A Century Searching for the Neurons Necessary for Wakefulness.

Wakefulness is necessary for consciousness, and impaired wakefulness is a symptom of many diseases. The neural circuits that maintain wakefulness remain incompletely understood, as do the mechanisms of impaired consciousness in many patients. In contrast to the influential concept of a diffuse "reticular activating system," the past century of neuroscience research has identified a focal region of the upper brainstem that, when damaged, causes coma. This region contains diverse neuronal populations with different axonal projections, neurotransmitters, and genetic identities. Activating some of these populations promotes wakefulness, but it remains unclear which specific neurons are necessary for sustaining consciousness. In parallel, pharmacological evidence has indicated a role for special neurotransmitters, including hypocretin/orexin, histamine, norepinephrine, serotonin, dopamine, adenosine and acetylcholine. However, genetically targeted experiments have indicated that none of these neurotransmitters or the neurons producing them are individually necessary for maintaining wakefulness. In this review, we emphasize the need to determine the specific subset of brainstem neurons necessary for maintaining arousal. Accomplishing this will enable more precise mapping of wakefulness circuitry, which will be useful in developing therapies for patients with coma and other disorders of arousal.

Direct Parabrachial-Cortical Connectivity.

The parabrachial nucleus (PB) in the upper brain stem tegmentum includes several neuronal subpopulations with a wide variety of connections and functions. A subpopulation of PB neurons projects axons directly to the cerebral cortex, and limbic areas of the cerebral cortex send a return projection directly to the PB. We used retrograde and Cre-dependent anterograde tracing to identify genetic markers and characterize this PB-cortical interconnectivity in mice. Cortical projections originate from glutamatergic PB neurons that contain Lmx1b (81%), estrogen receptor alpha (26%), and Satb2 (20%), plus mRNA for the neuropeptides cholecystokinin (Cck, 48%) and calcitonin gene-related peptide (Calca, 13%), with minimal contribution from FoxP2+ PB neurons (2%). Axons from the PB produce an extensive terminal field in an unmyelinated region of the insular cortex, extending caudally into the entorhinal cortex, and arcing rostrally through the dorsolateral prefrontal cortex, with a secondary terminal field in the medial prefrontal cortex. In return, layer 5 neurons in the insular cortex and other prefrontal areas, along with a dense cluster of cells dorsal to the claustrum, send a descending projection to subregions of the PB that contain cortically projecting neurons. This information forms the neuroanatomical basis for testing PB-cortical interconnectivity in arousal and interoception.

Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival.

PURPOSE: Despite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype-growth before treatment and during each treatment to recurrence -has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK. PATIENTS AND METHODS: From our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment. RESULTS: There was a significant survival difference between the clusters ( P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis ( P = .027) and faster growth before treatment ( P = .003) but demonstrated a better response to adjuvant chemotherapy ( P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%). CONCLUSION: Dichotomizing the heterogeneity of GBMs into two populations-one faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival-suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments.