Transcranial Focused Ultrasound Remotely Modulates Extrastriate Visual Cortex with Subregion Specificity.

Low-intensity transcranial focused ultrasound (tFUS) has emerged as a powerful neuromodulation tool characterized by its deep penetration and precise spatial targeting to influence neural activity. Our study directed low-intensity tFUS stimulation onto a region of prefrontal cortex (the frontal eye field, or FEF) of a rhesus macaque to examine its impact on a remote site, the extrastriate visual cortex (area V4). This pair of cortical regions form a top-down modulatory circuit that has been studied extensively with electrical microstimulation. To measure the impact of tFUS stimulation, we recorded local field potentials (LFPs) and multi-unit spiking activities from a multi-electrode array implanted in the visual cortex. To deliver tFUS stimulation, we leveraged a customized 128-element random array ultrasound transducer with improved spatial targeting. We observed that tFUS stimulation in FEF produced modulation of V4 neuronal activity, either through enhancement or suppression, dependent on the pulse repetition frequency of the tFUS stimulation. Electronically steering the transcranial ultrasound focus through the targeted FEF cortical region produced changes in the level of modulation, indicating that the tFUS stimulation was spatially targeted within FEF. Modulation of V4 activity was confined to specific frequency bands, and this modulation was dependent on the presence or absence of a visual stimulus during tFUS stimulation. A control study targeting the insula produced no effect, emphasizing the region-specific nature of tFUS neuromodulation. Our findings shed light on the capacity of tFUS to modulate specific neural pathways and provide a comprehensive understanding of its potential applications for neuromodulation within brain networks.

Multimodal deep learning for Alzheimer's disease dementia assessment.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.