo were analyzed using micro-CT, histology analysis, and western blot. Enzyme-linked immunosorbent assay was used to evaluate serum inflammatory cytokines. Then, the RAW 264.7 macrophage cells were treated with lipopolysaccharide (LPS), Nell-1, and the c-Jun N-terminal kinases (JNK) inhibitor (SP600125). RT-PCR, western blot, and flow cytometry were performed to further analyze the effect of Nell-1 on macrophage polarization and the underlying mechanism in vitro. RESULTS: Local treatment with Nell-1 significantly alleviated the destruction of alveolar bone and fibers in periodontitis, and upregulated the ratio of M2/M1 macrophages in periodontal tissues (P < 0.05). In vitro, Nell-1 at the concentrations of 200 and 500 ng/mL could significantly inhibit the expression of M1-related inflammatory factors in LPS-stimulated macrophages, and increase the expression of M2-related markers, regulating the macrophage phenotype switch into M2 (P < 0.05). The mRNA of JNK and relative protein level of phospho-JNK/JNK were also upregulated by Nell-1 (P < 0.05). Additionally, the JNK inhibitor (SP600125) could reverse the effect of Nell-1 on macrophage polarization (P < 0.05). CONCLUSIONS: Nell-1 could modulate the ratio of M2/M1 macrophages possibly through the JNK/MAPK signaling pathway, subsequently attenuating the inflammation and destruction of periodontal tissues caused by periodontitis.

Neurophysiological effects of cognitive behavioral therapy in social anxiety: An ERP study using a dot-probe task.

BACKGROUND: Social anxious individuals show attention bias towards emotional stimuli, this phenomenon is considered to be an important cause of anxiety generation and maintenance. Cognitive-behavioral therapy (CBT) is a standard psychotherapy for social anxiety disorder. CBT decreases attention biases by correcting the maladaptive beliefs of socially anxious individuals, but it is not clear whether CBT alters neurophysiological features of socially anxious individuals at early automatic and/or late cognitive strategy stage of attentional processing. METHOD: To address this knowledge gap, we collected pre-treatment event-related potential data of 22 socially anxious individuals while they performed a dot-probe task. These participants then received eight weeks of CBT, and post-treatment ERP data were collected after completion of CBT treatment. We also included 29 healthy controls and compared them with individuals with social anxiety to determine the neural mechanisms underlying the effectiveness of CBT. RESULTS: Participants' social anxiety level was significantly alleviated with CBT. ERP results revealed that (1) compared to pre-treatment phase, P1 amplitudes induced by probes significantly decreased at post-treatment phase, whereas P3 amplitudes increased at post-treatment phase; the P1 amplitudes induced by probes following happy-neutral face pairs in socially anxious individuals after treatment was significantly different with that in healthy controls; (2) amplitude of components elicited by face pairs did not change significantly between pre-treatment and post-treatment phases; (3) changes of Liebowitz Social Anxiety Scale were positively correlated with changes of P1 amplitude, and negatively correlated with changes of N1 amplitude. LIMITATIONS: Our sample was university students and lacked randomization, which limits the generalizability of the results. CONCLUSION: The present results demonstrated that CBT may adjust cognitive strategies in the later stage of attentional processing, indicating by changed ERPs appeared in probe-presenting stage for social anxiety.

Event-related alpha power in early stage of facial expression processing in social anxiety: Influence of language context.

Accurate interpretation of the emotional information conveyed by others' facial expressions is crucial for social interactions. Event-related alpha power, measured by time-frequency analysis, is a frequently used EEG index of emotional information processing. However, it is still unclear how event-related alpha power varies in emotional information processing in social anxiety groups. In the present study, we recorded event-related potentials (ERPs) while participants from the social anxiety and healthy control groups viewed facial expressions (angry, happy, neutral) preceded by contextual sentences conveying either a positive or negative evaluation of the subject. The impact of context on facial expression processing in both groups of participants was explored by assessing behavioral ratings and event-related alpha power (0-200 ms after expression presentation). In comparison to the healthy control group, the social anxiety group exhibited significantly lower occipital alpha power in response to angry facial expressions in negative contexts and neutral facial expressions in positive contexts. The influence of language context on facial expression processing in individuals with social anxiety may occur at an early stage of processing.

Brain responses to intermittent fasting and the healthy living diet in older adults.

Diet may promote brain health in metabolically impaired older individuals. In an 8-week randomized clinical trial involving 40 cognitively intact older adults with insulin resistance, we examined the effects of 5:2 intermittent fasting and the healthy living diet on brain health. Although intermittent fasting induced greater weight loss, the two diets had comparable effects in improving insulin signaling biomarkers in neuron-derived extracellular vesicles, decreasing the brain-age-gap estimate (reflecting the pace of biological aging of the brain) on magnetic resonance imaging, reducing brain glucose on magnetic resonance spectroscopy, and improving blood biomarkers of carbohydrate and lipid metabolism, with minimal changes in cerebrospinal fluid biomarkers for Alzheimer's disease. Intermittent fasting and healthy living improved executive function and memory, with intermittent fasting benefiting more certain cognitive measures. In exploratory analyses, sex, body mass index, and apolipoprotein E and SLC16A7 genotypes modulated diet effects. The study provides a blueprint for assessing brain effects of dietary interventions and motivates further research on intermittent fasting and continuous diets for brain health optimization. For further information, please see ClinicalTrials.gov registration: NCT02460783.

