Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

刘明月, 靳沙沙, 王志勇, 付艳鑫, 张一唯, 倪隽, 武亮LIU Mingyue, JIN Shasha, WANG Zhiyong, FU Yanxin, ZHANG Yiwei, NI Jun, WU Liang

重症卒中后呼吸功能障碍常合并呼吸中枢驱动失调、呼吸泵衰竭及气道保护失效等多类复杂且异质的病理机制，传统康复模式难以满足个体化临床需求。本文综述了以精准评估为导向的整合性呼吸康复框架。该框架以多模态生理评估为基础，构建重症卒中患者呼吸功能特征图谱，进而为多学科团队制订个体化干预方案提供依据。尽管目前该领域仍面临高级别证据不足等挑战，但未来结合人工智能辅助决策与靶向神经调控技术的精准康复模式，或将为重症卒中呼吸康复的发展提供新思路。Respiratory dysfunction after severe stroke often involves multiple complex and heterogeneous pathological mechanisms, including dysregulated central respiratory drive, respiratory pump failure, and airway protection failure. Conventional rehabilitation models are difficult to meet individualized clinical demands. This paper reviews an integrated respiratory rehabilitation framework guided by precision assessment. Based on multi-modal physiological assessments, this framework constructs a respiratory function profile for patients with severe stroke, thereby providing evidence for the multi-disciplinary team to formulate individualized intervention protocols. Although the field still faces challenges such as insufficient high-level evidence, precision rehabilitation models, which integrate artificial intelligence-assisted decision-making and targeted neuromodulation technology, may provide new insights into the development of respiratory rehabilitation in severe stroke.

Petra Winnette, Petra Winnette, Petr Bob et al.

Amounting findings on maternal separation and early disturbed caregiving suggest that this type of early experience negatively influences socioemotional development and may be associated with behavioral and mental health problems in later life. Concerning previously published studies, we have assessed if maternal separation and disrupted caregiving before adoption in infancy could be related to heightened levels of dissociative symptoms and behavioral problems in middle childhood. We involved 30 children (sample S1) who had experienced maternal separation after birth and short-term institutional or foster care prior to adoption before 16.7 months of age. Based on the parents’ reports, they had not experienced any other significant adversities by the time of evaluation. These children were compared to a control group of children who have lived with their biological mothers in complete families (sample S2; N = 25). Although the findings are correlational and not causal, they indicate that specific adverse experiences, maternal separation after birth, and relatively short disruptive caregiving prior to successful adoption in infancy could be associated with significantly heightened levels of dissociative symptoms and behavioral problems in school-aged children. Our data also contribute to the literature on child socioemotional development and the etiology of dissociative disorders.

Nayoung Ryoo, Ji Yong Park, Chunghwee Lee et al.

Abstract Background Subjective cognitive decline (SCD) has been recognized as a preclinical stage of Alzheimer’s disease. However, the identification of early functional brain changes remains challenging. This study investigated the functional brain changes in SCD using longitudinal EEG and evaluate the feasibility of EEG features as scalable biomarkers for identifying amyloid burden and cognitive decline using an interpretable machine learning framework. Methods We analyzed 120 individuals with SCD enrolled in a multicenter prospective cohort (the CoSCo study) at baseline and after a 2-year follow-up. Participants were classified as amyloid-positive (A + SCD) or amyloid-negative (A − SCD). Spectral power and graph theory-based network analyses were conducted. Also, we trained machine learning classifiers to distinguish between the groups and interpreted the predictions of classifiers using SHAP. Results At both baseline and follow-up, the A + SCD group exhibited elevated low-frequency (delta and theta) activity and reduced alpha activity compared to the A − SCD group. The EEG-based classifiers distinguished A + SCD from A-SCD individuals with high performance, outperforming a classifier based on demographic data. The results of SHAP analysis confirmed the importance and relative contribution of selected EEG features. Conclusions Longitudinal EEG, when combined with interpretable machine learning, can detect and track the functional alterations of brain related to amyloid pathology in preclinical AD. Our findings support the feasibility of EEG as a non-invasive, scalable, and sensitive biomarker for risk stratification, before overt cognitive impairment emerges. Trial registration This study was registered at the Clinical Research Information Service (CRIS) (cris.nih.go.kr/cris; # KCT0003397, Registration Date: December 21, 2018).

Dylan Hamitouche, Tihare Zamorano, Youcef Barkat et al.

