Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Julyan H. E. Cartwright, Charles S. Cockell, Julie G. Cosmidis et al.

Both abiotic self-organization and biological mechanisms have been put forward as the origin of a number of geological patterns. It is important to comprehend the formation mechanisms of such structures both to understand geological self-organization and in order to differentiate them from biological patterns -- fossils and bio-influenced structures -- seen in geological systems. Being able to distinguish the traces of biological activity from geological self-organization is fundamental both for understanding the origin of life on Earth and for the search for life beyond Earth.

Fumiko Ono, Miyu Okamura

Abstract Schizophrenia is a severe mental disorder with substantial clinical, economic, and humanistic impacts. This targeted literature review evaluated the burden of schizophrenia on patients and caregivers in Japan. Data were collected from PubMed, Ichushi, CiNii, J-STAGE, and the Cochrane Database (2013–2023) and supplementary materials from medical associations, government agencies, and patient organizations (2018–2023). The review focused on epidemiology, clinical management, societal, humanistic, and economic burdens experienced by patients and caregivers. The review identified 156 journal publications, 73 conference proceedings, and 37 additional data sources. Obesity, depression, and type 2 diabetes were highlighted as frequent comorbidities. Cognitive impairment in schizophrenia, assessed by the Brief Assessment of Cognition in Schizophrenia, indicated severe functional deficits with a Z-score of -2.1. Issues related to long-term hospitalization, including social isolation and inadequate post-discharge support, were also reported. Interventions aimed at improving cognitive function, fostering self-care, and strengthening community cooperation were identified as key factors in reducing early readmission rates. Caregivers experienced significant productivity losses, particularly due to presenteeism, leading to an estimated annual loss of JPY 2.4 million. The hand search further revealed a lack of stakeholder-driven initiatives to address the comprehensive burdens of schizophrenia, such as awareness campaigns, educational programs, and multidisciplinary approaches. This review underscores the multifaceted burdens of schizophrenia in Japan, emphasizing the urgent need for coordinated, evidence-based countermeasures involving multiple stakeholders, including patients, caregivers, healthcare professionals, and policymakers. To reduce burdens and improve healthcare, further research is needed to bridge the gap between required interventions and stakeholder engagement.

Marine Bouyssi-Kobar, Yan Zhang, Luke Norman et al.

Abstract Recent evidence suggests the brain’s major excitatory neurotransmitter, glutamate, plays a key role in attention-deficit/hyperactivity disorder (ADHD). Here we ask if glutamate also plays a role in the variable clinical courses of ADHD. While some children ‘grow out’ of ADHD by adolescence, others experience persistent symptoms into adulthood. Prior work implicates structural and functional differences in medial prefrontal cortex as pivotal in these different ADHD symptom courses, and we now ask if glutamate developmental change also contributes. Given the role of glutamate in neurotransmission, we also investigate potential impacts on the brain’s intrinsic connectivity. Using a glutamate-specific magnetic resonance spectroscopy sequence at 3 T, we analyzed 241 spectra on 161 participants, including 69 with persistent ADHD, 20 with remitting ADHD, and 72 never affected controls. Intrinsic functional connectivity was also assessed in a subset of 104 participants with 141 functional MRI scans. Using linear mixed models, we found an age-related increase in medial prefrontal glutamate in the persistent ADHD group which differed significantly from an age-related decrease among those who remitted and the never affected controls. Furthermore, altered prefrontal glutamate concentrations were associated with changes in intrinsic connectivity between the default mode network (which includes medial frontal cortex) and subcortical regions. These findings may indicate altered maturation of glutamate in the medial prefrontal cortex in youth with persistent ADHD.

Jun Tsukiji, Shiro Koizume, Tomoko Takahashi et al.

