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Physician and writer Oliver Sacks recounts his experiences as a young neurologist; his physical passions--weight lifting and swimming; his love affairs, both romantic and intellectual; his guilt over leaving his family to come to America; his bond with his schizophrenic brother; and the writers and scientists--Thom Gunn, A.R. Luria, W.H. Auden, Gerald M. Edelman, Francis Crick--who influenced him.

\"In July 2013, Oliver Sacks turned eighty and wrote [a] ... piece in The New York Times about the prospect of old age and the freedom he envisioned for himself in binding together the thoughts and feelings of a lifetime. Eighteen months later, he was given a diagnosis of terminal cancer--which he announced publically in another piece in The New York Times. Gratitude is Sacks's meditation on why life [continued] to enthrall him even as he [faced] the all-too-close presence of his own death, and how to live out the months that [remained] in the richest and deepest way possible\"-- Provided by publisher.

Deep learning has the potential to augment clinician performance in medical imaging interpretation and reduce time to diagnosis through automated segmentation. Few studies to date have explored this topic. To develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxel-by-voxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians' intracranial aneurysm diagnostic performance. In this diagnostic study, a 3-dimensional convolutional neural network architecture was developed using a training set of 611 head CTA examinations to generate aneurysm segmentations. Segmentation outputs from this support model on a test set of 115 examinations were provided to clinicians. Between August 13, 2018, and October 4, 2018, 8 clinicians diagnosed the presence of aneurysm on the test set, both with and without model augmentation, in a crossover design using randomized order and a 14-day washout period. Head and neck examinations performed between January 3, 2003, and May 31, 2017, at a single academic medical center were used to train, validate, and test the model. Examinations positive for aneurysm had at least 1 clinically significant, nonruptured intracranial aneurysm. Examinations with hemorrhage, ruptured aneurysm, posttraumatic or infectious pseudoaneurysm, arteriovenous malformation, surgical clips, coils, catheters, or other surgical hardware were excluded. All other CTA examinations were considered controls. Sensitivity, specificity, accuracy, time, and interrater agreement were measured. Metrics for clinician performance with and without model augmentation were compared. The data set contained 818 examinations from 662 unique patients with 328 CTA examinations (40.1%) containing at least 1 intracranial aneurysm and 490 examinations (59.9%) without intracranial aneurysms. The 8 clinicians reading the test set ranged in experience from 2 to 12 years. Augmenting clinicians with artificial intelligence-produced segmentation predictions resulted in clinicians achieving statistically significant improvements in sensitivity, accuracy, and interrater agreement when compared with no augmentation. The clinicians' mean sensitivity increased by 0.059 (95% CI, 0.028-0.091; adjusted P = .01), mean accuracy increased by 0.038 (95% CI, 0.014-0.062; adjusted P = .02), and mean interrater agreement (Fleiss κ) increased by 0.060, from 0.799 to 0.859 (adjusted P = .05). There was no statistically significant change in mean specificity (0.016; 95% CI, -0.010 to 0.041; adjusted P = .16) and time to diagnosis (5.71 seconds; 95% CI, 7.22-18.63 seconds; adjusted P = .19). The deep learning model developed successfully detected clinically significant intracranial aneurysms on CTA. This suggests that integration of an artificial intelligence-assisted diagnostic model may augment clinician performance with dependable and accurate predictions and thereby optimize patient care.

\"How do the billions of connections between neurons in our brain change as we learn and remember? This is the story of the discovery and the discoverer of synaptic pruning, the process of synapse elimination central to making us who we are. Taking the reader from Professor Peter Huttenlocher's childhood in wartime and post-war Germany to his emigration to the US to reunite with his mother and the launch and progress of a career in medicine and research, we uncover the motivations and process of scientific discovery that led to an unexpected leap in our understanding of the human brain. Decades after the discovery, the importance of synaptic pruning to early learning, autism, schizophrenia, Alzheimer's disease and other conditions are now in the process of being uncovered. Accessible examples illustrate how, decades later, researchers are discovering the importance of synaptic pruning in early learning, autism, schizophrenia and Alzheimer's disease.\" -- Back cover.