Neuroscience & Biobehavioral Reviews

Electroencephalography (EEG) has emerged as a key method for investigating the neural mechanisms through which oxytocin influences cognition and behaviour. EEG is cost-effective, has excellent temporal precision, and may elucidate neural correlates of emotional and cognitive processes. EEG studies evaluating oxytocins electrophysiological effects have, however, yielded mixed results, which is likely driven by heterogeneity in EEG measures, study designs, dosages, and samples. To investigate the effect of oxytocin administration on EEG measures, we performed two multilevel random effects meta-analyses: The first meta-analysis synthesized studies investigating the effects of oxytocin administration on different neural correlates of social and cognitive processing; the second meta-analysis synthesized studies evaluating effects of oxytocin administration on exploratory, less task-specific neural activity measures, such as the modulation of microstates. Across both meta-analyses, we synthesized 161 effect sizes from 28 randomised controlled trials with a total of 1361 participants from different population groups. These multilevel meta-analyses yielded small effect sizes of oxytocin administration across different EEG measures reflecting social and cognitive processes (Hedges g = 0.14), and exploratory neural activity (Hedges g = 0.28) with significant heterogeneity estimates (p < 0.01 and p < 0.001, respectively). Moderator analyses revealed that the different EEG measurements of interest (e.g., event-related potentials) and the proportion of female participants were found to significantly moderate the effect of oxytocin on neural EEG activity. Altogether, these meta-analyses present tentative evidence for oxytocin administration modulating a wide range of neural activity. We observed substantial heterogeneity across studies - in terms of study designs, experimental paradigms and EEG measurements, and participant characteristics. More research is warranted to map out the context-specific effects of oxytocin administration on different neural markers, to better understand the neurobiological mechanisms of oxytocin.

BackgroundElicit AI aims to simplify and accelerate the systematic review process without compromising accuracy. However, research on Elicits performance is limited. ObjectivesTo determine whether Elicit AI is a viable tool for systematic literature searches. MethodsWe compared the included studies in four systematic reviews to those identified searching with Elicit. We calculated sensitivity, precision and observed patterns in the performance of Elicit. ResultsElicit had an average of 39.6% precision (26.7% - 46.2%) which was higher than the 7.55% average of the original reviews (0.65% - 14.7%). However, the sensitivity of Elicit was poor, averaging 37.9% (25.5% - 69.2%) compared to 93.5% (87.2% - 98.0%) in the original reviews. Elicit also identified some included studies not identified by the original searches. DiscussionAt the time of this evaluation, Elicit did not search with high enough sensitivity to replace traditional literature searching. However, the high precision of searching in Elicit could prove useful for preliminary searches, and the unique studies identified mean that Elicit can be used by researchers as a useful adjunct. ConclusionWhilst Elicit searches are currently not sensitive enough to replace traditional searching, Elicit is continually improving, and further evaluations should be undertaken as new developments take place. Key MessagesO_LIAI tools, such as Elicit, have been developed to improve the efficiency of systematic review processes, including the identification of studies. C_LIO_LIUsing four case study systematic reviews Elicit searches had a sensitivity between 25.5% and 69.2% (37.9% average) and precision between 26.7% and 46.2% (39.6% average). C_LIO_LIElicit identified some unique studies that met the inclusion criteria for each of the case study systematic reviews. C_LIO_LIElicit is constantly improving and developing its systems, thus independent researchers should continue to evaluate its performance. C_LI

We investigated the neural substrates underlying the brain network shared by social exclusion and physiological needs, both viewed as instances of deprivation. Using activation likelihood estimation (ALE) meta-analyses, we examined brain activation patterns from studies where participants perceived food/water while hungry/thirsty and social interactions while experiencing exclusion. This analysis revealed overlapping activation in the mid-posterior insula, caudate head and ventral anterior cingulate cortex (ACC), as regions consistently engaged when perceiving relevant stimuli across both physiological and social deprivation. Furthermore, we found high spatial correlation between this shared network and the distribution of dopamine receptors, and we identified a significant positive correlation with the 5HT4 among serotonin receptors. Our findings suggest that perceiving deprivation-related stimuli activates brain regions and neurotransmitters involved in aversive affect and goal-directed behavior. These results highlight a neural bridge linking basic physiological drives with complex social needs, offering new insights into the neurobiological architecture of human affective states--while representing just one part of a much larger puzzle.

