No abstract

Background: Dorsal root ganglion stimulation (DRGS) treats discrete, localized areas of neuropathic pain. But there are no long-term results available so far. Objectives: We studied the long-term outcome of DRGS used in the treatment of chronic neuropathic pain. Study Design: A prospective, longitudinal single center investigation. Setting: Academic medical center in Germany. Methods: Patients (age >18 years) with chronic neuropathic pain in the hands, back, legs, knees and feet were prospectively examined. After a successful test-trial (duration of 3-14 days, pain decrease > 50%), a permanent generator was implanted. The patients were re-examined after 1 year, 2 years and 3 years. We used the Visual Analogue Scale (VAS), the Pain Disability Index (PDI), the Pain Catastrophizing Scale (PCS), the Brief Pain Inventory (BPI), and, the Beck Depression Inventory (BDI) for our assessments. Results: We included 62 consecutive patients (27 females, 35 males, mean age 56.8 years, with an age range from 28 to 82 years, 62/51 to permanent conversion) during the time period from March 2012 until March 2016. Fifty-one patients had a successful test-trial and a generator was implanted subsequently. Results after 3 years: the VAS dropped from Mdn = 8 to Mdn = 4 (P = 0.0001). The PDI decreased from Mdn = 45 to Mdn = 23 (P = 0.003). The PCS decreased from Mdn = 34 to Mdn = 21 (P = 0.001). The BPI dropped from Mdn = 73 to Mdn = 30 (P = 0.003). The BDI decreased from Mdn = 36 to Mdn = 21 (P = 0.010). Fourteen patients showed complications (27.4%). Limitations: This study is limited by the small number of patients in the single groups of the different pain locations. Conclusion: DRGS may be an effective long-term method of treating discrete, localized areas of chronic neuropathic pain. We would recommend DRGS for the treatment of chronic neuropathic pain in such areas. Key words: Knee pain, foot pain, hand pain, groin pain, neuromodulation, dorsal root ganglion stimulation, chronic neuropathic pain, paresthesia mapping

Background: Frontal midline theta (FMT) oscillations (4–8 Hz) are strongly related to cognitive and executive control during mental tasks such as memory processing, arithmetic problem solving or sustained attention. While maintenance of temporal order information during a working memory (WM) task was recently linked to FMT phase, a positive correlation between FMT power, WM demand and WM performance was shown. However, the relationship between these measures is not well understood, and it is unknown whether purposeful FMT phase manipulation during a WM task impacts FMT power and WM performance. Here we present evidence that FMT phase manipulation mediated by transcranial alternating current stimulation (tACS) can block WM demand-related FMT power increase (FMTΔpower) and disrupt normal WM performance.Methods: Twenty healthy volunteers were assigned to one of two groups (group A, group B) and performed a 2-back task across a baseline block (block 1) and an intervention block (block 2) while 275-sensor magnetoencephalography (MEG) was recorded. After no stimulation was applied during block 1, participants in group A received tACS oscillating at their individual FMT frequency over the prefrontal cortex (PFC) while group B received sham stimulation during block 2. After assessing and mapping phase locking values (PLV) between the tACS signal and brain oscillatory activity across the whole brain, FMT power and WM performance were assessed and compared between blocks and groups.Results: During block 2 of group A but not B, FMT oscillations showed increased PLV across task-related cortical areas underneath the frontal tACS electrode. While WM task-related FMTΔpower and WM performance were comparable across groups in block 1, tACS resulted in lower FMTΔpower and WM performance compared to sham stimulation in block 2.Conclusion: tACS-related manipulation of FMT phase can disrupt WM performance and influence WM task-related FMTΔpower. This finding may have important implications for the treatment of brain disorders such as depression and attention deficit disorder associated with abnormal regulation of FMT activity or disorders characterized by dysfunctional coupling of brain activity, e.g., epilepsy, Alzheimer’s or Parkinson’s disease (AD/PD).

We present a computational, biophysical model of neuron-astrocyte-vessel interaction. Unlike other cells, neurons convey “hunger” signals to the vascular network via an intervening layer of glial cells (astrocytes); vessels dilate and release glucose which fuels neuronal firing. Existing computational models focus on only parts of this loop (neuron→astrocyte→vessel→neuron), whereas the proposed model describes the entire loop. Neuronal firing causes release of a neurotransmitter like glutamate which triggers release of vasodilator by astrocytes via a cascade of biochemical events. Vasodilators released from astrocytic endfeet cause blood vessels to dilate and release glucose into the interstitium, part of which is taken up by the astrocyticendfeet. Glucose is converted into lactate in the astrocyte and transported into the neuron. Glucose from the interstitium and lactate (produced from glucose) influx from astrocyte are converted into ATP in the neuron. Neuronal ATP is used to drive the Na+/K+ATPase pumps, which maintain ionic gradients necessary for neuronal firing. When placed in the metabolic loop, the neuron exhibits sustained firing only when the stimulation current is more than a minimum threshold. For various combinations of initial neuronal [ATP] and external current, the neuron exhibits a variety of firing patterns including sustained firing, firing after an initial pause, burst firing etc. Neurovascular interactions under conditions of constricted vessels are also studied. Most models of cerebral circulation describe neurovascular interactions exclusively in the “forward” neuron→vessel direction. The proposed model indicates possibility of “reverse” influence also, with vasomotion rhythms influencing neural firing patterns. Another idea that emerges out of the proposed work is that brain's computations may be more comprehensively understood in terms of neuro-glial-vascular dynamics and not in terms of neural firing alone.

No abstract