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Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

Articles 56-59 of Henry of Ghent's 'Summa' is devoted to the trinitarian properties. Henry was the most important Christian theological thinker in the last quarter of the 13th century and his works were influential not only in his lifetime, but also in the following century and into the Renaissance. Henry's 'Quaestiones ordinariae' ('Summa'), articles 56-59 deal with the trinitarian properties and relations, topics of Henry's lectures at the university in Paris. In these articles, dated around 1286, Henry treats generation, a property unique to the Father, and being generated, a property unique to the Son. The university in Paris distributed articles 56-59 by means of two successive exemplars divided into 'peciae'. Manuscripts copied from each have survived and the text of the critical edition has been established based upon the reconstructed texts of these two exemplars.

Dementia is a leading cause of disability and death worldwide. At present there is no disease modifying treatment for any of the most common types of dementia such as Alzheimer’s disease (AD), Vascular dementia, Lewy Body Dementia (LBD) and Frontotemporal dementia (FTD). Early and accurate diagnosis of dementia subtype is critical to improving clinical care and developing better treatments. Structural and molecular imaging has contributed to a better understanding of the pathophysiology of neurodegenerative dementias and is increasingly being adopted into clinical practice for early and accurate diagnosis. In this review we summarise the contribution imaging has made with particular focus on multimodal magnetic resonance imaging (MRI) and positron emission tomography imaging (PET). Structural MRI is widely used in clinical practice and can help exclude reversible causes of memory problems but has relatively low sensitivity for the early and differential diagnosis of dementia subtypes. 18 F-fluorodeoxyglucose PET has high sensitivity and specificity for AD and FTD, while PET with ligands for amyloid and tau can improve the differential diagnosis of AD and non-AD dementias, including recognition at prodromal stages. Dopaminergic imaging can assist with the diagnosis of LBD. The lack of a validated tracer for α-synuclein or TAR DNA-binding protein 43 (TDP-43) imaging remain notable gaps, though work is ongoing. Emerging PET tracers such as 11 C-UCB-J for synaptic imaging may be sensitive early markers but overall larger longitudinal multi-centre cross diagnostic imaging studies are needed.

Brain network dysfunction is increasingly recognised in Alzheimer’s disease (AD). However, the causes of brain connectivity disruption are still poorly understood. Recently, neuroinflammation has been identified as an important factor in AD pathogenesis. Microglia participate in the construction and maintenance of healthy neuronal networks, but pro-inflammatory microglia can also damage these circuits. We hypothesised that microglial activation is independently associated with brain connectivity disruption in AD. We performed a cross-sectional multimodal imaging study and interrogated the relationship between imaging biomarkers of neuroinflammation, Aβ deposition, brain connectivity and cognition. 42 participants (12 Aβ-positive MCI, 14 Aβ-positive AD and 16 Aβ-negative healthy controls) were recruited. Participants had 11 C-PBR28 and 18 F-flutemetamol PET to quantify Aβ deposition and microglial activation, T1-weighted, diffusion tensor and resting-state functional MRI to assess structural network and functional network. 11 C-PBR28 uptake, structural network integrity and functional network orgnisation were compared across diagnostic groups and the relationship between neuroinflammation and brain network was tested in 26 Aβ-positive patients. Increased 11 C-PBR28 uptake, decreased FA, network small-worldness and local efficiency were observed in AD patients. Cortical 11 C-PBR28 uptake correlated negatively with structural integrity (standardised β  = −0.375, p  = 0.037) and network local efficiency (standardised β  = −0.468, p  < 0.001), independent of cortical thickness and Aβ deposition, while Aβ was not. Network structural integrity, small-worldness and local efficiency, and cortical thickness were positively associated with cognition. Our findings suggest cortical neuroinflammation coincide with structural and functional network disruption independent of Aβ and cortical atrophy. These findings link the brain connectivity change and pathological process in Alzheimer’s disease, and suggest a pathway from neuroinflammation to systemic brain dysfunction.

Cognitive deficits are a core feature of schizophrenia, account for much of the impaired functioning associated with the disorder and are not responsive to existing treatments. In this review, we first describe the clinical presentation and natural history of these deficits. We then consider aetiological factors, highlighting how a range of similar genetic and environmental factors are associated with both cognitive function and schizophrenia. We then review the pathophysiological mechanisms thought to underlie cognitive symptoms, including the role of dopamine, cholinergic signalling and the balance between GABAergic interneurons and glutamatergic pyramidal cells. Finally, we review the clinical management of cognitive impairments and candidate novel treatments.

