A rare variation of the infraorbital nerve, entrapping the infraorbital artery

Anatomical variations are a common feature of the human anatomy. Variation can explain some pathological conditions and is important to keep them in mind during surgical procedures. The relations between nerves and their adjacent arteries have been proposed to play a role in the generation of peripheral trigger migraines. Close opposition between nerves and arteries can lead to vascular compression of the nerve that triggers episodes of pain. We did a routine dissection of the infratemporal fossa and orbital floor by opening the maxillary sinus. Here we report a case where the infraorbital nerves form a nervous loop entrapping the infraorbital artery in the infraorbital channel. Similar cases of close nervous and arterial apposition are described for the auriculotemporal and occipital nerves. We think that accumulating knowledge of these rare variations could expand our understanding of rare conditions such as primary infraorbital neuralgia.

Group independent components underpin responses to items from a depression scale

OBJECTIVE

The aim of the present study is to investigate the brain circuits or networks that underpin diagnostically specific tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesised that there will be neural network patterns of activation and deactivation, which correspond to real-time performance on clinical self-evaluation scales.

METHODS

In total, 20 healthy controls (HC) and 22 patients with major depressive episode have been included. All subjects were scanned with functional magnetic resonance imaging (fMRI) with paradigm composed of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping.

RESULTS

The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with HC.

CONCLUSION

Potential neuroimaging of state-dependent biomarkers has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the convergent capacity of both methods to distinguish groups by means of incremental translational cross-validation.

Prevalence and prognosis of PIK3CA mutations in Bulgarian patients with metastatic breast cancer receiving endocrine therapy in first-line setting

BACKGROUND AND AIMS

In approximately 40% of patients with HER2-negative/HR-positive breast cancer tumors, the PIK3CA gene is mutated. Despite this, clinical outcomes vary between studies in this cohort. We aimed to ascertain the prevalence of PIK3CA mutations in patients with metastatic HR+/HER2- breast in Bulgaria, as well the evaluation and comparison of progression free survival (PFS) between wild-type (WT) and mutation-positive groups in the real-world setting.

METHODS

Three oncology centers in Bulgaria collected 250 tissue samples between 2016 and 2022 for this multicentric retrospective study. PIK3CA mutations were identified using Real-Time qPCR. The median follow-up period was 35 months.

RESULTS

The mean age of the mutant cohort was 57.6 ± 11.6 years, compared to 56.5 ± 12.2 years for the WT cohort (p = .52). The percentage of patients with visceral metastasis was 58.8% (n = 147). Approximately 84.3% (n = 210) of the patients had reached postmenopause. 29.2% (n = 73) of the patients had PIK3CA mutations. The predominant mutation was present in exon 20, H1047R (46.5%). We found a significant correlation only between the presence of a mutation and the metastatic diseases at diagnosis (p = .002). As first-line therapy, 67.1% of patients received endocrine therapy (ET) plus cyclin dependent kinase (CDK4/6) inhibitor, while the remainder received ET alone. The median PFS of patients in the group with the mutation was 32 months (95%, CI: 22-40) compared to 24 months in the WT cohort ((95%, CI: 21-36) (p = .45)); HR = 0.86 (95%, CI: 0.5-1.3) (p = .46). We corroborated our conclusion using propensity matching score analysis, (36 months [95% CI: 20-40] vs. 26 months [95% CI: 21-38], [p = .69]).

CONCLUSIONS

We found that the prevalence of PIK3CA mutations in our patients was comparable to what has been reported in other nations. Our results suggest that PIK3CA mutational status has no bearing to ET efficacy in first-line setting.

Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale

BACKGROUND

Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive, affective and behavioral tasks, adapted for the functional magnetic resonance imaging (MRI) (fMRI) experimental environment. There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders.

AIM

To investigate whether there exist specific neural circuits which underpin differential item responses to depressive, paranoid and neutral items (DN) in patients respectively with schizophrenia (SCZ) and major depressive disorder (MDD).

METHODS

60 patients were recruited with SCZ and MDD. All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm, comprised of block design, including blocks with items from diagnostic paranoid (DP), depression specific (DS) and DN from general interest scale. We performed a two-sample t-test between the two groups-SCZ patients and depressive patients. Our purpose was to observe different brain networks which were activated during a specific condition of the task, respectively DS, DP, DN.

RESULTS

Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task. We identified one component that is task-related and independent of condition (shared between all three conditions), composed by regions within the temporal (right superior and middle temporal gyri), frontal (left middle and inferior frontal gyri) and limbic/salience system (right anterior insula). Another component is related to both diagnostic specific conditions (DS and DP) e.g. It is shared between DEP and SCZ, and includes frontal motor/language and parietal areas. One specific component is modulated preferentially by to the DP condition, and is related mainly to prefrontal regions, whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus, several occipital areas, including lingual and fusiform gyrus, as well as parahippocampal gyrus. Finally, component 12 appeared to be unique for the neutral condition. In addition, there have been determined circuits across components, which are either common, or distinct in the preferential processing of the sub-scales of the task.

CONCLUSION

This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes

Introduction

In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54).

Methods

We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups.

Results

As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups.

Discussion

Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.

Topology switching during window thresholding fMRI-based functional networks of patients with major depressive disorder: Consensus network approach

We present a novel method for analyzing brain functional networks using functional magnetic resonance imaging data, which involves utilizing consensus networks. In this study, we compare our approach to a standard group-based method for patients diagnosed with major depressive disorder (MDD) and a healthy control group, taking into account different levels of connectivity. Our findings demonstrate that the consensus network approach uncovers distinct characteristics in network measures and degree distributions when considering connection strengths. In the healthy control group, as connection strengths increase, we observe a transition in the network topology from a combination of scale-free and random topologies to a small-world topology. Conversely, the MDD group exhibits uncertainty in weak connections, while strong connections display small-world properties. In contrast, the group-based approach does not exhibit significant differences in behavior between the two groups. However, it does indicate a transition in topology from a scale-free-like structure to a combination of small-world and scale-free topologies. The use of the consensus network approach also holds immense potential for the classification of MDD patients, as it unveils substantial distinctions between the two groups.

Conversion and Obsessive-Phobic Symptoms Predict IL-33 and IL-28A Levels in Individuals Diagnosed with COVID-19

The first epidemiological wave of the incidence of COVID-19 in Bulgaria was registered in June 2020. After the wave peak, we conducted a study in persons diagnosed with COVID-19 (N = 52). They were assessed with the anxiety-depressive scale (ADS), including basic (BS), vegetative (VS), conversion (CS), obsessive-phobic (OPS), and depressive (DS) symptoms. ADS assessment of individuals diagnosed with SARS-CoV-2 indicated a correlation between OPS and IL-33 values. IL-10 levels were higher than reference ranges in all patients. Multiple linear regression analyses demonstrated that combination of CS and OPS explained 28% of IL-33 levels, while combination of symptoms from all ADS dimensions explained 24% of IL-33 levels. It was also found that 21% of IL-28A levels was explained from the combination by all ADS dimensions, whereas OPS was the predictor for lower concentrations. The obtained results revealed meaningful correlations between psycho neuro-immunological factors in pathogenesis of illness from the coronavirus infection.