The human nervous system evolved to optimise its interactions with the natural world. However, as civilisations over recent millennia have developed, the human brain has become steadily disconnected from these natural stimuli. Consequently, detrimental effects on individual health are increasingly recognised. Strategies that promote reconnection with Nature have yielded significant gains when it comes to psychological and general physical health, although much less is known about their direct impact on neurological health. In this systematic review, we identified 17 studies that assessed at least one Nature-based intervention to treat any neurological disease. The predominant study design was quasi-experimental (n=9). Interventions included horticultural therapy, petal arranging and farm visits. The majority of studies had a moderate risk of bias. While 88% (n=15) of studies reported improvements in measures such as quality of life, mood, agitation, apathy or cognitive function, there was such an overrepresentation of studies recruiting patients with dementia (94%; n=16) that for many common neurological disorders, including epilepsy, migraine and multiple sclerosis, the current literature leaves us none the wiser. Considering the extensive crosstalk that exists between physical and psychological disease, we argue that the potential benefit of formal Nature Prescriptions as an accessible, cheap and harmless endorsement of Mother Natures healing hand warrants greater attention in neurology. Indeed, this aligns with the wider societal and environmental need for humans to reconnect with the natural world.

Background: Chronic pain is a multidimensional condition that often persists despite conventional treatment and adversely affects multiple domains of daily life. Music listening has emerged as a promising non-pharmacological intervention, with accumulating evidence supporting its beneficial effects on pain and associated psychological outcomes. However, despite growing evidence of efficacy, the translation of music listening into routine clinical practice remains limited, partly because intervention reporting has received comparatively little attention. Objective: To evaluate the effectiveness of music listening interventions for chronic pain and systematically assess the methodological quality and completeness of intervention reporting to identify barriers to reproducibility and clinical implementation. Methods: Systematic searches were conducted in PubMed, Cochrane Library, CINAHL, and Web of Science through June 2025, with no date restrictions on publication. Randomized controlled trials involving adults with chronic pain receiving music listening interventions were included. Two independent reviewers screened studies, extracted data, and assessed risk of bias. Intervention reporting was evaluated using the TIDieR checklist, and a random-effects meta-analysis was performed for pain intensity outcomes. Results: Ten RCTs involving 538 participants were included. Music listening interventions varied substantially in delivery, duration, and music selection procedures, reflecting considerable heterogeneity in intervention design. Most studies reported significant improvements in pain and psychological outcomes. Meta-analysis of eight trials (10 effect estimates), demonstrated a moderate reduction in pain intensity (SMD = -0.53, 95% CI: -0.96 to -0.11, p = 0.014; I2 = 76.2%). Although intervention rationale and procedures were generally well described, reporting of intervention modifications, treatment fidelity, and adherence was frequently incomplete. These reporting deficiencies may compromise reproducibility and limit translation into clinical practice. Conclusions: Music listening appears to be a safe, accessible, and scalable non-pharmacological intervention for chronic pain management, with benefits extending beyond pain reduction to psychological wellbeing, quality of life, and functioning. However, incomplete reporting of key intervention components may limit reproducibility and hinder clinical implementation. Future trials should adopt standardized and transparent reporting standards to facilitate implementation into clinical practice.