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.

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 (ß = -0.08; 95% CI, -0.13 to -0.02; P = .02 for false discovery rate) and IQ (ß = 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.

Subgenual cingulate connectivity as a treatment predictor during low-frequency right dorsolateral prefrontal rTMS: A concurrent TMS-fMRI study.

INTRODUCTION: Repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) is effective in alleviating treatment-resistant depression (TRD). It has been proposed that regions within the left DLPFC that are anti-correlated with the right subgenual anterior cingulate cortex (sgACC) may represent optimal individualized target sites for high-frequency left rTMS (HFL). OBJECTIVE/HYPOTHESIS: This study aimed to explore the effects of low-frequency right rTMS (LFR) on left sgACC connectivity during concurrent TMS-fMRI. METHODS: 34 TRD patients underwent an imaging session that included both a resting-state fMRI run (rs-fMRI0) and a run during which LFR was applied to the right DLPFC (TMS-fMRI). Participants subsequently completed four weeks of LFR treatment. The left sgACC functional connectivity was compared between the rs-fMRI0 run and TMS-fMRI run. Personalized e-fields and a region-of-interest approach were used to calculate overlap of left sgACC functional connectivity at the TMS target and to assess for a relationship with treatment effects. RESULTS: TMS-fMRI increased left sgACC functional connectivity to parietal regions within the ventral attention network; differences were not significantly associated with clinical improvements. Personalized e-fields were not significant in predicting treatment outcomes (p = 0.18). CONCLUSION: This was the first study to examine left sgACC anti-correlation with the right DLPFC during an LFR rTMS protocol. In contrast to studies that targeted the left DLPFC, we did not find that higher anti-correlation was associated with clinical outcomes. Our results suggest that the antidepressant mechanism of action of LFR to the right DLPFC may be different than for HFL.

The associations of coffee consumption, coffee types, and caffeine metabolites with periodontitis: Results from NHANES 2009-2014.

BACKGROUND: Coffee is one of the world's most popular beverages and is the main dietary source of caffeine for most people. The various molecular effects of caffeine suggest that it may enhance bone loss. The purpose of the present study was to investigate the relationship of coffee, coffee types, and caffeine metabolites with periodontitis. METHODS: Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2009-2014. Total coffee and different types of coffee consumption were acquired through a 24-h dietary recall. Urinary caffeine metabolites were quantified using high-performance liquid chromatography-electrospray ionization-tandem quadrupole mass spectrometry (HPLC-ESI-MS/MS). The association of coffee, coffee types, and caffeine metabolites with periodontitis and its severity were assessed using multivariable logistic regression. RESULTS: A total of 3309 eligible participants were included. After adjusting for potential confounding variables, a positive association was observed between coffee consumption (particularly certain types of coffee) and periodontitis. Notably, a positive correlation was also found between total coffee intake and the severity of periodontitis. Additionally, for urinary caffeine metabolites, there was a significant positive association between 1-methyluric acid (1-MU), 1,3-dimethyluric acid (1,3-DMU), 3,7-dimethyluric acid (3,7-DMU), 1,7-dimethylxanthine (1,7-DMX), or 5-actlyamino-6-amino-3-methyluracil (AAMU) and periodontitis, with adjusted odds ratios and 95% confidence intervals of 1.10 (1.02, 1.19), 1.86 (1.05, 3.29), 0.94 (0.90, 0.98), 1.29 (1.03, 1.62), and 1.15 (1.05, 1.26), respectively. CONCLUSIONS: The present study suggests a positive association of coffee intake (especially certain coffee types) and caffeine metabolites (1-MU, 1,3-DMU, 3,7-DMU, 1,7-DMX, and AAMU) with periodontitis and its severity.

Effects of intermittent theta-burst transcranial magnetic stimulation on cognition and hippocampal volumes in bipolar depression.

INTRODUCTION: Repetitive transcranial magnetic stimulation (TMS) is increasingly used to treat neurocognitive symptoms in mood disorders. Intermittent theta burst stimulation (iTBS) is a brief version of TMS that may preferentially target cognitive functions. This study evaluated whether iTBS leads to cognitive improvements and associated increased hippocampal volumes in bipolar depression. METHODS: In a two-site double-blind randomised sham controlled trial (NCT02749006), 16 patients received active iTBS to the Left Dorsolateral Prefrontal Cortex (DLPF) and 15 patients received sham stimulation across four weeks. A composite neuropsychological score and declarative memory scores served as the cognitive outcomes. Hippocampal volumes were derived from T1 weighted MRI scans using the longitudinal ComBat method to harmonise data across sites. RESULTS: No significant improvements were observed in any cognitive variables in the active relative to the sham group; however, there was a trend for increased left hippocampal volume in the former. Left hippocampal volume increases were associated with improvements in nonverbal memory in the active group. CONCLUSIONS: Although cognitive improvements were not associated with iTBS, the finding that hippocampal volume increases were associated with memory improvement suggests there may be some level of prefrontal-temporal neuroplasticity that could support cognitive change in future studies of iTBS in bipolar disorder.

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/).

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 (|ß|<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.