Functional Connectivity of the Anterior Cingulate Cortex and the Right Anterior Insula Differentiates between Major Depressive Disorder, Bipolar Disorder and Healthy Controls

Background: This study aimed to explore possible differences of the whole-brain functional connectivity of the anterior cingulate cortex (ACC) and anterior insula (AI), in a sample of depressed patients with major depressive disorder (MDD), bipolar disorder (BD) and healthy controls (HC). Methods: A hundred and three subjects (n_MDD = 35, n_BD = 25, and n_HC = 43) between the ages of eighteen and sixty-five years old underwent functional magnetic resonance imaging. The CONN Toolbox was used to process and analyze the functional connectivity of the ACC and AI. Results: The comparison between the patients (MDD/BD) and HC yielded increased resting-state functional connectivity (rsFC) between the ACC and the motor and somatosensory cortices (SSC), superior parietal lobule (SPL), precuneus, and lateral occipital cortex, which was driven by the BD group. In addition, hyperconnectivity between the right AI and the motor and SSC was found in BD, as compared to HC. In MDD, as compared to HC, hyperconnectivity between ACC and SPL and the lateral occipital cortex was found, with no statistical rsFC differences for the AI seed. Compared to BD, the MDD group showed ACC-cerebellum hyperconnectivity and a trend for increased rsFC between the right AI and the bilateral superior frontal cortex. Conclusions: Considering the observed hyperconnectivity between the ACC/somatosensory cortex in the patient group, we suggest depression may be related to an impairment of the sensory-discriminative function of the SSC, which results in the phenomenological signature of mental pain in both MDD and BD. These findings suggest that future research should investigate this particular network with respect to motor functions and executive control, as a potential differential diagnostic biomarker for MDD and BD.

Toward interpretability of machine learning methods for the classification of patients with major depressive disorder based on functional network measures

We address the interpretability of the machine learning algorithm in the context of the relevant problem of discriminating between patients with major depressive disorder (MDD) and healthy controls using functional networks derived from resting-state functional magnetic resonance imaging data. We applied linear discriminant analysis (LDA) to the data from 35 MDD patients and 50 healthy controls to discriminate between the two groups utilizing functional networks' global measures as the features. We proposed the combined approach for feature selection based on statistical methods and the wrapper-type algorithm. This approach revealed that the groups are indistinguishable in the univariate feature space but become distinguishable in a three-dimensional feature space formed by the identified most important features: mean node strength, clustering coefficient, and the number of edges. LDA achieves the highest accuracy when considering the network with all connections or only the strongest ones. Our approach allowed us to analyze the separability of classes in the multidimensional feature space, which is critical for interpreting the results of machine learning models. We demonstrated that the parametric planes of the control and MDD groups rotate in the feature space with increasing the thresholding parameter and that their intersection increases with approaching the threshold of 0.45, for which classification accuracy is minimal. Overall, the combined approach for feature selection provides an effective and interpretable scenario for discriminating between MDD patients and healthy controls using measures of functional connectivity networks. This approach can be applied to other machine learning tasks to achieve high accuracy while ensuring the interpretability of the results.

Retrospective Analysis of the Psychological Predictors of Public Health Support in Bulgarians at the Beginning of the Coronavirus Pandemic

The earliest critical context of the pandemic, preceding the first real epidemiological wave of contagion in Bulgaria, was examined using a socio-affective perspective. A retrospective and agnostic analytical approach was adopted. Our goal was to identify traits and trends that explain public health support (PHS) of Bulgarians during the first two months of the declared state of emergency. We investigated a set of variables with a unified method within an international scientific network named the International Collaboration on Social & Moral Psychology of COVID-19 (ICSMP) in April and May 2020. A total of 733 Bulgarians participated in the study (67.3% females), with an average age of 31.8 years (SD = 11.66). Conspiracy Theories Beliefs were a significant predictor of lower PHS. Psychological Well-Being was significantly associated with Physical Contact and Anti-Corona Policy Support. Physical Contact was significantly predicted by fewer Conspiracy Theories Beliefs, higher Collective Narcissism, Open-mindedness, higher Trait Self-Control, Moral Identity, Risk Perception and Psychological Well-Being. Physical Hygiene compliance was predicted by fewer Conspiracy Theories Beliefs, Collective Narcissism, Morality-as-Cooperation, Moral Identity and Psychological Well-Being. The results revealed two polar trends of support and non-support of public health policies. The contribution of this study is in providing evidence for the affective polarization and phenomenology of (non)precarity during the outbreak of the pandemic.

Social and moral psychology of COVID-19 across 69 countries

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

An adaptable research platform for ex vivo normothermic machine perfusion of the liver

PURPOSE

This paper presents an assessment of a low-cost organ perfusion machine designed for use in research settings. The machine is modular and versatile in nature, built on a robotic operating system (ROS2) pipeline allowing for the addition of specific sensors for different research applications. Here we present the system and the development stages to achieve viability of the perfused organ.

METHODS

The machine's perfusion efficacy was assessed by monitoring the distribution of perfusate in livers using methylene blue dye. Functionality was evaluated by measuring bile production after 90 min of normothermic perfusion, while viability was examined using aspartate transaminase assays to monitor cell damage throughout the perfusion. Additionally, the output of the pressure, flow, temperature, and oxygen sensors was monitored and recorded to track the health of the organ during perfusion and assess the system's capability of maintaining the quality of data over time.

RESULTS

The results show the system is capable of successfully perfusing porcine livers for up to three hours. Functionality and viability assessments show no deterioration of liver cells once normothermic perfusion had occurred and bile production was within normal limits of approximately 26 ml in 90 min showing viability.

CONCLUSION

The developed low-cost perfusion system presented here has been shown to keep porcine livers viable and functional ex vivo. Additionally, the system is capable of easily incorporating several sensors into its framework and simultaneously monitor and record them during perfusion. The work promotes further exploration of the system in different research domains.

Advances in the Diagnosis and Management of Psychosis

Psychosis research in the contemporary sense of scientific inquiry may be traced as far as the formulation of the "unitary psychosis" concept, or Einheitpsychose, which is usually attributed to Wilhelm Griesinger, Ernst von Zeller, and Heinrich Neumann [...].