Background: Symptom rating scales in psychiatry are limited by reliance on self-report, and lack of predictive power. Actigraphy, a passive wearable-based method for measuring sleep and physical activity, offers objective, high-resolution behavioral data that may better reflect symptom fluctuations, but most studies have focused on narrow diagnostic groups or fixed time windows, limiting clinical translation. Objective: To examine whether actigraphy-derived sleep and activity features correlate with psychiatric symptom severity in a transdiagnostic psychiatric sample, and to identify which features are most clinically relevant across multiple temporal resolutions. Methods: We present a feasibility case series study with preliminary data from eight outpatients enrolled in the DeeP-DD study, a transdiagnostic study of digital phenotyping. Participants wore GENEActiv actigraphy devices and symptom severity was measured using a variety of validated scales. We performed intra-individual Spearman correlations and inter-individual repeated measures correlations across daily, weekly, monthly, and full-duration averages. Results: Intra-individual analyses revealed that later rise times were significantly associated with higher weekly PHQ-9 scores in participant #7 (\r{ho} = 0.74, P=.0003) and participant #4 (\r{ho} = 0.78, P=.022), as well as higher weekly GAD-7 scores in participant #7 (\r{ho} = 0.59, P=.026). Inter-individual analyses showed that weeks with later average rise time correlated with higher PHQ-9 (r = 0.48, P=.0003) and GAD-7 scores (r = 0.38, P=.032). Increased light physical activity was linked to lower PHQ-9 scores weekly (r = -0.44, P=.001) and monthly (r = -0.53, P=.014). Conclusion: Consistent associations between actigraphy features and symptoms across temporal scales and diagnostic groups underscore their potential utility for scalable, real-world clinical monitoring.

Zhijian Wei, Runze Ma, Zichen Wang et al.

Virtual cell modeling aims to predict cellular responses to perturbations. Existing virtual cell models rely heavily on large-scale single-cell datasets, learning explicit mappings between gene expression and perturbations. Although recent models attempt to incorporate multi-source biological information, their generalization remains constrained by data quality, coverage, and batch effects. More critically, these models often function as black boxes, offering predictions without interpretability or consistency with biological principles, which undermines their credibility in scientific research. To address these challenges, we present VCWorld, a cell-level white-box simulator that integrates structured biological knowledge with the iterative reasoning capabilities of large language models to instantiate a biological world model. VCWorld operates in a data-efficient manner to reproduce perturbation-induced signaling cascades and generates interpretable, stepwise predictions alongside explicit mechanistic hypotheses. In drug perturbation benchmarks, VCWorld achieves state-of-the-art predictive performance, and the inferred mechanistic pathways are consistent with publicly available biological evidence.

Mahmut Pekedis

This study proposes a simple and practical approach based on in-vitro measurements and in-silico simulation using the likelihood-free Bayesian inference with the finite element method simultaneously for stochastic calibration and uncertainty quantification of the mechanical response of biological materials. We implement the approach for distal, middle, and proximal human Achilles tendon specimens obtained from diabetic patients post-amputation. A wide range of in-vitro loading conditions are considered, including one-step and two-step relaxation, as well as incremental cyclic loading tests. In-silico simulations are performed for the tendons assuming a fiber-reinforced viscoelastic response, which is modeled for the ground matrix and fiber components. Initially, the calibration of the specimen-specific parameters is predicted using Bayesian optimization and the sensitivity of each parameter is evaluated using the Sobol index and random forest. Then, these parameters are used as priors, and coupled with in-vitro data in simulation-based approximate Bayesian computation to calibrate and quantify the uncertainty parameters for three loading cases. The results demonstrate that in-silico simulations using the posterior parameters of approximate Bayesian computation can capture the uncertainty bounds of in-vitro measurements. This approach provides a useful framework for stochastic calibration of constitutive material model parameters without the need to derive a likelihood function, regardless of the specimen's geometry or loading conditions.

Nathan Bigaud, Vincent Cabeli, Meltem Gürel et al.

While large language models (LLMs) are rapidly advancing scientific research, they continue to struggle with core biological reasoning tasks essential for translational and biomedical discovery. To address this limitation, we created and curated eight comprehensive benchmark datasets comprising over 300,000 verifiable question-and-answer pairs, each targeting critical challenges in drug discovery including target druggability, modality suitability, and drug perturbation effects. Using this resource, we developed the OwkinZero models by post-training open-source LLMs through a Reinforcement Learning from Verifiable Rewards strategy. Our results demonstrate that specialized 8-32B OwkinZero models substantially outperform larger, state-of-the-art commercial LLMs on these biological benchmarks. Remarkably, we uncover evidence of a key aspect of generalization: specialist models trained on a single task consistently outperform their base models on previously unseen tasks. This generalization effect is further amplified in our comprehensive OwkinZero models, which were trained on a mixture of datasets and achieve even broader cross-task improvements. This study represents a significant step toward addressing the biological reasoning blind spot in current LLMs, demonstrating that targeted reinforcement learning on carefully curated data can unlock generalizable performance in specialized models, thereby accelerating AI-driven biological discovery.

