Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Baiyu Chen, Yujie Wu, Siyuan Xu et al.

Neuroscience and artificial intelligence represent distinct yet complementary pathways to general intelligence. However, amid the ongoing boom in AI research and applications, the translational synergy between these two fields has grown increasingly elusive-hampered by a widening infrastructural incompatibility: modern AI frameworks lack native support for biophysical realism, while neural simulation tools are poorly suited for gradient-based optimization and neuromorphic hardware deployment. To bridge this gap, we introduce BrainFuse, a unified infrastructure that provides comprehensive support for biophysical neural simulation and gradient-based learning. By addressing algorithmic, computational, and deployment challenges, BrainFuse exhibits three core capabilities: (1) algorithmic integration of detailed neuronal dynamics into a differentiable learning framework; (2) system-level optimization that accelerates customizable ion-channel dynamics by up to 3,000x on GPUs; and (3) scalable computation with highly compatible pipelines for neuromorphic hardware deployment. We demonstrate this full-stack design through both AI and neuroscience tasks, from foundational neuron simulation and functional cylinder modeling to real-world deployment and application scenarios. For neuroscience, BrainFuse supports multiscale biological modeling, enabling the deployment of approximately 38,000 Hodgkin-Huxley neurons with 100 million synapses on a single neuromorphic chip while consuming as low as 1.98 W. For AI, BrainFuse facilitates the synergistic application of realistic biological neuron models, demonstrating enhanced robustness to input noise and improved temporal processing endowed by complex HH dynamics. BrainFuse therefore serves as a foundational engine to facilitate cross-disciplinary research and accelerate the development of next-generation bio-inspired intelligent systems.

Marc Gong Bacvanski, Liu Ziyin, Tomaso Poggio

Feedback alignment and related weight-transport-free algorithms are often proposed as biologically plausible alternatives to backpropagation, yet they are typically formulated in discrete phases with implicitly synchronized forward and error signals. We develop a continuous-time model of feedback-alignment-type learning in which neural activities and synaptic weights evolve together under coupled first-order dynamics with distinct propagation, plasticity, and decay time constants. We show that learning is governed by the temporal overlap between presynaptic drive and a locally projected error signal, providing an analytic explanation for robustness to moderate timing mismatch and for failure when mismatch eliminates overlap. Our results show that in order for feedback-alignment-type algorithms to work at biological timescales, they must obey the same temporal overlap principle that applies to other biological processes like eligibility traces.

Chunyu Zou

Cryogenic electron microscopy (Cryo-EM) has become an essential tool for capturing high-resolution biological structures. Despite its advantage in visualizations, the large storage size of Cryo-EM data file poses significant challenges for researchers and educators. This paper investigates the application of deep learning, specifically implicit neural representation (INR), to compress Cryo-EM biological data. The proposed approach first extracts the binary map of each file according to the density threshold. The density map is highly repetitive, ehich can be effectively compressed by GZIP. The neural network then trains to encode spatial density information, allowing the storage of network parameters and learnable latent vectors. To improve reconstruction accuracy, I further incorporate the positional encoding to enhance spatial representation and a weighted Mean Squared Error (MSE) loss function to balance density distribution variations. Using this approach, my aim is to provide a practical and efficient biological data compression solution that can be used for educational and research purpose, while maintaining a reasonable compression ratio and reconstruction quality from file to file.

