Institute of Psychology, Chinese Academy of Sciences
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Distinct electroencephalogram microstate in patients with methamphetamine use disorder and obsessive-compulsive disorder
No full textBackground Electroencephalogram (EEG) microstates reflect momentary localized brain activity and may indicate spontaneous fluctuations within large-scale neural networks. Methamphetamine use disorder (MUD) and obsessive-compulsive disorder (OCD) exhibit overlapping compulsive features, however, similarities or differences in whole-brain dynamics on subsecond timescales between patients with MUD and OCD remain unclear. Methods We recruited 127 subjects aged 16 to 55 years, including 45 OCD patients, 44 MUD patients and 39 healthy controls (HCs), and collected resting-state EEG data. The OCD symptoms were assessed using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), and drug craving in MUD was evaluated with the Drug Desire Questionnaire (DDQ). Results Compared to HCs, MUD patients showed reduced time coverage of microstate A. OCD patients exhibited lower time coverage and occurrence of microstate A than MUD patients, alongside altered transition probabilities (reduced B/C/D -> A; increased B -> D) (all p D transition probability and the mean duration of microstate D, but positively correlated with the occurrence of microstate C (all p < 0.05). For MUD, DDQ desire and intention scores were positively correlated with the occurrence and time coverage of microstate D (all p < 0.05). Conclusions Our findings reveal that distinct EEG microstate patterns in MUD and OCD, shedding light on the underlying neurodynamic and connectivity differences between the two disorders. The relationship between microstate D and clinical symptoms in both disorders further elucidates potential shared neural underpinnings. Microstate C shows potential as a neurophysiological biomarker for OCD
Turning the tide: A randomized controlled trial of a brief digital mindfulness intervention to reduce immediate and delayed negative interpretation bias
No full textObjective: The current study aimed to explore the effectiveness and mechanisms of mindfulness on alleviating both immediate and delayed negative interpretation biases (NIB). Method: A total of 105 college students (female: 84.62 %, age: 17-23) with high levels of depression were randomly assigned to a mindfulness-writing group, a mindfulness-only group and a waiting list group, and completed measures of mindfulness, depression, anxiety, NIB across three points in time (pre-, mid, and post interventions). The two intervention groups underwent two phases, each lasting 21 days, with daily self-guided digital interventions lasting 15-30 min. Results: In terms of intervention effects: (1) Both intervention groups significantly improved levels of observing, describing, non-judging, and non-reactivity. While the waiting list showed a significant decline in acting with awareness, both intervention groups remained stable. (2) The mindfulness-only group significantly reduced delayed NIB. (3) In terms of reaction time as an indicator, both interventions did not reduce immediate NIB. However, in terms of frequency as an indicator, the mindfulness-only group effectively alleviated immediate NIB. In terms of mechanisms: (1) Regarding delayed NIB, acting with awareness and non-reactivity significantly mediated the relationship between groups and delayed NIB. (2) For immediate NIB, when measured by frequency, non-judging and non-reactivity significantly mediated the relationship between groups and immediate NIB. Conclusion: Acting with awareness and non-judging, as mindfulness sub-dimensions, play unique roles in alleviating delayed and immediate NIB, respectively. The enhancement of non-reactivity contributes to the reduction of both immediate and delayed NIB.</p
BioMotion-SNN: Spiking neural network modeling for visual motion processing*
No full textNeuroscience remains to be challenged by the decoding of the neural processes underlying biological motion perception. To address this, we propose BioMotion-SNN, a spiking neural network (SNN) framework inspired by the MT-MST pathways, designed to model the dynamic interactions between these brain regions. Grounded in biological experimental phenomena, BioMotion-SNN processes event-driven stimuli in a manner closely resembling real sensory inputs, setting it apart from conventional models reliant on static datasets and predefined labels. The framework incorporates contrastive self-supervised learning with a motion-perception contrastive loss function to enhance feature representation, while L1-norm-based synaptic pruning mimics sparse biological connectivity by reducing redundant connections. Leveraging real electrophysiological data augmented through controlled transformations, BioMotion-SNN reduces the need for extensive biological data collection, enriches dataset diversity, and bridges the gap between experimental neuroscience and computational modeling. Achieving a classification accuracy of 93.00%, the framework effectively captures complex motion patterns and establishes a novel paradigm for integrating computational modeling with empirical neuroscience. Our data/codes are available at https://github.com/BrainCogLab/biomotion_snn
Biased minds, sensitive hearts: Divergent neural signatures of interpersonal sensitivity in atypical and non-atypical depression
