Pharmaco-fUS: Quantification of pharmacologically-induced dynamic changes in brain perfusion and connectivity by functional ultrasound imaging in awake mice

Rabut, Claire; Ferrier, Jérémy; Bertolo, Adrien; Osmanski, Bruno-Félix; Mousset, Xavier; Pezet, Sophie; Thomas, Daniel; Lenkei, Zsolt; Tanter, Mickaël

doi:10.1016/j.neuroimage.2020.117231

Cited by 33 publications

(41 citation statements)

References 47 publications

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“…Recent studies demonstrated fUS imaging's potential to characterize dynamic profiles of CNS drugs, including a drug combination of donepezil plus mefloquine for AD 464 and atomoxetine for attention‐deficit/hyperactivity disorder 465 . Moreover, Rabut et al 466 . adapted ML to analyze the rich data content provided by fUS connectivity imaging.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Vatansever

Schlessinger

Wacker

et al. 2020

Medicinal Research Reviews

180

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Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Vatansever

Schlessinger

Wacker

et al. 2020

Medicinal Research Reviews

180

View full text Add to dashboard Cite

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“…In creating sparse sequences, we chose to select only the initial portion of the original sequence over zeroing interleaved frames (e.g., discarding 3 frames every 4 in the case of 25% density) to take advantage of the shorter observation window. This approach has the benefit of reducing the occurrence of motion artifacts and signal degradation due to data scrubbing, which are inevitable factors in mobile rodent fUS imaging experiments 7,44 and handheld applications 5 and are more likely to appear with longer observation times.…”

Section: Discussionmentioning

confidence: 99%

Deep-fUS: Functional ultrasound imaging of the brain using deep learning and sparse data

Ianni

Airan

2020

Preprint

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Functional ultrasound imaging is rapidly establishing itself as a state-of-the-art neuroimaging modality owing to its ability to image neural activation in awake and mobile small animals, its relatively low cost, and its unequaled portability. To achieve high blood flow sensitivity in the brain microvasculature, functional ultrasound relies on long sequences of ultrasound data acquired at high frame rates, which pose high demands on the hardware architecture and effectively limit the practical utility and clinical translation of this imaging modality. Here we propose Deep-fUS, a deep learning approach that aims to significantly reduce the amount of ultrasound data necessary, while retaining the imaging performance. We trained a neural network to learn the power Doppler reconstruction function from sparse sequences of compound ultrasound data, using ground truth images from high-quality in vivo acquisitions in rats, and with a custom loss function. The network produces highly accurate images with restored sensitivity in the smaller blood vessels even when using heavily under-sampled data. We tested the network performance in a task-evoked functional neuroimaging application, demonstrating that time series of power Doppler images can be reconstructed with adequate accuracy to compute functional activity maps. Notably, the network reduces the occurrence of motion artifacts in awake functional ultrasound imaging experiments. The proposed platform can facilitate the development of this neuroimaging modality in any setting where dedicated hardware is not available or even in clinical scanners, opening the way to new potential applications based on this technology. To our knowledge, this is the first attempt to implement a convolutional neural network approach for power Doppler reconstruction from sparse ultrasonic datasets.

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“…Acquisition started and ended with a 5 min baseline followed by 10 phases of 30 s manual stimulation of the whiskers 76 with 1 min of resting in between. A fourth order polynomial detrending of the data was applied to remove drifts of baseline 77 .…”

Section: Methodsmentioning

confidence: 99%

Microglia control cerebral blood flow and neurovascular coupling via P2Y12R-mediated actions

Császár

Lénárt

Cserép

et al. 2021

Preprint

Self Cite

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Microglia, the main immunocompetent cells of the brain regulate neuronal function in health and disease, but their contribution to cerebral blood flow (CBF) remained elusive. Here we identify microglia as important modulators of CBF both under physiological conditions and during hypoperfusion. We show that microglia establish direct purinergic contacts with cells in the neurovascular unit that shape cerebral perfusion in both mice and humans. Surprisingly, the absence of microglia or blockade of microglial P2Y12 receptor (P2Y12R) substantially impairs neurovascular coupling in the barrel cortex after whisker stimulation. We also reveal that hypercapnia, which is associated with acidification, induces microglial adenosine production, while depletion of microglia reduces brain pH and impairs hypercapnia-induced vasodilation. Furthermore, the absence or dysfunction of microglia markedly impairs adaptation to hypoperfusion via P2Y12R after transient unilateral common carotid artery occlusion, which is also influenced by CX3CR1-mediated actions. Thus, our data reveal a previously unrecognized role for microglia in CBF regulation with broad implications for common neurological diseases.

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Pharmaco-fUS: Quantification of pharmacologically-induced dynamic changes in brain perfusion and connectivity by functional ultrasound imaging in awake mice

Cited by 33 publications

References 47 publications

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Deep-fUS: Functional ultrasound imaging of the brain using deep learning and sparse data

Microglia control cerebral blood flow and neurovascular coupling via P2Y12R-mediated actions

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