BackgroundAutism spectrum disorder (ASD) represents a panel of conditions that begin during the developmental period and result in impairments of personal, social, academic, or occupational functioning. Early diagnosis is directly related to a better prognosis. Unfortunately, the diagnosis of ASD requires a long and exhausting subjective process. ObjectiveTo review the state of the art for the automated autism diagnosis. MethodsIn February 2022, we searched multiple databases and several sources of grey literature for eligible studies. We used an adapted version of the QUADAS-2 tool to assess the risk of bias in the studies. A brief report of the methods and results of each study is presented. Data were synthesized for each modality separately using the Split Component Synthesis (SCS) method. We assessed heterogeneity using the I2 statistics and evaluated publication bias using trim and fill tests combined with ln DOR. Confidence in cumulative evidence was evaluated using the GRADE approach for diagnostic studies. ResultsWe included 344 studies from 186020 participants (51129 are estimated to be unique) for nine different modalities in this review, from which 232 reported sufficient data for meta-analysis. The area under the curve was in the range of 0.71-0.90 for all the modalities. The studies on EEG data provided the best accuracy, with the area under the curve ranging between 0.85 to 0.93. ConclusionsThe literature is rife with bias and methodological/reporting flaws. Recommendations are provided for future research to provide better studies and fill in the current knowledge gaps.

We looked at the overlap between brain areas related to perception of physiologically and socially (non-physiological) needed stimuli and how they might regulate serotonin levels. First, we conducted separate ALE meta-analyses on published results pertaining to brain activation patterns when participants perceived food while hungry or water while thirsty, and social interactions while being excluded. This allowed us to identify common consistent brain activation patterns for physiological and social needed stimuli. We also looked at significant spatial association between the common network and serotonin receptor distribution. We found that regions within the mid-posterior insula, the anterior cingulate cortex and the caudate are at the intersection of physiological (hunger and thirst) and social (exclusion) aspects of needing. Furthermore, we found a significant positive spatial correlation between that common network and 5HT4 receptor among serotonin receptors. While this was the highest for serotonin receptors, it was not the highest of all receptors. Our study suggests there is a common brain pattern during the processing of physiologically and socially needed stimuli, and discusses their spatial association with serotonin receptors and its possible implication.

Chronic pain is increasingly conceptualized within a stress-related framework. However, it remains unclear whether chronic pain and prototypical stress-related conditions--such as post-traumatic stress disorder (PTSD)--share common neurobiological substrates. To this end, we conducted a pre-registered transdiagnostic meta-analytic study of gray matter volume alterations in chronic pain (60 studies) and PTSD (20 studies). Disorder-specific meta-analyses revealed that chronic pain was associated with distributed volume reductions across ventromedial prefrontal, middle cingulate, and insular cortices, whereas PTSD exhibited a single cluster of reduced volume in the anterior cingulate/dorsomedial prefrontal cortices. A conjunction analysis revealed that both conditions converged onto an overlapping cluster of reduced volume in the bilateral medial orbitofrontal/anterior cingulate area. Using normative resting-state fMRI data (HCP 7T dataset), we found that chronic pain neuroanatomical abnormalities were embedded within a distributed architecture of large-scale circuits encompassing mesocorticolimbic/reward, default mode, salience, frontoparietal, dorsal attention, and somatosensory networks. On the other hand, the PTSD focal neuroanatomical alteration was embedded in a single large-scale circuit mapping onto the mesocorticolimbic/reward, default mode, salience, and visual networks. In both conditions, the mesocorticolimbic/reward circuit emerged as the most robustly involved large-scale network. Notably, the shared cluster of reduced volume showed functional integration within the mesocorticolimbic/reward and default mode networks, with neurochemical fingerprinting revealing robust spatial correspondence with dopaminergic, serotonergic, opioid, and endocannabinoid receptor/transporter maps. Overall, these findings suggest that chronic pain and PTSD, beyond disorder-specific alterations, converge on a shared large-scale network organization. The overlap between chronic pain and a prototypical stress-related disorder at the network level provides neurobiological support for conceptualizing chronic pain within a stress-related framework.