Background: Impaired smell/taste sensation (dysosmia/dysgeusia) are common manifestations of coronavirus disease 2019 (COVID-19). Scattered peripheral chemoreceptors and directly innervating central nerves from the brain to the receptors are responsible systems for perception in the human body. The shared neurotransmitter serotonin (5-HT) and neuroimmune modulators of the kynurenine (Kyn) pathway (KP) are metabolites derived from tryptophan (Trp). The synthesis of KP metabolites is initiated by the rate-limiting enzymes indoleamine 2,3-dioxygenase (IDO) and tryptophan 2,3-dioxygenase (TDO). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can activate Trp metabolism. Therefore, we investigated whether serum metabolites of Trp and IDO/TDO activity could serve as biomarkers for assessing smell/taste impairment (dysosmia/dysgeusia) in patients during the acute phase of COVID-19. Methods: We conducted a retrospective case-control study. Among patients admitted with acute COVID-19 to our hospital between September 13, 2021, and September 30, 2023, those whose chief complaints included dysosmia/dysgeusia at admission were identified. These symptoms were confirmed by the attending physician for COVID-19. Patients were stratified based on the presence or absence of dysosmia and/or dysgeusia.In both patient groups, serum concentrations of Trp, 5-HT, Kyn, kynurenic acid (KYNA), and quinolinic acid (QUIN) were measured using enzyme-linked immunosorbent assay. IDO/TDO activity was expressed as Kyn–Trp ratio (KTR). The relationships between these biomarkers and dysosmia/dysgeusia, as well as other clinical parameters and outcomes, were evaluated. Results: Of 520 patients admitted with COVID-19, 95 met the inclusion and exclusion criteria. Among them, 26 patients with dysosmia/dysgeusia (group A) and 26 patients without these symptoms (group B) were analyzed. No significant intergroup difference was observed in the average timepoint at blood sampling after COVID-19 onset (post-day from onset: pdo) (4.69 ± 2.51 days in group A vs. 3.62 ± 2.22 in group B). Group A showed significantly lower Trp levels [median 9.70 μg/mL (range 4.59–13.89) vs. 10.40 (7.52–13.34), p = 0.031], and higher KTR [61.34 (40.47–384.2) vs. 53.52 (26.13–86.64), p < 0.037] and QUIN levels [574.39 nM (100.39–11909) vs. 443.65 (83.09–998.3), p < 0.0169]. No significant differences were observed in 5-HT or KYNA levels between groups. Almost all cases of dysosmia involved anosmia/hyposmia and were significantly correlated with non-vaccination status with mRNA vaccine (p = 0.017). In contrast, dysgeusia exhibited heterogeneous manifestations, primarily ageusia or hypogeusia, followed by hypersensitivity to salty taste, and was not correlated with vaccination status. Conclusion: Clinically, serum KTR and QUIN levels may serve as useful biomarkers for assessing dysgeusia/dysomia during acute COVID-19. Furthermore, vaccination may play an important preventive role, particularly against dysosmia.

Ludovico Iannello, Luca Ciampi, Fabrizio Tonelli et al.

In this paper, we present a biologically grounded approach to reservoir computing (RC), in which a network of cultured biological neurons serves as the reservoir substrate. This system, referred to as biological reservoir computing (BRC), replaces artificial recurrent units with the spontaneous and evoked activity of living neurons. A multi-electrode array (MEA) enables simultaneous stimulation and readout across multiple sites: inputs are delivered through a subset of electrodes, while the remaining ones capture the resulting neural responses, mapping input patterns into a high-dimensional biological feature space. We evaluate the system through a case study on digit classification using a custom dataset. Input images are encoded and delivered to the biological reservoir via electrical stimulation, and the corresponding neural activity is used to train a simple linear classifier. To contextualize the performance of the biological system, we also include a comparison with a standard artificial reservoir trained on the same task. The results indicate that the biological reservoir can effectively support classification, highlighting its potential as a viable and interpretable computational substrate. We believe this work contributes to the broader effort of integrating biological principles into machine learning and aligns with the goals of human-inspired vision by exploring how living neural systems can inform the design of efficient and biologically plausible models.

Andrea Visonà, Robert Morel, Hélène Joisten et al.

