Intracortical Recording Devices

A key future use of neural electrode technology envisioned for nanomedicine and cognitive enhancement is intracortical recording devices that would capture the output signals of multiple neurons that are related to a given activity, for example signals associated with movement, or the intent of movement. Intracortical recording devices will require the next-generation of more robust and sophisticated neural interfaces combined with advanced signal processing, and algorithms to properly translate spontaneous neural action potentials into command signals [1]. Capturing, recording, and outputting neural signals would be a precursor to intervention and augmentation.

Toward the next-generation functionality necessary for intracortical recording devices, using organic rather than inorganic transistors, Bink et al. demonstrated flexible organic thin film transistors with sufficient performance for neural signal recording that can be directly interfaced with neural electrode arrays [2].

Since important brain network activity exists at temporal and spatial scales beyond the resolution of existing implantable devices, high-density active electrode arrays may be one way to provide a higher-resolution interface with the brain to access and influence this network activity. Integrating flexible electronic devices directly at the neural interface might possibly enable thousands of multiplexed electrodes to be connected with far fewer wires. Active electrode arrays have been demonstrated using traditional inorganic silicon transistors, but may not be cost-effective for scaling to large array sizes (8 × 8 cm).

Also, toward neural signal recording, Keefer et al. developed carbon nanotube coated electrodes, which increased the functional resolution, and thus the localized selectivity and potential influence of implanted neural electrodes. The team electrochemically populated conventional stainless steel and tungsten electrodes with carbon nanotubes which amplified both the recording of neural signals and the electronic stimulation of neurons (in vitro, and in rat and monkey models). The clinical electrical excitation of neuronal circuitry could be of significant benefit for epilepsy, Parkinson’s disease, persistent pain, hearing deficits, and depression. The team thus demonstrated an important advance for brain-machine communication: increasing the quality of electrode-neuronal interfaces by lowering the impedance and elevating the charge transfer of electrodes [3].

Full Article: Nanomedical Cognitive Enhancement

References:
[1] Donoghue, J.P., Connecting cortex to machines: Recent advances in brain interfaces. Nat. Neurosci. 5 (Suppl), 1085–1088, 2002.
[2] Bink, H., Lai, Y., Saudari, S.R., Helfer, B., Viventi, J., Van der Spiegel, J., Litt, B., and Kagan, C., Flexible organic electronics for use in neural sensing. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 5400–5403, 2011.
[3] Keefer, E.W., Botterman, B.R., Romero, M.I., Rossi, A.F., and Gross, G.W., Carbon nanotube coating improves neuronal recordings. Nat. Nanotechnol. 3(7), 434–439, 2008.

Complement proteins: possible predictive biomarker of Alzheimer’s disease

Recent research has uncovered more details about how the brain works. As brains develop, the many initially-formed synapses get pruned. How this occurs is important to understand as synapse pruning also occurs in neurodegenerative disease.

In synapse pruning, many mechanisms are operating together in a systems biology fashion, but one key dynamic is that protein molecules (complement proteins C1q and C3) are tagging weak synapses for elimination. For example, complement protein molecules are massively upregulated in Alzheimer’s disease. The Cq1 genes have been shown to come on very early in the case of glaucoma and are proposed to be a global dynamic of synaptic loss in neurodegenerative disease (e.g.; Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, ALS, etc.).

The early and prominent role of complement genes and proteins suggests the possibility of measuring them as a predictive biomarker of neurodegenerative disease.