Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression.

Visuospatial neglect is a disorder characterised by impaired awareness for visual stimuli located in regions of space and frames of reference. It is often associated with stroke. Patients can struggle with all aspects of daily living and community participation. Assessment methods are limited and show several shortcomings, considering they are mainly performed on paper and do not implement the complexity of daily life. Similarly, treatment options are sparse and often show only small improvements. We present an artificial intelligence solution designed to accurately assess a patient's visuospatial neglect in a three-dimensional setting. We implement an active learning method based on Gaussian process regression to reduce the effort it takes a patient to undergo an assessment. Furthermore, we describe how this model can be utilised in patient oriented treatment and how this opens the way to gamification, tele-rehabilitation and personalised healthcare, providing a promising avenue for improving patient engagement and rehabilitation outcomes. To validate our assessment module, we conducted clinical trials involving patients in a real-world setting. We compared the results obtained using our AI-based assessment with the widely used conventional visuospatial neglect tests currently employed in clinical practice. The validation process serves to establish the accuracy and reliability of our model, confirming its potential as a valuable tool for diagnosing and monitoring visuospatial neglect. Our VR application proves to be more sensitive, while intra-rater reliability remains high.

Muscae volitantes: A 19th-century precursor of visual snow syndrome?

PURPOSE: Visual snow syndrome comprises a whole-field static-like visual disturbance, with increased awareness of entopic phenomena, an inability to suppress the 'just seen' and photophobia. Visual snow syndrome is often associated with other problems such as headache, tinnitus, and anxiety. The earliest reported case of a patient experiencing symptoms consistent with visual snow syndrome dates only to 1995. This paper seeks to find patterns of experience in the medical literature of the past that are reminiscent of visual snow syndrome, to challenge the view that it is in any sense a novel disorder. Descriptions of subjective visual sensations such as experienced by patients suffering from visual snow syndrome were sought in treatises, textbooks and other literature generated by leading figures in 19th-century ophthalmology, physiology and physics. CONCLUSION: While retrospective diagnosis of modern illness categories in historical medical literature is an enterprise fraught with pitfalls, it is nonetheless possible to see patterns of experience in the 19th-century medical literature that are strongly reminiscent of visual snow syndrome.

A parcellation-based connectomic model of hemispatial neglect.

BACKGROUND AND PURPOSE: Hemispatial neglect is characterized by a reduced awareness to stimuli on the contralateral side. Current literature suggesting that damage to the right parietal lobe and attention networks may cause hemispatial neglect is conflicting and can be improved by investigating a connectomic model of the "neglect system" and the anatomical specificity of regions involved in it. METHODS: A meta-analysis of voxel-based morphometry magnetic resonance imaging (MRI) studies of hemispatial neglect was used to identify regions associated with neglect. We applied parcellation schemes to these regions and performed diffusion spectrum imaging (DSI) tractography to determine their connectivity. By overlaying neglect areas and maps of the attention networks, we studied the relationship between them. RESULTS: The meta-analysis generated a list of 13 right hemisphere parcellations. These 13 neglect-related parcellations were predominantly linked by the superior longitudinal fasciculus (SLF) throughout a fronto-parietal-temporal network. We found that the dorsal and ventral attention networks showed partial overlap with the neglect system and included various other higher-order networks. CONCLUSIONS: We provide an anatomically specific connectomic model of the neurobehavioral substrates underlying hemispatial neglect. Our model suggests a fronto-parietal-temporal network linked via the SLF supports the functions impaired in neglect and implicates various higher-order networks which are not limited to the attention networks.