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OBJECTIVES: Transcranial ultrasound neuromodulation (TUSN) is a noninvasive and spatially specific therapy that promises to deliver treatments tailored to the specific needs of individuals. To fulfill this promise, each treatment must be modified to adequately correct for variation across individual skulls and neural anatomy. This study examines the use of ultrasound-induced voltage potentials (measured with electroencephalography [EEG]) to guide TUSN therapies. MATERIALS AND METHODS: We measured EEG responses in two awake nonhuman primates during sonication of 12 targets surrounding two deep brain nuclei, the left and right lateral geniculate nucleus. RESULTS: We report reliable ultrasound evoked potentials measured with EEG after the deep brain ultrasonic modulation in nonhuman primates. Robust responses are observed after just ten repetitions of the ultrasonic stimuli. Moreover, these potentials are only evoked for specific deep brain targets. Furthermore, a behavioral study in one subject shows a direct correspondence between the target with maximal EEG response and ultrasound-based modulation of visual choice behavior. Thus, this study provides evidence for the feasibility of EEG-based guidance for ultrasound neuromodulation therapies.
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Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization - successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on speciï¬c features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid cell code (e.g., in the entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over the grid cell code using determinantal point process (DPP), that we call DPP attention (DPP-A) - a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in the grid cell code, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how the grid cell code in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artiï¬cial neural networks.
Assuntos
Células de Grade , Redes Neurais de Computação , Humanos , Células de Grade/fisiologia , Algoritmos , Modelos Neurológicos , Animais , Atenção/fisiologia , Encéfalo/fisiologia , Córtex Entorrinal/fisiologiaRESUMO
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between connectionist and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. In that approach, neural networks are constrained via their architecture to focus on relations between perceptual inputs, rather than the attributes of individual inputs. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain.
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Many areas of science and medicine would benefit from selective release of drugs in specific regions. Nanoparticle drug carriers activated by focused ultrasound-remotely applied, depth-penetrating energy-may provide such selective interventions. Here, we developed stable, ultrasound-responsive nanoparticles that can be used to release drugs effectively and safely in non-human primates. The nanoparticles were used to release propofol in deep brain visual regions. The release reversibly modulated the subjects' visual choice behavior and was specific to the targeted region and to the released drug. Gadolinium-enhanced MR imaging suggested an intact blood-brain barrier. Blood draws showed normal clinical chemistry and hematology. In summary, this study provides a safe and effective approach to release drugs on demand in selected deep brain regions at levels sufficient to modulate behavior.
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Encéfalo , Preparações de Ação Retardada , Propofol , Animais , Propofol/farmacocinética , Propofol/administração & dosagem , Propofol/sangue , Propofol/química , Encéfalo/metabolismo , Encéfalo/diagnóstico por imagem , Nanopartículas/administração & dosagem , Masculino , Liberação Controlada de Fármacos , Macaca mulatta , Portadores de Fármacos/química , Imageamento por Ressonância Magnética , Barreira Hematoencefálica/metabolismo , Sistemas de Liberação de Medicamentos , Gadolínio/administração & dosagem , Gadolínio/química , Gadolínio/farmacocinéticaRESUMO
Given over 2.77 million US service members have been deployed in the past 20 years and the intense process of reintegration to civilian life, understanding factors that contribute to Veterans' mental health and substance use is critical. This study sought to understand the effects of US identity exploration, US identity commitment, US identity affirmation, and US identity centrality on substance use and symptoms of depression and anxiety. The sample consisted of 195 US military Veterans (n = 184, 53.3% women; 73.3% White; Mage = 35.12 years, SD = 9.60 years). Bivariate correlations indicated US identity affirmation was negatively associated with substance use and symptoms of depression and anxiety whereas US identity centrality was positively correlated with alcohol use. Utilizing structural equation model, US identity affirmation and US identity centrality were, respectively, negatively and positively associated with alcohol use, substance use, and symptoms of depression and anxiety. Partially consistent with our hypothesis, US identity exploration was positively associated with symptoms of anxiety. In contrast to our hypothesis, US identity commitment was not significantly associated with any outcome. Results are discussed in terms of important directions for identity research in the transition to civilian life.