Neuropsychological performance and predictors of pharmacological treatment response in obsessive compulsive disorder

Prediction of pharmacological response in OCD using machine learning techniques and clinical and neuropsychological variables

INTRODUCTION

Obsessive compulsive disorder is associated with affected executive functioning, including memory, cognitive flexibility, and organizational strategies. As it was reported in previous studies, patients with preserved executive functions respond better to pharmacological treatment, while others need to keep trying different pharmacological strategies.

MATERIAL AND METHODS

In this work we used machine learning techniques to predict pharmacological response (OCD patients' symptomatology reduction) based on executive functioning and clinical variables. Among those variables we used anxiety, depression and obsessive-compulsive symptoms scores by applying State-Trait Anxiety Inventory, Hamilton Depression Rating Scale and Yale-Brown Obsessive Compulsive Scale respectively, while Rey-Osterrieth Complex Figure Test was used to assess organisation skills and non-verbal memory; Digits' subtests from Wechsler Adult Intelligence Scale-IV were used to assess short-term memory and working memory; and Raven's Progressive Matrices were applied to assess problem solving and abstract reasoning.

RESULTS

As a result of our analyses, we created a reliable algorithm that predicts Y-BOCS score after 12 weeks based on patients' clinical characteristics (sex at birth, age, pharmacological strategy, depressive and obsessive-compulsive symptoms, years passed since diagnostic and Raven's Progressive Matrices score) and Digits' scores. A high correlation (0.846) was achieved in predicted and true values.

CONCLUSIONS

The present study proves the viability to predict if a patient would respond or not to a certain pharmacological strategy with high reliability based on sociodemographics, clinical variables and cognitive functions as short-term memory and working memory. These results are promising to develop future prediction models to help clinical decision making.

Cognitive and clinical predictors of a long-term course in obsessive compulsive disorder: A machine learning approach in a prospective cohort study

BACKGROUND

Obsessive compulsive disorder (OCD) is a disabling illness with a chronic course, yet data on long-term outcomes are scarce. This study aimed to examine the long-term course of OCD in patients treated with different approaches (drugs, psychotherapy, and psychosurgery) and to identify predictors of clinical outcome by machine learning.

METHOD

We included outpatients with OCD treated at our referral unit. Demographic and neuropsychological data were collected at baseline using standardized instruments. Clinical data were collected at baseline, 12 weeks after starting pharmacological treatment prescribed at study inclusion, and after follow-up.

RESULTS

Of the 60 outpatients included, with follow-up data available for 5-17 years (mean = 10.6 years), 40 (67.7 %) were considered non-responders to adequate treatment at the end of the study. The best machine learning model achieved a correlation of 0.63 for predicting the long-term Yale-Brown Obsessive Compulsive Scale (Y-BOCS) score by adding clinical response (to the first pharmacological treatment) to the baseline clinical and neuropsychological characteristics.

LIMITATIONS

Our main limitations were the sample size, modest in the context of traditional ML studies, and the sample composition, more representative of rather severe OCD cases than of patients from the general community.

CONCLUSIONS

Many patients with OCD showed persistent and disabling symptoms at the end of follow-up despite comprehensive treatment that could include medication, psychotherapy, and psychosurgery. Machine learning algorithms can predict the long-term course of OCD using clinical and cognitive information to optimize treatment options.

Comorbidities and Disease Duration in Tourette Syndrome: Impact on Cognition and Quality of Life of Children

BACKGROUND

Cognitive functions represent foundational factors for mental health and quality of life (QoL). In Tourette syndrome (TS), psychiatric comorbidities are common and have been inconsistently reported to affect the cognition and QoL of patients, while the role of tic disorder duration has not been yet explored.

METHODS

To examine how comorbidities and TS duration may influence cognition and QoL, N = 80 children with TS (6-16 years) were evaluated using the Wechsler Intelligence Scale for Children (WISC-IV). Standardized questionnaires were used to assess the presence and severity of TS main comorbidities and QoL. Data were interpreted using linear correlations, regression, and mediation analysis.

RESULTS

Depression and attention-deficit/hyperactivity disorder (ADHD) symptoms accounted for poorer cognitive performance. Anxiety oppositely predicted better cognitive performance, while no significant role for obsessive compulsive disorder (OCD) was observed. Disease duration was associated with lower total IQ, verbal reasoning, and working memory abilities. Depression, anxiety, and TS duration also deeply influenced QoL measures.

CONCLUSIONS

TS common comorbidities have a differential impact on the cognitive abilities of children and adolescents, which translates into a complex influence on their perceived QoL. A longer clinical history of tics was related to worse cognitive outcomes, which prompts further consideration of disease duration in both clinical and research settings involving children and adolescents.

Direct changes of neurometabolic concentrations in the pregenual anterior cingulate cortex among obsessive-compulsive patients after repetitive transcranial magnetic stimulation treatment

BACKGROUND AND AIM

Although Repetitive Transcranial Magnetic Stimulation (rTMS) is a promising new noninvasive brain stimulation therapy, its underlying mechanisms of action remain unknown. OCD patients exhibit impaired response control and attention shifting, which is linked to some brain areas such as anterior cingulate cortex and basal ganglia. OCD patients also display altered neurometabolic concentrations in cortical cortical-striatal-thalamic-cortical (CSTC). In this study, we aimed to elucidate efficacy of rTMS treatment in alleviating related symptoms and pregenual anterior cingulate cortex (pACC) neurometabolites.

METHODS

OCD patients were randomly divided into either drug (n = 23) or drug + rTMS (n = 29) groups, and those in the latter group subjected to 4-week rTMS treatment. All participants were visited twice, at baseline and follow-up after four weeks. During both visits, all patients were subjected to 1H-MRS, then Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and the Global Assessment Function (GAF) used to assess severity of obsessive-compulsive symptoms. We also evaluated synchronous anxiety and depression by Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI), Hamilton Anxiety Scale (HAM-A) and Hamilton Depression Scale (HAM-D).

RESULTS

After 4 weeks of treatment, patients in the Drug + rTMS group displayed significantly lower Y-BOCS (p = 0.038), BDI (p = 0.009), HAM-D (p = 0.013), HAM-A (p = 0.012) scores than their counterparts in the Drug group. Conversely, patients in the Drug + rTMS group had significantly higher tNAA concentrations (p = 0.030) than those in the Drug group. Notably, the Drug + rTMS group exhibited higher, but insignificant Glu (p = 0.055) and Glx (p = 0.068) concentrations compared to the Drug group. Partial correlation analysis revealed a significant negative correlation between post HAM-A scores and 4-week change of pACC glutamate levels in the Drug + rTMS group (r = -0.434, p = 0.02).

CONCLUSION

rTMS treatment is an efficacious treatment therapy for OCD, mainly by inducing changes in neurometabolites.