Organ curvature sensing using pneumatically attachable flexible rails in robotic-assisted laparoscopic surgery

In robotic-assisted partial nephrectomy, surgeons remove a part of a kidney often due to the presence of a mass. A drop-in ultrasound probe paired to a surgical robot is deployed to execute multiple swipes over the kidney surface to localise the mass and define the margins of resection. This sub-task is challenging and must be performed by a highly-skilled surgeon. Automating this sub-task may reduce cognitive load for the surgeon and improve patient outcomes. The eventual goal of this work is to autonomously move the ultrasound probe on the surface of the kidney taking advantage of the use of the Pneumatically Attachable Flexible (PAF) rail system, a soft robotic device used for organ scanning and repositioning. First, we integrate a shape-sensing optical fibre into the PAF rail system to evaluate the curvature of target organs in robotic-assisted laparoscopic surgery. Then, we investigate the impact of the PAF rail's material stiffness on the curvature sensing accuracy, considering that soft targets are present in the surgical field. We found overall curvature sensing accuracy to be between 1.44% and 7.27% over the range of curvatures present in adult kidneys. Finally, we use shape sensing to plan the trajectory of the da Vinci surgical robot paired with a drop-in ultrasound probe and autonomously generate an Ultrasound scan of a kidney phantom.

Neuroprotection is improved by watertightness of fetal spina bifida repair in the sheep model

OBJECTIVES

A contributing factor to unsuccessful prenatal spina bifida aperta (SBA) repair via an open approach may be incomplete neurosurgical repair causing persistent in-utero leakage of cerebrospinal fluid (CSF) and exposure of the fetal spinal cord to amniotic fluid. We aimed to investigate the neurostructural and neurofunctional efficacy of watertight prenatal SBA repair in a validated SBA fetal lamb model.

METHODS

A well-powered superiority study was conducted in the validated SBA fetal lamb model (n = 7 per group). The outcomes of lambs which underwent watertight or non-watertight multilayer repair through an open approach were compared to those of unrepaired SBA lambs (historical controls) at delivery (term = 145 days). At ∼75 days, fetal lambs underwent standardized induction of lumbar SBA. At ∼100 days, they were assigned to an either watertight or non-watertight layered repair group based on an intraoperative watertightness test using subcutaneous fluorescein injection. At 1-2 days postnatally, as primary outcome, we assessed reversal of hindbrain herniation using magnetic resonance imaging (MRI). Secondary proxies of neuroprotection were: absence of CSF leakage at the repair site; hindlimb motor function based on joint-movement score, locomotor grade and Motor Evoked Potential (MEP); four-score neuroprotection scale, encompassing live birth, complete hindbrain herniation reversal, absence of CSF leakage and joint-movement score ≥ 9/15; and brain and spinal cord histology and immunohistochemistry. As the watertightness test cannot be used clinically due to its invasiveness, we developed a potential surrogate intraoperative three-score skin-repair-quality scale based on visual assessment of the quality of the skin repair (suture inter-run distance ≤ 3 mm, absence of tear and absence of ischemia), with high quality defined by a score ≥ 2/3 and low quality by a score < 2/3, and assessed its relationship with improved outcome.

RESULTS

Compared with unrepaired lambs, lambs with watertight repair achieved a high level of neuroprotection (neuroprotection score of 4/4 in 5/7 vs 0/7 lambs) as evidenced by: a significant 100% (vs 14%) reversal of hindbrain herniation on MRI; low CSF leakage (14% vs 100%); better hindlimb motor function, with higher joint-movement score, locomotor grade and MEP area under the curve and peak-to-peak amplitude; higher neuronal density in the hippocampus and corpus callosum; and higher reactive astrogliosis at the SBA lesion epicenter. Conversely, lambs with non-watertight SBA repair did not achieve the same level of neuroprotection (score of 4/4 in 1/7 lambs) compared with unrepaired lambs, with: a non-significant 86% (vs 14%) reversal of hindbrain herniation; high CSF leakage (43% vs 100%); no improvement in motor function; low brain neuron count in both the hippocampus and corpus callosum; and small spinal astroglial cell area at the epicenter. Both watertight layered repair and high (≥ 2/3) intraoperative skin-repair-quality score were associated with improved outcome, but the watertightness test and skin-repair-quality scale could not be used interchangeably due to result discrepancies.

CONCLUSIONS

Watertight layered fetal SBA repair is neuroprotective since it improves brain and spinal-cord structure and function in the fetal lamb model. This translational research has important clinical implications. A neurosurgical technique that achieves watertightness should be adopted in all fetal centers to improve neuroprotection. Future clinical studies could assess whether a high skin-repair-quality score (≥ 2/3) correlates with neuroprotection. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Effective Connectivity Between the Orbitofrontal Cortex and the Precuneus Differentiates Major Psychiatric Disorders: Results from a Transdiagnostic Spectral DCM Study

BACKGROUND & OBJECTIVE

We have previously identified aberrant connectivity of the left precuneus, ventrolateral prefrontal cortex, anterior cingulate cortex, and anterior insula in patients with either a paranoid (schizophrenia), or a depressive syndrome (both unipolar and bipolar). In the current study, we attempted to replicate and expand these findings by including a healthy control sample and separating the patients in a depressive episode into two groups: unipolar and bipolar depression. We hypothesized that the connections between those major nodes of the resting state networks would demonstrate different patterns in the three patient groups compared to the healthy subjects.

METHODS

Resting-state functional MRI was performed on a sample of 101 participants, of which 26 patients with schizophrenia (current psychotic episodes), 24 subjects with Bipolar Disorder (BD), 33 with Major Depressive Disorder (MDD) (both BD and MDD patients were in a current depressive episode), and 21 healthy controls. Spectral Dynamic Causal Modeling was used to calculate the coupling values between eight regions of interest, including the anterior precuneus (PRC), anterior hippocampus, anterior insula, angular gyrus, lateral Orbitofrontal Cortex (OFC), middle frontal gyrus, planum temporale, and anterior thalamus.

RESULTS & CONCLUSION

We identified disturbed effective connectivity from the left lateral orbitofrontal cortex to the left anterior precuneus that differed significantly between unipolar depression, where the influence was inhibitory, and bipolar depression, where the effect was excitatory. A logistic regression analysis correctly classified 75% of patients with unipolar and bipolar depression based solely on the coupling values of this connection. In addition, patients with schizophrenia demonstrated negative effective connectivity from the anterior PRC to the lateral OFC, which distinguished them from healthy controls and patients with major depression. Future studies with unmedicated patients will be needed to establish the replicability of our findings.

How to define today a medical disorder? Biological and psychosocial disadvantages as the paramount criteria

BACKGROUND

The 20th century has seen great developments in the concept of disease. Marked by the biopsychosocial paradigm, several strategies for disease definition were added to previous descriptive organic views, but a final concept is still out of reach.

METHOD

A critical review was carried out on thorough analysis of articles and textbooks to describe the main concepts and definitions of disease.

RESULTS

The concept 'disease' is a pragmatic construct, not a natural kind. Three main ways to define disease were identified, and characterized: Biological (disease as a lesion, disadvantage/deviation from normal and dysfunction), Psychosocial (distress and disability, existential potentials, descriptive prototype, and prototype typification), and values-based definition.

