A Review of Multidimensional Scaling (MDS) and its Utility in Various Psychological Domains

This paper aims to provide a non-technical overview of multidimensional scaling (MDS) so that a broader population of psychologists, in particular, will consider using this statistical procedure. A brief description regarding the type of data used in MDS, its acquisition and analyses via MDS is provided. Also included is a commentary on the unique challenges associated with assessing the output of MDS. Our second aim, by way of discussing representative studies, is to highlight and evaluate the utility of this method in various domains in psychology

Recognition of masked and unmasked facial expressions in males and females and relations with mental wellness

BackgroundWhile the effects of mask wearing/facial occlusion are known to impair facial expression recognition, little is known about the role of mental wellness on facial expression recognition, as well as the influence of sex on misattribution errors (i.e., confusions between emotions). In this large study, we aimed to address the relation between facial expression recognition and loneliness, perceived stress, anxiety, and depression symptoms in male and female adults.MethodsWe assessed the influence of mask-wearing on facial expression recognition [i.e., accuracy and response time (RT)] via an online study in N = 469 adult males and females across Canada.ResultsExpectedly, recognition was impaired under masked conditions (i.e., lower accuracy, longer RTs, more misattribution errors). Females were faster and more accurate than males, with less misattribution errors. A novel finding was that people with higher perceived stress were less accurate at identifying masked fearful faces. Perceived stress influenced the relation between sex and RT to masked happy faces; males with high stress scores were slower to recognize masked happy faces, the opposite was true for females. Finally, this study was among the first to show that higher loneliness predicted shorter RT to unmasked faces.ImpactOur results show that facial expression recognition is impaired by mask-wearing, and that sex and mental health features are important predictors of performance. Such insight could be detrimental in certain sectors of the population (e.g., health care or education), and inform policies being adopted in future pandemics

Brain responses in aggression-prone individuals: A systematic review and meta-analysis of functional magnetic resonance imaging (fMRI) studies of anger- and aggression-eliciting tasks

Reactive aggression in response to perceived threat or provocation is part of humans' adaptive behavioral repertoire. However, high levels of aggression can lead to the violation of social and legal norms. Understanding brain function in individuals with high levels of aggression as they process anger- and aggression-eliciting stimuli is critical for refining explanatory models of aggression and thereby improving interventions. Three neurobiological models of reactive aggression - the limbic hyperactivity, prefrontal hypoactivity, and dysregulated limbic-prefrontal connectivity models - have been proposed. However, these models are based on neuroimaging studies involving mainly non-aggressive individuals, leaving it unclear which model best describes brain function in those with a history of aggression. We conducted a systematic literature search (PubMed and Psycinfo) and Multilevel Kernel Density meta-analysis (MKDA) of nine functional magnetic resonance imaging (fMRI) studies (eight included in the between-group analysis [i.e., aggression vs. control groups], five in the within-group analysis). Studies examined brain responses to tasks putatively eliciting anger and aggression in individuals with a history of aggression alone and relative to controls. Individuals with a history of aggression exhibited greater activity in the superior temporal gyrus and in regions comprising the cognitive control and default mode networks (right posterior cingulate cortex, precentral gyrus, precuneus, right inferior frontal gyrus) during reactive aggression relative to baseline conditions. Compared to controls, individuals with a history of aggression exhibited increased activity in limbic regions (left hippocampus, left amygdala, left parahippocampal gyrus) and temporal regions (superior, middle, inferior temporal gyrus), and reduced activity in occipital regions (left occipital cortex, left calcarine cortex). These findings lend support to the limbic hyperactivity model in individuals with a history of aggression, and further indicate altered temporal and occipital activity in anger- and aggression-eliciting conditions involving face and speech processing

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data

Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge.Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features.Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha1/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha2 being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 “concentration difficulty” scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha2 and frontopolar alpha1.Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, “biomarker”-based treatment approaches

cis-Tetra­chloridobis(1H-imidazole-κN 3)platinum(IV)

