Psychiatric Neural Networks and Precision Therapeutics by Machine Learning

Artificial intelligence in psychiatry: A systematic review and meta-analysis of diagnostic and therapeutic efficacy

Background

Artificial Intelligence (AI) has demonstrated significant potential in transforming psychiatric care by enhancing diagnostic accuracy and therapeutic interventions. Psychiatry faces challenges like overlapping symptoms, subjective diagnostic methods, and personalized treatment requirements. AI, with its advanced data-processing capabilities, offers innovative solutions to these complexities.

Aims

This study systematically reviewed and meta-analyzed the existing literature to evaluate AI's diagnostic accuracy and therapeutic efficacy in psychiatric care, focusing on various psychiatric disorders and AI technologies.

Methods

Adhering to PRISMA guidelines, the study included a comprehensive literature search across multiple databases. Empirical studies investigating AI applications in psychiatry, such as machine learning (ML), deep learning (DL), and hybrid models, were selected based on predefined inclusion criteria. The outcomes of interest were diagnostic accuracy and therapeutic efficacy. Statistical analysis employed fixed- and random-effects models, with subgroup and sensitivity analyses exploring the impact of AI methodologies and study designs.

Results

A total of 14 studies met the inclusion criteria, representing diverse AI applications in diagnosing and treating psychiatric disorders. The pooled diagnostic accuracy was 85% (95% CI: 80%-87%), with ML models achieving the highest accuracy, followed by hybrid and DL models. For therapeutic efficacy, the pooled effect size was 84% (95% CI: 82%-86%), with ML excelling in personalized treatment plans and symptom tracking. Moderate heterogeneity was observed, reflecting variability in study designs and populations. The risk of bias assessment indicated high methodological rigor in most studies, though challenges like algorithmic biases and data quality remain.

Conclusion

AI demonstrates robust diagnostic and therapeutic capabilities in psychiatry, offering a data-driven approach to personalized mental healthcare. Future research should address ethical concerns, standardize methodologies, and explore underrepresented populations to maximize AI's transformative potential in mental health.

Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA

While functional near-infrared spectroscopy (fNIRS) had previously been suggested for major depressive disorder (MDD) diagnosis, the clinical application to predict antidepressant treatment response (ATR) is still unclear. To address this, the aim of the current study is to investigate MDD ATR at three response levels using fNIRS and micro-ribonucleic acids (miRNAs). Our proposed algorithm includes a custom inter-subject variability reduction based on the principal component analysis (PCA). The principal components of extracted features are first identified for non-responders' group. The first few components that sum up to 99% of explained variance are discarded to minimize inter-subject variability while the remaining projection vectors are applied on all response groups (24 non-responders, 15 partial-responders, 13 responders) to obtain their relative projections in feature space. The entire algorithm achieved a better performance through the radial basis function (RBF) support vector machine (SVM), with 82.70% accuracy, 78.44% sensitivity, 86.15% precision, and 91.02% specificity, respectively, when compared with conventional machine learning approaches that combine clinical, sociodemographic and genetic information as the predictor. The performance of the proposed custom algorithm suggests the prediction of ATR can be improved with multiple features sources, provided that the inter-subject variability is properly addressed, and can be an effective tool for clinical decision support system in MDD ATR prediction. Clinical and Translational Impact Statement-The fusion of neuroimaging fNIRS features and miRNA profiles significantly enhances the prediction accuracy of MDD ATR. The minimally required features also make the personalized medicine more practical and realizable.

