Progressive functional decline in the epilepsies is largely unexplained. We formed the ENIGMA-Epilepsy consortium to understand factors that influence brain measures in epilepsy, pooling data from 24 research centres in 14 countries across Europe, North and South America, Asia, and Australia. Structural brain measures were extracted from MRI brain scans across 2149 individuals with epilepsy, divided into four epilepsy subgroups including idiopathic generalized epilepsies (n =367), mesial temporal lobe epilepsies with hippocampal sclerosis (MTLE; left, n = 415; right, n = 339), and all other epilepsies in aggregate (n = 1026), and compared to 1727 matched healthy controls. We ranked brain structures in order of greatest differences between patients and controls, by meta-analysing effect sizes across 16 subcortical and 68 cortical brain regions. We also tested effects of duration of disease, age at onset, and age-by-diagnosis interactions on structural measures. We observed widespread patterns of altered subcortical volume and reduced cortical grey matter thickness. Compared to controls, all epilepsy groups showed lower volume in the right thalamus (Cohen’s d = −0.24 to −0.73; P < 1.49 × 10⁻⁴), and lower thickness in the precentral gyri bilaterally (d = −0.34 to −0.52; P < 4.31 × 10⁻⁶). Both MTLE subgroups showed profound volume reduction in the ipsilateral hippocampus (d = −1.73 to −1.91, P < 1.4 × 10⁻¹⁹), and lower thickness in extrahippocampal cortical regions, including the precentral and paracentral gyri, compared to controls (d = −0.36 to −0.52; P < 1.49 × 10⁻⁴). Thickness differences of the ipsilateral temporopolar, parahippocampal, entorhinal, and fusiform gyri, contralateral pars triangularis, and bilateral precuneus, superior frontal and caudal middle frontal gyri were observed in left, but not right, MTLE (d = −0.29 to −0.54; P < 1.49 × 10⁻⁴). Contrastingly, thickness differences of the ipsilateral pars opercularis, and contralateral transverse temporal gyrus, were observed in right, but not left, MTLE (d = −0.27 to −0.51; P < 1.49 × 10⁻⁴). Lower subcortical volume and cortical thickness associated with a longer duration of epilepsy in the all-epilepsies, all-other-epilepsies, and right MTLE groups (beta, b < −0.0018; P < 1.49 × 10⁻⁴). In the largest neuroimaging study of epilepsy to date, we provide information on the common epilepsies that could not be realistically acquired in any other way. Our study provides a robust ranking of brain measures that can be further targeted for study in genetic and neuropathological studies. This worldwide initiative identifies patterns of shared grey matter reduction across epilepsy syndromes, and distinctive abnormalities between epilepsy syndromes, which inform our understanding of epilepsy as a network disorder, and indicate that certain epilepsy syndromes involve more widespread structural compromise than previously assumed.

One objective of modern neuroimaging is to identify markers that can aid in diagnosis, disease progression monitoring and long-term drug impact analysis. In this study, Parkinson-associated physiopathological modifications were characterized in six subcortical structures by simultaneously measuring quantitative magnetic resonance parameters sensitive to complementary tissue characteristics (i.e. volume atrophy, iron deposition and microstructural damage). Thirty patients with Parkinson’s disease and 22 control subjects underwent 3-T magnetic resonance imaging with T₂*-weighted, whole-brain T₁-weighted and diffusion tensor imaging scans. The mean R₂* value, mean diffusivity and fractional anisotropy in the pallidum, putamen, caudate nucleus, thalamus, substantia nigra and red nucleus were compared between patients with Parkinson’s disease and control subjects. Comparisons were also performed using voxel-based analysis of R₂*, mean diffusivity and fractional anisotropy maps to determine which subregion of the basal ganglia showed the greater difference for each parameter. Averages of each subregion were then used in a logistic regression analysis. Compared with control subjects, patients with Parkinson’s disease displayed significantly higher R₂* values in the substantia nigra, lower fractional anisotropy values in the substantia nigra and thalamus, and higher mean diffusivity values in the thalamus. Voxel-based analyses confirmed these results and, in addition, showed a significant difference in the mean diffusivity in the striatum. The combination of three markers was sufficient to obtain a 95% global accuracy (area under the receiver operating characteristic curve) for discriminating patients with Parkinson’s disease from controls. The markers comprising discriminating combinations were R₂* in the substantia nigra, fractional anisotropy in the substantia nigra and mean diffusivity in the putamen or caudate nucleus. Remarkably, the predictive markers involved the nigrostriatal structures that characterize Parkinson’s physiopathology. Furthermore, highly discriminating combinations included markers from three different magnetic resonance parameters (R₂*, mean diffusivity and fractional anisotropy). These findings demonstrate that multimodal magnetic resonance imaging of subcortical grey matter structures is useful for the evaluation of Parkinson’s disease and, possibly, of other subcortical pathologies.

Artificial Intelligence (ai) systems are precious support for decision-making, with many applications also in the medical domain. The interaction betweenmds andaienjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based knowledge of the context, design, and psychological mechanisms that craft an optimal human–aicollaboration. In this multicentric study, 21 endoscopists reviewed 504 videos of lesions prospectively acquired from real colonoscopies. They were asked to provide an optical diagnosis with and without the assistance of anaisupport system. Endoscopists were influenced byai( $$\textsc {or}=3.05$$OR=3.05 ), but not erratically: they followed theaiadvice more when it was correct ( $$\textsc {or}=3.48$$OR=3.48 ) than incorrect ( $$\textsc {or}=1.85$$OR=1.85 ). Endoscopists achieved this outcome through a weighted integration of their and theaiopinions, considering the case-by-case estimations of the two reliabilities. This Bayesian-like rational behavior allowed the human–aihybrid team to outperform both agents taken alone. We discuss the features of the human–aiinteraction that determined this favorable outcome.

White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data.

Background: The corpus callosum (CC) has been shown to be susceptible to atrophy in Alzheimer disease (AD) as a correlate of wallerian degeneration or retrogenesis. However, when and where these 2 mechanisms intervene is still unclear. Methods: In 3 memory clinics, we recruited 38 patients with amnestic mild cognitive impairment (MCI), 38 patients with mild AD, and 40 healthy controls (HC). Combining voxel-based morphometry and diffusion tensor imaging, we investigated CC white matter (WM) density and fractional anisotropy (FA), radial diffusivity (DR), and axial diffusivity (DA). Results: Compared with HC, patients with amnestic MCI showed reduced WM density in the anterior CC subregion; however, FA, DR, and DA did not differ between the 2 groups. Significant changes were found in patients with mild AD compared with HC in the anterior and posterior CC regions. These differences were evident in both voxel-based morphometry and diffusion tensor imaging analyses. Specifically, we found reduced callosal WM density in the genu, posterior body, and splenium; decreased FA and increased DR in the anterior CC subregion; and increased DA, with no difference in the FA, in the posterior CC subregion. Conclusions: Callosal changes are already present in patients with amnestic mild cognitive impairment (MCI) and mild Alzheimer disease (AD). The precocious involvement of the anterior callosal subregion in amnestic MCI extends to posterior regions in mild AD. Two different mechanisms might contribute to the white matter changes in mild AD: wallerian degeneration in posterior subregions of the corpus callosum (suggested by increased axial diffusivity without fractional anisotropy modifications) and a retrogenesis process in the anterior callosal subregions (suggested by increased radial diffusivity without axial diffusivity modifications).