Magnetically driven microparticles provide a versatile platform for probing and manipulating biological systems, yet the physical framework governing their actuation in complex environments remains only partially explored. Within the field of cellular magneto-mechanical stimulation, vortex microdiscs have emerged as particularly promising candidates for developing novel therapeutic approaches. Here, we introduce a simplified two-dimensional model describing the magneto-mechanical response of such particles embedded in viscoelastic media under varying magnetic fields. Using a Maxwell description of the medium combined with simplified elasticity assumptions, we derive analytical expressions and support them with numerical simulations of particle motion under both oscillating and rotating magnetic fields. Our results show that rotating fields typically induce oscillatory dynamics and that the transition to asynchronous motion occurs at a critical frequency determined by viscosity and stiffness. The amplitude and phase of this motion is governed by the competition between magnetic and viscoelastic contributions, with particle motion being strongly impaired when the latter dominate. Energy-based considerations further demonstrate that, within the frequency range explored of few tens of Hertz, no heat is generated -- distinguishing this approach from magnetic hyperthermia -- while the elastic energy transferred to the surrounding medium is, in principle, sufficient to perturb major cellular processes. This work provides a simple framework to anticipate the first-order influence of rheological properties on magnetically driven microdisc dynamics, thereby enabling a better understanding of their impact in cells or extracellular materials and bridging the gap between experimental observations and theoretical modelling.

Ligong Zhao, Mohamed Sennary, Dina Hussein et al.

Ultrafast electron microscopy (UEM) has found widespread applications in physics, chemistry, and materials science, enabling real-space imaging of dynamics on ultrafast timescales. Recent advances have pushed the temporal resolution of UEM into the attosecond regime, enabling the attomicroscopy technique to directly visualize electron motion. In this work, we extend the capabilities of this powerful imaging tool to investigate ultrafast electron dynamics in a biological system by imaging and controlling light induced electronic and chemical changes in the conductive network of multicellular cable bacteria. Using electron energy loss spectroscopy (EELS), we first observed a laser induced increase in π-electron density, accompanied by spectral peak broadening and a blueshift features indicative of enhanced conductivity and structural modification. We also traced the effect of ultrafast laser pumping on bulk plasmon electron oscillations by monitoring changes in the plasmon like resonance peak. Additionally, we visualized laser induced chemical structural changes in cable bacteria in real space. The imaging results revealed carbon enrichment alongside a depletion of nitrogen and oxygen, highlighting the controllability of chemical dynamics. Moreover, time resolved EELS measurements further revealed a picosecond scale decay and recovery of both π-electron and plasmonic features, attributed to electron phonon coupling. In addition to shedding light on the mechanism of electron motion in cable bacteria, these findings demonstrate ultrafast modulation and switching of conductivity, underscoring their potential as bio-optoelectronic components operating on ultrafast timescales.

Mika Rubinov

Modern scientific fields face the challenge of integrating a wealth of data, analyses, and results. We recently showed that a neglect of this integration can lead to circular analyses and redundant explanations. Here, we help advance scientific integration by describing equivalences that unify diverse analyses of datasets and networks. We describe equivalences across analyses of clustering and dimensionality reduction, network centrality and dynamics, and popular models in imaging and network neuroscience. First, we equate foundational objectives across unsupervised learning and network science (from k means to modularity to UMAP), fuse classic algorithms for optimizing these objectives, and extend these objectives to simplify interpretations of popular dimensionality reduction methods. Second, we equate basic measures of connectional magnitude and dispersion with six measures of communication, control, and diversity in network science and network neuroscience. Third, we describe three semi-analytical vignettes that clarify and simplify the interpretation of structural and dynamical analyses in imaging and network neuroscience. We illustrate our results on example brain-imaging data and provide abct, an open multi-language toolbox that implements our analyses. Together, our study unifies diverse analyses across unsupervised learning, network science, imaging neuroscience, and network neuroscience.

Yuhan Helena Liu, Guangyu Robert Yang, Christopher J. Cueva

Understanding how the brain learns may be informed by studying biologically plausible learning rules. These rules, often approximating gradient descent learning to respect biological constraints such as locality, must meet two critical criteria to be considered an appropriate brain model: (1) good neuroscience task performance and (2) alignment with neural recordings. While extensive research has assessed the first criterion, the second remains underexamined. Employing methods such as Procrustes analysis on well-known neuroscience datasets, this study demonstrates the existence of a biologically plausible learning rule -- namely e-prop, which is based on gradient truncation and has demonstrated versatility across a wide range of tasks -- that can achieve neural data similarity comparable to Backpropagation Through Time (BPTT) when matched for task accuracy. Our findings also reveal that model architecture and initial conditions can play a more significant role in determining neural similarity than the specific learning rule. Furthermore, we observe that BPTT-trained models and their biologically plausible counterparts exhibit similar dynamical properties at comparable accuracies. These results underscore the substantial progress made in developing biologically plausible learning rules, highlighting their potential to achieve both competitive task performance and neural data similarity.