Pegylated chitosan nanoparticles of fluoxetine enhance cognitive performance and hippocampal brain derived neurotrophic factor levels in a rat model of local demyelination

Cognitive impairment is a common feature in neurodegenerative diseases such as multiple sclerosis (MS). This study aims to explore the potential of enhancing the beneficial effects of fluoxetine (FLX), a neuroprotective agent known for its ability to increase neural plasticity by utilizing nanoparticles. The study specifically focuses on the synthesis and evaluation of PEGylated chitosan nanoparticles of FLX and its effect on demyelination and the subsequent cognitive impairment (CI) in the hippocampus of rats induced by local injection of lysophosphatidylcholine (LPC). Chitosan/polyethylene glycol nanoparticles were synthesized, and their properties were analyzed. Demyelination was induced in rats via hippocampal injections of lysolecithin. Behavioral assessments included open field maze, elevated plus maze, and novel object recognition memory (NORM) tests. Hippocampal levels of insulin-like growth factor (IGF-1) and brain-derived neurotrophic factor (BDNF) were measured using enzyme-linked immunoassay (ELISA). The extent of remyelination was quantified using Luxol fast blue staining. Nanoparticle size measured 240.2 nm with 53 % encapsulation efficacy. Drug release exhibited a slow pattern, with 76 % released within 4 h. Nanoparticle-treated rats displayed reduced anxiety-like behavior, improved memory, increased BDNF levels, and a reduced extent of demyelination, with no change in IGF- levels. In addition, FLX -loaded chitosan nanoparticles had better effect on cognitive improvement, BDNF levels in the hippocampus that FLX. Altering pharmacokinetics and possibly pharmacodynamics. These findings highlight the potential of innovative drug delivery systems, encouraging further research in this direction.

The Genomic Intersection of Oligodendrocyte Dynamics in Schizophrenia and Aging Unravels Novel Pathological Mechanisms and Therapeutic Potentials

Schizophrenia is a significant worldwide health concern, affecting over 20 million individuals and contributing to a potential reduction in life expectancy by up to 14.5 years. Despite its profound impact, the precise pathological mechanisms underlying schizophrenia continue to remain enigmatic, with previous research yielding diverse and occasionally conflicting findings. Nonetheless, one consistently observed phenomenon in brain imaging studies of schizophrenia patients is the disruption of white matter, the bundles of myelinated axons that provide connectivity and rapid signalling between brain regions. Myelin is produced by specialised glial cells known as oligodendrocytes, which have been shown to be disrupted in post-mortem analyses of schizophrenia patients. Oligodendrocytes are generated throughout life by a major population of oligodendrocyte progenitor cells (OPC), which are essential for white matter health and plasticity. Notably, a decline in a specific subpopulation of OPC has been identified as a principal factor in oligodendrocyte disruption and white matter loss in the aging brain, suggesting this may also be a factor in schizophrenia. In this review, we analysed genomic databases to pinpoint intersections between aging and schizophrenia and identify shared mechanisms of white matter disruption and cognitive dysfunction.

The Impact of C-3 Side Chain Modifications on Kynurenic Acid: A Behavioral Analysis of Its Analogs in the Motor Domain

The central nervous system (CNS) is the final frontier in drug delivery because of the blood-brain barrier (BBB), which poses significant barriers to the access of most drugs to their targets. Kynurenic acid (KYNA), a tryptophan (Trp) metabolite, plays an important role in behavioral functions, and abnormal KYNA levels have been observed in neuropsychiatric conditions. The current challenge lies in delivering KYNA to the CNS owing to its polar side chain. Recently, C-3 side chain-modified KYNA analogs have been shown to cross the BBB; however, it is unclear whether they retain the biological functions of the parent molecule. This study examined the impact of KYNA analogs, specifically, SZR-72, SZR-104, and the newly developed SZRG-21, on behavior. The analogs were administered intracerebroventricularly (i.c.v.), and their effects on the motor domain were compared with those of KYNA. Specifically, open-field (OF) and rotarod (RR) tests were employed to assess motor activity and skills. SZR-104 increased horizontal exploratory activity in the OF test at a dose of 0.04 μmol/4 μL, while SZR-72 decreased vertical activity at doses of 0.04 and 0.1 μmol/4 μL. In the RR test, however, neither KYNA nor its analogs showed any significant differences in motor skills at either dose. Side chain modification affects affective motor performance and exploratory behavior, as the results show for the first time. In this study, we showed that KYNA analogs alter emotional components such as motor-associated curiosity and emotions. Consequently, drug design necessitates the development of precise strategies to traverse the BBB while paying close attention to modifications in their effects on behavior.

