Disease versus dimension in diagnosis

One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry

Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.

Kleefstra syndrome in three adult patients: further delineation of the behavioral and neurological phenotype shows aspects of a neurodegenerative course

Kleefstra syndrome (KS), previously known as the 9q subtelomeric deletion syndrome (9qSTDS) is caused by haploinsufficiency of the EHMT1 gene. Both a single mutation and 9q34 microdeletions encompassing the entire gene can be responsible for this syndrome which is characterized by intellectual disability, hypotonia, and typical dysmorphisms, and may be associated with congenital heart and/or renal defects and epilepsy. Its behavioral phenotype has recently been described and comprises particular sleep disturbances and apathy. In this report, the evolution of the behavioral profile of KS is outlined by the description of three female patients aged 19, 33, and 43 years, respectively. In two patients, the syndrome was caused by an intragenic mutation and in the third by a 9q34 microdeletion encompassing the EHMT1 gene. MRI scanning of the brain in the two eldest patients demonstrated multifocal subcortical signal abnormalities. In general, the severity of the behavioral and motor deficiencies increased over time and became apparent after adolescence. It is concluded that the "regressive" phenotype of KS seems to be associated with the EHMT1 gene in particular. In addition, the utility of uncritical use of a classificatory diagnostic approach is discussed in the context of the motor and motivational disturbances that are prominent in this syndrome.