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Unilateral spatial neglect (USN) is a syndrome characterized by inattention to or inaction in one side of space that affects between 23-46% of acute stroke survivors, most commonly involving injury to the right cerebral hemisphere and resulting in a tendency to ignore the left side of space and/or the survivor's body1,2. Although the majority of patients with USN experience significant recovery in the short term, subtle USN symptoms often persist3. USN can increase patient risk for falls and impede activities of daily living2,4 It has also been shown to negatively impact both motor and global functional outcomes5,6.
Deficits in USN can be conceptualized as existing across multiple dimensions, such as whether a person ignores one side of space with respect to their own body (egocentric) or with respect to an external stimulus (allocentric)7,8,9, or whether a person is unable to direct their attention (attentional) or actions (intentional) toward one side of space10. Patients often exhibit a complex constellation of symptoms that can be characterized along more than one of these dimensions. This variability of USN syndromes is thought to result from varying degrees of injury to specific neuroanatomical structures and neuronal networks, which are complex11. Allocentric neglect has been associated with lesions of the angular gyrus (AG) and superior temporal gyrus (STG), while the posterior parietal cortex (PPC) including the supramarginal gyrus (SMG) has been implicated in egocentric processing12,13,14,15. Attentional neglect is thought to involve lesions in the right IPL16, while intentional neglect is thought to be secondary to damage of the right frontal lobe17 or basal ganglia18.
Clinical assessment of USN currently relies on pen-and-paper neuropsychological instruments. These conventional assessment tools may be less sensitive than more technologically sophisticated tools, resulting in misdiagnosing or under-diagnosing some patients with USN19. Better characterization of residual deficits could facilitate the delivery of therapy to patients with milder USN and potentially improve their overall recovery, but such characterization would require very sensitive diagnostic tools. USN poses similar challenges in the laboratory setting, where it can be difficult to isolate from the motor and visual impairments that commonly accompany USN among stroke patients.
Virtual reality (VR) presents a unique opportunity to develop new tools for the diagnosis and characterization of USN. VR is a multisensory 3D environment presented in the first person with real time interactions in which individuals are able to perform tasks involving ecologically valid objects20. It is a promising tool for assessing USN; the ability to precisely control what the user sees and hears allows developers to present a wide variety of virtual tasks to the user. In addition, the sophisticated hardware and software packages currently available allow for real-time collection of a wealth of data about the user's actions, including eye, head, and limb movements, far exceeding the metrics offered by traditional diagnostic tests21. These data streams are instantaneously available, opening up the possibility for real-time adjustment of diagnostic tasks based on user performance (e.g. targeting the ideal difficulty level for a given task). This feature can facilitate task adaptation to the wide range of severity seen in USN, which is regarded as a priority in the development of new diagnostic tools for USN22. In addition, immersive VR tasks may impose an increased burden on the patients' attentional resources23,24, resulting in increased errors which can facilitate the detection of neglect symptoms; indeed, some VR tasks have been shown to have increased sensitivity when compared to conventional paper-and-pencil measures of USN24,25.
In this study, the goal was to create an assessment tool that requires no expertise in neurology to operate and that can reliably detect and characterize even subtle cases of USN. We built a virtual reality-based, game-like task. We then induced a USN-like syndrome in healthy subjects with transcranial magnetic stimulation (TMS), a noninvasive brain stimulation technique that utilizes electromagnetic pulses emitted from a handheld stimulation coil, which pass through the scalp and skull of the subject and induce electric currents in the subject's brain that stimulate neurons26,27. This technique has been utilized in the study of USN by others13,17,28,29,30, though to our knowledge never in conjunction with a VR-based assessment tool.
Many researchers are already working on diagnostic and therapeutic applications of VR systems. Recent reviews31,32 explored a number of projects aimed at the assessment of USN with VR-based techniques, and a number of other studies with this aim have been published33,34,35,36,37,38,39,40,41. The majority of these studies do not utilize the full complement of VR technology that is currently available to the consumer market (e.g., a head-mounted display (HMD) and eye-tracking inserts), limiting their datasets to a smaller number of easily-quantifiable metrics. In addition, all of these studies were performed on patients with acquired brain injury leading to USN, requiring screening methods to assure that patients could at least participate in the assessment tasks (e.g., excluding patients with large visual field deficits or cognitive impairment). It is possible that more subtle cognitive, motor, or visual deficits passed under the threshold of these screening methods, possibly confounding the results of these studies. It is also possible that such screening biased the samples of participants in these studies toward a particular subtype of USN.
To avoid the screening biases of prior studies, we recruited healthy subjects and artificially simulated USN symptoms with a standard TMS protocol that is well-described in a recent manuscript15, with the goal of inducing allocentric USN-like symptoms by targeting the STG and egocentric USN-like symptoms by targeting the SMG. We designed the task to actively adjust its difficulty trial to trial and to differentiate between different subtypes of USN, specifically allocentric vs. egocentric symptoms. We also used standard paper & pencil assessments of USN to formally demonstrate that the deficits we induced with rTMS are USN-like. We believe the method will be useful to other researchers who want to test novel VR tools for the assessment and rehabilitation of USN.