Agnieszka Mateja, Barbara Gawda

IntroductionIn addition to anxiety disorders and depressive symptoms, transgender people are also shown to have pathological personality profiles. These patterns are due to functioning under chronic stress, exposure to discrimination, victimization, the inability to affirm gender identity, and insufficient social support. The internalized transphobia predisposes transgender individuals to psychological decompensation. The study aims to assess Dark Personality Trait among transgender individuals and to establish the relationships between Dark Tetrad traits and resilience.Materials and MethodsThe Dark Tetrad (narcissism, psychopathy, Machiavellianism, sadism) was assessed using The Short Dark Tetrad Scale (SD4-PL). Resilience was measured using The Resilience Measurement Scale (SPP-25) questionnaire. The dimensions of psychological resilience were also evaluated, including perseverance, determination in action, openness to new experiences, sense of humor, personal competence in coping, tolerance of negative emotions, tolerance for failure, viewing life as a challenge, optimism, and the ability to mobilize in difficult situations. In the statistical analysis, a Multivariate Analysis of Covariance (MANCOVA) was conducted. Correlations between dark personality traits in the transgender and cisgender groups were compared using Fisher’s z-test.ResultsThe study results indicate a slightly lower level of narcissism and Machiavellianism in transgender women compared with cisgender women, and a slightly increased level of sadism in all men, regardless of whether they are transgender or cisgender. No differences were observed between the transgender and cisgender groups in terms of dark personality traits. Transgender individuals exhibited significantly lower level of general resilience than cisgender individuals.ConclusionsThe results of participants from the transgender group indicate lower level of dark personality traits. Observed differences in dark personality traits are related to gender and are independent of transgenderism. Psychological resilience provides a subtle protective function against the development of dark personality traits.

Khadijah Shanazz, Kun Xie, Tucker Oliver et al.

Maria Sol Vidal-Saez, Oscar Vilarroya, Jordi Garcia-Ojalvo

One of the defining features of living systems is their adaptability to changing environmental conditions. This requires organisms to extract temporal and spatial features of their environment, and use that information to compute the appropriate response. In the last two decades, a growing body or work, mainly coming from the machine learning and computational neuroscience fields, has shown that such complex information processing can be performed by recurrent networks. In those networks, temporal computations emerge from the interaction between incoming stimuli and the internal dynamic state of the network. In this article we review our current understanding of how recurrent networks can be used by biological systems, from cells to brains, for complex information processing. Rather than focusing on sophisticated, artificial recurrent architectures such as long short-term memory (LSTM) networks, here we concentrate on simpler network structures and learning algorithms that can be expected to have been found by evolution. We also review studies showing evidence of naturally occurring recurrent networks in living organisms. Lastly, we discuss some relevant evolutionary aspects concerning the emergence of this natural computation paradigm.

Weixi Xiang, Xueting Han, Xiujuan Chai et al.

Modeling biological sequences such as DNA, RNA, and proteins is crucial for understanding complex processes like gene regulation and protein synthesis. However, most current models either focus on a single type or treat multiple types of data separately, limiting their ability to capture cross-modal relationships. We propose that by learning the relationships between these modalities, the model can enhance its understanding of each type. To address this, we introduce BSM, a small but powerful mixed-modal biological sequence foundation model, trained on three types of data: RefSeq, Gene Related Sequences, and interleaved biological sequences from the web. These datasets capture the genetic flow, gene-protein relationships, and the natural co-occurrence of diverse biological data, respectively. By training on mixed-modal data, BSM significantly enhances learning efficiency and cross-modal representation, outperforming models trained solely on unimodal data. With only 110M parameters, BSM achieves performance comparable to much larger models across both single-modal and mixed-modal tasks, and uniquely demonstrates in-context learning capability for mixed-modal tasks, which is absent in existing models. Further scaling to 270M parameters demonstrates even greater performance gains, highlighting the potential of BSM as a significant advancement in multimodal biological sequence modeling.

Jerry John Nutor, Robert Kaba Alhassan, Rachel G A Thompson et al.

Depression among people living with HIV/AIDS in higher-income countries is associated with suboptimal adherence to antiretroviral therapy and though counterintuitive. Yet, less is known regarding how depression, social support, and other sociodemographic factors influence outcomes among people living with HIV, particularly in resource-limited settings like Ghana. In view of this gap, this study investigated factors associated with depressive symptoms among people living with HIV in the Volta region of Ghana. A total of 181 people living with HIV from a local antiretroviral clinic was purposively sampled for the study. The questionnaire included the Center for Epidemiologic Studies Depression Scale, the Internalized Stigma of HIV/AIDS Tool, and the Interpersonal Support Evaluation List-12. An independent student t-test, one-way analysis of variance, and chi-square test were conducted to ascertain the associations among the variables of interest. The magnitude of association was evaluated with multiple linear regression. The average depression score among the participants was 9.1±8.8 and 20.4% reported signs of depression. Majority (78%) of participants who were depressed were male compared to females (p = 0.031). In the multiple linear regression, every one-year increase in age was significantly associated with an estimated 0.012 standard deviation increase in depression scores (95% CI: 0.002-0.021) after adjusting for all other variables in the model. Every unit standard deviation increase in social support was significantly associated with an estimated 0.659 standard deviation increase in depression scores (95% CI:0.187-1.132), after adjusting for all other variables in the model. We found a high prevalence of depressive symptoms among people living with HIV especially among males. An increase in age and social support was associated with an increase in depressive symptoms among people living with HIV in this study. We recommend further study using longitudinal approach to understand this unexpected association between depression and social support among people living with HIV in Ghana.