Preclinical Evidence for the Role of the Yin/Yang Angiotensin System Components in Autism Spectrum Disorder: A Therapeutic Target of Astaxanthin

Autism spectrum disorder (ASD) prevalence is emerging with an unclear etiology, hindering effective therapeutic interventions. Recent studies suggest potential renin-angiotensin system (RAS) alterations in different neurological pathologies. However, its implications in ASD are unexplored. This research fulfills the critical gap by investigating dual arms of RAS and their interplay with Notch signaling in ASD, using a valproic acid (VPA) model and assessing astaxanthin's (AST) modulatory impacts. Experimentally, male pups from pregnant rats receiving either saline or VPA on gestation day 12.5 were divided into control and VPA groups, with subsequent AST treatment in a subset (postnatal days 34-58). Behavioral analyses, histopathological investigations, and electron microscopy provided insights into the neurobehavioral and structural changes induced by AST. Molecular investigations of male pups' cortices revealed that AST outweighs the protective RAS elements with the inhibition of the detrimental arm. This established the neuroprotective and anti-inflammatory axes of RAS (ACE2/Ang1-7/MasR) in the ASD context. The results showed that AST's normalization of RAS components and Notch signaling underscore a novel therapeutic avenue in ASD, impacting neuronal integrity and behavioral outcomes. These findings affirm the integral role of RAS in ASD and highlight AST's potential as a promising treatment intervention, inviting further neurological research implications.

From CGRP to PACAP, VIP, and Beyond: Unraveling the Next Chapters in Migraine Treatment

Migraine is a neurovascular disorder that can be debilitating for individuals and society. Current research focuses on finding effective analgesics and management strategies for migraines by targeting specific receptors and neuropeptides. Nonetheless, newly approved calcitonin gene-related peptide (CGRP) monoclonal antibodies (mAbs) have a 50% responder rate ranging from 27 to 71.0%, whereas CGRP receptor inhibitors have a 50% responder rate ranging from 56 to 71%. To address the need for novel therapeutic targets, researchers are exploring the potential of another secretin family peptide, pituitary adenylate cyclase-activating polypeptide (PACAP), as a ground-breaking treatment avenue for migraine. Preclinical models have revealed how PACAP affects the trigeminal system, which is implicated in headache disorders. Clinical studies have demonstrated the significance of PACAP in migraine pathophysiology; however, a few clinical trials remain inconclusive: the pituitary adenylate cyclase-activating peptide 1 receptor mAb, AMG 301 showed no benefit for migraine prevention, while the PACAP ligand mAb, Lu AG09222 significantly reduced the number of monthly migraine days over placebo in a phase 2 clinical trial. Meanwhile, another secretin family peptide vasoactive intestinal peptide (VIP) is gaining interest as a potential new target. In light of recent advances in PACAP research, we emphasize the potential of PACAP as a promising target for migraine treatment, highlighting the significance of exploring PACAP as a member of the antimigraine armamentarium, especially for patients who do not respond to or contraindicated to anti-CGRP therapies. By updating our knowledge of PACAP and its unique contribution to migraine pathophysiology, we can pave the way for reinforcing PACAP and other secretin peptides, including VIP, as a novel treatment option for migraines.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Building Predictive Models for Schizophrenia Diagnosis with Peripheral Inflammatory Biomarkers

Machine learning and artificial intelligence technologies are known to be a convenient tool for analyzing multi-domain data in precision psychiatry. In the case of schizophrenia, the most commonly used data sources for such purposes are neuroimaging, voice and language patterns, and mobile phone data. Data on peripheral markers can also be useful for building predictive models. Here, we have developed five predictive models for the binary classification of schizophrenia patients and healthy individuals. Data on serum concentrations of cytokines, chemokines, growth factors, and age were among 38 parameters used to build these models. The sample consisted of 217 schizophrenia patients and 90 healthy individuals. The models architecture was involved logistic regression, deep neural networks, decision trees, support vector machine, and k-nearest neighbors algorithms. It was shown that the algorithm based on a deep neural network (consisting of five layers) showed a slightly higher sensitivity (0.87 ± 0.04) and specificity (0.52 ± 0.06) than other algorithms. Combining all variables into a single classifier showed a cumulative effect that exceeded the effectiveness of individual variables, indicating the need to use multiple biomarkers to diagnose schizophrenia. Thus, the data obtained showed the promise of using data on peripheral biomarkers and machine learning methods for diagnosing schizophrenia.