BackgroundMindfulness-based interventions (MBIs) have been increasingly adopted in educational settings to support cognitive development in youth. Executive function (EF)--encompassing inhibitory control, working memory, and cognitive flexibility--is a plausible target of MBI given its reliance on attention regulation. However, prior reviews have yielded mixed conclusions, partly due to inconsistent construct definitions and the pooling of heterogeneous outcome measures. ObjectivesTo (1) estimate the pooled effect of MBI on EF in youth aged 3-18 years using only construct-validated, direct EF measures, (2) examine potential moderators including age group, EF domain, and risk of bias, and (3) test dose-response relationships via meta-regression on intervention duration. MethodsWe searched PubMed, PsycINFO, CINAHL, Scopus, and Web of Science from inception to March 2026, supplemented by reference-list searches from two existing systematic reviews and a scoping review. Only English-language publications were eligible. Eligible studies were randomised controlled trials (RCTs) or quasi-RCTs of MBI (excluding yoga-only interventions) in typically developing youth, with at least one direct behavioural or computerised EF outcome. Risk of bias was assessed using Cochrane RoB 2. Hedges g was computed for each study, and pooled using a DerSimonian-Laird random-effects model. Subgroup analyses by age group, EF domain, and risk of bias were conducted, alongside leave-one-out sensitivity analyses, Eggers regression test, trim-and-fill, and Knapp-Hartung-adjusted meta-regression on intervention duration. Evidence certainty was rated using GRADE. ResultsThirteen RCTs (nine school-age, four preschool; total N = 1,560) met inclusion criteria. The pooled effect was g = 0.365 (95% CI 0.264 to 0.465; p < .00001), with negligible heterogeneity (I2 = 0.0%; Q = 6.76, p = .87). Effects were consistent across age groups (school-age g = 0.389; preschool g = 0.318) and EF domains (inhibitory control, working memory, cognitive flexibility; pbetween = .60). Meta-regression on intervention duration (4-20 weeks) was non-significant (p = .79). The effect was robust in leave-one-out analyses, in the low risk-of-bias subgroup (g = 0.361; k = 8), and after trim-and-fill adjustment (g = 0.354). The 95% prediction interval (0.252 to 0.477) was entirely positive. GRADE certainty was rated MODERATE, downgraded once for risk of bias. ConclusionsMBIs appear to produce a small, statistically significant improvement in EF in youth aged 3-18 years, with moderate certainty of evidence per the GRADE framework. The effect is consistent across preschool and school-age samples and across EF domains, with no significant dose-response relationship within the 4-20 week range studied. Emerging mediation evidence suggests that EF improvement may serve as an important pathway through which MBI supports emotion regulation, though this requires replication. Further large-scale, pre-registered RCTs with active control conditions and longitudinal follow-up are warranted.

A persistent effort in neuroscience has been to pinpoint the neurobiological substrates that support mental processes. The Research Domain Criteria (RDoC) aims to develop a new framework based on fundamental neurobiological dimensions. However, results from several meta-analysis of task-based fMRI showed substantial spatial overlap between several mental processes including emotion and anticipatory processes, irrespectively of the valence. Consequently, there is a crucial need to better characterize the core neurobiological processes using a data-driven techniques, given that these analytic approaches can capture the core neurobiological processes across neuroimaging literature that may not be identifiable through expert-driven categories. Therefore, we sought to examine the main data-driven co-activation networks across the past 20 years of published meta-analyses on task-based fMRI studies. We manually extracted 19,822 coordinates from 1,347 identified meta-analytic experiments. A Correlation-Matrix-Based Hierarchical Clustering was conducted on spatial similarity between these meta-analytic experiments, to identify the main co-activation networks. Activation likelihood estimation was then used to identify spatially convergent brain regions across experiments in each network. Across 1,347 meta-analyses, we found 13 co-activation networks which were further characterized by various psychological terms and distinct association with receptor density maps and intrinsic functional connectivity networks. At a fMRI activation resolution, neurobiological processes seem more similar than different across various mental functions. We discussed the potential limitation of linking brain activation to psychological labels and investigated potential avenues to tackle this long-lasting research question.