Judith M. Gault, Nicola Cascella, Nidal Moukaddam et al.

Abstract Introduction Ethical concerns have been raised by both current and historically controversial neurosurgical interventions for treatment-refractory schizophrenia and schizoaffective disorder (TR-SZ). Considering advances in next-generation deep brain stimulation (DBS), initial success in treating a few cases of TR-SZ, and how challenging trial enrollment is, transparency and disseminating knowledge about DBS is important, as is input from involved groups. Here information was presented about DBS as an experimental treatment option for TR-SZ to stakeholders to gauge enthusiasm after consideration of potential risks and benefits. Methods Stakeholders were presented with information about DBS (total n = 629). Opinions about whether DBS should be an option for people with TR-SZ and acceptable response rates considering DBS risks were collected from research participants with SZ, treatment-refractory Parkinson’s disease (TR-PD) approved for DBS, caregivers for either SZ or TR-PD participants, and attendees at medical school presentations. In addition, the attendees were asked to decide whether DBS is appropriate for 4 cases who want DBS, one with PD, 2 with OCD and 1 with SZ. Chi-square, pairwise comparisons, and Duncan Multiple Range Test were performed with significance at p < 0.05. Results Most (83%) research participants and presentation audience members agreed that DBS should be an option for TR-SZ and 40% thought the potential benefits outweigh the risks of DBS with at least a 41–60% response rate. Audience approval of DBS was similar for the PD (30%), SZ (52%) and the OCD case with psychosis (56%), but there was a higher rate of approval (77%) for the OCD case whose compulsions involved self-harm. The majority (73–86%) of the audience thought that they would want to try DBS if they had TR-PD, TR-OCD, or TR-SZ. Conclusions Despite difficulty in recruiting patients for DBS clinical trials for TR-SZ, the consensus among 83% of stakeholders was that DBS should be an option for people with severe TR-SZ. Our approach to disseminate general knowledge then gather opinions among diverse stakeholders was to ensure the development of DBS clinical trials for the new indication TR-SZ is a relevant option despite the known difficulties in enrollment. These findings may help prevent disparities in access to advanced DBS therapeutics.

Kalab Yigermal Gete, Asnakew Achaw Ayele

Abstract Background Non-contrast CT (NCCT) is first-line imaging for suspected acute ischemic stroke (AIS) but has limited early sensitivity; deep learning (DL) may improve patient-level detection. Objectives To estimate the diagnostic accuracy of DL applied to NCCT for patient-level AIS detection and to examine prespecified sources of between-study heterogeneity. Methods We searched MEDLINE, Embase, and Web of Science (January 2010–May 2025). Eligible prospective or retrospective diagnostic studies evaluated DL on NCCT against an appropriate reference standard and reported (or allowed reconstruction of) patient-level 2 × 2 data. Two-gate case–control and lesion-only reports were excluded. Dual reviewers screened/extracted data; risk of bias was assessed with QUADAS-2, and AI-reporting against items adapted from STARD-AI/CLAIM/CONSORT-AI. Bivariate random-effects/HSROC models summarized sensitivity and specificity. Prespecified moderators were posterior-fossa inclusion, reference-standard robustness, and validation type. Sensitivity analyses included external-only cohorts, robust standards, posterior-fossa inclusion, and a “Direct AIS” construct subset. Results Of 1,899 records, 16 studies met inclusion; 13 contributed patient-level data to meta-analysis. Summary sensitivity was 0.91 (95% CI, 0.81–0.96) and specificity 0.90 (0.85–0.94). Sensitivity was lower for externally validated models than internally validated ones (0.82 [0.67–0.91] vs. 0.95 [0.89–0.98]) with similar specificity (0.88 [0.83–0.92] vs. 0.93 [0.82–0.97]). Findings were directionally robust across sensitivity analyses. QUADAS-2 frequently indicated concerns in patient selection and index-test domains; AI-reporting quality was mostly moderate, and explicit external validation remained uncommon. Conclusions DL applied to NCCT shows high accuracy for patient-level AIS detection. However, generalizability is the principal gap; broader external validation and guideline-concordant reporting are needed to support safe clinical adoption.

