INTRODUCTION:Functional brain network alterations associated with Alzheimer's disease (AD) pathology, including amyloid beta (Aβ) and phosphorylated tau (p-tau), are difficult to interpret due to overlapping aging-associated and non-amyloid biological processes. METHODS:We analyzed resting-state functional magnetic resonance imaging (fMRI) from 289 older adults classified as Aβ-positive (A+, n = 129) or Aβ-negative (A-, n = 160) based on cerebrospinal fluid biomarkers. A contrastive deep learning framework was used to identify A+-specific network dimensions and predict individual Aβ and p-tau levels. RESULTS:A+-specific signatures were localized to the right superior temporal and anterior cingulate cortices and linked to attention and memory functions, with transcriptomic enrichment implicating synaptic dysfunction and glial activity. In contrast, signature dimensions shared between A+ and A- individuals involved language-related regions and aging-associated molecular pathways. CONCLUSION:These findings suggest that contrastive graph learning may help separate amyloid-associated functional network variation from broader background biological variability, providing insight into the heterogeneity of AD-related biomarkers and cognitive dysfunction.

In high-stakes visual search tasks, such as reading a medical image or screening luggage at an airport security checkpoint, targets are often rare, leading to disproportionately high miss rates—a phenomenon known as the low-prevalence effect (LPE). While the LPE is traditionally attributed to conservative shifts in decision criterion (c), recent research on prevalence-induced concept change (PICC) suggests that low prevalence can lead to liberal decision criterion shifts in certain contexts. We propose that trial-level performance feedback is a critical mechanism governing these divergent outcomes. We conducted a meta-analysis of 62 effects from 29 publications, using robust variance estimation (RVE) to account for statistical dependencies. Our results confirm a large and reliable conservative criterion shift under low-prevalence visual search conditions, with no consistent effect on sensitivity (d’). Critically, meta-regression revealed that trial-level feedback significantly moderated the magnitude of these shifts; the presence of immediate feedback amplified the LPE, whereas its absence mitigated it. Additionally, the magnitude of the shift was tied to the numerical difference in prevalence, but remained robust across variations in stimulus type, observer expertise, and time constraints. These findings suggest that trial-level feedback reinforces expectations of target scarcity, amplifying the conservative biases that drive the LPE. This resolves theoretical discrepancies between the LPE in visual search and PICC findings in categorization and highlights a potential “intervention paradox”: While generally intended to improve accuracy, standard trial-level feedback may unintentionally exacerbate misses in rare target detection. In many critical real-world visual search environments like medical imaging or airport security, the most important targets are often the rarest. Decades of research on the low-prevalence effect have shown that as targets become rare, observers develop a bias toward reporting them as absent, which leads to an increase in missed target detections. This meta-analysis shows that feedback about search accuracy, a tool often used to improve search performance, actually exacerbates the increase in missed targets in low-prevalence settings by strengthening this reporting bias. By consistently reinforcing the absence of a target in low-prevalence search, feedback appears to bolster the observer's expectation of target scarcity rather than sharpening their target detection when searching for these proverbial needles in a haystack. These meta-analytic results have implications for how professional visual searchers should be trained, as well as how basic science studies of low-prevalence visual search will generalize differently in applied settings depending on the availability and type of feedback.

Major depressive disorder (MDD) is a common and debilitating condition with limited treatment precision. While brain imaging has linked neural features to MDD diagnosis and treatment response, the underlying circuits and their early modulation by treatment remain unclear. To examine changes in functional connectivity within the first two weeks of treatment, we analyzed two independent cohorts of MDD patients receiving antidepressants or placebo. Across patients, a visual-precuneus-thalamus network showed increased functional connectivity regardless of treatment arm or clinical outcome. Placebo response involved attention and striatal systems, while drug-specific effects were localized to the amygdala, mid-cingulate, orbitofrontal cortex, and cerebellum, emerging only in a subset of medicated patients. Notably, the responses of those without drug-specific changes can be predicted with a placebo response prediction model. These early functional connectivity changes reveal common and distinct mechanisms of treatment effects, offering insights that could inform more personalized interventions for MDD.