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are approved for treating type 2 diabetes and obesity and are under investigation as potential treatments for substance use disorders (SUD). GLP-1 RA-induced weight loss is thought to arise from both peripheral effects on gastrointestinal function and central modulation of appetite and reward circuits, though the exact mechanisms are unclear. Functional magnetic resonance imaging (fMRI) studies examining brain responses to reward-related cues can help clarify the central mechanisms through which GLP-1 RAs influence reward-seeking behavior. We systematically reviewed the fMRI literature examining how GLP-1 RAs affect brain responses to reward-related cues. We identified 1,209 records through a comprehensive literature search. After screening, only 11 studies met eligibility criteria. The vast majority assessed reactivity to food-related cues, with only one examining drug-related cues (alcohol), leaving neural mechanisms relevant to SUD largely unexplored. None included non-food emotional stimuli as control conditions. Several methodological limitations emerged. Most studies enrolled 20 or fewer participants per group, limiting statistical power. Treatment protocols varied substantially, with some assessing cue responses after single-dose administration and others after chronic treatment. Heterogeneity in medications used further confounds interpretation. The limited evidence tentatively suggests that acute GLP-1 RA administration may reduce brain reactivity to food cues in appetite and reward regions. However, effects appear inconsistent and may attenuate over time. Future studies should recruit larger samples, standardize agents and dosing, and assess responses to diverse motivationally relevant stimuli.

ObjectiveDespite the promising results of neurofeedback with real-time functional magnetic resonance imaging (rt-fMRI-NF) in the treatment of various psychiatric and neurological disorders, few studies have investigated its effects in acute and chronic pain and with mixed results. The lack of clear neuromodulation targets, rooted in the still poorly understood neurophysiopathology of chronic pain, has probably contributed to these inconsistent findings. In contrast, functional neurosurgery (funcSurg) approaches targeting specific brain regions have been shown to reduce pain in a considerable number of patients with chronic pain, however, their invasiveness limits their use to patients in critical situations. In this work, we sought to redefine, in an unbiased manner, rt-fMRI-NF future targets informed by the long tradition of funcSurg approaches. Methodsusing independent systematic reviews, we identified the targets of the rt-fMRI-NF (in acute and chronic pain) and funcSurg (in chronic pain) studies and characterized their underlying functional networks using a subset of high spatial resolution resting-state fMRI data (7T MRI data from the Human Connectome Project). After applying principal component analysis to reduce the number of identified networks, we performed a quantitative functional and anatomical annotation of these networks with a large-scale meta-analytic approach. Finally, we characterized the functional networks, defining their degree of overlap with canonical intrinsic brain networks (default mode, salience, and somatosensory) and their neurotransmitter profile. ResultsAs expected, the rt-fMRI-NF and funcSurg targets were different, except for the middle cingulate cortex, and showed different characteristics in terms of their functional connectivity. Our findings indicate that targets of rt-fMRI-NF primarily encompass hubs within the default mode network and, to a lesser extent, within the salience network. In contrast, funcSurg targets predominantly involve hubs within the sensorimotor system (primarily the motor system), with less robust involvement of the salience network. Notably, 3 out of 4 derived funcSurg rs-fMRI networks correlated significantly with the distribution map of noradrenaline transporters, further supporting the functional relevance of the funcSurg networks as targets for the treatment of chronic pain. ConclusionKey hubs of the sensorimotor networks, in particular the motor system, may represent promising targets for the therapeutic application of rt-fMRI-NF in chronic pain in particular in neuropathic pain patients. Our results also suggest that the antinociceptive effects of the funcSurg approaches could be, at least partially, linked to the restoration of abnormal noradrenergic system activation.

