Using supervised machine learning on neuropsychological data to distinguish OCD patients with and without sensory phenomena from healthy controls

The Premonitory Urge to Tic in Children and Adolescents: Measuring, Describing, and Correlating

BACKGROUND

This study aimed to explore the premonitory urges (PUs) experienced by children with tic disorders (TDs), with the aim of describing and correlating these urges with various factors.

METHODS

First-episode and drug-naive patients with TDs were recruited. We conducted a comprehensive study utilizing the Premonitory Urge for Tics Scale to measure the severity of PUs. Regression analysis was performed to explore the relationships between PUs and other relevant factors, such as demographic characteristics, tic severity, and comorbidities.

RESULTS

The linear regression model revealed that age (β = 0.090, P = 0.004), the severity of motor tics (β = 0.112, P < 0.001), the severity of vocal tics (β = 0.074, P = 0.020), and the severity of tic-related impairments (β = 0.112, P = 0.001) were significant predictors of PUs.

CONCLUSIONS

This study provides insights into the nature of PUs in children with TD. Future research should focus on PUs across different age groups and develop and evaluate targeted treatments that aim to reduce the severity of tics.

The relationship between sensory phenomena and interoception across the obsessive-compulsive spectrum: a systematic review

BACKGROUND

Uncomfortable sensations preceding repetitive behaviours, known as sensory phenomena, have been documented across the obsessive-compulsive spectrum. Indirect evidence suggests altered interoception may play a role in these shared experiences of sensory phenomena; however, research explicitly measuring this relationship is limited. The current systematic review aimed to establish the nature of sensory phenomena and interoception in obsessive-compulsive and related disorders (OCRDs) and tic disorders as potential maintaining factors of these disorders.

METHODS

PsycINFO, PubMed, and Scopus databases were searched from 2007 to April 2024, yielding 65 studies.

RESULTS

While the majority of studies presented low risk of bias, significant overlap and ambiguity characterised the measurement and conceptualisation of sensory phenomena and interoception. Overall, higher sensory phenomena was associated with greater symptom severity in several obsessive-compulsive spectrum disorders. Obsessive-compulsive disorder and tic disorder samples were characterised by lower interoceptive accuracy, with mixed findings on interoceptive sensibility. Some limited research emerged suggesting altered interoceptive abilities may be associated with greater sensory phenomena in obsessive-compulsive disorder and tic disorders.

CONCLUSIONS

Sensory phenomena are experienced across the obsessive-compulsive spectrum. Future research should explore interoceptive abilities across the OCRDs, and build upon evidence supporting a relationship between sensory phenomena and interoception in OCD and tic disorders.

TRIAL REGISTRATION

CRD42023422817.

Evaluating The Predictive Reliability of Neural Networks in Psychological Research With Random Datasets

Psychologists are emphasizing the importance of predictive conclusions. Machine learning methods, such as supervised neural networks, have been used in psychological studies as they naturally fit prediction tasks. However, we are concerned about whether neural networks fitted with random datasets (i.e., datasets where there is no relationship between ordinal independent variables and continuous or binary-dependent variables) can provide an acceptable level of predictive performance from a psychologist's perspective. Through a Monte Carlo simulation study, we found that this kind of erroneous conclusion is not likely to be drawn as long as the sample size is larger than 50 with continuous-dependent variables. However, when the dependent variable is binary, the minimum sample size is 500 when the criteria are balanced accuracy ≥ .6 or balanced accuracy ≥ .65, and the minimum sample size is 200 when the criterion is balanced accuracy ≥ .7 for a decision error less than .05. In the case where area under the curve (AUC) is used as a metric, a sample size of 100, 200, and 500 is necessary when the minimum acceptable performance level is set at AUC ≥ .7, AUC ≥ .65, and AUC ≥ .6, respectively. The results found by this study can be used for sample size planning for psychologists who wish to apply neural networks for a qualitatively reliable conclusion. Further directions and limitations of the study are also discussed.

Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19

The risk factors for post-COVID-19 cognitive impairment have been poorly described. This study aimed to identify the sociodemographic, clinical, and lifestyle characteristics that characterize a group of post-COVID-19 condition (PCC) participants with neuropsychological impairment. The study sample included 426 participants with PCC who underwent a neurobehavioral evaluation. We selected seven mental speed processing and executive function variables to obtain a data-driven partition. Clustering algorithms were applied, including K-means, bisecting K-means, and Gaussian mixture models. Different machine learning algorithms were then used to obtain a classifier able to separate the two clusters according to the demographic, clinical, emotional, and lifestyle variables, including logistic regression with least absolute shrinkage and selection operator (LASSO) (L1) and Ridge (L2) regularization, support vector machines (linear/quadratic/radial basis function kernels), and decision tree ensembles (random forest/gradient boosting trees). All clustering quality measures were in agreement in detecting only two clusters in the data based solely on cognitive performance. A model with four variables (cognitive reserve, depressive symptoms, obesity, and change in work situation) obtained with logistic regression with LASSO regularization was able to classify between good and poor cognitive performers with an accuracy and a weighted averaged precision of 72%, a recall of 73%, and an area under the curve of 0.72. PCC individuals with a lower cognitive reserve, more depressive symptoms, obesity, and a change in employment status were at greater risk for poor performance on tasks requiring mental processing speed and executive function. Study registration: www.ClinicalTrials.gov , identifier NCT05307575.

mHealth and technology innovations for anxiety and OC spectrum disorders

Interdisciplinary mobile health (mHealth) technologies and intervention approaches are changing the nature of health research, providing the opportunity to shift from more reactive approaches for patient care to a more proactive stance. As with the larger field of medicine, mHealth and technology-enhanced approaches in psychiatry and clinical psychology are opening an unparalleled number of avenues to help reduce the risk for psychiatric disease, treat psychological disorders, and increase well-being of our patients. While promising, this research is characterized by complex challenges across the domains of concept development, initial design and testing, and downstream implementation and scaled-up dissemination. This Special Issue in the British Journal of Clinical Psychology was designed to highlight the development and implementation of mHealth research in the anxiety and obsessive-compulsive spectrum disorders. In addition to informing readers about important advances that have been made, the present special issue also draws attention to the myriad challenges that will need to be considered in future research. Three domains relevant for mHealth research are addressed, including a careful consideration of where the research currently stands and what challenges we should prepare for, the adaptation of traditional and adjunctive treatments to mobile or online platforms, and the ability for technology and associated methodological approaches to provide further insight into aetiological investigations.

Computational Predictions for OCD Pathophysiology and Treatment: A Review

Obsessive compulsive disorder (OCD) can manifest as a debilitating disease with high degrees of co-morbidity as well as clinical and etiological heterogenity. However, the underlying pathophysiology is not clearly understood. Computational psychiatry is an emerging field in which behavior and its neural correlates are quantitatively analyzed and computational models are developed to improve understanding of disorders by comparing model predictions to observations. The aim is to more precisely understand psychiatric illnesses. Such computational and theoretical approaches may also enable more personalized treatments. Yet, these methodological approaches are not self-evident for clinicians with a traditional medical background. In this mini-review, we summarize a selection of computational OCD models and computational analysis frameworks, while also considering the model predictions from a perspective of possible personalized treatment. The reviewed computational approaches used dynamical systems frameworks or machine learning methods for modeling, analyzing and classifying patient data. Bayesian interpretations of probability for model selection were also included. The computational dissection of the underlying pathology is expected to narrow the explanatory gap between the phenomenological nosology and the neuropathophysiological background of this heterogeneous disorder. It may also contribute to develop biologically grounded and more informed dimensional taxonomies of psychopathology.