Takuya Isomura

Characterising the intelligence of biological organisms is challenging. This work considers intelligent algorithms developed evolutionarily within neural systems. Mathematical analyses unveil a natural equivalence between canonical neural networks, variational Bayesian inference under a class of partially observable Markov decision processes, and differentiable Turing machines, by showing that they minimise the shared Helmholtz energy. Consequently, canonical neural networks can biologically plausibly equip Turing machines and conduct variational Bayesian inferences of external Turing machines in the environment. Applying Helmholtz energy minimisation at the species level facilitates deriving active Bayesian model selection inherent in natural selection, resulting in the emergence of adaptive algorithms. In particular, canonical neural networks with two mental actions can separately memorise transition mappings of multiple external Turing machines to form a universal machine. These propositions were corroborated by numerical simulations of algorithm implementation and neural network evolution. These notions offer a universal characterisation of biological intelligence emerging from evolution in terms of Bayesian model selection and belief updating.

Jialin Wen, Qianqian Jiang, Lijun Yang et al.

Background: Hypoxic-ischemic injury of neurons is a pathological process observed in several neurological conditions, including ischemic stroke and neonatal hypoxic-ischemic brain injury (HIBI). An optimal treatment strategy for these conditions remains elusive. The present study delved deeper into the molecular alterations occurring during the injury process in order to identify potential therapeutic targets. Methods: Oxygen-glucose deprivation/reperfusion (OGD/R) serves as an established in vitro model for the simulation of HIBI. This study utilized RNA sequencing to analyze rat primary hippocampal neurons that were subjected to either 0.5 or 2 h of OGD, followed by 0, 9, or 18 h of reperfusion. Differential expression analysis was conducted to identify genes dysregulated during OGD/R. Time-series analysis was used to identify genes exhibiting similar expression patterns over time. Additionally, functional enrichment analysis was conducted to explore their biological functions, and protein-protein interaction (PPI) network analyses were performed to identify hub genes. Quantitative real-time polymerase chain reaction (qRT-PCR) was used for validation of hub-gene expression. Results: The study included a total of 24 samples. Analysis revealed distinct transcriptomic alterations after OGD/R processes, with significant dysregulation of genes such as Txnip, Btg2, Egr1 and Egr2. In the OGD process, 76 genes, in two identified clusters, showed a consistent increase in expression; functional analysis showed involvement of inflammatory responses and signaling pathways like tumor necrosis factor (TNF), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and interleukin 17 (IL-17). PPI network analysis suggested that Ccl2, Jun, Cxcl1, Ptprc, and Atf3 were potential hub genes. In the reperfusion process, 274 genes, in three clusters, showed initial upregulation followed by downregulation; functional analysis suggested association with apoptotic processes and neuronal death regulation. PPI network analysis identified Esr1, Igf-1, Edn1, Hmox1, Serpine1, and Spp1 as key hub genes. qRT-PCR validated these trends. Conclusions: The present study provides a comprehensive transcriptomic profile of an in vitro OGD/R process. Key hub genes and pathways were identified, offering potential targets for neuroprotection after hypoxic ischemia.

Chunyu Pan, Ying Ma, Lifei Wang et al.

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain’s ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.

Yang Yang, Yafei Shangguan, Xiaoming Wang et al.