BackgroundEarly life stress (ELS) refers to exposure to negative childhood experiences, such as neglect, disaster, and physical, mental, or emotional abuse. ELS can permanently alter the brain, leading to cognitive impairment, increased sensitivity to future stressors, and mental health risks. The prefrontal cortex (PFC) is a key brain region implicated in the effects of ELS. MethodsTo better understand the effects of ELS on the PFC, we ran a meta-analysis of publicly available transcriptional profiling datasets. We identified five datasets (GSE89692, GSE116416, GSE14720, GSE153043, GSE124387) that characterized the long-term effects of multi-day postnatal ELS paradigms (maternal separation, limited nesting/bedding) in male and female laboratory rodents (rats, mice). The outcome variable was gene expression in the PFC later in adulthood as measured by microarray or RNA-Seq. To conduct the meta-analysis, preprocessed gene expression data were extracted from the Gemma database. Following quality control, the final sample size was n=89: n=42 controls & n=47 ELS: GSE116416 n=23 (no outliers); GSE116416 n=44 (2 outliers); GSE14720 n=7 (no outliers); GSE153043 n=9 (1 outlier), and GSE124387 n=6 (no outliers). Differential expression was calculated using the limma pipeline followed by an empirical Bayes correction. For each gene, a random effects meta-analysis model was then fit to the ELS vs. Control effect sizes (Log2 Fold Changes) from each study. ResultsOur meta-analysis yielded stable estimates for 11,885 genes, identifying five genes with differential expression following ELS (false discovery rate< 0.05): transforming growth factor alpha (Tgfa), IQ motif containing GTPase activating protein 3 (Iqgap3), collagen, type XI, alpha 1 (Col11a1), claudin 11 (Cldn11) and myelin associated glycoprotein (Mag), all of which were downregulated. Broadly, gene sets associated with oligodendrocyte differentiation, myelination, and brain development were downregulated following ELS. In contrast, genes previously shown to be upregulated in Major Depressive Disorder patients were upregulated following ELS. ConclusionThese findings suggest that ELS during critical periods of development may produce long-term effects on the efficiency of transmission in the PFC and drive changes in gene expression similar to those underlying depression. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/624315v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1619926org.highwire.dtl.DTLVardef@8da510org.highwire.dtl.DTLVardef@14feffcorg.highwire.dtl.DTLVardef@114aae3_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIEarly life stress (ELS) can have long-term effects on the prefrontal cortex (PFC) and its related cognitive and emotional functions. C_LIO_LITo elucidate these long-term effects, we conducted a meta-analysis of five publicly available PFC transcriptional profiling datasets from adult rodents that had previously experienced ELS. C_LIO_LIThis meta-analysis revealed a consistent downregulation of myelin-related genes in the PFC following ELS, and an upregulation of genes related to Major Depressive Disorder. C_LI Plain Language SummaryEarly life stress refers to exposure to negative childhood experiences, such as neglect, disaster, and physical, mental, or emotional abuse. Early life stress can permanently alter the brain, including the prefrontal cortex, which can lead to cognitive and emotional dysfunction that lasts into adulthood. We performed a meta-analysis using five public datasets to identify consistent long-term effects of early life stress on gene expression (mRNA) in the prefrontal cortex of adult rodents. In these studies, rodents that had experienced early life stress consistently showed a decreased amount of mRNA for genes related to myelin. Myelin is the fatty layer that insulates the axons of neurons, allowing them to transmit electrical signals more efficiently. These gene expression changes may suggest long-term effects of early life stress on the efficiency of prefrontal neurotransmission, disrupting cognitive and emotional processing.

Delay discounting is a promising paradigm for transdiagnostic research because both excessive and insufficient tendency to discount future rewards have been reported across diagnoses. Because delay discounting involves multiple neurocognitive functions, researchers have used many strategies to characterize brain activity during delay discounting. However, which of these analytic approaches yield truly robust and replicable findings remains unclear. To this end, we conducted a meta-analysis of 80 fMRI studies of delay discounting, testing which statistical contrasts give rise to reliable effects across studies. Despite being a widely used analytic approach, comparing impulsive and patient choices did not reliably yield the expected effects. Instead, subjective value contrasts reliably engaged the valuation network, and task versus baseline and choice difficulty contrasts reliably engaged regions in the frontoparietal and salience networks. We strongly recommend that future neuroimaging studies of delay discounting use these analytic approaches shown to reliably identify specific networks. In addition, we provide all cluster maps from our meta-analysis for use as a priori regions of interest for future experiments.

