As depressive symptoms among adolescents continue to rise, increasing attention has been directed toward educational contexts that may support psychological well-being. Augmented reality, characterized by immersive, interactive, and multisensory affordances, has been increasingly incorporated into fine arts education. However, it remains insufficiently understood how adolescents’ perceived AR-based fine arts education experiences are associated with psychological resilience, emotional regulation, perceived reduction in rumination, and perceived reduction in depressive symptoms. Using post-course self-report questionnaire data from 518 Chinese adolescents, this study tested a cross-sectional associational model with structural equation modeling, without making causal or clinical claims. The empirical results indicate that adolescents’ perceived AR-based fine arts education experiences were positively associated with self-reported perceived reduction in depressive symptoms. Additionally, these experiences were positively associated with self-reported psychological resilience and perceived reduction in rumination. Self-reported psychological resilience and perceived reduction in rumination were positively associated with self-reported emotional regulation, and this regulation was positively associated with self-reported perceived reduction in depressive symptoms. These findings support a correlational psychological model linking perceived AR-based fine arts education experiences with adolescents’ self-reported psychological perceptions and perceived reduction in depressive symptoms. Theoretically, this study contributes to the literature on arts education, educational technology, and adolescent mental health. Practically, the findings suggest that immersive art-learning environments may be perceived by adolescents as supportive contexts for psychological resources. The innovation of this study lies in reframing AR-based fine arts education experiences as psychologically meaningful educational experiences associated with adolescents’ self-reported psychological perceptions
Neurophysiological effects of transcranial alternating current stimulation combined with multidisciplinary rehabilitation in Parkinson’s disease
IntroductionMotor impairment in Parkinson’s disease (PD) often becomes inadequately controlled as the disease progresses. Rehabilitation therapy combined with noninvasive neuromodulation has shown benefits, yet the neural mechanisms underlying this remain unclear. This study aimed to identify neurophysiological and brain network features associated with motor improvement following high-intensity transcranial alternating current stimulation (Hi-tACS) combined with multidisciplinary intensive rehabilitation therapy (MIRT).MethodsThis secondary analysis was based on a randomized controlled trial (ChiCTR2300071969). Thirty patients receiving Hi-tACS combined with MIRT were included. Responders were defined as patients achieving a minimal clinically important difference improvement greater than 3.25 points on the Unified Parkinson’s Disease Rating Scale part III. Electroencephalographic spectral analysis, resting-state functional connectivity, and dynamic brain states assessed using hidden Markov modeling were performed. Partial correlation analyses were conducted for group-differentiating measures, controlling for age and disease duration.ResultsCompared with non-responders, responders showed increased temporal high beta frequencies relative power (95%CI 0.85(0.01–1.66), P = 0.047), reduced connectivity between the default mode and sensorimotor networks (95%CI -0.89(-1.71–0.05), P = 0.038), and decreased fractional occupancy of state 3 (95%CI 0.03(0.00–0.28), P = 0.033) and 6 (95%CI 0.02(0.00–0.35), P = 0.038). After Bonferroni correction, only changes in temporal high beta frequencies relative power were negatively correlated with changes in total UPDRS part III scores (R = -0.59, PBON = 0.012) and rigidity subscores (R = -0.74, PBON < 0.001).DiscussionThese results suggest that motor improvement following combined Hi-tACS and MIRT in PD patients is associated with alterations in brain oscillations and network connectivity, particularly in high-beta frequencies. The findings contribute to understanding the neural mechanisms underlying rehabilitation and neuromodulation in PD. Future studies should investigate the long-term effects of this intervention and its applicability in larger, more diverse populations. Moreover, exploring additional biomarkers may help identify individuals who are most likely to respond to this treatment.Clinical Trial Registrationhttps://www.chictr.org.cn, identifer ChiCTR2300071969.