CONCLUSION

All the paradigms have advantages and flaws, but progressive use of all criteria in disease definition adds validity and reliability to diagnostic constructs. Such constructs must be, above all, useful for practice and research. Biological paradigm is relevant, but fails to cover all the complexity that involves human illness and the treatment process. An emphasis on distress, dysfunction, and carefully selected value-laden characteristics might be the right direction for useful diagnostic construct conceptions.

Resting-State Functional Connectivity Impairment in Patients with Major Depressive Episode

AIM

This study aims to develop new approaches to characterize brain networks to potentially contribute to a better understanding of mechanisms involved in depression.

METHOD AND SUBJECTS

We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed investigating functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were developed at a functional network level, using a false discovery rate method. Linear discriminant analysis was used to differentiate between the two groups.

RESULTS AND DISCUSSION

Significant differences in functional connectivity (FC) between depressed patients vs. healthy controls was demonstrated, with brain regions including the lingual gyrus, cerebellum, midcingulate cortex and thalamus more prominent in healthy subjects as compared to depression where the orbitofrontal cortex emerged as a key node. Linear discriminant analysis demonstrated that full-connectivity matrices were the most precise in differentiating between depression vs. health subjects.

CONCLUSION

The study provides supportive evidence for impaired functional connectivity networks in MDE patients.

Differences in subjective well-being between individuals with distinct Joint Personality (temperament-character) networks in a Bulgarian sample

Background

Personality is the major predictor of people's subjective well-being (i.e., positive affect, negative affect, and life satisfaction). Recent research in countries with high-income and strong self-transcendent values shows that well-being depends on multidimensional configurations of temperament and character traits (i.e., Joint Personality Networks) that regulate the way people learn to adapt their habits to be in accord with their goals and values, rather than individual traits. To evaluate the prevalence and the associations of different Joint Personality (temperament-character) Networks with well-being in a low-income country with weak self-transcendent values, we tested their association in Bulgarian adults, a population known to have strong secular-rationalist values but weak self-transcendent values.

Method

The sample consisted of 443 individuals from Bulgaria (68.70% females) with a mean age of 34 years (SD = 15.05). Participants self-reported personality (Temperament and Character Inventory), affect (Positive Affect Negative Affect Schedule), and life satisfaction (Satisfaction with Life Scale). The personality scores were used for profiling through latent profile analysis and latent class analysis based on temperament configurations (i.e., Temperament Profiles) of high/low scores of Novelty Seeking (N/n), Harm Avoidance (H/h), Reward Dependence (R/r), and Persistence (P/s); and character configurations (i.e., Character Profiles) of high/low scores of Self-Directedness (S/s), Cooperativeness (C/c), and Self-Transcendence (T/t).

Results

We found two Temperament Profiles and two Character Profiles that clustered into two distinctive Joint Personality Networks. All individuals in Joint Personality Network 1 had a Reliable (nhRP) Temperament Profile in combination with an Organized (SCt) Character Profile (i.e., a stable temperament and a healthy character configuration). About 71.9% in Joint Personality Network 2 had an Apathetic (sct) Character Profile in combination with Methodical (nHrp) or Reliable (nhRP) Temperament Profiles, while 28.1% had a Methodical (nHrp) Temperament Profile in combination with an Organized (SCt) Character Profile. Few people with high self-expressive values (i.e., high in all three character traits; SCT) were found. Individuals with a Joint Personality Network 1 with strong secular-rationalist values reported higher levels of positive affect and life satisfaction (p < .001), while individuals with a Joint Personality Network 2 reported higher levels of negative affect (p < .001).

Conclusions

Although a stable temperament and a healthy character were separately important for well-being, it was clear that it was the interaction between such temperament and character configuration that yielded greater levels of subjective well-being. Nevertheless, future research needs to investigate this interaction further to evaluate other cultures with variable configurations of personality traits and values.

Microsurgery for intracranial aneurysms: A qualitative survey on technical challenges and technological solutions

Introduction

Microsurgery for the clipping of intracranial aneurysms remains a technically challenging and high-risk area of neurosurgery. We aimed to describe the technical challenges of aneurysm surgery, and the scope for technological innovations to overcome these barriers from the perspective of practising neurovascular surgeons.

Materials and Methods

Consultant neurovascular surgeons and members of the British Neurovascular Group (BNVG) were electronically invited to participate in an online survey regarding surgery for both ruptured and unruptured aneurysms. The free text survey asked three questions: what do they consider to be the principal technical barriers to aneurysm clipping? What technological advances have previously contributed to improving the safety and efficacy of aneurysm clipping? What technological advances do they anticipate improving the safety and efficacy of aneurysm clipping in the future? A qualitative synthesis of responses was performed using multi-rater emergent thematic analysis.

Results

The most significant reported historical advances in aneurysm surgery fell into five themes: (1) optimising clip placement, (2) minimising brain retraction, (3) tissue handling, (4) visualisation and orientation, and (5) management of intraoperative rupture. The most frequently reported innovation by far was indocyanine green angiography (84% of respondents). The three most commonly cited future advances were hybrid surgical and endovascular techniques, advances in intraoperative imaging, and patient-specific simulation and planning.

Conclusions

While some surgeons perceive that the rate of innovation in aneurysm clipping has been dwarfed in recent years by endovascular techniques, surgeons surveyed highlighted a broad range of future technologies that have the potential to continue to improve the safety of aneurysm surgery in the future.

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

Evaluation of A Novel Organ Perfusion Research Platform

This paper presents a novel, low cost, organ perfusion machine designed for use in research. The modular and versatile nature of the system allows for additional sensing equipment to be added or adapted for specific use. Here we introduce the system and present its preliminary evaluation by assessing its ability to maintain a predetermined input pressure. A proportional-integral-derivative (PID) controller was implemented and tested on a porcine liver to maintain input pressure to the hepatic artery and compared to bench tests. The results confirmed the effectiveness of the controller for maintaining input through the hepatic artery (HA) in a timely manner. Clinical Relevance-Machine Perfusion (MP) is proving to be an invaluable adjunct in clinical practice. With its ongoing success in the transplant arena, we propose MP for use in research. A cost-effective, versatile system that can be modified for specific research use to test new pharmacological therapies, imaging techniques or develop simulation training would be beneficial.

Neural Activation in the Ventromedial Prefrontal Cortex Precedes Conscious Experience of Being in or out of a Transient Hallucinatory State

BACKGROUND AND HYPOTHESES

Auditory verbal hallucinations (AVHs) is not only a common symptom in schizophrenia but also observed in individuals in the general population. Despite extensive research, AVHs are poorly understood, especially their underlying neuronal architecture. Neuroimaging methods have been used to identify brain areas and networks that are activated during hallucinations. A characteristic feature of AVHs is, however, that they fluctuate over time, with varying frequencies of starts and stops. An unanswered question is, therefore, what neuronal events co-occur with the initiation and inhibition of an AVH episode.