No full textBackground: Atypical depression (AD) is a distinct subtype of depression, with interpersonal sensitivity as one of its core characteristics. However, the electrophysiological mechanisms that underlie interpersonal sensitivity in AD remain insufficiently explored. Therefore, in the present study, we systematically investigated the neurophysiological differences in interpersonal sensitivity between individuals with AD and non-atypical depression (non-AD) using electroencephalography (EEG). Methods: We assessed 93 patients (50 with AD and 43 with non-AD) using standardized scales, including the Rejection Sensitivity Questionnaire and the Symptom Checklist 90 (SCL-90) Interpersonal Sensitivity subscale. The Cyberball task combined with EEG recordings, followed by the Positive and Negative Affect Schedule, was used to evaluate electrophysiological and emotional responses. Exploratory analyses examined correlations between interpersonal sensitivity scores and behavioral/electrophysiological measures. In addition, a machine learning model was applied to identify key features for distinguishing between AD and non-AD. Results: The AD group had significantly higher scores on the SCL-90 Interpersonal Sensitivity subscale compared with the non-AD group. Electrophysiological analyses detected distinct response patterns in the P3 amplitude and theta wave activity during both inclusion and exclusion blocks in the AD group compared with the non-AD group. Moreover, a random forest model developed by using features such as Interpersonal Sensitivity subscale scores achieved an accuracy of 83.3 % in distinguishing between AD and non-AD. Conclusion: Thus, AD patients exhibited greater interpersonal sensitivity than non-AD patients, which was supported by cognitive and neurological evidence. Our findings provide critical insights for developing more precise diagnostic and therapeutic strategies for AD.</p
Large-scale brain network alterations in young individuals with comorbid social anxiety and depression: Evidence from resting-state EEG spectral and microstate analyses
No full textSocial anxiety and depression have each been linked to alterations in large-scale brain network connectivity, yet the neural mechanisms underlying their comorbidity remain poorly understood. To address this gap, we analyzed resting-state EEG data from 420 young adults classified as healthy controls (HC), individuals with social anxiety without depression (SA-noDp), and those with comorbid social anxiety and depression (SA-Dp). Social anxiety and depressive symptoms were assessed using the Liebowitz Social Anxiety Scale (LSAS) and the Patient Health Questionnaire-9 (PHQ-9), respectively. We applied both spectral and microstate analyses to characterize large-scale brain network dynamics during eyes-open resting-state. Spectral analysis revealed reduced delta amplitude in the SA-Dp group relative to HC (p < .05), suggesting that decreased delta may be a neural marker of SA-Dp and diminished approach motivation. Microstate analysis showed reduced microstate F in SA-noDp relative to HC, while SA-Dp exhibited higher occurrence than SA-noDp despite lower delta activity, indicating a shift from anxiety-related vigilance to depression-related maladaptive self-focus linked to DMN activity. Additionally, SA-noDp displayed reduced transitions between microstates C to F or G relative to HC (all ps < 0.05). These findings reveal differential disruptions in resting-state brain dynamics in social anxiety with and without comorbid depression, highlighting diminished approach and elevated avoidance motivation in comorbid individuals versus predominantly elevated avoidance in non-comorbid social anxiety. Together, spectral and microstate measures may serve as neurobiological markers for comorbid social anxiety and depression, supporting early identification and tailored interventions, and emphasizing the importance of approach-avoidance motivational balance in these populations.</p
BioMotion-SNN: Spiking neural network modeling for visual motion processing
No full textNeuroscience remains to be challenged by the decoding of the neural processes underlying biological motion perception. To address this, we propose BioMotion-SNN, a spiking neural network (SNN) framework inspired by the MT-MST pathways, designed to model the dynamic interactions between these brain regions. Grounded in biological experimental phenomena, BioMotion-SNN processes event-driven stimuli in a manner closely resembling real sensory inputs, setting it apart from conventional models reliant on static datasets and predefined labels. The framework incorporates contrastive self-supervised learning with a motion-perception contrastive loss function to enhance feature representation, while L1-norm-based synaptic pruning mimics sparse biological connectivity by reducing redundant connections. Leveraging real electrophysiological data augmented through controlled transformations, BioMotion-SNN reduces the need for extensive biological data collection, enriches dataset diversity, and bridges the gap between experimental neuroscience and computational modeling. Achieving a classification accuracy of 93.00%, the framework effectively captures complex motion patterns and establishes a novel paradigm for integrating computational modeling with empirical neuroscience. </p
Altered posterior mid-cingulate cortex activation during adaptive coding in individuals with schizotypal traits, subthreshold depression and autistic traits