As populations age, identifying the neurobiological basis of cognitive resilience is critical for delaying or preventing Alzheimer's disease (AD). While most older adults experience memory decline, a subset known as superagers (SA) maintains youthful memory into late life, offering a unique window into protective mechanisms against neurodegeneration. Here, we identified a functional connectivity (FC) signature, termed Alzheimer's-resilient connectome (ARC), that robustly differentiates SA from age-matched patients with AD. Using resting-state fMRI in a discovery cohort (N = 290), we identified ARC derived from machine learning classifiers that distinguished SA from AD with high accuracy (AUC = 0.85), and validated the replicability of the ARC in an independent replication cohort (N = 143). ARC involved prefrontal, temporal and insular networks and was strongly associated with brain age. When applied to cognitively unimpaired (CU) adults (discovery cohort: N = 818 and replication cohort: N = 497), ARC-based subtyping revealed SA-like and AD-like subgroups with similar baseline cognitive performance but markedly divergent longitudinal trajectories. SA-like CU individuals showed slower cognitive decline, reduced amyloid-β accumulation, and lower risk of conversion to mild cognitive impairment and AD, reinforcing the ARC signature as a potential early indicator of resilience. Genome-wide association analysis identified CLYBL and FRMD6 as novel genetic modulators associated with these divergent aging phenotypes. Together, our findings position ARC as a sensitive and generalizable biomarker of resilience, enabling early risk stratification and precision prevention for AD.

Major depressive disorder (MDD) presents a substantial health burden with low treatment response rates. Predicting antidepressant efficacy is challenging due to MDD’s complex and varied neuropathology. Identifying biomarkers for antidepressant treatment requires thorough analysis of clinical trial data. Multimodal neuroimaging, combined with advanced data-driven methods, can enhance our understanding of the neurobiological processes influencing treatment outcomes. To address this, we analyzed resting-state fMRI and EEG connectivity data from 130 patients treated with sertraline and 135 patients with placebo from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. A deep learning framework was developed using graph neural networks to integrate data-augmented connectivity and cross-modality correlation, aiming to predict individual symptom changes by revealing multimodal brain network signatures. The results showed that our model demonstrated promising prediction accuracy, with an R2 value of 0.24 for sertraline and 0.20 for placebo. It also exhibited potential in transferring predictions using only EEG. Key brain regions identified for predicting sertraline response included the inferior temporal gyrus (fMRI) and posterior cingulate cortex (EEG), while for placebo response, the precuneus (fMRI) and supplementary motor area (EEG) were critical. Additionally, both modalities identified the superior temporal gyrus and posterior cingulate cortex as significant for sertraline response, while the anterior cingulate cortex and postcentral gyrus were common predictors in the placebo arm. Additionally, variations in the frontoparietal control, ventral attention, dorsal attention, and limbic networks were notably associated with MDD treatment. By integrating fMRI and EEG, our study established novel multimodal brain network signatures to predict individual responses to sertraline and placebo in MDD, providing interpretable neural circuit patterns that may guide future targeted interventions. Trial Registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) ClinicalTrials.gov Identifier: NCT#01407094.

Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment due to complex interactions between clinical symptoms and neurobiological features. To address this, we apply contrastive principal-component analysis to fMRI-based resting-state functional connectivity, isolating disorder-specific variations by contrasting data from 233 MDD patients and 285 healthy controls. Subsequently, we use sparse canonical correlation analysis to identify two significant dimensions linking distinct brain circuits with clinical profiles. One dimension relates to an internalizing-externalizing symptom spectrum involving visual and limbic networks and is associated with cognitive task reaction times. The other dimension, linked to personality traits protective against depression (e.g., extraversion), is driven by dorsal attention network connections and correlates with cognitive control and psychomotor performance. This approach illuminates stable symptom dimensions and their neurophysiological underpinnings, aiding in precision phenotyping for MDD and supporting the development of targeted, individualized therapeutic strategies for mental health care.

