Age-dependent alterations in the induction of long-term potentiation (LTP) are well documented, providing a likely neural basis for memory decline associated with aging. Studies of neural plasticity are also important to understand the neural basis of individual differences in aging, ranging from significant cognitive impairment to preservation of function on a par with younger adults. To examine the cellular mechanisms that distinguish such outcomes, we studied the induction of LTP in male outbred young and aged rats behaviorally characterized in hippocampal-dependent spatial learning. We evaluated, in vitro, the magnitude of NMDA receptor (NMDAR)-dependent and -independent forms of LTP induced in the Schaffer collateral to CA1 synapses. We found that age substantially reduces NMDAR-dependent LTP across the spectrum of cognitive outcomes, whereas increased NMDAR-independent LTP occurs distinctively in high-performing aged rats. Moreover, in young rats, behavioral performance correlates strongly with the magnitude of NMDAR-LTP, whereas NMDAR-independent LTP correlates with behavioral performance only in aged rats. Together with similar previous findings on the mechanisms for LTD in this model, these results support the notion that a shift from NMDAR-dependent to NMDAR-independent mechanisms for neural plasticity during aging is associated with better cognitive outcomes.

Objective Many forms of epilepsy are associated with aberrant neuronal connections, but the relationship between such pathological connectivity and the underlying physiological predisposition to seizures is unclear. We sought to characterize the cortical excitability profile of a developmental form of epilepsy known to have structural and functional connectivity abnormalities. Methods We employed transcranial magnetic stimulation (TMS) with simultaneous EEG recording in eight patients with epilepsy from periventricular nodular heterotopia (PNH) and matched healthy controls. We used connectivity imaging findings to guide TMS targeting and compared the evoked responses to single-pulse stimulation from different cortical regions. Results Heterotopia patients with active epilepsy demonstrated a relatively augmented late cortical response that was greater than that of matched controls. This abnormality was specific to cortical regions with connectivity to subcortical heterotopic gray matter. Topographic mapping of the late response differences showed distributed cortical networks that were not limited to the stimulation site, and source analysis in one subject revealed that the generator of abnormal TMS-evoked activity overlapped with the spike and seizure onset zone. Interpretation Our findings indicate that patients with epilepsy from gray matter heterotopia have altered cortical physiology consistent with hyperexcitability, and that this abnormality is specifically linked to the presence of aberrant connectivity. These results support the idea that TMS-EEG could be a useful biomarker in epilepsy in gray matter heterotopia, expand our understanding of circuit mechanisms of epileptogenesis, and have potential implications for therapeutic neuromodulation in similar epileptic conditions associated with deep lesions.

Hypothalamic neurons of the arcuate nucleus control food intake, releasing orexigenic and anorexigenic neuropeptides in response to changes in glucose concentration. Several studies have suggested that the glucosensing mechanism is governed by a metabolic interaction between neurons and glial cells via lactate flux through monocarboxylate transporters (MCTs). Hypothalamic glial cells (tanycytes) release lactate through MCT1 and MCT4; however, similar analyses in neuroendocrine neurons have yet to be undertaken. Using primary rat hypothalamic cell cultures and fluorimetric assays, lactate incorporation was detected. Furthermore, the expression and function of MCT2 was demonstrated in the hypothalamic neuronal cell line, GT1-7, using kinetic and inhibition assays. Moreover, MCT2 expression and localization in the Sprague Dawley rat hypothalamus was analyzed using RT-PCR, in situ hybridization and Western blot analyses. Confocal immunohistochemistry analyses revealed MCT2 localization in neuronal but not glial cells. Moreover, MCT2 was localized to ∼90% of orexigenic and ∼60% of anorexigenic neurons as determined by immunolocalization analysis of AgRP and POMC with MCT2-positives neurons. Thus, MCT2 distribution coupled with lactate uptake by hypothalamic neurons suggests that hypothalamic neurons control food intake using lactate to reflect changes in glucose levels.

BackgroundAutism spectrum disorders (ASD) are increasingly prevalent and have a significant impact on the lives of patients and their families. Currently, the diagnosis is determined by clinical judgment and no definitive physiological biomarker for ASD exists. Quantitative biomarkers obtainable from clinical neuroimaging data – such as the scalp electroencephalogram (EEG) - would provide an important aid to clinicians in the diagnosis of ASD. The interpretation of prior studies in this area has been limited by mixed results and the lack of validation procedures. Here we use retrospective clinical data from a well-characterized population of children with ASD to evaluate the rhythms and coupling patterns present in the EEG to develop and validate an electrophysiological biomarker of ASD.MethodsEEG data were acquired from a population of ASD (n = 27) and control (n = 55) children 4–8 years old. Data were divided into training (n = 13 ASD, n = 24 control) and validation (n = 14 ASD, n = 31 control) groups. Evaluation of spectral and functional network properties in the first group of patients motivated three biomarkers that were computed in the second group of age-matched patients for validation.ResultsThree biomarkers of ASD were identified in the first patient group: (1) reduced posterior/anterior power ratio in the alpha frequency range (8–14 Hz), which we label the “peak alpha ratio”, (2) reduced global density in functional networks, and (3) a reduction in the mean connectivity strength of a subset of functional network edges. Of these three biomarkers, the first and third were validated in a second group of patients. Using the two validated biomarkers, we were able to classify ASD subjects with 83 % sensitivity and 68 % specificity in a post-hoc analysis.ConclusionsThis study demonstrates that clinical EEG can provide quantitative biomarkers to assist diagnosis of autism. These results corroborate the general finding that ASD subjects have decreased alpha power gradients and network connectivities compared to control subjects. In addition, this study demonstrates the necessity of using statistical techniques to validate EEG biomarkers identified using exploratory methods.Electronic supplementary materialThe online version of this article (doi:10.1186/s12883-015-0355-8) contains supplementary material, which is available to authorized users.