BackgroundThe new antiseizure medications (ASMs) and non-invasive brain stimulation (NIBS) are controversial in controlling seizures. So, this network meta-analysis aimed to evaluate the efficacy and safety of five third-generation ASMs and two NIBS therapies for the treatment of refractory epilepsy.MethodsWe searched PubMed, EMBASE, Cochrane Library and Web of Science databases. Brivaracetam (BRV), cenobamate (CNB), eslicarbazepine acetate (ESL), lacosamide (LCM), perampanel (PER), repetitive transcranial magnetic stimulation (rTMS), and transcranial direct current stimulation (tDCS) were selected as additional treatments for refractory epilepsy in randomized controlled studies and other cohort studies. Randomized, double-blind, placebo-controlled, add-on studies that evaluated the efficacy or safety of medication and non-invasive brain stimulation and included patients with seizures were uncontrolled by one or more concomitant ASMs were identified. A random effects model was used to incorporate possible heterogeneity. The primary outcome was the change in seizure frequency from baseline, and secondary outcomes included the proportion of patients with ≥50% reduction in seizure frequency, and the rate of treatment-emergent adverse events.ResultsForty-five studies were analyzed. The five ASMs and two NIBS decreased seizure frequency from baseline compared with placebo. The 50% responder rates of the five antiseizure drugs were significantly higher than that of placebo, and the ASMs were associated with fewer adverse events than placebo (p &lt; 0.05). The surface under the cumulative ranking analysis revealed that ESL was most effective in decreasing the seizure frequency from baseline, whereas CNB provided the best 50% responder rate. BRV was the best tolerated. No significant publication bias was identified for each outcome index.ConclusionThe five third-generation ASMs were more effective in controlling seizures than placebo, among which CNB, ESL, and LCM were most effective, and BRV exhibited better safety. Although rTMS and tDCS did not reduce seizure frequency as effectively as the five drugs, their safety was confirmed.Systematic review registrationPROSPERO, https://www.crd.york.ac.uk/prospero/ (CRD42023441097).

Taslim Murad, Sarwan Ali, Murray Patterson

Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, like viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become pandemics globally. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based methods undergo challenges with data imbalance, generally associated with biological sequence datasets, which hinders their performance. Although various strategies are present to address this issue, like the SMOTE algorithm, which creates synthetic data, however, they focus on local information rather than the overall class distribution. In this work, we explore a novel approach to handle the data imbalance issue based on Generative Adversarial Networks (GANs) which use the overall data distribution. GANs are utilized to generate synthetic data that closely resembles the real one, thus this generated data can be employed to enhance the ML models' performance by eradicating the class imbalance problem for biological sequence analysis. We perform 3 distinct classification tasks by using 3 different sequence datasets (Influenza A Virus, PALMdb, VDjDB) and our results illustrate that GANs can improve the overall classification performance.

Xavier Sánchez-Sáez, Isabel Ortuño-Lizarán, Carla Sánchez-Castillo et al.

Abstract Background The main clinical symptoms characteristic of Parkinson’s disease (PD) are bradykinesia, tremor, and other motor deficits. However, non-motor symptoms, such as visual disturbances, can be identified at early stages of the disease. One of these symptoms is the impairment of visual motion perception. Hence, we sought to determine if the starburst amacrine cells, which are the main cellular type involved in motion direction selectivity, are degenerated in PD and if the dopaminergic system is related to this degeneration. Methods Human eyes from control (n = 10) and PD (n = 9) donors were available for this study. Using immunohistochemistry and confocal microscopy, we quantified starburst amacrine cell density (choline acetyltransferase [ChAT]-positive cells) and the relationship between these cells and dopaminergic amacrine cells (tyrosine hydroxylase-positive cells and vesicular monoamine transporter-2-positive presynapses) in cross-sections and wholemount retinas. Results First, we found two different ChAT amacrine populations in the human retina that presented different ChAT immunoreactivity intensity and different expression of calcium-binding proteins. Both populations are affected in PD and their density is reduced compared to controls. Also, we report, for the first time, synaptic contacts between dopaminergic amacrine cells and ChAT-positive cells in the human retina. We found that, in PD retinas, there is a reduction of the dopaminergic synaptic contacts into ChAT cells. Conclusions Taken together, this work indicates degeneration of starburst amacrine cells in PD related to dopaminergic degeneration and that dopaminergic amacrine cells could modulate the function of starburst amacrine cells. Since motion perception circuitries are affected in PD, their assessment using visual tests could provide new insights into the diagnosis of PD.

Peter Lindqvist, Frederick Thienpont, Parker Ellingson et al.