Disrupted self-referential processing and empathy-based interventions in mental disorders: neural mechanisms, cognitive biases, and therapeutic integration
Self-referential processing (SRP), the mental activity of linking experiences and stimuli to the self, is fundamental to shaping identity, regulating affect, and navigating complex social environments. Disruptions in SRP are increasingly recognized as a transdiagnostic hallmark across psychiatric disorders, including schizophrenia spectrum disorders, major depressive disorder, and anxiety disorders. Such disruptions often manifest as distorted self-perception, negative cognitive-affective biases, and dysfunctional connectivity within key neural networks, particularly the default mode network (DMN) and the medial prefrontal cortex (mPFC). Emerging evidence reveals a dynamic bidirectional relationship between SRP and empathy. Impairments in self-processing may hinder the capacity to accurately infer others’ emotional and cognitive states, while deficits in empathic attunement may reinforce maladaptive self-focus. This interplay contributes to social withdrawal, interpersonal dysfunction, and emotional dysregulation across diagnostic boundaries. Recent therapeutic advances suggest that interventions targeting the interface between SRP and empathy may effectively modulate shared neurocognitive mechanisms. Such interventions include metacognitive therapy, mindfulness-based interventions, and psychedelic-assisted psychotherapy. These therapeutic approaches show preliminary but promising evidence for reducing maladaptive self-focused attention, enhancing emotion regulation, and improving social functioning. This review synthesizes empirical evidence regarding the neural underpinnings of SRP, delineates disorder-specific alterations in self-referential processing, and examines the bidirectional interplay between SRP and empathic functioning. We further outline a conceptual framework for empathy-based interventions targeting SRP dysfunction and propose future directions for precision psychiatry research. Emphasis is placed on developing personalized, mechanism-driven therapeutic strategies informed by computational modeling, transdiagnostic biomarker identification, and digital therapeutic platforms.
Preliminary testing of a prespecified liability architecture for autism: theory-guided pathogenetic triad models outperform strength-matched alternatives
BackgroundNeuropsychiatric conditions are heterogeneous and mechanistically diverse, yet predictive modeling studies commonly aggregate features without evaluating prespecified liability architectures. The Pathogenetic Triad (PT) is a multilevel framework proposing that diagnostic outcomes reflect the joint configuration of a trait-related domain, cognitive capacity (CC), and neuropathological burden (NB). In autism, the trait-related domain corresponds to autistic personality (AP). We evaluated this framework using out-of-sample prediction as a structured empirical test of architectural coherence rather than as a purely data-driven exercise in classification.MethodsWe analyzed a case–comparison cohort (n = 42, 21 autistic) with dense multimodal characterization, including behavioral phenotyping, psychometric measures, autonomic physiology, structural MRI morphometry, and magnetoencephalographic indices. AP was indexed by the Autism-Spectrum Quotient, CC by Wechsler’s scales of intelligence, and NB by heart-rate variability as a proxy indicator. This multimodal structure enabled direct comparison of theory-constrained PT models with strength-matched, domain-restricted atheoretical combinations. Predictive discrimination was evaluated using leakage-free nested cross-validation with permutation inference.ResultsAcross multiverse specifications, low-dimensional PT models ranked among the strongest models of comparable size and achieved discrimination broadly comparable to higher-dimensional models within this dataset. Matched comparisons indicated that including all three PT domains was associated with systematic advantages relative to alternatives with similar univariate input strength, consistent with complementary configurational information relating to case status.ConclusionsThese findings provide preliminary evidence supporting the Pathogenetic Triad as a multilevel architecture of autism liability. Although based on a small and demographically restricted cohort, this densely characterized dataset permitted explicit comparison of theory-guided and atheoretical model spaces. The results illustrate how prespecified multilevel frameworks can be operationalized and empirically evaluated in neuropsychiatric samples.
Commentary: Associations between attention-deficit/hyperactivity disorder and allergic diseases: a two-sample Mendelian randomization study
Author Correction: Sustained nitric oxide production by engineered E. coli remodels the tumor microenvironment and potentiates immunotherapy
Nature Biotechnology, Published online: 06 July 2026; doi:10.1038/s41587-026-03247-5
Author Correction: Sustained nitric oxide production by engineered E. coli remodels the tumor microenvironment and potentiates immunotherapy
Quantum computing in transition
Nature Biotechnology, Published online: 06 July 2026; doi:10.1038/s41587-026-03233-x
Hybrid quantum–classical computers will achieve quantum advantage in biotechnology, bridging the time until a full quantum computer becomes available.
