Purpose: The current gold standard for the localization of the cortical regions responsible for the initiation and propagation of the ictal activity is through the use of invasive electrocorticography (ECoG). This method is utilized to guide surgical intervention in cases of medically intractable epilepsy by identifying the location and extent of the epileptogenic focus. Recent studies have proposed mechanisms in which the activity of epileptogenic cortical networks, rather than discrete focal sources, contributes to the generation of the ictal state. If true, selective modulation of key network components could be employed for the prevention and termination of the ictal state. Methods: Here, we have applied graph theory methods as a means to identify critical network nodes in cortical networks during both ictal and interictal states. ECoG recordings were obtained from a cohort of 25 patients undergoing presurgical monitoring for the treatment of intractable epilepsy at the Mayo Clinic (Rochester, MN, U.S.A.). Key Findings: One graph measure, the betweenness centrality, was found to correlate with the location of the resected cortical regions in patients who were seizure‐free following surgical intervention. Furthermore, these network interactions were also observed during random nonictal periods as well as during interictal spike activity. These network characteristics were found to be frequency dependent, with high frequency gamma band activity most closely correlated with improved postsurgical outcome as has been reported in previous literature. Significance: These findings could lead to improved understanding of epileptogenesis. In addition, this theoretically allows for more targeted therapeutic interventions through the selected modulation or disruption of these epileptogenic networks.

Unlocking the dynamic inner workings of the brain continues to remain a grand challenge of the 21st century. To this end, functional neuroimaging modalities represent an outstanding approach to better understand the mechanisms of both normal and abnormal brain functions. The ability to image brain function with ever increasing spatial and temporal resolution has made a significant leap over the past several decades. Further delineation of functional networks could lead to improved understanding of brain function in both normal and diseased states. This paper reviews recent advancements and current challenges in dynamic functional neuroimaging techniques, including electrophysiological source imaging, multimodal neuroimaging integrating fMRI with EEG/MEG, and functional connectivity imaging.

Understanding the neural mechanisms responsible for human social interactions is difficult, since the brain activities of two or more individuals have to be examined simultaneously and correlated with the observed social patterns. We introduce the concept of hyper-brain network, a connectivity pattern representing at once the information flow among the cortical regions of a single brain as well as the relations among the areas of two distinct brains. Graph analysis of hyper-brain networks constructed from the EEG scanning of 26 couples of individuals playing the Iterated Prisoner's Dilemma reveals the possibility to predict non-cooperative interactions during the decision-making phase. The hyper-brain networks of two-defector couples have significantly less inter-brain links and overall higher modularity—i.e., the tendency to form two separate subgraphs—than couples playing cooperative or tit-for-tat strategies. The decision to defect can be “read” in advance by evaluating the changes of connectivity pattern in the hyper-brain network.

Frequency-derived identification of the propagation of information between brain regions has quickly become a popular area in the neurosciences. Of the various techniques used to study the propagation of activation within the central nervous system, the directed transfer function (DTF) has been well used to explore the functional connectivity during a variety of brain states and pathological conditions. However, the DTF method assumes the stationarity of the neural electrical signals and the time invariance of the connectivity among different channels over the investigated time window. Such assumptions may not be valid in the abnormal brain signals such as seizures and interictal spikes in epilepsy patients. In the present study, we have developed an adaptive DTF (ADTF) method through the use of a multivariate adaptive autoregressive model to study the time-variant propagation of seizures and interictal spikes in simulated electrocorticogram (ECoG) networks. The time-variant connectivity reconstruction is achieved by the Kalman filter algorithm, which can incorporate time-varying state equations. We study the performance of the proposed method through simulations with various propagation models using either sample seizures or interictal spikes as the source waveform. The present results suggest that the new ADTF method correctly captures the temporal dynamics of the propagation models, while the DTF method cannot, and even returns erroneous results in some cases. The present ADTF method was tested in real epileptiform ECoG data from an epilepsy patient, and the ADTF results are consistent with the clinical assessments performed by neurologists.

Summary: Purpose:  Determination of the origin of extratemporal neocortical onset seizures is often challenging due to the rapid speed at which they propagate throughout the cortex. Typically, these patients are poor surgical candidates and many times experience recurrences of seizure activity following resection of the assumed seizure focus.Methods:  We applied a causal measurement technique—the directed transfer function (DTF)—in an effort to determine the cortical location responsible for the propagation of the seizure activity. Intracranial seizure recordings were obtained from a group of 11 pediatric patients with medically intractable neocortical‐onset epilepsy. Time windows were selected from the recordings following onset of the ictal activity. The DTF was applied to the selected time windows, and the frequency‐specific statistically significant source activity arising from each cortical recording site was quantified. The DTF‐estimated source activity was then compared with the seizure‐onset zone(s) identified by the epileptologists.Results:  In an analysis of the 11 pediatric patients, the DTF was shown to identify estimated ictal sources that were highly correlated with the clinically identified foci. In addition, it was observed that in the patients with multiple ictal foci, the topography of the casual source activity from the analyzed seizures was associated with the separate clinically identified seizure‐onset zones.Discussion:  Although localization of neocortical‐onset seizures is typically challenging, the causal measures employed in this study—namely the directed transfer function—identified generators of the ictal activity that were highly correlated with the cortical regions identified as the seizure‐onset zones by the epileptologists. This technique could prove useful in the identification of seizure‐specific propagation pathways in the presurgical evaluation of patients with epilepsy.

Radiotherapy is ubiquitous in the treatment of patients with both primary brain tumors as well as disease which is metastatic to the brain. This therapy is not without cost, however, as cognitive decline is frequently associated with cranial radiation, particularly with whole brain radiotherapy (WBRT). The precise mechanisms responsible for radiation-induced morbidity remain incompletely understood and continue to be an active area of ongoing research. In this article, we review the hypothetical means by which cranial radiation induces cognitive decline as well as potential therapeutic approaches to prevent, minimize, or reverse treatment-induced cognitive deterioration. We additionally review advances in imaging modalities that can potentially be used to identify site-specific radiation-induced anatomic or functional changes in the brain and their correlation with clinical outcomes.