STUDY DESIGN

We investigated brain activation with fMRI in 66 individuals who experienced multiple AVH-episodes while in the scanner. We extracted time-series fMRI-data and monitored changes second-by-second from 10 s before to 15 s after participants indicated the start and stop of an episode, respectively, by pressing a hand-held response-button.

STUDY RESULTS

We found a region in the ventromedial prefrontal cortex (VMPFC) which showed a significant increase in activation initiated a few seconds before participants indicated the start of an episode, and a corresponding decrease in activation initiated a few seconds before the end of an episode.

CONCLUSIONS

The consistent increase and decrease in activation in this area in advance of the consciously experienced presence or absence of the "voice" imply that this region may act as a switch in turning episodes on and off. The activation is unlikely to be confounded by motor responses. The findings could have clinical implications for brain stimulation treatments, like transcranial magnetic stimulation.

False dogmas in mood disorders research: Towards a nomothetic network approach

The current understanding of major depressive disorder (MDD) and bipolar disorder (BD) is plagued by a cacophony of controversies as evidenced by competing schools to understand MDD/BD. The DSM/ICD taxonomies have cemented their status as the gold standard for diagnosing MDD/BD. The aim of this review is to discuss the false dogmas that reign in current MDD/BD research with respect to the new, data-driven, machine learning method to model psychiatric illness, namely nomothetic network psychiatry (NNP). This review discusses many false dogmas including: MDD/BD are mind-brain disorders that are best conceptualized using a bio-psycho-social model or mind-brain interactions; mood disorders due to medical disease are attributable to psychosocial stress or chemical imbalances; DSM/ICD are the gold standards to make the MDD/BD diagnosis; severity of illness should be measured using rating scales; clinical remission should be defined using threshold values on rating scale scores; existing diagnostic BD boundaries are too restrictive; and mood disorder spectra are the rule. In contrast, our NNP models show that MDD/BD are not mind-brain or psycho-social but systemic medical disorders; the DSM/ICD taxonomies are counterproductive; a shared core, namely the reoccurrence of illness (ROI), underpins the intertwined recurrence of depressive and manic episodes and suicidal behaviors; mood disorders should be ROI-defined; ROI mediates the effects of nitro-oxidative stress pathways and early lifetime trauma on the phenome of mood disorders; severity of illness and treatment response should be delineated using the NNP-derived causome, pathway, ROI and integrated phenome scores; and MDD and BD are the same illness.

National identity predicts public health support during a global pandemic

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = -0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.

Perspectives before incremental trans-disciplinary cross-validation of clinical self-evaluation tools and functional MRI in psychiatry: 10 years later

Translational validity (or trans-disciplinary validity) is defined as one possible approach to achieving incremental validity by combining simultaneous clinical state-dependent measures and functional MRI data acquisition. It is designed under the assumption that the simultaneous administration of the two methods may produce a dataset with enhanced synchronization and concordance. Translational validation aims at "bridging" the explanatory gap by implementing validated psychometric tools clinically in the experimental settings of fMRI and then translating them back to clinical utility. Our studies may have identified common diagnostic task-specific denominators in terms of activations and network modulation. However, those common denominators need further investigation to determine whether they signify disease or syndrome-specific features (signatures), which, at the end of the day, raises one more question about the poverty of current conventional psychiatric classification criteria. We propose herewith a novel algorithm for translational validation based on our explorative findings. The algorithm itself includes pre-selection of a test based on its psychometric characteristics, adaptation to the functional MRI paradigm, exploration of the underpinning whole brain neural correlates in healthy controls as compared to a patient population with certain diagnoses, and finally, investigation of the differences between two or more diagnostic classes.

Differential aberrant connectivity of precuneus and anterior insula may underpin the diagnosis of schizophrenia and mood disorders

BACKGROUND

Over the past decade, resting-state functional magnetic resonance imaging (rs-fMRI) has concentrated on brain networks such as the default mode network (DMN), the salience network (SN), and the central executive network (CEN), allowing for a better understanding of cognitive deficits observed in mental disorders, as well as other characteristic psychopathological phenomena such as thought and behavior disorganization.

AIM

To investigate differential patterns of effective connectivity across distributed brain networks involved in schizophrenia (SCH) and mood disorders.

METHODS

The sample comprised 58 patients with either paranoid syndrome in the context of SCH (n = 26) or depressive syndrome (Ds) (n = 32), in the context of major depressive disorder or bipolar disorder. The methods used include rs-fMRI and subsequent dynamic causal modeling to determine the direction and strength of connections to and from various nodes in the DMN, SN and CEN.

RESULTS

A significant excitatory connection from the dorsal anterior cingulate cortex to the anterior insula (aI) was observed in the SCH patient group, whereas inhibitory connections from the precuneus to the ventrolateral prefrontal cortex and from the aI to the precuneus were observed in the Ds group.

CONCLUSION

The results delineate specific patterns associated with SCH and Ds and offer a better explanation of the underlying mechanisms of these disorders, and inform differential diagnosis and precise treatment targeting.

Pharmaco-Magnetic Resonance as a Tool for Monitoring the Medication-Related Effects in the Brain May Provide Potential Biomarkers for Psychotic Disorders

The neurodegenerative and neurodevelopmental hypotheses represent the basic etiological framework for the origin of schizophrenia. Additionally, the dopamine hypothesis, adopted more than two decades ago, has repeatedly asserted the position of dopamine as a pathobiochemical substrate through the action of psychostimulants and neuroleptics on the mesolimbic and mesocortical systems, giving insight into the origin of positive and negative schizophrenic symptoms. Meanwhile, cognitive impairments in schizophrenia remain incompletely understood but are thought to be present during all stages of the disease, as well as in the prodromal, interictal and residual phases. On the other hand, observations on the effects of NMDA antagonists, such as ketamine and phencyclidine, reveal that hypoglutamatergic neurotransmission causes not only positive and negative but also cognitive schizophrenic symptoms. This review aims to summarize the different hypotheses about the origin of psychoses and to identify the optimal neuroimaging method that can serve to unite them in an integral etiological framework. We systematically searched Google scholar (with no concern to the date published) to identify studies investigating the etiology of schizophrenia, with a focus on impaired central neurotransmission. The complex interaction between the dopamine and glutamate neurotransmitter systems provides the long-needed etiological concept, which combines the neurodegenerative hypothesis with the hypothesis of impaired neurodevelopment in schizophrenia. Pharmaco-magnetic resonance imaging is a neuroimaging method that can provide a translation of scientific knowledge about the neural networks and the disruptions in and between different brain regions, into clinically applicable and effective therapeutic results in the management of severe psychotic disorders.