Parkinsons disease is a progressive neurodegenerative disorder characterised by motor dysfunction and cognitive disruption among other non-motor symptoms. No cure for Parkinsons disease exists. Deep Brain Stimulation of the Subthalamic Nucleus (DBS STN) has been utilised for control of motor symptoms. However, cognitive deficits are commonly reported after implantation, and few exhaustive analyses exist to quantify and explain them. Our systematic review, meta-analyses, and metaregressions examine within-subjects change across thirteen cognitive domains, from 70 studies and 3000 participants at baseline measurements. Improvement was not observed in any domain, but substantial decline at 12 months was observed for phonemic and categorical fluency, which appeared to stabilise 24 to 36 months. Meta-regression suggests that few study characteristics are predictive of longitudinal outcomes, and we propose that further research into specific surgical or placement effects is necessary to mitigate short-term cognitive change after DBS STN in Parkinsons disease.

1.Attention Deficit Hyperactivity Disorder (ADHD) affects over 400 million individuals worldwide, yet reliable biomarkers for diagnosis remain elusive. Recent studies have suggested elevated homocysteine levels may serve as a potential biomarker, given its role in one-carbon metabolism and neurotransmitter synthesis. Objective is to conduct a comprehensive systematic review and meta-analysis examining homocysteine levels in ADHD patients compared to healthy controls, and to identify shared molecular pathways through protein-protein interaction network analysis. A systematic search was conducted across PubMed, Scopus, Web of Science, and Embase databases from inception to June 2025. Studies comparing plasma/serum homocysteine levels between ADHD patients and healthy controls were included. Meta-analysis was performed using random-effects models with standardized mean differences (SMD). Protein-protein interaction networks were constructed using STRING database for genes common to ADHD and hyperhomocysteinemia, with hub gene identification through CytoHubba analysis. Six studies comprising 796 ADHD patients and 488 controls from three countries were included. Meta-analysis revealed no statistically significant difference in homocysteine levels between groups (SMD = -0.38, 95% CI [-1.24, 0.48], p = 0.386), with substantial heterogeneity (I{superscript 2} = 85.4%). Results showed a biphasic distribution, with three studies demonstrating lower homocysteine in ADHD and two showing higher levels. Network analysis identified 485 common genes between ADHD and hyperhomocysteinemia, revealing 25 hub genes enriched in inflammatory pathways (TNF signaling), growth factor signaling (FGF family), and MAPK cascades. In conclusion homocysteine levels do not serve as a reliable standalone biomarker for ADHD diagnosis due to significant inter-study variability and population-specific factors. However, shared molecular networks suggest complex mechanistic relationships involving neuroinflammation, one-carbon metabolism, and neurotransmitter regulation. Future diagnostic approaches should consider multidimensional biomarker panels incorporating genetic, metabolic, and inflammatory markers rather than single metabolites.

Depression is a highly prevalent and disabling disorder affecting approximately 5% of the adult population worldwide. Despite its impact, the underlying pathophysiology remains insufficiently understood, and current treatments are only partially effective. Brain-based biomarkers offer promise for clarifying mechanisms of depression and guiding novel treatment approaches, including neuromodulation. EEG is particularly attractive for this purpose due to its wide availability, cost-effectiveness, and potential for direct neuromodulatory targeting. We conducted a PROSPERO-registered systematic review in accordance with PRISMA guidelines to assess resting-state EEG biomarkers in adult patients with depression, diagnosed according to DSM-IV/V or ICD-10/11. Included studies reported cross-sectional or correlational data on quantitative EEG measures such as power, cordance, peak frequency, and alpha asymmetry. Semiquantitative analyses using modified albatross plots and meta-analyses were performed. Study quality was assessed with a modified Newcastle-Ottawa Scale. Fifty-two studies met the inclusion criteria. Findings indicated increased low-frequency (delta, theta) and high-frequency (beta, gamma) power, and left frontal alpha asymmetry in depressed patients compared to healthy controls. Meta-analysis confirmed a significant increase in beta power. However, results regarding disease severity correlations and data on peak alpha frequency and cordance were insufficient for interpretation. Risk of bias across studies was high. Our results support increased beta and potentially also theta oscillations and alpha asymmetry as candidate diagnostic EEG biomarkers for depression. These oscillations may reflect disrupted corticolimbic control and reward processing and partially overlap with mechanisms implicated in chronic pain and fatigue. Further investigation is warranted into their potential as diagnostic tools and neuromodulatory treatment targets.

