Neural correlates of adherence to extended-release naltrexone pharmacotherapy in heroin dependence.

Injectable extended-release naltrexone (XRNTX) presents an effective therapeutic strategy for opioid addiction, however its utility could be hampered by poor adherence. To gain a better insight into this phenomenon, we utilized blood oxygenation level-dependent functional magnetic resonance imaging (fMRI) in conjunction with a validated cue-induced craving procedure to examine neural correlates of XRNTX adherence. We operationalized treatment adherence as the number of monthly XRNTX injections (range: 0-3) administered to a group of fully detoxified heroin-dependent subjects (n=32). Additional outcomes included urine toxicology screening and self-reported tobacco use. The presented heroin-related visual cues reliably elicited heroin craving in all tested subjects. Nine, five, three and 15 of the participants, respectively, received zero, one, two and three XRNTX injections, predicted by the individual baseline fMRI signal change in response to the cues in the medial prefrontal cortex, a brain region involved in inhibitory self-control and emotional appraisal. The incidence of opioid-positive urines during the XRNTX therapy was low and remained about half the pre-treatment rate after the XRNTX ended. During the treatment, cigarette smoking behaviors followed patterns of opioid use, while cocaine consumption was increased with reductions in opioid use. The present data support the hypothesis that medial prefrontal cortex functions are involved in adherence to opioid antagonist therapy. A potential role of concurrent non-opioid addictive substances consumption during the XRNTX pharmacotherapy warrants further investigation. Our findings set the stage for further bio-behavioral investigations of the mechanisms of relapse prevention in opioid dependence.

Brain T2 relaxation times correlate with regional cerebral blood volume.

We previously reported cerebellar and putaminal transverse relaxation time (T2) differences in children with ADHD and in adults with childhood trauma. As brain T2 can be altered by deoxyhemoglobin concentration ([dHb]) and because [dHb] is proportional to regional cerebral blood volume (rCBV), at steady state we attributed those differences to rCBV changes. Studies in other species have established a correlation between T2 and rCBV; however this has yet to be demonstrated in human brain. Echo planar imaging (EPI) T2 relaxometry and dynamic susceptibility-contrast (DSC) MRI were used to measure T2 and rCBV in 11 healthy adults. Significant T2-rCBV correlations were observed in both cerebellar vermis and putamen (r = 0.759,p = 0.007;r = 0.782,p = 0.004, respectively). These correlations predict 9 +/- 3% and 10 +/- 3% rCBV changes, respectively, for each 1-msec change in T2. Consequently, brain T2 measurements may be useful for estimating steady-state rCBV.

Fractal features of dark, maintained, and driven neural discharges in the cat visual system.

We employ a number of statistical measures to characterize neural discharge activity in cat retinal ganglion cells (RGCs) and in their target lateral geniculate nucleus (LGN) neurons under various stimulus conditions, and we develop a new measure to examine correlations in fractal activity between spike-train pairs. In the absence of stimulation (i.e., in the dark), RGC and LGN discharges exhibit similar properties. The presentation of a constant, uniform luminance to the eye reduces the fractal fluctuations in the RGC maintained discharge but enhances them in the target LGN discharge, so that neural activities in the pair cease to be mirror images of each other. A drifting-grating stimulus yields RGC and LGN driven spike trains similar in character to those observed in the maintained discharge, with two notable distinctions: action potentials are reorganized along the time axis so that they occur only during certain phases of the stimulus waveform, and fractal activity is suppressed. Under both uniform-luminance and drifting-grating stimulus conditions (but not in the dark), the discharges of pairs of LGN cells are highly correlated over long time scales; in contrast discharges of RGCs are nearly uncorrelated with each other. This indicates that action-potential activity at the LGN is subject to a common fractal modulation to which the RGCs are not subjected.

Fractal ion-channel behavior generates fractal firing patterns in neuronal models.

Fractal behavior has been observed in both ion-channel gating and neuronal spiking patterns, but a causal relationship between the two has not yet been established. Here, we examine the effects of fractal ion-channel activity in modifications of two classical neuronal models: Fitzhugh-Nagumo (FHN) and Hodgkin-Huxley (HH). For the modified FHN model, the recovery variable was represented as a population of ion channels with either fractal or Markov gating characteristics. Fractal gating characteristics changed the form of the interspike interval histogram (ISIH) and also induced fractal behavior in the firing rate. For the HH model, the K+ conductance was represented as a collection of ion channels with either quasifractal or Markov gating properties. Fractal gating induced fractal-rate behavior without changing the ISIH. We conclude that fractal ion-channel gating activity is sufficient to account for fractal-rate firing behavior.

Quantal neurotransmitter secretion rate exhibits fractal behavior.