Jingyang Ma, Songting Li, Douglas Zhou

Dendrites are crucial structures for computation of an individual neuron. It has been shown that the dynamics of a biological neuron with dendrites can be approximated by artificial neural networks (ANN) with deep structure. However, it remains unclear whether a neuron can be further captured by a simple, biologically plausible ANN. In this work, we develop a two-layer ANN, named as dendritic bilinear neural network (DBNN), to accurately predict both the sub-threshold voltage and spike time at the soma of biological neuron models with dendritic structure. Our DBNN is found to be interpretable and well captures the dendritic integration process of biological neurons including a bilinear rule revealed in previous works. In addition, we show DBNN is capable of performing diverse tasks including direction selectivity, coincidence detection, and image classification. Our work proposes a biologically interpretable ANN that characterizes the computation of biological neurons, which can be potentially implemented in the deep learning framework to improve computational ability.

Rong Ji, Rong Ji, Wei-chao Chen et al.

BackgroundDepression is a prevalent mental health disorder. Although Internet use has been associated with depression, there is limited data on the association between smartphone use and depressive symptoms. The present study aimed to investigate the relationship between smartphone use and depressive symptoms among older individuals in China.Methods5,244 Chinese older individuals over the age of 60 were selected as the sample from the China Longitudinal Aging Social Survey (CLASS) 2018 dataset. The dependent variable “depression symptoms” was measured using the 9-item Center for Epidemiologic Studies-Depression (CES-D) scale. The study employed multiple linear regression to investigate the relationship between smartphone use (independent variable) and depressive symptoms in older people. Thorough analyses of robustness, sensitivity, and heterogeneity were conducted to ensure the robustness and sensitivity of the findings. Additionally, mediating effect analysis was performed to elucidate the mechanism through which the dependent and independent variables were related.ResultsEmpirical study indicated that smartphone use had a negative impact on depressive symptoms among older adults, specifically leading to a reduction in such symptoms. The above-mentioned result was verified through endogenous and robustness tests. The heterogeneity analysis revealed that older individuals aged 70 years and above, male, and residing in urban areas exhibited a stronger association between smartphone use and depressive symptoms. Furthermore, the mediating effect model indicated that political participation, voluntary participation, and active leisure participation mediated the relationship between smartphone use and lower levels of depression symptoms among the older adults. However, passive leisure participation had a suppressing effect on the relationship between smartphone use and reduced depressive symptoms among the older adults.LimitationsThe causal relationship between variables required further investigation with a longitudinal design.ConclusionThese findings suggested that smartphone use may be considered an intervention to reduce depression symptoms among older people by increasing levels of political participation, voluntary participation, and active leisure participation.

Jia-Li Xu, Jia-Ping Gu, Li-Yan Wang et al.

