Bone response to loaded implants with non-matching implant-abutment diameters in the canine mandible

BACKGROUND: One way to evaluate various implant restorations is to measure the amount of bone change that occurs at the crestal bone. The objective of this study was to histologically evaluate the alveolar bone change around a bone-level, non-matching implant-abutment diameter configuration that incorporated a horizontal offset and a Morse taper internal connection. METHODS: The study design included extraction of all mandibular premolars and first molars in five canines. After 3 months, 12 dental implants were placed at three levels in each dog: even with the alveolar crest, 1 mm above the alveolar crest, and 1 mm below the alveolar crest. The implants were submerged on one side of the mandible. On the other side, healing abutments were exposed to the oral cavity (non-submerged). Gold crowns were attached 2 months after implant placement. The dogs were sacrificed 6 months postloading, and specimens were processed for histologic and histometric analyses. RESULTS: Evaluation of the specimens indicated that the marginal bone remained near the top of the implants under submerged and non-submerged conditions. The amount of bone change for submerged implants placed even with, 1 mm below, and 1 mm above the alveolar crest was -0.34, -1.29, and 0.04 mm, respectively (negative values indicate bone loss). For non-submerged implants, the respective values were -0.38, -1.13, and 0.19 mm. For submerged and non-submerged implants, there were significant differences in the amount of bone change among the three groups (P <0.05). The percentage of bone-to-implant contact for submerged implants was 73.3%, 71.8%, and 71.5%. For non-submerged implants, the respective numbers were 73.2%, 74.5%, and 76%. No significant differences occurred with regard to the percentage of bone contact. CONCLUSIONS: Minimal histologic bone loss occurred when dental implants with non-matching implant-abutment diameters were placed at the bone crest and were loaded for 6 months in the canine. The bone loss was significantly less (five- to six-fold) than that reported for bone-level implants with matching implant-abutment diameters (butt-joint connections).

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.