Neurophotonics applications to motor cortex research

Neurophotonics: a comprehensive review, current challenges and future trends

The human brain, with its vast network of billions of neurons and trillions of synapses (connections) between diverse cell types, remains one of the greatest mysteries in science and medicine. Despite extensive research, an understanding of the underlying mechanisms that drive normal behaviors and response to disease states is still limited. Advancement in the Neuroscience field and development of therapeutics for related pathologies requires innovative technologies that can provide a dynamic and systematic understanding of the interactions between neurons and neural circuits. In this work, we provide an up-to-date overview of the evolution of neurophotonic approaches in the last 10 years through a multi-source, literature analysis. From an initial corpus of 243 papers retrieved from Scopus, PubMed and WoS databases, we have followed the PRISMA approach to select 56 papers in the area. Following a full-text evaluation of these 56 scientific articles, six main areas of applied research were identified and discussed: (1) Advanced optogenetics, (2) Multimodal neural interfaces, (3) Innovative therapeutics, (4) Imaging devices and probes, (5) Remote operations, and (6) Microfluidic platforms. For each area, the main technologies selected are discussed according to the photonic principles applied, the neuroscience application evaluated and the more indicative results of efficiency and scientific potential. This detailed analysis is followed by an outlook of the main challenges tackled over the last 10 years in the Neurophotonics field, as well as the main technological advances regarding specificity, light delivery, multimodality, imaging, materials and system designs. We conclude with a discussion of considerable challenges for future innovation and translation in Neurophotonics, from light delivery within the brain to physical constraints and data management strategies.

Better understanding the neurobiology of primary lateral sclerosis

Primary lateral sclerosis (PLS) is a rare neurodegenerative disease characterized by progressive degeneration of upper motor neurons (UMNs). Recent studies shed new light onto the cellular events that are particularly important for UMN maintenance including intracellular trafficking, mitochondrial energy homeostasis and lipid metabolism. This review summarizes these advances including the role of Alsin as a gene linked to atypical forms of juvenile PLS, and discusses wider aspects of cellular pathology that have been observed in adult forms of PLS. The review further discusses the prospects of new transgenic upper motor neuron reporter mice, human stem cell-derived UMN cultures, cerebral organoids and non-human primates as future model systems to better understand and ultimately treat PLS.

Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex

Neuronal persistent activity has been primarily assessed in terms of electrical mechanisms, without attention to the complex array of molecular events that also control cell excitability. We developed a multiscale neocortical model proceeding from the molecular to the network level to assess the contributions of calcium (Ca(2+)) regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels in providing additional and complementary support of continuing activation in the network. The network contained 776 compartmental neurons arranged in the cortical layers, connected using synapses containing AMPA/NMDA/GABAA/GABAB receptors. Metabotropic glutamate receptors (mGluR) produced inositol triphosphate (IP3) which caused the release of Ca(2+) from endoplasmic reticulum (ER) stores, with reuptake by sarco/ER Ca(2+)-ATP-ase pumps (SERCA), and influence on HCN channels. Stimulus-induced depolarization led to Ca(2+) influx via NMDA and voltage-gated Ca(2+) channels (VGCCs). After a delay, mGluR activation led to ER Ca(2+) release via IP3 receptors. These factors increased HCN channel conductance and produced firing lasting for ∼1min. The model displayed inter-scale synergies among synaptic weights, excitation/inhibition balance, firing rates, membrane depolarization, Ca(2+) levels, regulation of HCN channels, and induction of persistent activity. The interaction between inhibition and Ca(2+) at the HCN channel nexus determined a limited range of inhibition strengths for which intracellular Ca(2+) could prepare population-specific persistent activity. Interactions between metabotropic and ionotropic inputs to the neuron demonstrated how multiple pathways could contribute in a complementary manner to persistent activity. Such redundancy and complementarity via multiple pathways is a critical feature of biological systems. Mediation of activation at different time scales, and through different pathways, would be expected to protect against disruption, in this case providing stability for persistent activity.

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.