Background: Nonalcoholic fatty liver disease (NAFLD) has been proven to be associated with an increased risk of cognitive impairment and dementia, and this association is more significant in non-obese NAFLD populations, but its pathogenesis remains unclear. Our study aimed to explore the abnormalities of spontaneous brain activity in non-obese NAFLD patients by resting-state fMRI (RS-fMRI) and their relationship with cognitive function. Methods: 19 non-obese NAFLD, 25 obese NAFLD patients, and 20 healthy controls (HC) were enrolled. All subjects underwent RS-fMRI scan, psychological scale assessment, and biochemical examination. After RS-fMRI data were preprocessed, differences in low-frequency fluctuation amplitude (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were compared among the three groups. Furthermore, the relationship between RS-fMRI indicators and cognitive and clinical indicators were performed using correlation analysis. Results: The cognitive function was declined in both NAFLD groups. Compared with obese NAFLD patients, non-obese NAFLD patients showed increased ALFF and ReHo in the left middle temporal gyrus (MTG), increased ReHo in the sensorimotor cortex and reduced FC between left MTG and right inferior frontal gyrus (IFG). Compared with HC, non-obese NAFLD patients showed increased ALFF and ReHo in the left calcarine cortex and fusiform gyrus (FG), decreased ALFF in the bilateral cerebellum, and reduced FC between left FG and right IFG and left angular gyrus. In addition to the same results, obese patients showed increased activity in different regions of the bilateral cerebellum, while decreased ALFF in the right superior frontal gyrus and ReHo in the right orbitofrontal cortex (OFC). Correlation analysis showed that in non-obese patients, the ALFF values in the FG and the FC values between the left MTG and the right IFG were associated with cognitive decline, insulin resistance, and fasting glucose disorder. Conclusions: Non-obese NAFLD patients showed abnormal local spontaneous activity and FC in regions involved in the sensorimotor, temporo-occipital cortex, cerebellum, and reward system (such as OFC), some of which may be the potential neural mechanism difference from obese NAFLD patients. In addition, the temporo-occipital cortex may be a vulnerable target for cognitive decline in non-obese NAFLD patients.

Swati Saroha, Dipasri Bhattacharya, Prashant Arya et al.

Background: Pain originating in the facet joint accounts for an estimated 15%–45% of cases of low back pain. Radiofrequency ablation (RFA) of the medial branch nerves (MBN) is used in refractory cases. However, very few studies have compared the clinical outcomes of cooled versus conventional/traditional RFA (T-RFA) for the treatment of lumbar facet joint pain. Objective: To determine the clinical outcomes of MBN cooled RFA (C-RFA) compared with T-RFA, as measured by improvements in pain and physical function. Methodology: Forty patients with positive diagnostic MBN blocks were allocated to C-RFA or T-RFA group. Reduction in pain (NRS “Numerical Rating Scale” score), improvement in quality of life (Oswestry Disability Index [ODI]), proportion of responders/successful treatment (≥50% NRS reduction, and or ≥30% or ≥15 point reduction in ODI at 6 months follow up) in the two groups were recorded. Results: Total 34 patients were analysed, C-RFA (n = 18) and T-RFA (n = 16). There was significant reduction in pain scores as well as improvement in quality of life in both the groups, but the difference between the two groups was not significant. NRS reduction of ≥50% was observed in 72.22% and 68.5% of participants in the C-RFA and T-RFA groups, respectively (P = 0.824). A ≥15-point or ≥30% reduction in ODI score was observed in 77.77% and 75% of participants in the C-RFA and T-RFA groups, respectively (P = 0.849). Conclusions: Both the groups showed significant improvement in pain scores and quality of life. C-RFA resulted in greater treatment success rate than conventional RFA, but the difference was not significant.

Leonard E. van Dyck, Walter R. Gruber

Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground and recent efforts have started to transfer this achievement to research on biological face recognition. In this regard, face detection can be investigated by comparing face-selective biological neurons and brain areas to artificial neurons and model layers. Similarly, face identification can be examined by comparing in vivo and in silico multidimensional "face spaces". In this review, we summarize the first studies that use DCNNs to model biological face recognition. On the basis of a broad spectrum of behavioral and computational evidence, we conclude that DCNNs are useful models that closely resemble the general hierarchical organization of face recognition in the ventral visual pathway and the core face network. In two exemplary spotlights, we emphasize the unique scientific contributions of these models. First, studies on face detection in DCNNs indicate that elementary face selectivity emerges automatically through feedforward processing even in the absence of visual experience. Second, studies on face identification in DCNNs suggest that identity-specific experience and generative mechanisms facilitate this particular challenge. Taken together, as this novel modeling approach enables close control of predisposition (i.e., architecture) and experience (i.e., training data), it may be suited to inform long-standing debates on the substrates of biological face recognition.

Ke Li, Jiayu Wang, Lei Chen et al.