Gina M. Gehling, Keesha Powell-Roach, Diana J. Wilkie et al.

BackgroundScientists have speculated genetic variants may contribute to an individual's unique pain experience. Although research exists regarding the relationship between single nucleotide polymorphisms and sickle cell disease-related pain, this literature has not been synthesized to help inform future precision health research for sickle cell disease-related pain. Our primary aim of this systematic review was to synthesize the current state of scientific literature regarding single nucleotide polymorphisms and their association with sickle cell disease-related pain.MethodsUsing the Prisma guidelines, we conducted our search between December 2021–April 2022. We searched PubMed, Web of Science, CINAHL, and Embase databases (1998–2022) and selected all peer-reviewed articles that included reports of associations between single nucleotide polymorphisms and sickle cell disease-related pain outcomes.ResultsOur search yielded 215 articles, 80 of which were duplicates, and after two reviewers (GG, JD) independently screened the 135 non-duplicate articles, we retained 22 articles that met the study criteria. The synthesis of internationally generated evidence revealed that this scientific area remains predominantly exploratory in nature, with only three studies reporting sufficient power for genetic association. Sampling varied across studies with a range of children to older adults with SCD. All of the included articles (n = 22) examined acute pain, while only nine of those studies also examined chronic pain.ConclusionCurrently, the evidence implicating genetic variation contributing to acute and chronic sickle cell disease-related pain is characterized by modestly powered candidate-gene studies using rigorous SCD-pain outcomes. Effect sizes and directions vary across studies and are valuable for informing the design of future studies. Further research is needed to replicate these associations and extend findings with hypothesis-driven research to inform precision health research.

Shan Meng, Hui Cao, Yichen Huang et al.

Abstract Background Traumatic brain injury (TBI) is a significant worldwide public health concern that necessitates attention. Apoptosis signal-regulating kinase 1 (ASK1), a key player in various central nervous system (CNS) diseases, has garnered interest for its potential neuroprotective effects against ischemic stroke and epilepsy when deleted. Nonetheless, the specific impact of ASK1 on TBI and its underlying mechanisms remain elusive. Notably, mutation of ATP-binding sites, such as lysine residues, can lead to catalytic inactivation of ASK1. To address these knowledge gaps, we generated transgenic mice harboring a site-specific mutant ASK1 Map3k5-e (K716R), enabling us to assess its effects and elucidate potential underlying mechanisms following TBI. Methods We employed the CRIPR/Cas9 system to generate a transgenic mouse model carrying the ASK1-K716R mutation, aming to investigate the functional implications of this specific mutant. The controlled cortical impact method was utilized to induce TBI. Expression and distribution of ASK1 were detected through Western blotting and immunofluorescence staining, respectively. The ASK1 kinase activity after TBI was detected by a specific ASK1 kinase activity kit. Cerebral microvessels were isolated by gradient centrifugation using dextran. Immunofluorescence staining was performed to evaluate blood–brain barrier (BBB) damage. BBB ultrastructure was visualized using transmission electron microscopy, while the expression levels of endothelial tight junction proteins and ASK1 signaling pathway proteins was detected by Western blotting. To investigate TBI-induced neuroinflammation, we conducted immunofluorescence staining, quantitative real-time polymerase chain reaction (qRT-PCR) and flow cytometry analyses. Additionally, immunofluorescence staining and electrophysiological compound action potentials were conducted to evaluate gray and white matter injury. Finally, sensorimotor function and cognitive function were assessed by a battery of behavioral tests. Results The activity of ASK1-K716R was significantly decreased following TBI. Western blotting confirmed that ASK1-K716R effectively inhibited the phosphorylation of ASK1, JNKs, and p38 in response to TBI. Additionally, ASK1-K716R demonstrated a protective function in maintaining BBB integrity by suppressing ASK1/JNKs activity in endothelial cells, thereby reducing the degradation of tight junction proteins following TBI. Besides, ASK1-K716R effectively suppressed the infiltration of peripheral immune cells into the brain parenchyma, decreased the number of proinflammatory-like microglia/macrophages, increased the number of anti-inflammatory-like microglia/macrophages, and downregulated expression of several proinflammatory factors. Furthermore, ASK1-K716R attenuated white matter injury and improved the nerve conduction function of both myelinated and unmyelinated fibers after TBI. Finally, our findings demonstrated that ASK1-K716R exhibited favorable long-term functional and histological outcomes in the aftermath of TBI. Conclusion ASK1-K716R preserves BBB integrity by inhibiting ASK1/JNKs pathway in endothelial cells, consequently reducing the degradation of tight junction proteins. Additionally, it alleviates early neuroinflammation by inhibiting the infiltration of peripheral immune cells into the brain parenchyma and modulating the polarization of microglia/macrophages. These beneficial effects of ASK1-K716R subsequently result in a reduction in white matter injury and promote the long-term recovery of neurological function following TBI.