Machine learning methods to predict outcomes of pharmacological treatment in psychosis

In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.

Antipsychotics-Induced Changes in Synaptic Architecture and Functional Connectivity: Translational Implications for Treatment Response and Resistance

Schizophrenia is a severe mental illness characterized by alterations in processes that regulate both synaptic plasticity and functional connectivity between brain regions. Antipsychotics are the cornerstone of schizophrenia pharmacological treatment and, beyond occupying dopamine D2 receptors, can affect multiple molecular targets, pre- and postsynaptic sites, as well as intracellular effectors. Multiple lines of evidence point to the involvement of antipsychotics in sculpting synaptic architecture and remodeling the neuronal functional unit. Furthermore, there is an increasing awareness that antipsychotics with different receptor profiles could yield different interregional patterns of co-activation. In the present systematic review, we explored the fundamental changes that occur under antipsychotics' administration, the molecular underpinning, and the consequences in both acute and chronic paradigms. In addition, we investigated the relationship between synaptic plasticity and functional connectivity and systematized evidence on different topographical patterns of activation induced by typical and atypical antipsychotics.

Pushing the boundaries of behavioral analysis could aid psychiatric drug discovery

Drug discovery for psychiatric conditions is stagnating. Behavioral changes could be used as a primary phenotypic screen for new drug candidates, if enough useful data can be generated from behavioral models. Could machine learning be the answer to extracting the data we need?

Impact of direct-acting antivirals on neuropsychiatric and neurocognitive dysfunction in chronic hepatitis C patients

Background

Hepatitis C virus (HCV) infection is associated with psychiatric and cognitive dysfunctions. We aimed to investigate depression, anxiety, and cognitive function of chronic hepatitis C (CHC) patients before and after treatment with direct-acting antivirals (DAAs). Forty CHC patients (20 non-cirrhotic and 20 cirrhotic) who had undergone DAA treatment in our outpatient clinic and ten controls. We administered the Hospital Anxiety and Depression questionnaires to measure the anxiety and depression symptoms and the Cognitive Abilities Screening Instruments (CASI) to measure the cognitive function at the beginning and 3 months after the end of the treatment.

Results

Sustained virological response (SVR) was achieved in all patients. Post-treatment anxiety and depression scores showed a significant improvement than pre-treatment ones in CHC patients. Regarding CASI, before and after the treatment, a statistical significance was found in short-term memory (P = 0.001), concentration (P = 0.033), abstract thinking and judgment (P = 0.024), total (P = 0.001) in non-cirrhotic, Also, an improvement was seen in long-term memory (P = 0.015), short-term memory (P < 0.001), concentration (P = 0.024) and total (P = 0.01) in cirrhotic. However, these changes were still impaired in post-treated cirrhotic compared to controls.

Conclusions

CHC patients' anxiety, depression, and cognitive function partially improved after DAA therapy. Besides, improving the status of CHC, reversibility of cognitive dysfunction in non-cirrhotic patients may indicate the importance of treatment in early stages of liver disease.

Cognitive Impairment in Multiple Sclerosis: An Update on Assessment and Management

Cognitive impairment (CI) is a core feature of multiple sclerosis (MS) and affects up to 65% of patients in every phase of the disease, having a deep impact on all aspects of patients' lives. Cognitive functions most frequently involved include information processing speed, learning and memory, visuospatial abilities, and executive function. The precise pathogenetic mechanisms underpinning CI in MS are still largely unknown, but are deemed to be mainly related to pathological changes in lesioned and normal-appearing white matter, specific neuronal grey matter structures, and immunological alterations, with particular impact on synaptic transmission and plasticity. Moreover, much research is needed on therapeutic strategies. Small to moderate efficacy has been reported for disease-modifying therapies, particularly high-efficacy drugs, and symptomatic therapies (dalfampridine), while the strongest benefit emerged after cognitive training. The present narrative review provides a concise, updated overview of more recent evidence on the prevalence, profile, pathogenetic mechanisms, and treatment of CI in people with MS. CI should be screened on a regular basis as part of routine clinical assessments, and brief tools are now widely available (such as the Symbol Digit Modalities Test). The main goal of cognitive assessment in MS is the prompt implementation of preventive and treatment interventions.

