Neural Functioning in Late-Life Depression: An Activation Likelihood Estimation Meta-Analysis.

Late-life depression (LLD) is a relatively common and debilitating mental disorder, also associated with cognitive dysfunctions and an increased risk of mortality. Considering the growing elderly population worldwide, LLD is increasingly emerging as a significant public health issue, also due to the rise in direct and indirect costs borne by healthcare systems. Understanding the neuroanatomical and neurofunctional correlates of LLD is crucial for developing more targeted and effective interventions, both from a preventive and therapeutic standpoint. This ALE meta-analysis aims to evaluate the involvement of specific neurofunctional changes in the neurophysiopathology of LLD by analysing functional neuroimaging studies conducted on patients with LLD compared to healthy subjects (HCs). We included 19 studies conducted on 844 subjects, divided into 439 patients with LLD and 405 HCs. Patients with LLD, compared to HCs, showed significant hypoactivation of the right superior and medial frontal gyri (Brodmann areas (Bas) 8, 9), left cingulate cortex (BA 24), left putamen, and left caudate body. The same patients exhibited significant hyperactivation of the left superior temporal gyrus (BA 42), left inferior frontal gyrus (BA 45), right anterior cingulate cortex (BA 24), right cerebellar culmen, and left cerebellar declive. In summary, we found significant changes in activation patterns and brain functioning in areas encompassed in the cortico-limbic-striatal network in LLD. Furthermore, our results suggest a potential role for areas within the cortico-striatal-cerebellar network in the neurophysiopathology of LLD.

Is Antipsychotic Drug Use During Pregnancy Associated with Increased Malformation Rates and Worsening of Maternal and Infant Outcomes? A Systematic Review.

There is much debate about continuing antipsychotic medication in patients who need it when they become pregnant because benefits must be weighed against potential teratogenic and malformation effects related to antipsychotics themselves. To address this, we conducted a systematic review on the PubMed, PsycINFO and CINHAL databases and the ClinicalTrials.gov register using the following strategy: (toxicity OR teratogenicity OR malformation* OR "birth defect*" OR "congenital abnormality" OR "congenital abnormalities" OR "brain changes" OR "behavioral abnormalities" OR "behavioral abnormalities") AND antipsychotic* AND (pregnancy OR pregnant OR lactation OR delivery OR prenatal OR perinatal OR post-natal OR puerperium) on September 27, 2023. We found 38 studies to be eligible. The oldest was published in 1976, while most articles were recent. Most studies concluded that the antipsychotics, especially the second-generation antipsychotics, were devoid of teratogenic potential, while few studies were inconclusive and recommended replication. Most authoritative articles were from the Boston area, where large databases were implemented to study the malformation potential of psychiatric drugs. Other reliable databases are from Northern European registers. Overall conclusions are that antipsychotics are no more related to malformations than the disorders themselves; most studies recommend that there are no reasons to discontinue antipsychotic medications in pregnancy.

New pharmacotherapies to tackle the unmet needs in bipolar disorder: a focus on acute suicidality.

INTRODUCTION: Suicidal behavior is relatively frequent in patients with bipolar disorder (BD) and constitutes their most frequent cause of death. Suicide rates remain high in patients with BD despite adherence to guidelines recommending lithium as first line, and/or antidepressants, antipsychotics, psychotherapy, psychosocial interventions, and electroconvulsive therapy. Hence the need to identify more effective and rapid anti-suicide interventions. AREAS COVERED: To tackle the unmet needs of pharmacotherapy, we investigated the PubMed database on 24-25 January 2024 using strategies like ('acute suicid*'[ti] OR 'suicide crisis syndrome' OR 'acute suicidal affective disturbance') AND (lithium[ti] OR clozapine[ti]), which obtained 3 results, and ('acute suicid*'[ti] OR 'suicide crisis syndrome' OR 'acute suicidal affective disturbance') AND (ketamine[ti] OR esketamine[ti] OR NMDA[ti] OR glutamat*[ti]), which yielded 14 results. We explored glutamatergic abnormalities in BD and suicide and found alterations in both. The noncompetitive NMDS antagonist ketamine and its S-enantiomer esketamine reportedly decrease acute suicidality. EXPERT OPINION: Intranasal esketamine or subcutaneous ketamine, single-bolus or intravenous, and possibly other glutamate receptor modulators may improve suicidal behavior in patients with unipolar and bipolar depression. This may be achieved through prompt remodulation of glutamate activity. The correct use of glutamatergic modulators could reduce acute suicidality and mortality in patients with BD.

Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.

Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.

DRD2, DRD3, and HTR2A Single-Nucleotide Polymorphisms Involvement in High Treatment Resistance to Atypical Antipsychotic Drugs.

BACKGROUND: The objective of this study was to investigate the DRD2 rs1800497, rs1799732, rs1801028, DRD3 rs6280, and HTR2A rs6314, rs7997012, and rs6311 single-nucleotide polymorphism (SNP) correlations with resistance to second-generation antipsychotics (SGAs) in a real-world sample of patients with treatment-resistant mental disorders. METHODS: We divided 129 participants into a high treatment resistance (HTR) group (current treatment with two SGAs, or clozapine, or classic neuroleptics for a failure of previous SGAs trials) and a low treatment resistance (LTR) group (current treatment with one atypical antipsychotic). We used Next-Generation Sequencing on DNA isolated from peripheral blood samples to analyze the polymorphisms. We performed logistic regression to search for predictors of HTR membership. RESULTS: A diagnosis of schizophrenia significantly predicted the HTR membership compared to other diagnoses. Other predictors were the DRD3 rs6280 C|T (OR = 22.195) and T|T (OR = 18.47) vs. C|C, HTR2A rs7997012 A|G vs. A|A (OR = 6.859) and vs. G|G (OR = 2.879), and DRD2 rs1799732 I|I vs. D|I (OR = 12.079) genotypes. CONCLUSIONS: A diagnosis of schizophrenia and the DRD2 rs1799732, DRD3 rs6280, and HTR2A rs7997012 genotypes can predict high treatment resistance to SGAs.

Neuroanatomical correlates of autism spectrum disorders: A meta-analysis of structural magnetic resonance imaging (MRI) studies.

Autism spectrum disorders (ASD) are neurodevelopmental disorders correlated to various neuroanatomical modifications. We aimed to identify neuroanatomical changes assessed in magnetic resonance imaging (MRI) studies of autism spectrum disorder (ASD) through Activation Likelihood Estimate (ALE) meta-analysis. We included 19 peer-reviewed magnetic resonance imaging (MRI) studies that analyzed cortical volume in patients with ASD compared to healthy control subjects (HCs). The between-group analyses comparing subjects with ASD to HCs showed a volumetric reduction of a large cluster in the right brain, including the uncus/amygdala, parahippocampal gyrus, and entorhinal cortex, and putamen. The anomalies are primarily found in the right hemisphere, involved in social cognitive function, particularly impaired in ASD. These results correlate with several clinical aspects of ASD. These volumetric alterations can be considered a major correlate of disease in the context of multifactorial etiology. Further studies on brain lateralization in ASD are needed, considering the clinical phenotype variability of these disorders.