Hui Xiong, Congying Chu, Lingzhong Fan et al.

In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems. Cutting-edge advancements in neuroscience research have revealed the intricate relationship between brain structure and function, while the success of artificial neural networks highlights the importance of network architecture. Now is the time to bring them together to better unravel how intelligence emerges from the brain's multiscale repositories. In this review, we propose the Digital Twin Brain (DTB) as a transformative platform that bridges the gap between biological and artificial intelligence. It consists of three core elements: the brain structure that is fundamental to the twinning process, bottom-layer models to generate brain functions, and its wide spectrum of applications. Crucially, brain atlases provide a vital constraint, preserving the brain's network organization within the DTB. Furthermore, we highlight open questions that invite joint efforts from interdisciplinary fields and emphasize the far-reaching implications of the DTB. The DTB can offer unprecedented insights into the emergence of intelligence and neurological disorders, which holds tremendous promise for advancing our understanding of both biological and artificial intelligence, and ultimately propelling the development of artificial general intelligence and facilitating precision mental healthcare.

Ramdas Ransing, Vikas Menon, Sujita Kumar Kar et al.

Qiaowei Wu, Chunxu Li, Shancai Xu et al.

ObjectiveTo compare the safety, angiographic, and long-term clinical outcomes of intradural large vertebrobasilar artery (VBA) aneurysms following flow diversion (FD) or conventional stent-assisted coiling (SAC).MethodsWe performed a retrospective study of 66 consecutive patients with intradural large VBA aneurysms between 2014 and 2021 who underwent FD or SAC. Patients' characteristics, postprocedural complications, and clinical and angiographic outcome details were reviewed.ResultsA total of 66 intradural large VBA aneurysms were included, including 42 (63.6%), which were treated with SAC (SAC group) and 24 (36.4%), which were treated with FD (FD group). Clinical follow-up was obtained at the median of 24.0 [interquartile range (IQR) 12.0–45.0] months, with 34 (81.0%) patients achieved the modified Rankin Scale (mRS) ≤ 2 in the SAC group and 21 (87.5%) patients in the FD group. Thirteen (19.7%) patients experienced neurological complications, of which 9 (13.6%) patients first occurred during the periprocedural phase and 4 (6.1%) patients first occurred during follow-up. The overall complication rate and periprocedural complication rate were both higher in the SAC group, but did not reach statistical significance (23.8 vs. 12.5%, P = 0.430; 16.7 vs. 8.3%, P = 0.564). The mortality rates were similar between the groups (11.9 vs. 12.5%). Angiographic follow-up was available for 46 patients at the median of 7 (IQR 6–14) months, with a numerically higher complete occlusion rate in the SAC group (82.1 vs. 55.6%, P = 0.051) and similar adequate aneurysm occlusion rates between the groups (85.7 vs. 83.3%, P = 1.000). In the multivariate analysis, ischemic onset (P = 0.019), unilateral vertebral artery sacrifice (P = 0.008), and older age (≥60 years) (P = 0.031) were significantly associated with complications.ConclusionThere was a trend toward lower complication rate and lower complete occlusion rate for intradural large VBA aneurysms following FD as compared to SAC. FD and SAC have comparable mortality rates and favorable outcomes. Ischemic onset, unilateral vertebral artery sacrifice, and older age could increase the risk of complications.

Rauan Kaiyrzhanov, Luke Perry, Clarissa Rocca et al.