Intelligent System for Detecting Deterioration of Life Satisfaction as Tool for Remote Mental-Health Monitoring

The research described in this article is a continuation of work on a computational model of quality of life (QoL) satisfaction. In the proposed approach, overall life satisfaction is aggregated to personal life satisfaction (PLUS). The model described in the article is based on well-known and commonly used clinimetric scales (e.g., in psychiatry, psychology and physiotherapy). The simultaneous use of multiple scales, and the complexity of describing the quality of life with them, require complex fuzzy computational solutions. The aim of the study is twofold: (1) To develop a fuzzy model that allows for the detection of changes in life satisfaction scores (data on the influence of the COVID-19 pandemic and the war in the neighboring country were used). (2) To develop more detailed guidelines than the existing ones for further similar research on more advanced intelligent systems with computational models which allow for sensing, detecting and evaluating the psychical state. We are concerned with developing practical solutions with higher scientific and clinical utility for both small datasets and big data to use in remote patient monitoring. Two exemplary groups of specialists at risk of occupational burnout were assessed three times at different intervals in terms of life satisfaction. The aforementioned assessment was made on Polish citizens because the specific data could be gathered: before and during the pandemic and during the war in Ukraine (a neighboring country). That has a higher potential for presenting a better analysis and reflection on the practical application of the model. A research group (physiotherapists, n = 20) and a reference group (IT professionals, n = 20) participated in the study. Four clinimetric scales were used for assessment: the Perceived Stress Scale (PSS10), the Maslach Burnout Scale (MBI), the Satisfaction with Life Scale (SWLS), and the Nordic Musculoskeletal Questionnaire (NMQ). The assessment was complemented by statistical analyses and fuzzy models based on a hierarchical fuzzy system. Although several models for understanding changes in life satisfaction scores have been previously investigated, the novelty of this study lies in the use of data from three consecutive time points for the same individuals and the way they are analyzed, based on fuzzy logic. In addition, the new hierarchical structure of the model used in the study provides flexibility and transparency in the process of remotely monitoring changes in people's mental well-being and a quick response to observed changes. The aforementioned computational approach was used for the first time.

Integrating Armchair, Bench, and Bedside Research for Behavioral Neurology and Neuropsychiatry: Editorial

"To learning much inclined, who went to see the Elephant (though all of them were blind) that each by observation might satisfy the mind" [...].

Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section

The correlation between the kind of cesarean section and post-traumatic stress disorder (PTSD) in Greek women after a traumatic birth experience has been recognized in previous studies along with other risk factors, such as perinatal conditions and traumatic life events. Data from early studies have suggested some possible links between some vulnerable factors and the potential development of postpartum PTSD. The classification of each case in three possible states (PTSD, profile PTSD, and free of symptoms) is typically performed using the guidelines and the metrics of the version V of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) which requires the completion of several questionnaires during the postpartum period. The motivation in the present work is the need for a model that can detect possible PTSD cases using a minimum amount of information and produce an early diagnosis. The early PTSD diagnosis is critical since it allows the medical personnel to take the proper measures as soon as possible. Our sample consists of 469 women who underwent emergent or elective cesarean delivery in a university hospital in Greece. The methodology which is followed is the application of random decision forests (RDF) to detect the most suitable and easily accessible information which is then used by an artificial neural network (ANN) for the classification. As is demonstrated from the results, the derived decision model can reach high levels of accuracy even when only partial and quickly available information is provided.

