Recent advances in neuroimaging biomarkers in geriatric psychiatry

Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning

Motivation: Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI. Advantages are the timely availability of results while maintaining a clinically relevant accuracy. Materials and Methods: An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). A silver-standard ground truth was generated with FreeSurfer 6.0. Results: The CNN predicts a total of 165 morphometric measures directly from raw MR images. Analysis of the results using intraclass correlation coefficients showed, in general, good correlation with FreeSurfer generated ground truth data, with some of the regions nearly reaching human inter-rater performance (ICC > 0.75). Cortical thicknesses predicted by the CNN showed cross-sectional annual age-related gray matter atrophy rates both globally (thickness change of -0.004 mm/year) and regionally in agreement with the literature. A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. Conclusions: We demonstrate the general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds. A comparison of the results to other publications shows accuracies of comparable magnitudes for the subcortical volumes and cortical thicknesses.

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders.

Principles and management of neuropsychiatric symptoms in Alzheimer's dementia

Neuropsychiatric symptoms of Alzheimer's disease (NPS-AD) are highly prevalent and lead to poor medical and functional outcomes. In spite of the burdensome nature of NPS-AD, we are continuing to refine the nosology and only beginning to understand the underlying pathophysiology. Cluster analyses have frequently identified three to five subsyndromes of NPS-AD: behavioral dysfunction (for example, agitation/aggressiveness), psychosis (for example, delusions and hallucinations), and mood disturbance (for example, depression or apathy). Recent neurobiological studies have used new neuroimaging techniques to elucidate behaviorally relevant circuits and networks associated with these subsyndromes. Several fronto-subcortical circuits, cortico-cortical networks, and neurotransmitter systems have been proposed as regions and mechanisms underlying NPS-AD. Common to most of these subsyndromes is the broad overlap of regions associated with the salience network (anterior cingulate and insula), mood regulation (amygdala), and motivated behavior (frontal cortex). Treatment strategies for dysregulated mood syndromes (depression and apathy) have primarily targeted serotonergic mechanisms with antidepressants or dopaminergic mechanisms with psychostimulants. Psychotic symptoms have largely been targeted with anti-psychotic medications despite controversial risk/benefit tradeoffs. Management of behavioral dyscontrol, including agitation and aggression in AD, has encompassed a wide range of psychoactive medications as well as non-pharmacological approaches. Developing rational therapeutic approaches for NPS-AD will require a firmer understanding of the underlying etiology in order to improve nosology as well as provide the empirical evidence necessary to overcome regulatory and funding challenges to further study these debilitating symptoms.