W. Lutz, Julian A. Rubel, Anne-Katharina Deisenhofer et al.

Continuous outcome measurement in psychotherapies has become a central research topic only in the last two decades. Here we provide a short introduction to the relevant concepts and discuss the opportunities and challenges of their implementation in clinical practice. Most continuous outcome measurement systems comprise short self-report questionnaires which assess patient progress on a session-by-session basis. Feeding this psychometric information back to therapists enables them to evaluate whether their current approach is successful or adaptations are necessary. In order to help therapists judge whether a particular patient is improving or at risk for ultimate treatment failure, many routine outcome monitoring (ROM) systems include feedback and empirically-based decision rules. Decision rules are generated based on datasets from clinical practice settings. Based on such large archival datasets, expected recovery curves can be estimated and used to build thresholds indicating which scores are reflective of an increased risk for treatment failure. Having identified a patient as at risk, some ROM/feedback systems provide therapists with additional clinical support tools. These support tools have incorporated process measures designed to assess specific change factors within and outside treatment that impact outcome. Originally, these tools comprised two elements to help therapists adapt treatments specifically for patients at risk for treatment failure: a) an additional assessment of potential problem areas (e.g., suicidal ideation, motivation) to elucidate the patient’s individual risk profile, and b) a decision tree directing therapists to specific interventions depending on the identified risk profile. New developments have built on these ideas and included multimedia instruction materials and machine learning prediction models in order to help therapists provide the specific interventions that are most promising for a particular patient. Over 40 randomized clinical trials (RCTs) and several metaanalyses provide a compelling evidence base for ROM and feedback. Feedback-informed treatments have been shown to result in improved outcomes, reduced dropout, and higher efficiency than standard evidence-based treatments. The most recent and comprehensive meta-analysis reported a significant effect size advantage of d=0.15 for progress feedback compared to treatment as usual. This effect was slightly higher for the subgroup of patients showing an initial treatment non-response (d=0.17). When evaluating the size of these effects, it is important to keep two issues in mind. First, these effects come on top of the effects of effective evidence-based treatments. Second, feedback is a minimal low-cost technological intervention that does not put much of a burden on either patients or therapists. Accordingly, the largest RCT to date (N=2,233) demonstrated the cost-effecout necessarily reducing them to only intra-personal processes. Beyond these recent developments, we can also wonder what are the next steps for multi-brain neuroscience, and especially what potential avenues it can open for psychiatric research and clinical practice. First, while early work was done in humans, the recent increased interest in IBC comes from multiple papers published with animal models. Not only have these studies replicated the early observation of inter-brain correlates in humans, but they have also uncovered for the first time cellular mechanisms. This move from mesoscopic to microscopic levels opens possibilities to decipher which biological mechanisms can be targeted pharmacologically to potentially enhance IBC and with them neurobehavioral inter-personal dynamics. Second, another recent trend is the move from multi-brain recording to multi-brain stimulations. The burgeoning field of hyper-stimulation may thus represent the next technological step to go from inter-brain correlational measurement to direct causal manipulation. Preliminary results already demonstrate that induction of inter-brain synchronization of neural processes shapes social interaction within groups of mice, and facilitates motor coordination in humans. If multi-brain electromagnetic stimulation provides insights about the causal factors modulating IBC and eventually sheds light onto biological mechanisms, a long-term challenge will be to move even beyond the traditional “correlation vs. causation” debate and provide an integrative explanation of the IBC phenomenon. Ultimately, inter-personal neuromodulation through pharmacological compounds, electromagnetic stimulations, and even both, could open the way to new forms of therapeutics in psychiatry. We have seen how the nascent multi-brain neuroscience may lead to transformative applications in psychiatry, from interbrain measures for clinical characterization to inter-brain neuromodulation for treatments. Interestingly, this inter-personal psychiatry will also help take seriously our biological grounding as much as our social embedding.

Marco Aldinucci, Livio Bioglio, Cristina Calcagno et al.

The modelling and analysis of biological systems has deep roots in Mathematics, specifically in the field of Ordinary Differential Equations. Alternative approaches based on formal calculi, often derived from process algebras or term rewriting systems, provide a quite complementary way to analyse the behaviour of biological systems. These calculi allow to cope in a natural way with notions like compartments and membranes, which are not easy (sometimes impossible) to handle with purely numerical approaches, and are often based on stochastic simulation methods. The Calculus of Wrapped Compartments is a framework based on stochastic multiset rewriting in a compartmentalised setting used for the description of biological and ecological systems. We provide an extended presentation of the Calculus of Wrapped Compartments, sketch a few modelling guidelines to encode biological and ecological interactions, show how spatial properties can be handled within the framework and define a hybrid simulation algorithm. Several applications in Biology and Ecology are proposed as modelling case studies.