The study of structural and functional magnetic resonance imaging data has greatly benefitted from the development of sophisticated and efficient algorithms aimed at automating and optimizing the analysis of brain data. We address, in the context of the segmentation of brain from non-brain tissue (i.e., brain extraction, also known as skull-stripping), the tension between the increased theoretical and clinical interest in patient data, and the difficulty of conventional algorithms to function optimally in the presence of gross brain pathology. Indeed, because of the reliance of many algorithms on priors derived from healthy volunteers, images with gross pathology can severely affect their ability to correctly trace the boundaries between brain and non-brain tissue, potentially biasing subsequent analysis. We describe and make available an optimized brain extraction script for the pathological brain (optiBET) robust to the presence of pathology. Rather than attempting to trace the boundary between tissues, optiBET performs brain extraction by (i) calculating an initial approximate brain extraction; (ii) employing linear and non-linear registration to project the approximate extraction into the MNI template space; (iii) back-projecting a standard brain-only mask from template space to the subject’s original space; and (iv) employing the back-projected brain-only mask to mask-out non-brain tissue. The script results in up to 94% improvement of the quality of extractions over those obtained with conventional software across a large set of severely pathological brains. Since optiBET makes use of freely available algorithms included in FSL, it should be readily employable by anyone having access to such tools.

Although the connectivity of hippocampal circuits has been extensively studied, the way in which these connections give rise to large-scale dynamic network activity remains unknown. Here, we used optogenetic fMRI to visualize the brain network dynamics evoked by different frequencies of stimulation of two distinct neuronal populations within dorsal and intermediate hippocampus. Stimulation of excitatory cells in intermediate hippocampus caused widespread cortical and subcortical recruitment at high frequencies, whereas stimulation in dorsal hippocampus led to activity primarily restricted to hippocampus across all frequencies tested. Sustained hippocampal responses evoked during high-frequency stimulation of either location predicted seizure-like afterdischarges in video-EEG experiments, while the widespread activation evoked by high-frequency stimulation of intermediate hippocampus predicted behavioral seizures. A negative BOLD signal observed in dentate gyrus during dorsal, but not intermediate, hippocampus stimulation is proposed to underlie the mechanism for these differences. Collectively, our results provide insight into the dynamic function of hippocampal networks and their role in seizures.

Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal explores the maze, quickly discovers the location of a reward, and executes correct 10-bit choices after only 10 reward experiences - a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules.

Objective: We employed functional MRI (fMRI) to assess whether (1) patients with disorders of consciousness (DOC) retain the ability to willfully engage in top-down processing and (2) what neurophysiologic factors distinguish patients who can demonstrate this ability from patients who cannot.Methods: Sixteen volunteers, 8 patients in vegetative state (VS), 16 minimally conscious patients (MCS), and 4 exit from MCS (eMCS) patients were enrolled in a prospective cross-sectional fMRI study. Participants performed a target detection task in which they counted the number of times a (changing) target word was presented amidst a set of distractors.Results: Three of 8 patients diagnosed as being in a VS exhibited significant activations in response to the task, thereby demonstrating a state of consciousness. Differential activations across tasks were also observed in 6 MCS patients and 1 eMCS patient. A psycho-physiologic interaction analysis revealed that the main factor distinguishing patients who responded to the task from those who did not was a greater connectivity between the anterior section of thalamus and prefrontal cortex.Conclusions: In our sample of patients, the dissociation between overt behavior observable in clinical assessments and residual cognitive faculties is prevalent among DOC patients (37%). A substantial number of patients, including some diagnosed with VS, can demonstrate willful engagement in top-down cognition. While neuroimaging data are not the same as observable behavior, this suggests that the mental status of some VS patients exceeds what can be appreciated clinically. Furthermore, thalamo-frontal circuits might be crucial to sustaining top-down functions. Neurology ® 2015;84:167-173 GLOSSARY DOC 5 disorders of consciousness; eMCS 5 exit from minimally conscious state; FA 5 flip angle; fMRI 5 functional MRI; GLM 5 generalized linear model; MCS 5 minimally conscious state; MNI 5 Montreal Neurological Institute; PPI 5 psychophysiologic interaction approach; ROI 5 region of interest; TE 5 echo time; TR 5 repetition time; VS 5 vegetative state.