Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1

The Lack of Ad Hoc Neuropsychological Assessment in Adults with Neurofibromatosis: A Systematic Review

Background: Neurofibromatosis Type 1 (NF1) is a genetic autosomal dominant disorder that affects both the central and peripheral nervous systems. Children and adolescents with NF1 commonly experience neuropsychological, motor, and behavioral deficits. The cognitive profile hallmark of this disorder includes visuospatial and executive function impairments. These cognitive disorders may persist into adulthood. This study aims to analyze previous research studies that have described cognitive dysfunctions in adults with NF1. The purpose of this analysis is to review the neuropsychological and psychological assessment methods used. Methods: A total of 327 articles were identified based on the search terms in their titles and abstracts. The evaluation was conducted by scrutinizing each article's title, abstract, and text. Results: Only 16 articles were found to be eligible for inclusion based on the pre-defined criteria. The selected studies primarily focus on the development of diagnostic protocols for individuals with NF1. Conclusions: The management of NF1 disease requires a multidisciplinary approach to address symptoms, preserve neurological functions, and ensure the best possible quality of life. However, cognitive impairment can negatively affect psychological well-being. This study suggested that cognitive functions in NF1 patients were not tested using specific measures, but rather were evaluated through intelligence scales. Additionally, the findings revealed that there is no standardized neuropsychological assessment for adults with NF1. To address this gap, it would be helpful to create a specific neuropsychological battery to study cognitive function in NF1 patients during clinical studies. This battery could also serve as a tool to design models for cognitive rehabilitation by using reliable and sensitive measures of cognitive outcomes.

Accurate Prediction of Momentary Cognition From Intensive Longitudinal Data

BACKGROUND

Deficits in cognitive performance are implicated in the development and maintenance of psychopathology. Emerging evidence further suggests that within-person fluctuations in cognitive performance may represent sensitive early markers of neuropsychiatric decline. Incorporating routine cognitive assessments into standard clinical care-to identify between-person differences and monitor within-person fluctuations-has the potential to improve diagnostic screening and treatment planning. In support of these goals, it is critical to understand to what extent cognitive performance varies under routine, remote assessment conditions (i.e., momentary cognition) in relation to a wide range of possible predictors.

METHODS

Using data-driven, high-dimensional methods, we ranked strong predictors of momentary cognition and evaluated out-of-sample predictive accuracy. Our approach leveraged innovations in digital technology, including ambulatory assessment of cognition and behavior 1) at scale (n = 122 participants, n = 94 females), 2) in naturalistic environments, and 3) within an intensive longitudinal study design (mean = 25.5 assessments/participant).

RESULTS

Reaction time (R2 > 0.70) and accuracy (0.56 >R2 > 0.35) were strongly predicted by age, between-person differences in mean performance, and time of day. Effects of self-reported, intraindividual fluctuations in environmental (e.g., noise) and internal (e.g., stress) states were also observed.

CONCLUSIONS

Our results provide robust estimates of effect size to characterize sources of cognitive variability, to support the identification of optimal windows for psychosocial interventions, and to possibly inform clinical evaluation under remote neuropsychological assessment conditions.

A review of the application of machine learning in molecular imaging

Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Optical molecular imaging (OMI) and nuclear medical imaging are two key research fields of MI. OMI technology refers to the optical information generated by the imaging target (such as tumors) due to drug intervention and other reasons. By collecting the optical information, researchers can track the motion trajectory of the imaging target at the molecular level. Owing to its high specificity and sensitivity, OMI has been widely used in preclinical research and clinical surgery. Nuclear medical imaging mainly detects ionizing radiation emitted by radioactive substances. It can provide molecular information for early diagnosis, effective treatment and basic research of diseases, which has become one of the frontiers and hot topics in the field of medicine in the world today. Both OMI and nuclear medical imaging technology require a lot of data processing and analysis. In recent years, artificial intelligence technology, especially neural network-based machine learning (ML) technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of ML methods in OMI and nuclear medical imaging.

Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities

The last four decades have witnessed tremendous growth in research studies applying neuroimaging methods to evaluate pathophysiological and treatment aspects of psychiatric disorders around the world. This article provides a brief history of psychiatric neuroimaging research in Brazil, including quantitative information about the growth of this field in the country over the past 20 years. Also described are the various methodologies used, the wealth of scientific questions investigated, and the strength of international collaborations established. Finally, examples of the many methodological advances that have emerged in the field of in vivo neuroimaging are provided, with discussion of the challenges faced by psychiatric research groups in Brazil, a country of limited resources, to continue incorporating such innovations to generate novel scientific data of local and global relevance.

Discriminating between neurofibromatosis-1 and typically developing children by means of multimodal MRI and multivariate analyses

Neurofibromatosis Type 1 leads to brain anomalies involving both gray and white matter. The extent and granularity of these anomalies, together with their possible impact on brain activity, is still unknown. In this multicentric cross-sectional study we submitted a sample of 42 typically developing and 38 neurofibromatosis-1 children to a multimodal MRI assessment including T1, diffusion weighted and resting state functional sequences. We used a pipeline involving several features selection steps coupled with multivariate statistical analysis (supporting vector machine) to discriminate between the two groups while having interpretable models. We used MRI indexes measuring macro (gray matter volume) and microstructural (fractional anisotropy, mean diffusivity) characteristics of the brain, as well as indexes of brain activity (fractional amplitude of low frequency fluctuations) and connectivity (local and global correlation) at rest. We found that structural indexes could discriminate between the two groups, with the mean diffusivity leading to performance as high as the combination of all structural indexes combined (accuracy = 0.86), while functional indexes had worse performances. The MRI signature of NF1 brain pathology is a combination of gray and white matter abnormalities, as measured with gray matter volume, fractional anisotropy, and mean diffusivity.