In the title complex, cis-[PtCl4(C3H4N2)2], the PtIV ion lies on a twofold rotation axis and is coordinated in a slightly distorted octa­hedral geometry. The dihedral angle between the imidazole rings is 69.9 (2)°. In the crystal, mol­ecules are linked by N—H⋯Cl hydrogen bonds, forming a three-dimensional network

Znaczenie zgodności HLA na wyniki transplantacji komórek hematopoetycznych od dawców niespokrewnionych u dzieci z ostrymi białaczkami i niewydolnościami szpiku

BackgroundIn case of the lack of matched family donors (MFD), hematopoietic stem cell transplantation (HSCT) from unrelated donor (UD) is an established procedure for many acquired and congenital disorders of the hematopoietic system, including malignancies and bone marrow failure (BMF) syndromes.ObjectiveThe analysis of the results of HSCT in patients with acute leukemia or BMF syndromes from UDs with respect to human leukocyte antigen (HLA) match.Patients and methodsA total number of 97 of HSCT from UDs performed in single center between 2007 and 2015 in children and adolescents with acute lymphoblastic (ALL) or myeloblastic leukemia (AML) and BMF syndromes were included into this analysis. HLA match between donor and recipient was analyzed at the allele level and classified as 10/10, 9/10 or 8/10. Data were compared to results of 56 MFD-HSCTs. Probability of overall survival (pOS) was given for 3-year and 1-year (as required by JACIE standards) time periods.ResultsThe mean survival for all patients estimated by Kaplan–Meier method was 4.8 years (95%CI=4.1–5.5 years). The 3-year pOS after all UD-HSCT was 0,60±0,05, and with respect to 10/10, 9/10 and 8/10 HLA match: 0,61±0,06; 0,59±0,09 and 0,60±0,22, respectively (ns). In patients with AML, 3-year pOS reached 52%, 60% and 60%, respectively. In patients with ALL, 3-year pOS was 73% and 62% (ns) for 10/10 and 9/10 HLA match, respectively, while for BMF syndromes 86% and 57% (ns), respectively.ConclusionCurrent data suggest that results of mismatched and matched UD-HSCT in children with acute leukemia might be comparable

Repetitive Transcranial Magnetic Stimulation in Youth With Treatment Resistant Major Depression

Background: Major depressive disorder (MDD) is common in youth and treatment options are limited. We evaluated the effectiveness and safety of repetitive transcranial magnetic stimulation (rTMS) in adolescents and transitional aged youth with treatment resistant MDD.Methods: Thirty-two outpatients with moderate to severe, treatment-resistant MDD, aged 13–21 years underwent a three-week, open-label, single center trial of rTMS (ClinicalTrials.gov identifier NCT01731678). rTMS was applied to the left dorsolateral prefrontal cortex (DLPFC) using neuronavigation and administered for 15 consecutive week days (120% rest motor threshold; 40 pulses over 4 s [10 Hz]; inter-train interval, 26 s; 75 trains; 3,000 pulses). The primary outcome measure was change in the Hamilton Depression Rating Scale (Ham-D). Treatment response was defined as a >50% reduction in Ham-D scores. Safety and tolerability were also examined.Results: rTMS was effective in reducing MDD symptom severity (t = 8.94, df = 31, p < 0.00001). We observed 18 (56%) responders (≥ 50% reduction in Ham-D score) and 14 non-responders to rTMS. Fourteen subjects (44%) achieved remission (Ham-D score ≤ 7 post-rTMS). There were no serious adverse events (i.e., seizures). Mild to moderate, self-limiting headaches (19%) and mild neck pain (16%) were reported. Participants ranked rTMS as highly tolerable. The retention rate was 91% and compliance rate (completing all study events) was 99%.Conclusions: Our single center, open trial suggests that rTMS is a safe and effective treatment for youth with treatment resistant MDD. Larger randomized controlled trials are needed.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT0173167