Postoperative delirium (POD) is a common and serious postoperative complication in elderly patients, and its underlying mechanism is elusive and without effective therapy at present. In recent years, the neuroinflammatory hypothesis has been developed in the pathogenesis of POD, in which the damaged blood-brain barrier (BBB) plays an important role. Netrin-1 (NTN-1), an axonal guidance molecule, has been reported to have strong inflammatory regulatory and neuroprotective effects. We applied NTN-1 (45 μg/kg) to aged mice using a POD model with a simple laparotomy to assess their systemic inflammation and neuroinflammation by detecting interleukin-6 (IL-6), interleukin-10 (IL-10), and high mobility group box chromosomal protein-1 (HMGB-1) levels. We also assessed the reactive states of microglia and the permeability of the BBB by detecting cell junction proteins and the leakage of dextran. We found that a single dose of NTN-1 prophylaxis decreased the expression of IL-6 and HMGB-1 and upregulated the expression of IL-10 in the peripheral blood, hippocampus, and prefrontal cortex. Nerin-1 reduced the activation of microglial cells in the hippocampus and prefrontal cortex and improved POD-like behavior. NTN-1 also attenuated the anesthesia/surgery-induced increase in BBB permeability by upregulating the expression of tight junction-associated proteins such as ZO-1, claudin-5, and occludin. These findings confirm the anti-inflammatory and BBB protective effects of NTN-1 in an inflammatory environment in vivo and provide better insights into the pathophysiology and potential treatment of POD.

Anette Hardy-Sosa, Anette Hardy-Sosa, Karen León-Arcia et al.

BackgroundBecause of high prevalence of Alzheimer’s disease (AD) in low- and middle-income countries (LMICs), there is an urgent need for inexpensive and minimally invasive diagnostic tests to detect biomarkers in the earliest and asymptomatic stages of the disease. Blood-based biomarkers are predicted to have the most impact for use as a screening tool and predict the onset of AD, especially in LMICs. Furthermore, it has been suggested that panels of markers may perform better than single protein candidates.MethodsMedline/Pubmed was searched to identify current relevant studies published from January 2016 to December 2020. We included all full-text articles examining blood-based biomarkers as a set of protein markers or panels to aid in AD’s early diagnosis, prognosis, and characterization.ResultsSeventy-six articles met the inclusion criteria for systematic review. Majority of the studies reported plasma and serum as the main source for biomarker determination in blood. Protein-based biomarker panels were reported to aid in AD diagnosis and prognosis with better accuracy than individual biomarkers. Conventional (amyloid-beta and tau) and neuroinflammatory biomarkers, such as amyloid beta-42, amyloid beta-40, total tau, phosphorylated tau-181, and other tau isoforms, were the most represented. We found the combination of amyloid beta-42/amyloid beta-40 ratio and APOEε4 status to be most represented with high accuracy for predicting amyloid beta-positron emission tomography status.ConclusionAssessment of Alzheimer’s disease biomarkers in blood as a non-invasive and cost-effective alternative will potentially contribute to early diagnosis and improvement of therapeutic interventions. Given the heterogeneous nature of AD, combination of markers seems to perform better in the diagnosis and prognosis of the disease than individual biomarkers.

Brenna Hansen, Katelyn M. Inch, Brenna A. Kaschor

Abstract Background Using traditional pharmacotherapy to treat Borderline Personality Disorder (BPD) such as mood stabilizers and second-generation antipsychotics has a lack of supporting evidence. Buprenorphine/Naloxone (BUP/N), a combination medication consisting of a partial opioid agonist, and a full opioid antagonist, is an effective treatment for opioid use disorder. It has also been found effective for treatment-resistant mood disorders. Previous studies suggest a relationship between BPD and endogenous opioids, therefore our case report investigates the effect of BUP/N on a patient diagnosed with BPD. Case presentation A 26-year-old female diagnosed with BPD, having recurrent visits to the emergency department (ED) for self-harm/suicidality was treated with BUP/N. Usage of crisis services, ED visits, and hospital admissions were tracked from 15 months prior to BUP/N to 15 months after using BUP/N. Since starting BUP/N, the length and frequency of mental health-related hospital admissions decreased drastically, as did the number of times that she reached out to community crisis services. Since the dosing adjustment to 6 mg in Oct 2020, there have been no calls to the community crisis lines. Conclusions We suggest pharmacological treatment targeting BPD as a disorder of distress tolerance and self-soothing mediated by the opioid system is an effective individual healing attempt. An important note is that this patient did not use opioids prior to BUP/N and had never been diagnosed with an opioid use disorder. However, she exhausted multiple other pharmacologic therapies and was open to trying whatever was available to improve her quality of life.