Abstract Ultra‐rare biallelic pathogenic variants in geranylgeranyl diphosphate synthase 1 (GGPS1) have recently been associated with muscular dystrophy/hearing loss/ovarian insufficiency syndrome. Here, we describe 11 affected individuals from four unpublished families with ultra‐rare missense variants in GGPS1 and provide follow‐up details from a previously reported family. Our cohort replicated most of the previously described clinical features of GGPS1 deficiency; however, hearing loss was present in only 46% of the individuals. This report consolidates the disease‐causing role of biallelic variants in GGPS1 and demonstrates that hearing loss and ovarian insufficiency might be a variable feature of the GGPS1‐associated muscular dystrophy.

Shengjie Zheng, Lang Qian, Pingsheng Li et al.

Recently, stemming from the rapid development of artificial intelligence, which has gained expansive success in pattern recognition, robotics, and bioinformatics, neuroscience is also gaining tremendous progress. A kind of spiking neural network with biological interpretability is gradually receiving wide attention, and this kind of neural network is also regarded as one of the directions toward general artificial intelligence. This review introduces the following sections, the biological background of spiking neurons and the theoretical basis, different neuronal models, the connectivity of neural circuits, the mainstream neural network learning mechanisms and network architectures, etc. This review hopes to attract different researchers and advance the development of brain-inspired intelligence and artificial intelligence.

Marwin Schindler, Thorsten Korpitsch, Renata G. Raidou et al.

In this paper, we present a new workflow for the computer-aided generation of physicalizations, addressing nested configurations in anatomical and biological structures. Physicalizations are an important component of anatomical and biological education and edutainment. However, existing approaches have mainly revolved around creating data sculptures through digital fabrication. Only a few recent works proposed computer-aided pipelines for generating sculptures, such as papercrafts, with affordable and readily available materials. Papercraft generation remains a challenging topic by itself. Yet, anatomical and biological applications pose additional challenges, such as reconstruction complexity and insufficiency to account for multiple, nested structures--often present in anatomical and biological structures. Our workflow comprises the following steps: (i) define the nested configuration of the model and detect its levels, (ii) calculate the viewpoint that provides optimal, unobstructed views on inner levels, (iii) perform cuts on the outer levels to reveal the inner ones based on the viewpoint selection, (iv) estimate the stability of the cut papercraft to ensure a reliable outcome, (v) generate textures at each level, as a smart visibility mechanism that provides additional information on the inner structures, and (vi) unfold each textured mesh guaranteeing reconstruction. Our novel approach exploits the interactivity of nested papercraft models for edutainment purposes.

Soo Min Kang, Richard P. Wildes

Animals locomote for various reasons: to search for food, find suitable habitat, pursue prey, escape from predators, or seek a mate. The grand scale of biodiversity contributes to the great locomotory design and mode diversity. Various creatures make use of legs, wings, fins and other means to move through the world. In this report, we refer to the locomotion of general biological species as biolocomotion. We present a computational approach to detect biolocomotion in unprocessed video. Significantly, the motion exhibited by the body parts of a biological entity to navigate through an environment can be modeled by a combination of an overall positional advance with an overlaid asymmetric oscillatory pattern, a distinctive signature that tends to be absent in non-biological objects in locomotion. We exploit this key trait of positional advance with asymmetric oscillation along with differences in an object's common motion (extrinsic motion) and localized motion of its parts (intrinsic motion) to detect biolocomotion. An algorithm is developed to measure the presence of these traits in tracked objects to determine if they correspond to a biological entity in locomotion. An alternative algorithm, based on generic features combined with learning is assembled out of components from allied areas of investigation, also is presented as a basis of comparison. A novel biolocomotion dataset encompassing a wide range of moving biological and non-biological objects in natural settings is provided. Also, biolocomotion annotations to an extant camouflage animals dataset are provided. Quantitative results indicate that the proposed algorithm considerably outperforms the alternative approach, supporting the hypothesis that biolocomotion can be detected reliably based on its distinct signature of positional advance with asymmetric oscillation and extrinsic/intrinsic motion dissimilarity.