Obesity and type 2 diabetes are the most frequent metabolic disorders, but their causes remain largely unclear. Insulin resistance, the common underlying abnormality, results from imbalance between energy intake and expenditure favouring nutrient-storage pathways, which evolved to maximize energy utilization and preserve adequate substrate supply to the brain. Initially, dysfunction of white adipose tissue and circulating metabolites modulate tissue communication and insulin signalling. However, when the energy imbalance is chronic, mechanisms such as inflammatory pathways accelerate these abnormalities. Here we summarize recent studies providing insights into insulin resistance and increased hepatic gluconeogenesis associated with obesity and type 2 diabetes, focusing on data from humans and relevant animal models. A Review of studies into insulin resistance and hepatic gluconeogenesis associated with obesity and type 2 diabetes.

Key Points Semantic cognition refers to our ability to use, manipulate and generalize knowledge that is acquired over the lifespan to support innumerable verbal and non-verbal behaviours. Semantic cognition relies on two principal interacting neural systems: representation and control. We refer to this two-system view as the controlled semantic cognition framework. Coherent, generalizable concepts are formed through the hub-and-spoke representational system with the hub localised to the anterior temporal region (bilaterally) and spokes localised in modality-specific association cortices that are distributed across the cortex. Convergent clinical and cognitive neuroscience data show that the anterior temporal lobe hub has graded variations of semantic function that follow its pattern of connectivity. Category-specific differences in semantic function reflect the contributions of different parts of the connectivity-constrained version of the hub-and-spoke framework. Semantic control is implemented within a distributed frontal and temporoparietal neural network. Semantic control supports executive mechanisms that constrain how activation propagates through the network for semantic representation. Our ability to use conceptual knowledge to support various behaviours is termed semantic cognition. In this Review, Lambon Ralph et al . argue that this ability arises from two interacting neural systems, one for representation and one for control. Semantic cognition refers to our ability to use, manipulate and generalize knowledge that is acquired over the lifespan to support innumerable verbal and non-verbal behaviours. This Review summarizes key findings and issues arising from a decade of research into the neurocognitive and neurocomputational underpinnings of this ability, leading to a new framework that we term controlled semantic cognition (CSC). CSC offers solutions to long-standing queries in philosophy and cognitive science, and yields a convergent framework for understanding the neural and computational bases of healthy semantic cognition and its dysfunction in brain disorders.

Key Points The currently accepted model of a dorsal–ventral (posterior–anterior) dichotomy of hippocampal function requires revision. There is evidence for two types of long-axis organization: gradients and discrete transitions. Anatomical studies in rodents and primates show that hippocampal extrinsic connectivity is organized as smooth topographical gradients. The relative size of spatial representation by place cells in the rat hippocampus gradually increases from dorsal to ventral. By contrast, rodent gene expression studies demonstrate multiple long-axis functional domains with sharply demarcated borders: at least nine in CA3 and three in CA1 and the dentate gyrus. Hippocampal intrinsic anatomical connectivity, as well as electrophysiological measures of coherence, show abrupt division from the ventral (anterior) one-third to dorsal (posterior) two-thirds of the hippocampus. Hippocampal involvement in unconditioned fear responses is limited to the ventral one-third. When these levels of organization are superimposed, a new model of hippocampal long-axis organization emerges: gradients with discrete domains, the latter dividing the hippocampus into (at least) three portions along the long axis. This model supersedes a simple dissociation of the dorsal from the ventral hippocampus and provides a potential framework for accommodating the multiple functions ascribed to the hippocampus in rodents and primates. It is commonly thought that the dorsal hippocampus is implicated in memory and spatial navigation and the ventral hippocampus in anxiety-related behaviours. On the basis of gene expression, anatomical and electrophysiology studies, Strange and colleagues propose a new model of hippocampal functional anatomy, in which functional long-axis gradients are superimposed on discrete functional domains. The precise functional role of the hippocampus remains a topic of much debate. The dominant view is that the dorsal (or posterior) hippocampus is implicated in memory and spatial navigation and the ventral (or anterior) hippocampus mediates anxiety-related behaviours. However, this 'dichotomy view' may need revision. Gene expression studies demonstrate multiple functional domains along the hippocampal long axis, which often exhibit sharply demarcated borders. By contrast, anatomical studies and electrophysiological recordings in rodents suggest that the long axis is organized along a gradient. Together, these observations suggest a model in which functional long-axis gradients are superimposed on discrete functional domains. This model provides a potential framework to explain and test the multiple functions ascribed to the hippocampus.