The Translational Potential of Non-coding RNAs and Multimodal MRI Data Sets as Diagnostic and Differential Diagnostic Biomarkers for Mood Disorders

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) have a high prevalence and detrimental socio-economic consequences for the patients and the community. Furthermore, the depressive symptomatology of both disorders is essentially identical, thus rendering the clinical differential diagnosis between the two significantly more difficult considering the concomitant lack of objective biomarkers. Mood disorders are multifactorial disorders the pathophysiology of which includes genetic, epigenetic, neurobiological, neuroimmunological, structural and functional brain alterations, etc. Aberrant genetic variants as well as changed differential expression of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been implicated in the pathophysiology of MDD and BD. MiRNAs as well as lncRNAs have regulatory and modulating functions on protein-- coding gene expression thus influencing the remodeling of the architecture, neurotransmission, immunomodulation, etc. in the Central Nervous System (CNS) which are essential in the development of psychiatric disorders including MDD and BD. Moreover, both shared and distinct structural, connectivity, task-related and metabolic features have been observed via functional magnetic resonance imaging and magnetic resonance spectroscopy, suggesting the possibility of a dimensional continuum between the two disorders instead of a categorical differentiation. Aberrant connectivity within and between the Default Mode Network, the Salience Network, Executive Network, etc. as well as dysfunctional emotion, cognitive and executive processing have been associated with mood disorders. Therefore, the aim of this review is to explore a more multidimensional framework in the scientific research of mood disorders, including epigenetic and neuroimaging data in order to shape an outline for their translational capacity in clinical practice.

Performance of image guided navigation in laparoscopic liver surgery - A systematic review

BACKGROUND

Compared to open surgery, minimally invasive liver resection has improved short term outcomes. It is however technically more challenging. Navigated image guidance systems (IGS) are being developed to overcome these challenges. The aim of this systematic review is to provide an overview of their current capabilities and limitations.

METHODS

Medline, Embase and Cochrane databases were searched using free text terms and corresponding controlled vocabulary. Titles and abstracts of retrieved articles were screened for inclusion criteria. Due to the heterogeneity of the retrieved data it was not possible to conduct a meta-analysis. Therefore results are presented in tabulated and narrative format.

RESULTS

Out of 2015 articles, 17 pre-clinical and 33 clinical papers met inclusion criteria. Data from 24 articles that reported on accuracy indicates that in recent years navigation accuracy has been in the range of 8-15 mm. Due to discrepancies in evaluation methods it is difficult to compare accuracy metrics between different systems. Surgeon feedback suggests that current state of the art IGS may be useful as a supplementary navigation tool, especially in small liver lesions that are difficult to locate. They are however not able to reliably localise all relevant anatomical structures. Only one article investigated IGS impact on clinical outcomes.

CONCLUSIONS

Further improvements in navigation accuracy are needed to enable reliable visualisation of tumour margins with the precision required for oncological resections. To enhance comparability between different IGS it is crucial to find a consensus on the assessment of navigation accuracy as a minimum reporting standard.

Implications from translational cross-validation of clinical assessment tools for diagnosis and treatment in psychiatry

Traditional therapeutic methods in psychiatry, such as psychopharmacology and psychotherapy help many people suffering from mental disorders, but in the long-term prove to be effective in a relatively small proportion of those affected. Therapeutically, resistant forms of mental disorders such as schizophrenia, major depressive disorder, and bipolar disorder lead to persistent distress and dysfunction in personal, social, and professional aspects. In an effort to address these problems, the translational approach in neuroscience has initiated the inclusion of novel or modified unconventional diagnostic and therapeutic techniques with promising results. For instance, neuroimaging data sets from multiple modalities provide insight into the nature of pathophysiological mechanisms such as disruptions of connectivity, integration, and segregation of neural networks, focusing on the treatment of mental disorders through instrumental biomedical methods such as electro-convulsive therapy (ECT), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS) and deep brain stimulation (DBS). These methodologies have yielded promising results that have yet to be understood and improved to enhance the prognosis of the severe and persistent psychotic and affective disorders. The current review is focused on the translational approach in the management of schizophrenia and mood disorders, as well as the adaptation of new transdisciplinary diagnostic tools such as neuroimaging with concurrently administered psychopathological questionnaires and integration of the results into the therapeutic framework using various advanced instrumental biomedical tools such as ECT, TMS, tDCS and DBS.

Does greenery experienced indoors and outdoors provide an escape and support mental health during the COVID-19 quarantine?

BACKGROUND

The COVID-19 pandemic has profoundly changed people's ability to recreate in public green spaces, which is likely to exacerbate the psychological impacts of the pandemic. In the current study, we seek to understand whether greenery can support mental health even with insufficient outdoor exposure in times of physical isolation from the outdoor environment.

METHODS

Between 17 May and 10 June, 2020, we conducted an online survey among 323 students (21.99 ± 3.10 years; 31% male) in health-related programs from two universities in the city of Plovdiv, Bulgaria. Severities of depressive and anxiety symptoms over the past two weeks were measured with the Patient Health Questionnaire 9-item and the Generalized Anxiety Disorder 7-item scale. We employed two self-reported measures of greenery experienced indoors (number of houseplants in the home and proportion of exterior greenery visible from inside the home) and two measures of greenery experienced outdoors (presence/absence of a domestic garden and availability of neighborhood greenery). Restorative quality of the home (the "being away" dimension of the Perceived Restorativeness Scale; PRS) and the neighborhood (the "being away" and "fascination" dimensions of the PRS), engagement with outdoor greenery (frequency of different types of interaction) and perceived social support were treated as mediators. Associations between greenery and mental health were tested using generalized linear regression and logistic regression. Structural equation modelling (SEM) techniques were used to test the theoretically-indicated relations among the variables.

RESULTS

Clinically-meaningful symptoms of moderate depression and anxiety were reported by approximately 33% and 20% of the students, respectively. The relative abundance of greenery visible from the home or in the neighborhood was associated with reduced depressive/anxiety symptoms and lower depression/anxiety rates. Having more houseplants or a garden was also associated with some of these markers of mental health. As hypothesized, the mental health-supportive effects of indoor greenery were largely explained by increased feelings of being away while at home. Neighborhood greenery contributed to neighborhood restorative quality, which in turn facilitated social support and more frequent engagement with greenery, and that led to better mental health.

CONCLUSIONS

Students who spent most of their time at home during the COVID-19 epidemic experienced better mental health when exposed to more greenery. Our findings support the idea that exposure to greenery may be a valuable resource during social isolation in the home. However, causal interpretation of these associations is not straightforward.

University Students' Self-Rated Health in Relation to Perceived Acoustic Environment during the COVID-19 Home Quarantine

BACKGROUND

Online education became mandatory for many students during the Coronavirus disease 2019 (COVID-19) pandemic and blurred the distinction between settings where processes of stress and restoration used to take place. The lockdown also likely changed perceptions of the indoor acoustic environment (i.e., soundscape) and raised its importance. In the present study, we seek to understand how indoor soundscape related to university students' self-rated health in Bulgaria around the time that the country was under a state of emergency declaration caused by the COVID-19 pandemic.

