Nearly one-third of patients with focal epilepsy experience disabling seizures that are refractory to pharmacotherapy. Drug-resistant focal epilepsy is, however, potentially curable by surgery. Although lesions associated with the epileptic focus can often be accurately detected by MRI, in many patients conventional imaging based on visual evaluation is unable to pinpoint the surgical target. Patients with so-called cryptogenic epilepsy represent one of the greatest clinical challenges in many tertiary epilepsy centers. In recent years, it has become increasingly clear that epilepsies that are considered cryptogenic are not necessarily nonlesional, the primary histopathological substrate being subtle cortical dysplasia. This Review considers the application of new advances in brain imaging, such as MRI morphometry, computational modeling and diffusion tensor imaging. By revealing dysplastic lesions that previously eluded visual assessments, quantitative structural MRI methods such as these have clearly demonstrated an increased diagnostic yield of epileptic lesions, and have provided successful surgical options to an increasing number of patients with 'cryptogenic' epilepsy.

Objective: To detect automatically focal cortical dysplasia (FCD) type II in patients with extratemporal epilepsy initially diagnosed as MRI-negative on routine inspection of 1.5 and 3.0T scans. Methods:We implemented an automated classifier relying on surface-based features of FCD morphology and intensity, taking advantage of their covariance. The method was tested on 19 patients (15 with histologically confirmed FCD) scanned at 3.0T, and cross-validated using a leave-one-out strategy. We assessed specificity in 24 healthy controls and 11 disease controls with temporal lobe epilepsy. Cross-dataset classification performance was evaluated in 20 healthy controls and 14 patients with histologically verified FCD examined at 1.5T.Results: Sensitivity was 74%, with 100% specificity (i.e., no lesions detected in healthy or disease controls). In 50% of cases, a single cluster colocalized with the FCD lesion, while in the remaining cases a median of 1 extralesional cluster was found. Applying the classifier (trained on 3.0T data) to the 1.5T dataset yielded comparable performance (sensitivity 71%, specificity 95%). Conclusion:In patients initially diagnosed as MRI-negative, our fully automated multivariate approach offered a substantial gain in sensitivity over standard radiologic assessment. The proposed method showed generalizability across cohorts, scanners, and field strengths. Machine learning may assist presurgical decision-making by facilitating hypothesis formulation about the epileptogenic zone. Classification of evidence:This study provides Class II evidence that automated machine learning of MRI patterns accurately identifies FCD among patients with extratemporal epilepsy initially diagnosed as MRI-negative. Neurology ® 2014;83:48-55 GLOSSARY FCD 5 focal cortical dysplasia; GM 5 gray matter; LDA 5 linear discriminant analysis; MNI 5 Montreal Neurological Institute; RI 5 relative intensity; SEEG 5 stereotactic implanted depth electrodes; TE 5 echo time; TLE 5 temporal lobe epilepsy; TR 5 repetition time; WM 5 white matter.Focal cortical dysplasia (FCD) type II, an epileptogenic developmental malformation, 1 is characterized by cortical dyslamination, hypertrophic and dysmorphic neurons, and balloon cells 2 ; it is the most common histopathology in surgical series of extratemporal lobe epilepsy.3 Reliable detection of this lesion is critical for successful surgery. 4 On MRI, FCD type II is characterized by cortical thickening, blurring of the gray-white matter junction, and hyperintense signal.5 Many lesions, however, elude best-practice neuroimaging protocols. To enhance visibility, previous studies have modeled its main features using voxel-based methods, such as texture and morphometric analysis. 6 The evaluation of multiple maps generated through these quantitative techniques is done visually, so that the yield and diagnostic confidence depend on the reader's familiarity with the algorithm. Limited generalizability also stems from the fact that these approaches have been validated mainly with lesion...

The adult functional connectome is well characterized by a macroscale spatial gradient of connectivity traversing from unimodal toward higher-order transmodal cortices that recapitulates known principles of hierarchical organization and myelination patterns. Despite an emerging literature assessing connectome properties in neonates, the presence of connectome gradients and particularly their correspondence to microstructure remains largely unknown. We derived connectome gradients using unsupervised techniques applied to functional connectivity data from 40 term-born neonates. A series of cortex-wide analysis examined associations to magnetic resonance imaging-derived morphological parameters (cortical thickness, sulcal depth, curvature), measures of tissue microstructure (intracortical T1w/T2w intensity, superficial white matter diffusion parameters), and subcortico-cortical functional connectivity. Our findings indicate that the primary neonatal connectome gradient runs between sensorimotor and visual anchors and captures specific associations to cortical and superficial white matter microstructure as well as thalamo-cortical connectivity. A second gradient indicated an anterior-to-posterior asymmetry in macroscale connectivity alongside an immature differentiation between unimodal and transmodal areas, indicating a connectome-level circuitry en route to an adult-like organization. Our findings reveal an important coordination of structural and functional interactions in the neonatal connectome across spatial scales. Observed associations were replicable across individual neonates, suggesting consistency and generalizability.