No full textAnhedonia is a shared symptom for schizophrenia, major depressive disorder and autism spectrum disorder. Adaptive coding concerns the rescaling of the neural output to the range of values expected in the current context, and impaired adaptive coding may contribute to anhedonia. Previous research seldom compared the neural correlates of adaptive coding between individuals with schizotypal trait (ST), subthreshold depression (SD), autistic trait (AT). Thirty-five ST, 35 SD, 23 AT and 34 HC completed the adaptive version of the Monetary Incentive Delay Task in fMRI. Adaptive coding performance for the expected value (EV) and outcome value (OV) was recorded. Another separate task was used to measure the adaptive coding performance behaviourally. Anhedonia was measured using self-reported questionnaires. ST, SD and AT groups showed hyper-activation of the posterior mid-cingulate cortex (pMCC) during EV adaptation of reward as compared to HC. SD showed hyperactivation in supplementary motor area (SMA) as compared to HC during OV adaptation to rewards. The neural and behavioural performance of adaptive coding were correlated with self-reported pleasure experience in ST, SD and AT groups. These findings suggested shared and distinct aberrant neural patterns of adaptive coding in individuals with ST, SD and AT. The atypical adaptive coding performance was linked to anhedonia in all subclinical groups. Adaptive coding may have an important role in intervention or prevention of anhedonia symptoms.</p
Impact of antipsychotics on prolactin and its associations with neurocognition in patients with schizophrenia
No full textBackground and objectives: Cognitive impairment is a core feature of schizophrenia (SCZ). Previous studies suggest a close relationship between prolactin and neurocognition. However, antipsychotics have a great impact on prolactin dysregulation. This study investigates how antipsychotics modulate prolactin and its associations with neurocognition in SCZ patients. Methods: A total of 425 SCZ patients were recruited from psychiatric hospitals. Neurocognition was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status. Blood samples were collected after an overnight fast to measure prolactin levels. Analyses were conducted using moderated regression models with two moderators: prolactin-raising antipsychotics and hyperprolactinemia. Results: The prolactin-raising group showed significantly higher prolactin levels than the prolactin-sparing group. Prolactin levels showed significant negative associations with immediate memory and delayed memory. Moreover, the association between prolactin levels and immediate memory was modulated by prolactin-raising antipsychotics and the interaction between prolactin-raising antipsychotics and hyperprolactinemia. Specifically, the negative association was significant only in patients taking prolactin-raising antipsychotics, especially when their prolactin levels exceeded the threshold of hyperprolactinemia. Conclusions: These findings underscore the need for regular serum prolactin monitoring in patients receiving prolactin-raising antipsychotics, particularly those with confirmed hyperprolactinemia. Clinicians should exercise particular caution when prescribing such agents to patients with memory impairment and consider prolactin-sparing alternatives where clinically appropriate
Sensorimotor synchronization in children with autism spectrum disorder: The role of timing and modality
No full textImpaired sensorimotor synchronization is observed in children with autism spectrum disorder (ASD), yet the underlying mechanism of this impairment remains unclear. The current study investigated the impact of the inter-stimulus interval and the modality of stimulus on synchronization performance in children with ASD. Twenty-one high-functioning children with ASD and 21 typically developing (TD) children participated in a finger-tapping task. There were no significant group differences in age, gender, or IQ. Results showed that children with ASD exhibited greater asynchrony at longer time intervals and lower efficiency in multisensory integration compared to TD children. Notably, children with ASD were able to benefit from multisensory cues to improve their sensorimotor synchronization at longer intervals. Children's synchronization performance was correlated with total IQ, fluid reasoning, and visual spatial ability. These findings shed light on the underlying mechanism of atypical synchronization in children with ASD and provide a new avenue for developing targeted training on sensorimotor synchronization for children with ASD
Enactment and bizarreness modulate familiarity and recollection in associative recognition: Evidence from FN400 and LPC
No full textIndividuals usually have superior memory for bizarre stimuli compared to common ones. However, the extent to which bizarreness influences memory for actions remains unclear. Recent evidence has shown that performed actions represented by an action phrase (verb-object pair) in subject-performed task (SPT) result in better associative memory than simply reading it in verbal task (VT). Here, we directly investigated the effect of bizarreness on associative memory for action components following SPT relative to VT and its underlying memory processes using EEGs. During studying, the participants studied ordinary and bizarre verb-object phrases (pairs) in an SPT or VT condition. During testing, they discriminated between intact, recombined, and new pairs. Behaviorally, associative recognition of verb-object phrases was better following SPT than VT for ordinary and bizarre phrases. Bizarreness improved associative recognition under VT (but not SPT). In the event related potentials (ERP), an early frontal old-new effect (FN400) for intact vs. new pairs was observed under SPT for ordinary and bizarre phrases, whereas for VT, this effect was only observed for bizarre phrase. The FN400 for intact vs. recombined pairs was only present under SPT for ordinary phrases. In the late time window, a parietal old-new effect (LPC) for intact vs. new pairs was obtained under all conditions. The LPC for intact vs. recombined pairs only occurred under VT for bizarre phrases. These results demonstrate that enactment and bizarreness enhance associative recognition through distinct mechanisms by differentially modulating the contributions of familiarity and recollection during retrieval of action-object associations.</p
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