METHODS

Between 17 May and 10 June 2020, we conducted a cross-sectional online survey among 323 students (median age 21 years; 31% male) from two universities in the city of Plovdiv, Bulgaria. Self-rated health (SRH) was measured with a single-item. Participants were asked how frequently they heard different types of sounds while at home and how pleasant they considered each of those sounds to be. Restorative quality of the home (the "being away" dimension of the Perceived Restorativeness Scale) was measured with a single-item. A priori confounders and effect modifiers included sociodemographics, house-related characteristics, general sensitivity to environmental influences, and mental health. Our analysis strategy involved sequential exploratory factor analysis (EFA), multivariate linear and ordinal regressions, effect modification tests, and structural equation modeling (SEM).

RESULTS

EFA supported grouping perceived sounds into three distinct factors-mechanical, human, and nature sounds. Regression analyses revealed that greater exposure to mechanical sounds was consistently associated with worse SRH, whereas no significant associations were found for human and nature sounds. In SEM, exposure to mechanical sounds related to lower restorative quality of the home, and then to poorer SRH, whereas nature sounds correlated with higher restorative quality, and in turn with better SRH.

CONCLUSIONS

These findings suggest a role of positive indoor soundscape and restorative quality for promoting self-rated health in times of social distancing.

University Students' Self-Rated Health in Relation to Perceived Acoustic Environment during the COVID-19 Home Quarantine

BACKGROUND

Online education became mandatory for many students during the Coronavirus disease 2019 (COVID-19) pandemic and blurred the distinction between settings where processes of stress and restoration used to take place. The lockdown also likely changed perceptions of the indoor acoustic environment (i.e., soundscape) and raised its importance. In the present study, we seek to understand how indoor soundscape related to university students' self-rated health in Bulgaria around the time that the country was under a state of emergency declaration caused by the COVID-19 pandemic.

METHODS

Between 17 May and 10 June 2020, we conducted a cross-sectional online survey among 323 students (median age 21 years; 31% male) from two universities in the city of Plovdiv, Bulgaria. Self-rated health (SRH) was measured with a single-item. Participants were asked how frequently they heard different types of sounds while at home and how pleasant they considered each of those sounds to be. Restorative quality of the home (the "being away" dimension of the Perceived Restorativeness Scale) was measured with a single-item. A priori confounders and effect modifiers included sociodemographics, house-related characteristics, general sensitivity to environmental influences, and mental health. Our analysis strategy involved sequential exploratory factor analysis (EFA), multivariate linear and ordinal regressions, effect modification tests, and structural equation modeling (SEM).

RESULTS

EFA supported grouping perceived sounds into three distinct factors-mechanical, human, and nature sounds. Regression analyses revealed that greater exposure to mechanical sounds was consistently associated with worse SRH, whereas no significant associations were found for human and nature sounds. In SEM, exposure to mechanical sounds related to lower restorative quality of the home, and then to poorer SRH, whereas nature sounds correlated with higher restorative quality, and in turn with better SRH.

CONCLUSIONS

These findings suggest a role of positive indoor soundscape and restorative quality for promoting self-rated health in times of social distancing.

How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry

Psychiatry remains in a permanent state of crisis, which fragmented psychiatry from the field of medicine. The crisis in psychiatry is evidenced by the many different competing approaches to psychiatric illness including psychodynamic, biological, molecular, pan-omics, precision, cognitive and phenomenological psychiatry, folk psychology, mind-brain dualism, descriptive psychopathology, and postpsychiatry. The current "gold standard" Diagnostic and Statistical Manual of Mental Disorders/International Classification of Diseases taxonomies of mood disorders and schizophrenia are unreliable and preclude to employ a deductive reasoning approach. Therefore, it is not surprising that mood disorders and schizophrenia research was unable to revise the conventional classifications and did not provide more adequate therapeutic approaches. The aim of this paper is to explain the new nomothetic network psychiatry (NNP) approach, which uses machine learning methods to build data-driven causal models of mental illness by assembling risk-resilience, adverse outcome pathways (AOP), cognitome, brainome, staging, symptomatome, and phenomenome latent scores in a causal model. The latter may be trained, tested and validated with Partial Least Squares analysis. This approach not only allows to compute pathway-phenotypes or biosignatures, but also to construct reliable and replicable nomothetic networks, which are, therefore, generalizable as disease models. After integrating the validated feature vectors into a well-fitting nomothetic network, clustering analysis may be applied on the latent variable scores of the R/R, AOP, cognitome, brainome, and phenome latent vectors. This pattern recognition method may expose new (transdiagnostic) classes of patients which if cross-validated in independent samples may constitute new (transdiagnostic) nosological categories.

Diagnostic Task Specific Activations in Functional MRI and Aberrant Connectivity of Insula with Middle Frontal Gyrus Can Inform the Differential Diagnosis of Psychosis

We constructed a novel design integrating the administration of a clinical self-assessment scale with simultaneous acquisition of functional Magnetic Resonance Imaging (fMRI), aiming at cross-validation between psychopathology evaluation and neuroimaging techniques. We hypothesized that areas demonstrating differential activation in two groups of patients (the first group exhibiting paranoid delusions in the context of paranoid schizophrenia-SCH-and second group with a depressive episode in the context of major depressive disorder or bipolar disorder-DEP) will have distinct connectivity patterns and structural differences. Fifty-one patients with SCH (n = 25) or DEP (n = 26) were scanned with three different MRI sequences: a structural and two functional sequences-resting-state and task-related fMRI (the stimuli represent items from a paranoid-depressive self-evaluation scale). While no significant differences were found in gray matter volumes, we were able to discriminate between the two clinical entities by identifying two significant clusters of activations in the SCH group-the left Precuneus (PreCu) extending to the left Posterior Cingulate Cortex (PCC) and the right Angular Gyrus (AG). Additionally, the effective connectivity of the middle frontal gyrus (MFG), a part of the Dorsolateral Prefrontal Cortex (DLPFC) to the Anterior Insula (AI), demonstrated a significant difference between the two groups with inhibitory connection demonstrated only in SCH. The observed activations of PreCu, PCC, and AG (involved in the Default Mode Network DMN) might be indirect evidence of the inhibitory connection from the DLPFC to AI, interfering with the balancing function of the insula as the dynamic switch in the DMN. The findings of our current study might suggest that the connectivity from DLPFC to the anterior insula can be interpreted as evidence for the presence of an aberrant network that leads to behavioral abnormalities, the manifestation of which depends on the direction of influence. The reduced effective connectivity from the AI to the DLPFC is manifested as depressive symptoms, and the inhibitory effect from the DLPFC to the AI is reflected in the paranoid symptoms of schizophrenia.

Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis

Traditional psychiatric diagnosis has been overly reliant on either self-reported measures (introspection) or clinical rating scales (interviews). This produced the so-called explanatory gap with the bio-medical disciplines, such as neuroscience, which are supposed to deliver biological explanations of disease. In that context the neuro-biological and clinical assessment in psychiatry remained discrepant and incommensurable under conventional statistical frameworks. The emerging field of translational neuroimaging attempted to bridge the explanatory gap by means of simultaneous application of clinical assessment tools and functional magnetic resonance imaging, which also turned out to be problematic when analyzed with standard statistical methods. In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity.

Construction of a nitro-oxidative stress-driven, mechanistic model of mood disorders: A nomothetic network approach

Major depression is accompanied by increased IgM-mediated autoimmune responses to oxidative specific epitopes (OSEs) and nitric oxide (NO)-adducts. These responses were not examined in bipolar disorder type 1 (BP1) and BP2. IgM responses to malondialdehyde (MDA), phosphatidinylinositol, oleic acid, azelaic acid, and NO-adducts were determined in 35 healthy controls, and 47 major depressed (MDD), 29 BP1, and 25 BP2 patients. We also measured serum peroxides, IgG to oxidized LDL (oxLDL), and IgM/IgA directed to lipopolysaccharides (LPS). IgM responses to OSEs and NO-adducts (OSENO) were significantly higher in MDD and BP1 as compared with controls, and IgM to OSEs higher in MDD than in BP2. Partial Least Squares (PLS) analysis showed that 57.7% of the variance in the clinical phenome of mood disorders was explained by number of episodes, a latent vector extracted from IgM to OSENO, IgG to oxLDL, and peroxides. There were significant specific indirect effects of IgA/IgM to LPS on the clinical phenome, which were mediated by peroxides, IgM OSENO, and IgG oxLDL. Using PLS we have constructed a data-driven nomothetic network which ensembled causome (increased plasma LPS load), adverse outcome pathways (namely neuro-affective toxicity), and clinical phenome features of mood disorders in a data-driven model. Based on those feature sets, cluster analysis discovered a new diagnostic class characterized by increased plasma LPS load, peroxides, autoimmune responses to OSENO, and increased phenome scores. Using the new nomothetic network approach, we constructed a mechanistically transdiagnostic diagnostic class indicating neuro-affective toxicity in 74.3% of the mood disorder patients.

Transitions in Mental Health Care: the European Psychiatric Association contribution to reform in Bulgaria

Background:

The Bulgarian Ministry of Health invited the European Psychiatric Association (EPA) to evaluate Bulgarian mental health care service provision in 2018. Bulgarian mental health services face very significant challenges including a legacy of historic underfunding, internal conflicts, poor planning, and the emigration of very high numbers of younger skilled staff that had followed accession to the European Union. There were significant disputes between stakeholders regarding the way forward and had been at least two unsuccessful previous external agency interventions that had attempted to find solutions.

Method:

This EPA position paper describes in detail the EPA mission to Bulgaria including methodology, findings, recommendations, and finally the positive actions and changes that are now underway as a result of the EPA report and intervention aimed at contributing towards improving Bulgarian mental health services.

Results:

After meetings with multiple stakeholders in the Bulgarian mental health system and analysis of data on service delivery, workforce, funding and configuration the EPA Panel agreed a list of twenty recommendations for change.

Conclusions:

The EPA mission, with the collaboration of multiple stakeholders in Bulgaria, was successful in stimulating high level government action to improve mental health services. Despite longstanding differences, it was possible to involve the stakeholders in constructive dialogue. The importance of “speaking with one voice” was a key lesson learned.

Comparison of manual and semi-automatic registration in augmented reality image-guided liver surgery: a clinical feasibility study

BACKGROUND

The laparoscopic approach to liver resection may reduce morbidity and hospital stay. However, uptake has been slow due to concerns about patient safety and oncological radicality. Image guidance systems may improve patient safety by enabling 3D visualisation of critical intra- and extrahepatic structures. Current systems suffer from non-intuitive visualisation and a complicated setup process. A novel image guidance system (SmartLiver), offering augmented reality visualisation and semi-automatic registration has been developed to address these issues. A clinical feasibility study evaluated the performance and usability of SmartLiver with either manual or semi-automatic registration.

METHODS

Intraoperative image guidance data were recorded and analysed in patients undergoing laparoscopic liver resection or cancer staging. Stereoscopic surface reconstruction and iterative closest point matching facilitated semi-automatic registration. The primary endpoint was defined as successful registration as determined by the operating surgeon. Secondary endpoints were system usability as assessed by a surgeon questionnaire and comparison of manual vs. semi-automatic registration accuracy. Since SmartLiver is still in development no attempt was made to evaluate its impact on perioperative outcomes.

RESULTS

The primary endpoint was achieved in 16 out of 18 patients. Initially semi-automatic registration failed because the IGS could not distinguish the liver surface from surrounding structures. Implementation of a deep learning algorithm enabled the IGS to overcome this issue and facilitate semi-automatic registration. Mean registration accuracy was 10.9 ± 4.2 mm (manual) vs. 13.9 ± 4.4 mm (semi-automatic) (Mean difference - 3 mm; p = 0.158). Surgeon feedback was positive about IGS handling and improved intraoperative orientation but also highlighted the need for a simpler setup process and better integration with laparoscopic ultrasound.

CONCLUSION

The technical feasibility of using SmartLiver intraoperatively has been demonstrated. With further improvements semi-automatic registration may enhance user friendliness and workflow of SmartLiver. Manual and semi-automatic registration accuracy were comparable but evaluation on a larger patient cohort is required to confirm these findings.

Dynamic Functional Connectivity Patterns in Schizophrenia and the Relationship With Hallucinations

There is a wealth of evidence showing aberrant functional connectivity (FC) in schizophrenia but with considerable variability in findings across studies. Dynamic FC is an extension of traditional static FC, in that such analyses allow for explorations of temporal changes in connectivity. Thereby they also provide more detailed information on connectivity abnormalities in psychiatric disorders such as schizophrenia. The current study investigated dynamic FC in a sample of 80 schizophrenia patients and 80 matched healthy control subjects, replicating previous findings of aberrant dwell times in specific FC states, and further supporting a role for default mode network (DMN) dysfunction. Furthermore, relationships with hallucinations, a core symptom of schizophrenia, were explored. Two measures of hallucinations were used, one measure of current hallucination severity assessed on the day of scanning, and one trait-measure where hallucinations were assessed repeatedly over the course of 1 year. Current hallucination severity did not show a significant relationship with dynamic FC. However, the trait-measure of hallucination proneness over 1 year showed a significant relationship with dynamic FC. Patients with high hallucination proneness spent less time in connectivity states characterized by strong anti-correlation between the DMN and task-positive networks. The findings support theoretical models of hallucinations which have proposed an instability of the DMN and impaired cognitive control in patients with hallucinations. Furthermore, the results point to hallucination proneness as a potential marker for identifying distinct subgroups of schizophrenia patients.