Diffusion-weighted imaging (DWI) has emerged as the most sensitive magnetic resonance imaging (MRI) sequence in the diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD),^1-3 with studies^4-6 showing its superiority for detecting signal change in the cortex. Visual inspection of the trace-weighted diffusion image demonstrates typically increased signal intensity (SI) in the cortex, with up to 95% of cases showing hyperintensity affecting the insula, cingulate, and superior frontal cortex independent of deep gray matter involvement.³ The extent of involvement and the distribution of signal changes vary among patients and are thought to be influenced by PRNP (OMIM 123400) genotype and strain type of the abnormal isoform of the prion protein.^7,8 However, few studies^9,10 have assessed the evolution of these signal abnormalities as sCJD progresses.

Fluid intelligence is arguably the defining feature of human cognition. Yet the nature of its relationship with the brain remains a contentious topic. Influential proposals drawing primarily on functional imaging data have implicated ‘multiple demand’ frontoparietal and more widely distributed cortical networks, but extant lesion-deficit studies with greater causal power are almost all small, methodologically constrained, and inconclusive. The task demands large samples of patients, comprehensive investigation of performance, fine-grained anatomical mapping, and robust lesion-deficit inference, yet to be brought to bear on it. We assessed 165 healthy controls and 227 frontal or non-frontal patients with unilateral brain lesions on the best-established test of fluid intelligence, Raven’s Advanced Progressive Matrices, employing an array of lesion-deficit inferential models responsive to the potentially distributed nature of fluid intelligence. Non-parametric Bayesian stochastic block models were used to reveal the community structure of lesion deficit networks, disentangling functional from confounding pathological distributed effects. Impaired performance was confined to patients with frontal lesions [F(2,387) = 18.491; P < 0.001; frontal worse than non-frontal and healthy participants P < 0.01, P <0.001], more marked on the right than left [F(4,385) = 12.237; P < 0.001; right worse than left and healthy participants P < 0.01, P < 0.001]. Patients with non-frontal lesions were indistinguishable from controls and showed no modulation by laterality. Neither the presence nor the extent of multiple demand network involvement affected performance. Both conventional network-based statistics and non-parametric Bayesian stochastic block modelling heavily implicated the right frontal lobe. Crucially, this localization was confirmed on explicitly disentangling functional from pathology-driven effects within a layered stochastic block model, prominently highlighting a right frontal network involving middle and inferior frontal gyrus, pre- and post-central gyri, with a weak contribution from right superior parietal lobule. Similar results were obtained with standard lesion-deficit analyses. Our study represents the first large-scale investigation of the distributed neural substrates of fluid intelligence in the focally injured brain. Combining novel graph-based lesion-deficit mapping with detailed investigation of cognitive performance in a large sample of patients provides crucial information about the neural basis of intelligence. Our findings indicate that a set of predominantly right frontal regions, rather than a more widely distributed network, is critical to the high-level functions involved in fluid intelligence. Further they suggest that Raven’s Advanced Progressive Matrices is a useful clinical index of fluid intelligence and a sensitive marker of right frontal lobe dysfunction.

Progress in neuro-oncology is increasingly recognized to be obstructed by the marked heterogeneity—genetic, pathological, and clinical—of brain tumours. If the treatment susceptibilities and outcomes of individual patients differ widely, determined by the interactions of many multimodal characteristics, then large-scale, fully-inclusive, richly phenotyped data—including imaging—will be needed to predict them at the individual level. Such data can realistically be acquired only in the routine clinical stream, where its quality is inevitably degraded by the constraints of real-world clinical care. Although contemporary machine learning could theoretically provide a solution to this task, especially in the domain of imaging, its ability to cope with realistic, incomplete, low-quality data is yet to be determined. In the largest and most comprehensive study of its kind, applying state-of-the-art brain tumour segmentation models to large scale, multi-site MRI data of 1251 individuals, here we quantify the comparative fidelity of automated segmentation models drawn from MR data replicating the various levels of completeness observed in real life. We demonstrate that models trained on incomplete data can segment lesions very well, often equivalently to those trained on the full completement of images, exhibiting Dice coefficients of 0.907 (single sequence) to 0.945 (complete set) for whole tumours and 0.701 (single sequence) to 0.891 (complete set) for component tissue types. This finding opens the door both to the application of segmentation models to large-scale historical data, for the purpose of building treatment and outcome predictive models, and their application to real-world clinical care. We further ascertain that segmentation models can accurately detect enhancing tumour in the absence of contrast-enhancing imaging, quantifying the burden of enhancing tumour with an R² > 0.97, varying negligibly with lesion morphology. Such models can quantify enhancing tumour without the administration of intravenous contrast, inviting a revision of the notion of tumour enhancement if the same information can be extracted without contrast-enhanced imaging. Our analysis includes validation on a heterogeneous, real-world 50 patient sample of brain tumour imaging acquired over the last 15 years at our tertiary centre, demonstrating maintained accuracy even on non-isotropic MRI acquisitions, or even on complex post-operative imaging with tumour recurrence. This work substantially extends the translational opportunity for quantitative analysis to clinical situations where the full complement of sequences is not available and potentially enables the characterization of contrast-enhanced regions where contrast administration is infeasible or undesirable.

White matter (WM) abnormalities are frequently seen on brain MRI of HIV positive (HIV+) patients. We aimed to determine the prevalence of unexplained WM abnormalities and their associations with HIV disease and cardiovascular risk factors. We conducted a retrospective, cross-sectional study of brain MRI of HIV+ patients conducted between 2004 and 2009 at our center. Clinical and laboratory data were compiled, and images were independently reviewed for WM lesions. Images were obtained from 254 patients: 70% male, 53% white, 40% black, mean age 42 years, median current CD4 count 240 cells/mm ³ , and 41% not taking antiretroviral therapy (ART). Hyperintense WM lesions were present in 161 patients (63.4%): 89 scans (35.0%) showed diffuse WM signal abnormality (DWMSA), 61 (24.0%) were consistent with small vessel disease (SVD, graded by Fazekas' scale), and 37 (14.6%) showed large asymmetrical focal WM lesions. SVD changes were associated with age and cardiovascular risk factors, and while cerebral SVD may be related to HIV infection, the MRI findings were not associated with HIV-related factors. The only risk factor for DWMSA was black race, and no correlation with cardiovascular risk factors, CD4 count, or clinical presentation was identified. DWMSA are therefore of uncertain neurological significance in HIV+ patients and could represent more than one clinicopathological entity.