Emotional visual stimuli evoke enhanced responses in the visual cortex. To test whether this reflects modulatory influences from the amygdala on sensory processing, we used event-related functional magnetic resonance imaging (fMRI) in human patients with medial temporal lobe sclerosis. Twenty-six patients with lesions in the amygdala, the hippocampus or both, plus 13 matched healthy controls, were shown pictures of fearful or neutral faces in task-releant or task-irrelevant positions on the display. All subjects showed increased fusiform cortex activation when the faces were in task-relevant positions. Both healthy individuals and those with hippocampal damage showed increased activation in the fusiform and occipital cortex when they were shown fearful faces, but this was not the case for individuals with damage to the amygdala, even though visual areas were structurally intact. The distant influence of the amygdala was also evidenced by the parametric relationship between amygdala damage and the level of emotional activation in the fusiform cortex. Our data show that combining the fMRI and lesion approaches can help reveal the source of functional modulatory influences between distant but interconnected brain regions.

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 metaanalysing 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 = À0.24 to À0.73; P 5 1.49 Â 10 À4 ), and lower thickness in the precentral gyri bilaterally (d = À0.34 to À0.52; P 5 4.31 Â 10 À6 ). Both MTLE subgroups showed profound volume reduction in the ipsilateral hippocampus (d = À1.73 to À1.91, P 5 1.4 Â 10 À19 ), and lower thickness in extrahippocampal cortical regions, including the precentral and paracentral gyri, compared to controls (d = À0.36 to À0.52; P 5 1.49 Â 10 À4 ). 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 = À0.29 to À0.54; P 5 1.49 Â 10 À4 ). Contrastingly, thickness differences of the ipsilateral pars opercularis, and contralateral transverse temporal gyrus, were observed in right, but not left, MTLE (d = À0.27 to À0.51; P 5 1.49 Â 10 À4 ). 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 5 À0.0018; P 5 1.49 Â 10 À4 ). 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.

Surgery is a valuable option for pharmacologically intractable epilepsy. However, significant post-operative improvements are not always attained. This is due in part to our incomplete understanding of the seizure generating (ictogenic) capabilities of brain networks. Here we introduce an in silico, model-based framework to study the effects of surgery within ictogenic brain networks. We find that factors conventionally determining the region of tissue to resect, such as the location of focal brain lesions or the presence of epileptiform rhythms, do not necessarily predict the best resection strategy. We validate our framework by analysing electrocorticogram (ECoG) recordings from patients who have undergone epilepsy surgery. We find that when post-operative outcome is good, model predictions for optimal strategies align better with the actual surgery undertaken than when post-operative outcome is poor. Crucially, this allows the prediction of optimal surgical strategies and the provision of quantitative prognoses for patients undergoing epilepsy surgery.

We have studied patients with variable degrees of left hippocampal and amygdala pathology who performed a verbal encoding task during functional magnetic resonance imaging (fMRI) to assess the impact of pathology on emotional-memory performance and encoding-evoked activity. The severity of left hippocampal pathology predicted memory performance for neutral and emotional items alike, whereas the severity of amygdala pathology predicted memory performance for emotional items alone. Encoding-related hippocampal activity for successfully remembered emotional items correlated with the degree of left amygdala pathology. Conversely, amygdala-evoked activity with respect to subsequently remembered emotional items correlated with the degree of left hippocampal pathology. Our data indicate a reciprocal dependence between amygdala and hippocampus during the encoding of emotional memories.

Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.

Diffusion Tensor Imaging (DTI) is being increasingly used to assess white matter integrity and it is therefore paramount to address the test–retest reliability of DTI measures. In this study we assessed inter- and intra-site reproducibility of two nominally identical 3 T scanners at different sites in nine healthy controls using a DTI protocol representative of typical current “best practice” including cardiac gating, a multichannel head coil, parallel imaging and optimized diffusion gradient parameters. We calculated coefficients of variation (CV) and intraclass correlation coefficients (ICC) of fractional anisotropy (FA) measures for the whole brain, for three regions of interest (ROI) and for three tracts derived from these ROI by probabilistic tracking. We assessed the impact of affine, nonlinear and template based methods for spatially aligning FA maps on the reproducibility. The intra-site CV for FA ranged from 0.8% to 3.0% with ICC from 0.90 to 0.99, while the inter-site CV ranged from 1.0% to 4.1% with ICC of 0.82 to 0.99. Nonlinear image coregistration improved reproducibility compared to affine coregistration. Normalization to template space reduced the between-subject variation, resulting in lower ICC values and indicating a possibly reduced sensitivity. CV from probabilistic tractography were about 50% higher than for the corresponding seed ROI.Reproducibility maps of the whole scan volume showed a low variation of less than 5% in the major white matter tracts but higher variations of 10–15% in gray matter regions.One of the two scanners showed better intra-site reproducibility, while the intra-site CV for both scanners was significantly better than inter-site CV. However, when using nonlinear coregistration of FA maps, the average inter-site CV was below 2%. There was a consistent inter-site bias, FA values on site 2 were 1.0–1.5% lower than on site 1. Correction for this bias with a global scaling factor reduced the inter-site CV to the range of intra-site CV. Our results are encouraging for multi-centre DTI studies in larger populations, but also illustrate the importance of the image processing pipeline for reproducibility.

Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs. In pre-surgical planning, an array of data modalities, often including intra-cranial EEG, is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures. These regions are then resected with the hope that the individual is rendered seizure free as a consequence. However, post-operative seizure freedom is currently sub-optimal, suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks. Herein we use mathematical models to uncover the relative contribution of regions of the brain to seizure generation and consequently which brain regions should be considered for resection. A critical advantage of this modeling approach is that the effect of different surgical strategies can be predicted and quantitatively compared in advance of surgery. Herein we seek to understand seizure generation in networks with different topologies and study how the removal of different nodes in these networks reduces the occurrence of seizures. Since this a computationally demanding problem, a first step for this aim is to facilitate tractability of this approach for large networks. To do this, we demonstrate that predictions arising from a neural mass model are preserved in a lower dimensional, canonical model that is quicker to simulate. We then use this simpler model to study the emergence of seizures in artificial networks with different topologies, and calculate which nodes should be removed to render the network seizure free. We find that for scale-free and rich-club networks there exist specific nodes that are critical for seizure generation and should therefore be removed, whereas for small-world networks the strategy should instead focus on removing sufficient brain tissue. We demonstrate the validity of our approach by analysing intra-cranial EEG recordings from a database comprising 16 patients who have undergone epilepsy surgery, revealing rich-club structures within the obtained functional networks. We show that the postsurgical outcome for these patients was better when a greater proportion of the rich club was removed, in agreement with our theoretical predictions.

The brain is in a constant state of dynamic change, for example switching between cognitive and behavioural tasks, and between wakefulness and sleep. The brains of people with epilepsy have additional features to their dynamic repertoire, particularly the paroxysmal occurrence of seizures. Substantial effort over decades has produced a detailed description of many human epilepsies and of specific seizure types; in some instances there are known causes, sometimes highly specific such as single gene mutations, but the mechanisms of seizure onset and termination are not known. A large number of in vivo animal models and in vitro models based on animal tissues can generate seizures and seizure-like phenomena. Although in some instances there is much known about the mechanism of seizure onset and termination, across the range of models there is a bewildering range of mechanisms. There is a pressing need to bridge the gap between microscale mechanisms in experimental models and mechanisms of human epilepsies. Computational models of epilepsy have advanced rapidly, allowing dynamic mechanisms to be revealed in a computer model that can then be tested in biological systems. These models are typically simplified, leaving a need to scale up these models to the large scale brain networks in which seizures become manifest. The emerging science of connectomics provides an approach to understanding the large scale brain networks in which normal and abnormal brain functions operate. The stage is now set to couple dynamics with connectomics, to reveal the abnormal dynamics of brain networks which allow seizures to occur.It seems likely that our current understanding of epilepsy will radically change in the next decade. Conventional clinical concepts in epilepsy are still founded on a scheme developed 30 years ago. Seizure types were assigned to two major categories: focal or generalised. Despite the clarity of this classification, even in the original description its unsatisfactory nature was recognised by the additional category of 'unclassifiable' seizures. Likewise, the disorders causing epilepsy were classified into presumed localised brain disturbances and those with a presumed generalised disturbance; nonetheless, some epilepsies defied this dichotomy and were labelled 'unclassifiable'. Given ever expanding knowledge about epilepsy, the need to re-examine these approaches to classification has been clear for several years. In particular, the identification of widespread and often bilateral phenomena in supposedly focal epilepsies 3 4 and the finding of focal features in supposedly generalised epilepsy syndromesdbest exemplified by the finding of a localised cortical focus which drives absence seizures in a rat model 5e7 dsuggest that even the focal/generalised dichotomy is not clearcut. Unfortunately, the wide range of gene mutations coupled with the vast array of experimental manipulations that can give rise to seizures results in a bewildering picture: unifying epilepsy mechanisms do not seem to have emerged yet...