Getting the best outcomes from epilepsy surgery.

Neurosurgery is an underutilized treatment that can potentially cure drug-refractory epilepsy. Careful, multidisciplinary presurgical evaluation is vital for selecting patients and to ensure optimal outcomes. Advances in neuroimaging have improved diagnosis and guided surgical intervention. Invasive electroencephalography allows the evaluation of complex patients who would otherwise not be candidates for neurosurgery. We review the current state of the assessment and selection of patients and consider established and novel surgical procedures and associated outcome data. We aim to dispel myths that may inhibit physicians from referring and patients from considering neurosurgical intervention for drug-refractory focal epilepsies. Ann Neurol 2018;83:676-690

Combining tractography and cortical measures to test system-specific hypotheses in multiple sclerosis

The objective was to test three motor system-specific hypotheses in multiple sclerosis patients: (i) corticospinal tract and primary motor cortex imaging measures differ between multiple sclerosis patients and controls; (ii) in patients, these measures correlate with disability; (iii) in patients, corticospinal tract measures correlate with measures of the ipsilateral primary motor cortex

The effect of gadolinium-based contrast-agents on automated brain atrophy measurements by FreeSurfer in patients with multiple sclerosis

Objective To determine whether reliable brain atrophy measures can be obtained from post-contrast 3D T1-weighted images in patients with multiple sclerosis (MS) using FreeSurfer. Methods Twenty-two patients with MS were included, in which 3D T1-weighted MR images were obtained during the same scanner visit, with the same acquisition protocol, before and after administration of gadolinium-based contrast agents (GBCAs). Two FreeSurfer versions (v.6.0.1 and v.7.1.1.) were applied to calculate grey matter (GM) and white matter (WM) volumes and global and regional cortical thickness. The consistency between measures obtained in pre- and post-contrast images was assessed by intra-class correlation coefficient (ICC), the difference was investigated by paired t-tests, and the mean percentage increase or decrease was calculated for total WM and GM matter volume, total deep GM and thalamus volume, and mean cortical thickness. Results Good to excellent reliability was found between all investigated measures, with ICC ranging from 0.926 to 0.996, all p values < 0.001. GM volumes and cortical thickness measurements were significantly higher in post-contrast images by 3.1 to 17.4%, while total WM volume decreased significantly by 1.7% (all p values < 0.001). Conclusion The consistency between values obtained from pre- and post-contrast images was excellent, suggesting it may be possible to extract reliable brain atrophy measurements from T1-weighted images acquired after administration of GBCAs, using FreeSurfer. However, absolute values were systematically different between pre- and post-contrast images, meaning that such images should not be compared directly. Potential systematic effects, possibly dependent on GBCA dose or the delay time after contrast injection, should be investigated.publishedVersio

A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence

A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence

Measurement of cortical thickness asymmetry in carotid occlusive disease

Despite being considered an important anatomical parameter directly related to neuronal density, cortical thickness is not routinely assessed in studies of the human brain in vivo. This paucity has been largely due to the size and convoluted shape of the human cortex, which has made it difficult to develop automated algorithms that can measure cortical thickness efficiently and reliably. Since the development of such an algorithm by Fischl and Dale in 2000, the number of studies investigating the relationship between cortical thickness and other physiological parameters in the brain has been on the rise. There have been no studies however that have validated cortical asymmetry against known vascular anatomy. To this aim, using high-resolution MRI, we measured cortical thickness and volume in the primary motor (M1) and primary visual (V1) cortex in patients with unilateral, high-grade carotid occlusive disease (n = 29, age = 74 ± 10 years). These regions were selected based on the hypothesis that there will be thinning of the cortical thickness of M1 in the territory supplied by the occluded carotid artery, whereas V1 will show no asymmetry since its blood supply is provided by unaffected posterior arteries. To test for an effect of handedness, cortical thickness and volume were also measured in healthy volunteers (n = 8, age = 37 ± 13 years). In patients, we found thinner cortex in M1 on the occluded side (mean = 2.07 ± 0.19 mm vs 2.15 ± 0.20 mm, p = 0.0008) but no hemispheric difference in V1 (1.80 ± 0.17 mm in occluded vs 1.78 ± 0.16 mm in unoccluded, p = 0.31). Although the mean cortical volume of M1 in the occluded hemisphere was also lower, the difference did not reach statistical significance (p = 0.09). Similarly, in healthy controls, the results showed no hemispheric asymmetry in either cortical thickness or volume in either region (p \u3e 0.1). To test for an orientation bias in the method, the analysis was repeated with images flipped from neurological to radiological orientation. While the algorithm did not yield identical results for the two orientations, the effect did not alter the findings of the study. These results provide a method for within-subject validation of a pathophysiological effect of carotid occlusive disease on the human cortex and warrant further investigation for underlying mechanisms

Early diffusion evidence of retrograde transsynaptic degeneration in the human visual system

We investigated whether diffusion tensor imaging (DTI) indices of white matter integrity would offer early markers of retrograde transsynaptic degeneration (RTD) in the visual system after stroke Objective: We investigated whether diffusion tensor imaging (DTI) indices of white matter integrity would offer early markers of retrograde transsynaptic degeneration (RTD) in the visual system after stroke. Methods: We performed a prospective longitudinal analysis of the sensitivity of DTI markers of optic tract health in 12 patients with postsynaptic visual pathway stroke, 12 stroke controls, and 28 healthy controls. We examined group differences in (1) optic tract fractional anisotropy (FA-asymmetry), (2) perimetric measures of visual impairment, and (3) the relationship between FA-asymmetry and perimetric assessment. Results: FA-asymmetry was higher in patients with visual pathway lesions than in control groups. These differences were evident 3 months from the time of injury and did not change significantly at 12 months. Perimetric measures showed evidence of impairment in participants with visual pathway stroke but not in control groups. A significant association was observed between FA-asymmetry and perimetric measures at 3 months, which persisted at 12 months. Conclusions: DTI markers of RTD are apparent 3 months from the time of injury. This represents the earliest noninvasive evidence of RTD in any species. Furthermore, these measures associate with measures of visual impairment. DTI measures offer a reproducible, noninvasive, and sensitive method of investigating RTD and its role in visual impairment