Zoylen Fernández-Fleites, Elizabeth Jiménez-Puig, Yunier Broche-Pérez et al.

ABSTRACT. The Frontal Assessment Battery (FAB) and the INECO Frontal Screening (IFS) are two instruments frequently used to explore cognitive deficits in different diseases. However, studies reporting their use in patients with mild cognitive impairment (MCI) are limited. Objective: To compare the sensitivity and specificity of FAB and IFS in mild cognitive impairment (multiple-domain amnestic MCI subtype — md-aMCI). Methods: IFS and FAB were administered to 30 md-aMCI patients and 59 healthy participants. Sensitivity and specificity were investigated using the Receiver Operating Characteristic (ROC) analysis. Results: The area under the ROC curve (AUC) of IFS for MCI patients was .82 (sensitivity=0.96; specificity=0.76), whereas the AUC of FAB was 0.74 (sensitivity=0.73; specificity=0.70). Conclusions: In comparison to FAB, IFS showed higher sensitivity and specificity for the detection of executive dysfunctions in md-aMCI subtype. The use of IFS in everyday clinical practice would allow detecting the frontal dysfunctions in MCI patients with greater precision, enabling the early intervention and impeding the transition to more severe cognitive alterations.

Lei Du, Lei Du, Zifang Zhao et al.

Background and Purpose: The purpose of this study was to explore the changes of iron level using quantitative susceptibility mapping (QSM) in the bilateral basal ganglia region in middle cerebral artery occlusion (MCAO) patients with long-term ischemia.Methods: Twenty-seven healthy controls and nine patients with MCAO were recruited, and their QSM images were obtained. The bilateral caudate nucleus (Cd), putamen (Pt), and globus pallidus (Gp) were selected as the regions of interest (ROIs). Susceptibility values of bilateral ROIs were calculated and compared between the affected side and unaffected side in patients with MCAO and between patients with MCAO and healthy controls. In addition, receiver operating characteristic (ROC) curves were performed to evaluate the diagnostic capability of susceptibility values in differentiating healthy controls and patients with MCAO by the area under the curve (AUC).Results: The susceptibility values of bilateral Cd were asymmetric in healthy controls; however, this asymmetry disappeared in patients with MCAO. In addition, compared with healthy controls, the average susceptibility values of the bilateral Pt in patients with MCAO were increased (P < 0.05), and the average susceptibility value of the bilateral Gp was decreased (P < 0.05). ROC curves showed that the susceptibility values of the Pt and Gp had a larger AUC (AUC = 0.700 and 0.889, respectively).Conclusion: As measured by QSM, the iron levels of the bilateral basal ganglia region were significantly changed in patients with MCAO. Iron dyshomeostasis in the basal ganglia region might be involved in the pathophysiological process of middle cerebral artery stenosis and occlusion. These findings may provide a novel insight to profoundly address the pathophysiological mechanisms of MCAO.

Hong Xu, Xiaoyan Xuan, Li Zhang et al.

Objective: There has become a consensus for detecting intellectual disability in its early stages and implementing effective intervention. However, there are many difficulties and limitations in the evaluation of intelligence-related scales in low-age children. Eye-tracking technology may effectively solve some of the pain points in the evaluation.Method: We used an eye-tracking technology for cognitive assessment. The subjects looked at a series of task pictures and short videos, the fixation points of which were recorded by the eye-movement analyzer, and the data were statistically analyzed. A total of 120 children aged between 1.5 and 4 years participated in the study, including 60 typically developing children and 60 children with global development delay, all of whom were assessed via the Bayley scale, Peabody Picture Vocabulary Test (PPVT), and Gesell scale.Results: Cognitive scores from eye-tracking technology are closely related to the scores of neuropsychological tests, which shows that the technique performs well as an early diagnostic test of children's intelligence.Conclusions: The results show that children's cognitive development can be quickly screened using eye-tracking technology and that it can track quantitative intelligence scores and sensitively detect intellectual impairment.