Data rights are the missing pillar for modernizing consent in medicine
Nature Medicine, Published online: 06 July 2026; doi:10.1038/s41591-026-04506-3
Data rights are the missing pillar for modernizing consent in medicine
Author Correction: Teclistamab-based induction treatment in transplant-eligible, newly diagnosed multiple myeloma: a phase 2 trial
Nature Medicine, Published online: 06 July 2026; doi:10.1038/s41591-026-04551-y
Author Correction: Teclistamab-based induction treatment in transplant-eligible, newly diagnosed multiple myeloma: a phase 2 trial
Biomarkers Could Help in Antidepressant Choice
Antidepression treatment based on a person’s individual biomarkers could help determine which of the world’s most popular medications to use, a clinical trial suggests.
The SMART Trial to Predict Anhedonia Response to Antidepressant Treatment results suggest that behavioral, brain, and clinical data could together determine the optimal antidepressant to choose before a person starts treatment.
There were no significant primary endpoint differences in depression outcomes between participants who received bupropion or sertraline based on biomarkers identified in the prior Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study.
But people negative for biomarkers with both drugs had significantly worse depression symptom trajectories than those who had at least one positive biomarker, regardless of the drug they received.
Response rates among participants with both biomarkers were almost double that of those without any biomarkers, with those who had at least one biomarker having an intermediate response.
“Our results suggest that we could boost response rate by using two sets of biomarkers previously identified in the EMBARC study, making an important contribution to advancing the goals of precision psychiatry,” reported Peter Zhukovsky, PhD, from Harvard Medical School, and co-workers in Nature Mental Health.
“Ultimately, we strongly hope these advances will enable personalized treatment guidance to accelerate and boost antidepressant benefits.”
Treatment for depression is often still trial and error, with symptoms improving in only half the people taking an antidepressant. This could be due to treatments not being chosen based on people’s individual characteristics.
Finding markers that predict response to different antidepressants could therefore provide patients and clinicians with valuable information to guide treatment choice.
The trial was among the first to investigate how treatment could be guided using clinical information such as responses to questionnaires, behavioral information such as performance in computerized tasks, and brain data such as magnetic resonance imaging (MRI) scans.
It was carried out as part of the Wellcome Leap Multi-Channel Psych Program effort to double the number of people who respond to the first treatment they try for depression via the integration of multimodal biomarkers.
Firstly, the researchers investigated biomarker models that predicted response to the selective serotonin reuptake inhibitor (SSRI) sertraline or the norepinephrine-dopamine reuptake inhibitor bupropion using the EMBARC study.
The treatment-assignment algorithm that was developed generated two marker-based indications for each patient—one for bupropion and sertraline—with the predictive model achieving a cross-validated area under the curve of 0.86 and 0.66, respectively.
The team then examined whether antidepressant response could be boosted using their created biomarker combination of a functional MRI imaging marker, reward learning and sensitivity, cognitive control, the clinical variables of depression severity and neuroticism, and the demographic variable of employment status.
After analyzing these biomarkers among participants, who had major depressive disorder, the group was randomly assigned to receive a full 8-week course of an SSRI or non-SSRI.
The primary outcome was the change in depression severity from pretreatment baseline to eight weeks after the start of treatment, with no significant differences in treatment outcomes for those assigned a drug consistent versus inconsistent with their biomarkers.
This, the researchers say was possibly due to the limited power to detect moderate effects.
However, significant differences emerged in symptom reduction trajectories for those with positive markers for both medications, with a response rate of 71.4% compared with 65.4% for those with a positive biomarker for either drug and 42.9% for those with two negative markers.
The authors concluded: “We found that, relative to patients with two negative markers, those with one or two markers were characterized by significantly larger reduction in depressive symptoms, showing that biomarker-guided treatment selection can boost efficacy for two of the most widely prescribed antidepressants around the world.”
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