Background Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatment-resistant cases. However, identifying translatable biomarkers predictive of treatment outcomes remains underexplored. Methods Participants with treatment resistant depression from the TDBRAIN dataset underwent either high frequency rTMS (10 Hz) at the left dorsolateral prefrontal cortex (DLPFC) (Protocol 1, n = 44) or low frequency rTMS (1 Hz) at the right DLPFC (Protocol 2, n = 73). Pre-treatment electroencephalograms (EEG) was collected, and changes in Beck Depression Inventory were measured post-treatment. EEG oscillations were decomposed into multiband intrinsic mode functions (IMF) and integrated under a latent space predictive modeling framework to identify signatures for predicting treatment outcomes. Results Multiband signatures significantly predicted rTMS outcomes (Protocol 1: r = 0.40, p < 0.01; Protocol 2: r = 0.26, p < 0.05). Key spatial patterns linked to treatment outcomes were identified, revealing three main oscillations: IMF-Alpha, IMF-Beta, and the residual signal. In Protocol 1, critical regions included the left frontal and parietal regions for IMF-Alpha, left frontal-central and right parietal regions for IMF-Beta, and bi-hemispheric central and left parietal-occipital regions for residual signals. In Protocol 2, critical regions involved the left frontal and parietal regions for IMF-Alpha, left frontal-central region IMF-Beta, and right frontal, left frontal-central, midline central, and left parietal-occipital regions for residual signals. These oscillatory features also showed correlations with specific personality measures, suggesting their potential clinical relevance. Conclusion Our findings demonstrate the promise of machine learning-driven multiband EEG signatures for personalized MDD treatment prediction, offering a translatable pathway for improved patient outcomes.

Major depressive disorder (MDD) is a prevalent condition that profoundly impairs quality of life across diverse populations. Despite widespread use, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates. Progress in developing effective therapies is hampered by the insufficiently understood heterogeneity of MDD and its elusive underlying mechanisms. Here, to address these challenges, we develop a novel machine learning framework that identifies structure–function covariation through target-oriented fusion of structural and functional connectivity, which robustly predicts individual-level antidepressant response (sertraline, R2 = 0.31; placebo, R2 = 0.22). Validation in an independent escitalopram-medicated MDD cohort confirms the biomarker’s generalizability (P = 0.01) and suggests an overlap of psychopharmacological signatures across selective serotonin reuptake inhibitors. Our models highlight the right precuneus as a common key region for both sertraline and placebo responses, with the right middle frontal gyrus and left fusiform gyrus specific to sertraline and the left inferior and middle frontal gyri to placebo. We also find that structural connectivity is more predictive of sertraline response, while functional connectivity better predicts placebo response. The framework further decomposes the overall predictive patterns into three constitutive network constellations (default-mode regulatory, affective and sensory processing), which exhibit distinct generalizable structure–function covariation and treatment-specific association with personality traits and behavioral/cognitive profiles. These findings provide unique insights to the structure–function covariation in patients with MDD, its association to the heterogeneity in antidepressant response and the dissection of the intricate MDD neuropsychopharmacology, paving the way for precision medicine and development of more targeted antidepressant therapeutics. Clinicaltrials.gov registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT01407094. Using a machine learning framework to predict antidepressant response in major depressive disorder by analyzing structural and functional connectivity, this study reveals distinct predictive patterns and highlights specific brain regions associated with treatment efficacy, advancing personalized therapeutic approaches.

Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R2 = 0.31; placebo: R2 = 0.22). Remarkably, the sertraline response biomarker is further tested on an independent escitalopram-medicated cohort of MDD patients, validating its generalizability (p = 0.01) and suggesting an overlap of psychopharmacological mechanisms across selective serotonin reuptake inhibitors. Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel and reliable insights into the intricate neuropsychopharmacology of antidepressant treatment, paving the way for advances in precision medicine and development of more targeted antidepressant therapeutics.