The Role of Cytotoxic T-Lymphocyte Antigen 4 in the Pathogenesis of Multiple Sclerosis

Multiple sclerosis (MS) is an autoimmune neurodegenerative disorder of the central nervous system that presents heterogeneous clinical manifestations and course. It has been shown that different immune checkpoints, including Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4), can be involved in the pathogenesis of MS. CTLA-4 is a critical regulator of T-cell homeostasis and self-tolerance and represents a key inhibitor of autoimmunity. In this scopingreview, we resume the current preclinical and clinical studies investigating the role of CTLA-4 in MS with different approaches. While some of these studies assessed the expression levels of CTLA-4 on T cells by comparing MS patients with healthy controls, others focused on the evaluation of the effects of common MS therapies on CTLA-4 modulation or on the study of the CTLA-4 blockade or deficiency in experimental autoimmune encephalomyelitis models. Moreover, other studies in this field aimed to discover if the CTLA-4 gene might be involved in the predisposition to MS, whereas others evaluated the effects of treatment with CTLA4-Ig in MS. Although these results are of great interest, they are often conflicting. Therefore, further studies are needed to reveal the exact mechanisms underlying the action of a crucial immune checkpoint such as CTLA-4 in MS to identify novel immunotherapeutic strategies for MS patients.

Ocrelizumab in Patients with Active Primary Progressive Multiple Sclerosis: Clinical Outcomes and Immune Markers of Treatment Response

Ocrelizumab is a B-cell-depleting monoclonal antibody approved for the treatment of relapsing-remitting multiple sclerosis (RRMS) and active primary progressive MS (aPPMS). This prospective, uncontrolled, open-label, observational study aimed to assess the efficacy of ocrelizumab in patients with aPPMS and to dissect the clinical, radiological and laboratory attributes of treatment response. In total, 22 patients with aPPMS followed for 24 months were included. The primary efficacy outcome was the proportion of patients with optimal response at 24 months, defined as patients free of relapses, free of confirmed disability accumulation (CDA) and free of T1 Gd-enhancing lesions and new/enlarging T2 lesions on the brain and cervical MRI. In total, 14 (63.6%) patients and 13 patients (59.1%) were classified as responders at 12 and 24 months, respectively. Time exhibited a significant effect on mean absolute and normalized gray matter cerebellar volume (F = 4.342, p = 0.23 and F = 4.279, p = 0.024, respectively). Responders at 24 months exhibited reduced peripheral blood ((%) of CD19+ cells) plasmablasts compared to non-responders at the 6-month point estimate (7.69 ± 4.4 vs. 22.66 ± 7.19, respectively, p = 0.043). Response to ocrelizumab was linked to lower total and gray matter cerebellar volume loss over time. Reduced plasmablast depletion was linked for the first time to sub-optimal response to ocrelizumab in aPPMS.

The Interplay between Vitamin D, Exposure of Anticholinergic Antipsychotics and Cognition in Schizophrenia

Vitamin D deficiency is a frequent finding in schizophrenia and may contribute to neurocognitive dysfunction, a core element of the disease. However, there is limited knowledge about the neuropsychological profile of vitamin D deficiency-related cognitive deficits and their underlying molecular mechanisms. As an inductor of cytochrome P450 3A4, a lack of vitamin D might aggravate cognitive deficits by increased exposure to anticholinergic antipsychotics. This cross-sectional study aims to assess the relationship between 25-OH-vitamin D-serum concentrations, anticholinergic drug exposure and neurocognitive functioning (Brief Assessment of Cognition in Schizophrenia, BACS, and Trail Making Test, TMT) in 141 patients with schizophrenia. The anticholinergic drug exposure was estimated by adjusting the concentration of each drug for its individual muscarinic receptor affinity. Using regression analysis, we observed a positive relationship between vitamin D levels and processing speed (TMT-A and BACS Symbol Coding) as well as executive functioning (TMT-B and BACS Tower of London). Moreover, a negative impact of vitamin D on anticholinergic drug exposure emerged, but the latter did not significantly affect cognition. When other cognitive items were included as regressors, the impact of vitamin D remained only significant for the TMT-A. Among the different cognitive impairments in schizophrenia, vitamin D deficiency may most directly affect processing speed, which in turn may aggravate deficits in executive functioning. This finding is not explained by a cytochrome P450-mediated increased exposure to anticholinergic antipsychotics.

Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis

Background: Depression is a prominent and highly prevalent nonmotor feature in patients with Parkinson’s disease (PD). The neural and pathophysiologic mechanisms of PD with depression (DPD) remain unclear. The current diagnosis of DPD largely depends on clinical evaluation. Methods: We proposed a new family of multinomial tensor regressions that leveraged whole-brain structural magnetic resonance imaging (MRI) data to discriminate among 196 non-depressed PD (NDPD) patients, 84 DPD patients, 200 healthy controls (HC), and to assess the special brain microstructures in NDPD and DPD. The method of maximum likelihood estimation coupled with state-of-art gradient descent algorithms was used to predict the individual diagnosis of PD and the development of DPD in PD patients. Results: The results reveal that the proposed efficient approach not only achieved a high prediction accuracy (0.94) with a multi-class AUC (0.98) for distinguishing between NDPD, DPD, and HC on the testing set but also located the most discriminative regions for NDPD and DPD, including cortical regions, the cerebellum, the brainstem, the bilateral basal ganglia, and the thalamus and limbic regions. Conclusions: The proposed imaging technique based on tensor regression performs well without any prior feature information, facilitates a deeper understanding into the abnormalities in DPD and PD, and plays an essential role in the statistical analysis of high-dimensional complex MRI imaging data to support the radiological diagnosis of comorbidity of depression with PD.

Altered Dynamic Functional Connectivity of Cuneus in Schizophrenia Patients: A Resting-State fMRI Study

Objective: Schizophrenia (SZ) is a functional mental condition that has a significant impact on patients’ social lives. As a result, accurate diagnosis of SZ has attracted researchers’ interest. Based on previous research, resting-state functional magnetic resonance imaging (rsfMRI) reported neural alterations in SZ. In this study, we attempted to investigate if dynamic functional connectivity (dFC) could reveal changes in temporal interactions between SZ patients and healthy controls (HC) beyond static functional connectivity (sFC) in the cuneus, using the publicly available COBRE dataset. Methods: Sliding windows were applied to 72 SZ patients’ and 74 healthy controls’ (HC) rsfMRI data to generate temporal correlation maps and, finally, evaluate mean strength (dFC-Str), variability (dFC-SD and ALFF) in each window, and the dwelling time. The difference in functional connectivity (FC) of the cuneus between two groups was compared using a two-sample t-test. Results: Our findings demonstrated decreased mean strength connectivity between the cuneus and calcarine, the cuneus and lingual gyrus, and between the cuneus and middle temporal gyrus (TPOmid) in subjects with SZ. Moreover, no difference was detected in variability (standard deviation and the amplitude of low-frequency fluctuation), the dwelling times of all states, or static functional connectivity (sFC) between the groups. Conclusions: Our verdict suggest that dynamic functional connectivity analyses may play crucial roles in unveiling abnormal patterns that would be obscured in static functional connectivity, providing promising impetus for understanding schizophrenia disease.

Optimization of Neurite Tracing and Further Characterization of Human Monocyte-Derived-Neuronal-like Cells

Deficits in neuronal structure are consistently associated with neurodevelopmental illnesses such as autism and schizophrenia. Nonetheless, the inability to access neurons from clinical patients has limited the study of early neurostructural changes directly in patients’ cells. This obstacle has been circumvented by differentiating stem cells into neurons, although the most used methodologies are time consuming. Therefore, we recently developed a relatively rapid (~20 days) protocol for transdifferentiating human circulating monocytes into neuronal-like cells. These monocyte-derived-neuronal-like cells (MDNCs) express several genes and proteins considered neuronal markers, such as MAP-2 and PSD-95. In addition, these cells conduct electrical activity. We have also previously shown that the structure of MDNCs is comparable with that of human developing neurons (HDNs) after 5 days in culture. Moreover, the neurostructure of MDNCs responds similarly to that of HDNs when exposed to colchicine and dopamine. In this manuscript, we expanded our characterization of MDNCs to include the expression of 12 neuronal genes, including tau. Following, we compared three different tracing approaches (two semi-automated and one automated) that enable tracing using photographs of live cells. This comparison is imperative for determining which neurite tracing method is more efficient in extracting neurostructural data from MDNCs and thus allowing researchers to take advantage of the faster yield provided by these neuronal-like cells. Surprisingly, it was one of the semi-automated methods that was the fastest, consisting of tracing only the longest primary and the longest secondary neurite. This tracing technique also detected more structural deficits. The only automated method tested, Volocity, detected MDNCs but failed to trace the entire neuritic length. Other advantages and disadvantages of the three tracing approaches are also presented and discussed.