SueYeon Chung, L. F. Abbott

Advances in experimental neuroscience have transformed our ability to explore the structure and function of neural circuits. At the same time, advances in machine learning have unleashed the remarkable computational power of artificial neural networks (ANNs). While these two fields have different tools and applications, they present a similar challenge: namely, understanding how information is embedded and processed through high-dimensional representations to solve complex tasks. One approach to addressing this challenge is to utilize mathematical and computational tools to analyze the geometry of these high-dimensional representations, i.e., neural population geometry. We review examples of geometrical approaches providing insight into the function of biological and artificial neural networks: representation untangling in perception, a geometric theory of classification capacity, disentanglement and abstraction in cognitive systems, topological representations underlying cognitive maps, dynamic untangling in motor systems, and a dynamical approach to cognition. Together, these findings illustrate an exciting trend at the intersection of machine learning, neuroscience, and geometry, in which neural population geometry provides a useful population-level mechanistic descriptor underlying task implementation. Importantly, geometric descriptions are applicable across sensory modalities, brain regions, network architectures and timescales. Thus, neural population geometry has the potential to unify our understanding of structure and function in biological and artificial neural networks, bridging the gap between single neurons, populations and behavior.

S. Grover, B. Gupta, S. Dhawan

The present study examined India's publications (2803) on schizophrenia, using various bibliometric indicators during 1990-2019. The study focuses on the number of publications, and citations received by the papers on schizophrenia, published by authors affiliated to Indian institutes by using Scopus data base. Additionally, an attempt was made to evaluate the performance of India's leading organizations and authors, and inter-collaborative linkages between them. Scopus database include publications of Indian Journal of Psychiatry and Indian Journal of Psychological Medicine from 2009 and 2011. Accordingly, the publications in these journals were included after these years. Analysis of the publications showed that India is globally ranked at 13th position in number of publications on schizophrenia with 2.04 % global share, depicting 14.21 % annual growth, with 22.8 % of publications having international collaboration. Publications from India published during the period of 1990-2019, registered a citation impact per paper (CPP) of 13.3. National Institute of Mental Health and Neurosciences, Bangalore (671 papers), Post Graduate Institute of Medical Education and Research, Chandigarh (271 papers) and Central Instittue of Psychiatry, Ranchi (136 papers) were the most productive institutes. However, the most impactful organizations in terms of citation per paper (CPP) and relative citation index (RCI), Indian Institute of Science, Bangalore (77.27 CPP and 5.78 RCI), Schizophrenia Research Foundation, Chennai (31.16 CPP and 2.55 RCI) and Banaras Hindu University, Varanasi (29.21 CPP and 2.18 RCI) were at the top. In terms of Individual authors, G. Venkatasubramanian (180 papers), and B.N. Gangadhar (162 papers) were the most productive authors and R.Thara (31.87 CPP and 2.38 RCI), B.K. Thelma (24.0 CPP and 1.8 RCI), M.S. Keshavan (23.91 CPP and 1.79 RCI) were the most impactful authors, among the top 15 authors. The journals which reported comparatively higher productivity for Indian publications included Indian Journal of Psychiatry (242 papers), followed by Asian Journal of Psychiatry (214 papers) and Indian Journal of Psychological Medicine (103 papers). In terms of most impactful Indian publications, these were published in The Lancet (97.7), Progress in Neuro Psychopharmacology & Biological Psychiatry (50) and Schizophrenia Bulletin (44.67).

J. V. Dongen

Empathy is a crucial human ability, because of its importance to prosocial behavior, and for moral development. A deficit in empathic abilities, especially affective empathy, is thought to play an important role in psychopathic personality. Empathic abilities have traditionally been studied within the social and behavioral sciences using behavioral methods, but recent work in neuroscience has begun to elucidate the neural underpinnings of empathic processing in relation to psychopathy. In this review, current knowledge in the social neuroscience of empathy is discussed and a comprehensive view of the neuronal mechanisms that underlie empathy in psychopathic personality is provided. Furthermore, it will be argued that using classification based on overt behavior, we risk failing to identify important mechanisms involved in the psychopathology of psychopathy. In the last decade, there is a growing attention in combining knowledge from (neuro)biological research areas with psychology and psychiatry, to form a new basis for categorizing individuals. Recently, a converging framework has been put forward that applies such approach to antisocial individuals, including psychopathy. In this bio-cognitive approach, it is suggested to use information from different levels, to form latent categories on which individuals are grouped, that may better reflect underlying (neurobiological) dysfunctions. Subsequently, these newly defined latent categories may be more effective in guiding interventions and treatment. In conclusion, in my view, the future understanding of the social brain of psychopaths lies in studying the complex networks in the brain in combination with the use of other levels of information (e.g., genetics and cognition). Based on that, profiles of individuals can be formed that can be used to guide neurophysiological informed personalized treatment interventions that ultimately reduce violent transgressions in individuals with psychopathic traits.

J. E. B. Busto, M. Pedroso, Roberto León Castellón et al.