Anxiety disorders (ANX) are a prevalent public health burden that significantly impair daily functioning and decrease quality of life. A growing body of research suggests that DNA methylation (DNAm), an epigenetic modification that can impact gene expression, may be altered in ANX. The current review used a systematic approach to identify and synthesize the literature regarding methylome-wide association studies (MWASs) of ANX in humans. We screened 804 articles returned by a search in PubMed in May 2025 and identified 12 studies for inclusion. All included studies examined ANX-associated DNAm in blood. In total, 2,023 DNAm sites corresponding to 985 genes were significantly associated with ANX. No DNAm sites significantly replicated across studies and four nominally replicated. This is likely a result of a lack of replication attempts, small sample sizes, and differences in data analysis choices. Findings suggest that ANX-associated DNAm may promote dysregulation of immune and inflammatory processes, some possibly sex-dependent. Collectively, the findings from studies included in this review provide preliminary evidence of ANX-related alterations to DNAm in whole blood and multiple blood cell-types. Future MWASs of ANX could benefit from larger sample sizes, a standardized analytic pipeline, longitudinal study designs, and the examination of DNAm in additional cell-types and tissues.

Meta-analyses are invaluable tools for navigating the rapidly expanding scientific literature. Given their high value, ensuring the quality of meta-analyses is paramount. We conducted a multifaceted overview, examining each step in a manual neuroimaging meta-analysis on a large scale. We used four novel datasets comprising over 14,000 papers, including fMRI meta-analyses, fMRI studies, studies included in meta-analyses, and studies associated with image data on NeuroVault. Regarding successes, two-thirds of meta-analyses stated that they followed PRISMA guidelines, and 65% included a flowchart describing their inclusion process. We point out several areas for improvement. Pre-registration was fairly rare (20%), and only half listed their exact search strategy. There could be a location bias in which papers are included, and many did not include enough studies to be robust against publication bias (68% of meta analyses have less than 30 studies included). We also offer ideas for future directions. As image based meta-analysis is the gold standard, we have indicated which topics have the most image data available. The potential redundancy of topics can be visualized in our paper, and we recommend future meta-analyses be in conversation with past ones by citing and discussing previous similar work. By addressing these findings, the neuroimaging community can collectively improve the field of neuroimaging meta-analyses.

The ability to adjust to changing environments (cognitive flexibility) and optimal decision-making are pivotal brain functions that govern successful human behavior. Anxiety and depressive disorders are strongly pervasive psychiatric conditions across the lifespan that profoundly disrupt mechanisms of attention, working memory, and decision-making. Although existing task evidence documents impaired decision-making and flexibility outcomes for both anxiety and depression, there is a growing need to systematically evaluate the role of anxiety and depression and to quantitatively compare the effects of these disorders on these domains. In the present study, we conducted a meta-analysis of anxiety and depression on decision-making and cognitive flexibility. We utilized a random-effects approach, given that a large amount of between-subject heterogeneity was anticipated. Given the scope of this meta-analysis, we used the machine learning tool asReview to more efficiently conduct a meta-analytic search. Across all outcomes, results showed anxiety and depression were associated with reduced cognitive flexibility and decision-making. These effect sizes were then tested for significance using a fixed-effects (plural) model. Subgroup analyses revealed no significant differences between anxiety and depression for either decision-making or flexibility outcomes, consistent with a transdiagnostic perspective. Results are contextualized in light of the biopsychosocial model and potential transdiagnostic factors.