The rate of exocytic events from both neurons and non-neuronal cells exhibits fluctuations consistent with fractal (self-similar) behavior in time, as evidenced by a number of statistical measures. We explicitly demonstrate this for neurotransmitter secretion at Xenopus neuromuscular junctions and for rat hippocampal synapses in culture; the exocytosis of exogenously supplied neurotransmitter from cultured Xenopus myocytes and from rat fibroblasts behaves similarly. The magnitude of the fluctuations of the rate of exocytic events about the mean decreases slowly as the rate is computed over longer and longer time periods, the periodogram decreases in power-law manner with frequency, and the Allan factor (relative variance of the number of exocytic events) increases as a power-law function of the counting time. These features are hallmarks of self-similar behavior. Their description requires models that exhibit long-range correlation (memory) in event occurrences. We have developed a physiologically plausible model that accords with all of the statistical measures that we have examined. The appearance of fractal behavior at synapses, as well as in systems comprising collections of synapses, indicates that such behavior is ubiquitous in neural signaling.

Fractal character of the neural spike train in the visual system of the cat.

We used a variety of statistical measures to identify the point process that describes the maintained discharge of retinal ganglion cells (RGC's) and neurons in the lateral geniculate nucleus (LGN) of the cat. These measures are based on both interevent intervals and event counts and include the interevent-interval histogram, rescaled range analysis, the event-number histogram, the Fano factor, Allan factor, and the periodogram. In addition, we applied these measures to surrogate versions of the data, generated by random shuffling of the order of interevent intervals. The continuing statistics reveal 1/f-type fluctuations in the data (long-duration power-law correlation), which are not present in the shuffled data. Estimates of the fractal exponents measured for RGC- and their target LGN-spike trains are similar in value, indicating that the fractal behavior either is transmitted form one cell to the other or has a common origin. The gamma-r renewal process model, often used in the analysis of visual-neuron interevent intervals, describes certain short-term features of the RGC and LGN data reasonably well but fails to account for the long-duration correlation. We present a new model for visual-system nerve-spike firings: a gamma-r renewal process whose mean is modulated by fractal binomial noise. This fractal, doubly stochastic point process characterizes the statistical behavior of both RGC and LGN data sets remarkably well.

The periodogram and Allan variance reveal fractal exponents greater than unity in auditory-nerve spike trains.

Auditory-nerve spike trains exhibit fractal behavior, and therefore traditional renewal-point-process models fail to describe them adequately. Previous measures of the fractal exponent of these spike trains are based on the Fano factor and consequently cannot exceed unity. Two estimates of the fractal exponent are considered which do not suffer from this limit: one derived from the Allan variance, which was developed by the authors, and one based on the periodogram. These measures indicate that fractal exponents do indeed exceed unity for some nerve-spike recordings from stimulated primary afferent cat auditory-nerve fibers.

A nonstationary Poisson point process describes the sequence of action potentials over long time scales in lateral-superior-olive auditory neurons.

The behavior of lateral-superior-olive (LSO) auditory neurons over large time scales was investigated. Of particular interest was the determination as to whether LSO neurons exhibit the same type of fractal behavior as that observed in primary VIII-nerve auditory neurons. It has been suggested that this fractal behavior, apparent on long time scales, may play a role in optimally coding natural sounds. We found that a nonfractal model, the nonstationary dead-time-modified Poisson point process (DTMP), describes the LSO firing patterns well for time scales greater than a few tens of milliseconds, a region where the specific details of refractoriness are unimportant. The rate is given by the sum of two decaying exponential functions. The process is completely specified by the initial values and time constants of the two exponentials and by the dead-time relation. Specific measures of the firing patterns investigated were the interspike-interval (ISI) histogram, the Fano-factor time curve (FFC), and the serial count correlation coefficient (SCC) with the number of action potentials in successive counting times serving as the random variable. For all the data sets we examined, the latter portion of the recording was well approximated by a single exponential rate function since the initial exponential portion rapidly decreases to a negligible value. Analytical expressions available for the statistics of a DTMP with a single exponential rate function can therefore be used for this portion of the data. Good agreement was obtained among the analytical results, the computer simulation, and the experimental data on time scales where the details of refractoriness are insignificant.(ABSTRACT TRUNCATED AT 250 WORDS)

Auditory-nerve action potentials form a nonrenewal point process over short as well as long time scales.

The firing patterns of auditory-nerve action potentials exhibit long-term fractal fluctuations that do not arise from the distribution of the interevent intervals, but rather from the ordering of these intervals. Using the serial interevent-interval correlation coefficient, the Fano-factor time curve, and shuffling of interevent intervals, it is shown that adjacent intervals for spontaneous firings exhibit significant correlation. The events are therefore nonrenewal over short as well as long time scales.