Importance Understanding the heterogeneity of neuropsychiatric symptoms (NPSs) and associated brain abnormalities is essential for effective management and treatment of dementia. Objective To identify dementia subtypes with distinct functional connectivity associated with neuropsychiatric subsyndromes. Design, Setting, and Participants Using data from the Open Access Series of Imaging Studies-3 (OASIS-3; recruitment began in 2005) and Alzheimer Disease Neuroimaging Initiative (ADNI; recruitment began in 2004) databases, this cross-sectional study analyzed resting-state functional magnetic resonance imaging (fMRI) scans, clinical assessments, and neuropsychological measures of participants aged 42 to 95 years. The fMRI data were processed from July 2022 to February 2024, with secondary analysis conducted from August 2022 to March 2024. Participants without medical conditions or medical contraindications for MRI were recruited. Main Outcomes and Measures A multivariate sparse canonical correlation analysis was conducted to identify functional connectivity-informed NPS subsyndromes, including behavioral and anxiety subsyndromes. Subsequently, a clustering analysis was performed on obtained latent connectivity profiles to reveal neurophysiological subtypes, and differences in abnormal connectivity and phenotypic profiles between subtypes were examined. Results Among 1098 participants in OASIS-3, 177 individuals who had fMRI and at least 1 NPS at baseline were included (78 female [44.1%]; median [IQR] age, 72 [67-78] years) as a discovery dataset. There were 2 neuropsychiatric subsyndromes identified: behavioral (r = 0.22; P = .002; P for permutation = .007) and anxiety (r = 0.19; P = .01; P for permutation = .006) subsyndromes from connectivity NPS-associated latent features. The behavioral subsyndrome was characterized by connections predominantly involving the default mode (within-network contribution by summed correlation coefficients = 54) and somatomotor (within-network contribution = 58) networks and NPSs involving nighttime behavior disturbance (R = -0.29; P < .001), agitation (R = -0.28; P = .001), and apathy (R = -0.23; P = .007). The anxiety subsyndrome mainly consisted of connections involving the visual network (within-network contribution = 53) and anxiety-related NPSs (R = 0.36; P < .001). By clustering individuals along these 2 subsyndrome-associated connectivity latent features, 3 subtypes were found (subtype 1: 45 participants; subtype 2: 43 participants; subtype 3: 66 participants). Patients with dementia of subtype 3 exhibited similar brain connectivity and cognitive behavior patterns to those of healthy individuals. However, patients with dementia of subtypes 1 and 2 had different dysfunctional connectivity profiles involving the frontoparietal control network (FPC) and somatomotor network (the difference by summed z values was 230 within the SMN and 173 between the SMN and FPC for subtype 1 and 473 between the SMN and visual network for subtype 2) compared with those of healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity (eg, the median [IQR] of the total score of NPSs was 2 [2-7] for subtype 3 vs 6 [3-8] for subtype 1; P = .04 and 5.5 [3-11] for subtype 2; P = .03) and longitudinal progression of cognitive impairment and behavioral dysfunction (eg, the overall interaction association between time and subtypes to orientation was F = 4.88; P = .008; using the time x subtype 3 interaction item as the reference level: beta = 0.05; t = 2. 6 for time x subtype 2; P = .01). These findings were further validated using a replication dataset of 193 participants (127 female [65.8%]; median [IQR] age, 74 [69-77] years) consisting of 154 newly released participants from OASIS-3 and 39 participants from ADNI. Conclusions and Relevance These findings may provide a novel framework to disentangle the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the timely development of targeted interventions for patients with dementia.

Cocaine use disorder (CUD) is prevalent, and repetitive transcranial magnetic stimulation (rTMS) shows promise in reducing cravings. However, the association between a consistent CUD-specific functional connectivity signature and treatment response remains unclear. Here we identify a validated functional connectivity signature from functional magnetic resonance imaging to discriminate CUD, with successful independent replication. We found increased connectivity within the visual and dorsal attention networks and between the frontoparietal control and ventral attention networks, alongside reduced connectivity between the default mode and limbic networks in patients with CUD. These connections were associated with drug use history and cognitive impairments. Using data from a randomized clinical trial, we also established the prognostic value of these functional connectivities for rTMS treatment outcomes in CUD, especially involving the frontoparietal control and default mode networks. Our findings reveal insights into the neurobiological mechanisms of CUD and link functional connectivity biomarkers with rTMS treatment response, offering potential targets for future therapeutic development.

Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo. This modest efficacy is partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment - the approved antidepressants only benefit a portion of patients, calling for personalized psychiatry based on individual-level prediction of treatment responses. Normative modeling, a framework that quantifies individual deviations in psychopathological dimensions, offers a promising avenue for the personalized treatment for psychiatric disorders. In this study, we built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients. We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between treatment responses. Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective MDD treatment.

Visual working memory (VWM) plays an important role during visual search, with some theories suggesting an equivalence between working memory representations and guidance from attentional templates. However, recent work has shown that participants can also use ‘negative templates’, the foreknowledge of distractor-features stored in VWM, to guide attention away from distractors during visual search. These negative templates must also be represented in working memory, but the question remains whether the quality of the working memory representations underlying negative and positive templates are similar, in spite of their opposite impacts on attention. In this study, participants (N = 33) engaged in a visual search task for a shape-defined target after receiving a positive cue (target color), negative cue (distractor color) or neutral cue (non-informative). In 20% of the trials, a color-wheel probe was presented instead of a search array to measure the quality of the cue representation stored in VWM. Our results revealed that participants were more likely to guess in response to neutral cues than negative cues. Yet, the comparison between positive and negative cues showed no significant differences. However, we found no difference in memory precision for the three cue types. More interestingly, the more the VWM quality is boosted by the negative cue, the greater the ability to guide attention away from distractors. Such a pattern of results might map to recent evidence of between-individuals differences in utilization of negative cues. These findings highlight the distinction between attentional templates and simple maintenance in working memory.

Attention allows us to focus on relevant information while ignoring distractions. Effective suppression of distracting information is crucial for efficient visual search. Recent studies have developed two paradigms to investigate attentional suppression: cued-suppression which is based on top-down control, and learned-suppression which is based on selection history. While both types of suppression reportedly engage proactive control, it remains unclear whether they rely on shared mechanisms. This study aimed to determine the relationship between cued- and learned-suppression. In a within-subjects design, 54 participants performed a cued-suppression task where pre-cues indicated upcoming target or distractor colors, and a learned-suppression task where a salient color distractor was present or absent. No significant correlation emerged between performance in the two tasks, suggesting distinct suppression mechanisms. Cued-suppression correlated with visual working memory capacity, indicating reliance on explicit control. In contrast, learned-suppression correlated with everyday distractibility, suggesting implicit control based on regularities. These results provide evidence for heterogeneous proactive control mechanisms underlying cued- and learned-suppression. While both engage inhibition, cued-suppression relies on deliberate top-down control modulated by working memory, whereas learned-suppression involves implicit suppression shaped by selection history and distractibility traits.

Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting functional abnormalities in various brain disorders. However, existing studies often focus on a single domain or modality, neglecting the valuable complementary information offered by multiple domains from both fMRI and EEG, which is crucial for a comprehensive representation of disorder pathology. This limitation poses a challenge in effectively leveraging the synergistic information derived from these modalities. To address this, we propose a Multi-modal Cross-domain Self-supervised Pre-training Model (MCSP), a novel approach that leverages self-supervised learning to synergize multi-modal information across spatial, temporal, and spectral domains. Our model employs cross-domain self-supervised loss that bridges domain differences by implementing domain-specific data augmentation and contrastive loss, enhancing feature discrimination. Furthermore, MCSP introduces cross-modal self-supervised loss to capitalize on the complementary information of fMRI and EEG, facilitating knowledge distillation within domains and maximizing cross-modal feature convergence. We constructed a large-scale pre-training dataset and pretrained MCSP model by leveraging proposed self-supervised paradigms to fully harness multimodal neuroimaging data. Through comprehensive experiments, we have demonstrated the superior performance and generalizability of our model on multiple classification tasks. Our study contributes a significant advancement in the fusion of fMRI and EEG, marking a novel integration of cross-domain features, which enriches the existing landscape of neuroimaging research, particularly within the context of mental disorder studies.