Co-Players in Chronic Pain: Neuroinflammation and the Tryptophan-Kynurenine Metabolic Pathway

Chronic pain is an unpleasant sensory and emotional experience that persists or recurs more than three months and may extend beyond the expected time of healing. Recently, nociplastic pain has been introduced as a descriptor of the mechanism of pain, which is due to the disturbance of neural processing without actual or potential tissue damage, appearing to replace a concept of psychogenic pain. An interdisciplinary task force of the International Association for the Study of Pain (IASP) compiled a systematic classification of clinical conditions associated with chronic pain, which was published in 2018 and will officially come into effect in 2022 in the 11th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-11) by the World Health Organization. ICD-11 offers the option for recording the presence of psychological or social factors in chronic pain; however, cognitive, emotional, and social dimensions in the pathogenesis of chronic pain are missing. Earlier pain disorder was defined as a condition with chronic pain associated with psychological factors, but it was replaced with somatic symptom disorder with predominant pain in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) in 2013. Recently clinical nosology is trending toward highlighting neurological pathology of chronic pain, discounting psychological or social factors in the pathogenesis of pain. This review article discusses components of the pain pathway, the component-based mechanisms of pain, central and peripheral sensitization, roles of chronic inflammation, and the involvement of tryptophan-kynurenine pathway metabolites, exploring the participation of psychosocial and behavioral factors in central sensitization of diseases progressing into the development of chronic pain, comorbid diseases that commonly present a symptom of chronic pain, and psychiatric disorders that manifest chronic pain without obvious actual or potential tissue damage.

Immune Influencers in Action: Metabolites and Enzymes of the Tryptophan-Kynurenine Metabolic Pathway

The tryptophan (TRP)-kynurenine (KYN) metabolic pathway is a main player of TRP metabolism through which more than 95% of TRP is catabolized. The pathway is activated by acute and chronic immune responses leading to a wide range of illnesses including cancer, immune diseases, neurodegenerative diseases and psychiatric disorders. The presence of positive feedback loops facilitates amplifying the immune responses vice versa. The TRP-KYN pathway synthesizes multifarious metabolites including oxidants, antioxidants, neurotoxins, neuroprotectants and immunomodulators. The immunomodulators are known to facilitate the immune system towards a tolerogenic state, resulting in chronic low-grade inflammation (LGI) that is commonly present in obesity, poor nutrition, exposer to chemicals or allergens, prodromal stage of various illnesses and chronic diseases. KYN, kynurenic acid, xanthurenic acid and cinnabarinic acid are aryl hydrocarbon receptor ligands that serve as immunomodulators. Furthermore, TRP-KYN pathway enzymes are known to be activated by the stress hormone cortisol and inflammatory cytokines, and genotypic variants were observed to contribute to inflammation and thus various diseases. The tryptophan 2,3-dioxygenase, the indoleamine 2,3-dioxygenases and the kynurenine-3-monooxygenase are main enzymes in the pathway. This review article discusses the TRP-KYN pathway with special emphasis on its interaction with the immune system and the tolerogenic shift towards chronic LGI and overviews the major symptoms, pro- and anti-inflammatory cytokines and toxic and protective KYNs to explore the linkage between chronic LGI, KYNs, and major psychiatric disorders, including depressive disorder, bipolar disorder, substance use disorder, post-traumatic stress disorder, schizophrenia and autism spectrum disorder.