Whole-brain spatial organization of hippocampal single-neuron projectomes.

Mapping single-neuron projections is essential for understanding brain-wide connectivity and diverse functions of the hippocampus (HIP). Here, we reconstructed 10,100 single-neuron projectomes of mouse HIP and classified 43 projectome subtypes with distinct projection patterns. The number of projection targets and axon-tip distribution depended on the soma location along HIP longitudinal and transverse axes. Many projectome subtypes were enriched in specific HIP subdomains defined by spatial transcriptomic profiles. Furthermore, we delineated comprehensive wiring diagrams for HIP neurons projecting exclusively within the HIP formation (HPF) and for those projecting to both intra- and extra-HPF targets. Bihemispheric projecting neurons generally projected to one pair of homologous targets with ipsilateral preference. These organization principles of single-neuron projectomes provide a structural basis for understanding the function of HIP neurons.

Biphasic regulation of epigenetic state by matrix stiffness during cell reprogramming.

We investigate how matrix stiffness regulates chromatin reorganization and cell reprogramming and find that matrix stiffness acts as a biphasic regulator of epigenetic state and fibroblast-to-neuron conversion efficiency, maximized at an intermediate stiffness of 20 kPa. ATAC sequencing analysis shows the same trend of chromatin accessibility to neuronal genes at these stiffness levels. Concurrently, we observe peak levels of histone acetylation and histone acetyltransferase (HAT) activity in the nucleus on 20 kPa matrices, and inhibiting HAT activity abolishes matrix stiffness effects. G-actin and cofilin, the cotransporters shuttling HAT into the nucleus, rises with decreasing matrix stiffness; however, reduced importin-9 on soft matrices limits nuclear transport. These two factors result in a biphasic regulation of HAT transport into nucleus, which is directly demonstrated on matrices with dynamically tunable stiffness. Our findings unravel a mechanism of the mechano-epigenetic regulation that is valuable for cell engineering in disease modeling and regenerative medicine applications.

Enhanced Beta2-band Oscillations Denote Auditory Hallucination in Schizophrenia Patients and a Monkey Model of Psychosis.

An electroencephalographic (EEG) signature of auditory hallucinations (AHs) is important for facilitating the diagnosis and treatment of AHs in schizophrenia. We recorded EEG from 25 schizophrenia patients with recurrent AHs. During the period of AHs, EEG recordings exhibited significantly elevated beta2-band power in the temporal region, as compared to those recorded in the absence of AHs or during stimulation with verbal sounds. We further generated methamphetamine-treated rhesus monkeys exhibiting psychosis-like behaviors, including repetitive sudden searching actions in the absence of external intrusion, suggesting the occurrence of AHs. Epidural EEG beta2-band power in the temporal region of these monkeys was enhanced immediately after methamphetamine treatment and positively correlated with the frequency of sudden searching actions. Thus, the enhancement of temporal beta2-band oscillations represents a signature for AHs in both patients and a monkey model of psychosis, and this monkey model can be used for developing closed-loop neuromodulation approaches for the treatment of refractory AHs in schizophrenia.

A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost.

Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation.

Reduction of Intracellular Tension and Cell Adhesion Promotes Open Chromatin Structure and Enhances Cell Reprogramming.

The role of transcription factors and biomolecules in cell type conversion has been widely studied. Yet, it remains unclear whether and how intracellular mechanotransduction through focal adhesions (FAs) and the cytoskeleton regulates the epigenetic state and cell reprogramming. Here, it is shown that cytoskeletal structures and the mechanical properties of cells are modulated during the early phase of induced neuronal (iN) reprogramming, with an increase in actin cytoskeleton assembly induced by Ascl1 transgene. The reduction of actin cytoskeletal tension or cell adhesion at the early phase of reprogramming suppresses the expression of mesenchymal genes, promotes a more open chromatin structure, and significantly enhances the efficiency of iN conversion. Specifically, reduction of intracellular tension or cell adhesion not only modulates global epigenetic marks, but also decreases DNA methylation and heterochromatin marks and increases euchromatin marks at the promoter of neuronal genes, thus enhancing the accessibility for gene activation. Finally, micro- and nano-topographic surfaces that reduce cell adhesions enhance iN reprogramming. These novel findings suggest that the actin cytoskeleton and FAs play an important role in epigenetic regulation for cell fate determination, which may lead to novel engineering approaches for cell reprogramming.

Origin of the efficiency of spike timing-based neural computation for processing temporal information.

Although the advantage of spike timing-based over rate-based network computation has been recognized, the underlying mechanism remains unclear. Using Tempotron and Perceptron as elementary neural models, we examined the intrinsic difference between spike timing-based and rate-based computations. For more direct comparison, we modified Tempotron computation into rate-based computation with the retention of some temporal information. Previous studies have shown that spike timing-based computation are computationally more powerful than rate-based computation in terms of the number of computational units required and the capability in classifying random patterns. Our study showed that spike timing-based and rate-based Tempotron computations provided similar capability in classifying random spike patterns, as well as in text sentiment classification and spam text detection. However, spike timing-based computation is superior in performing a task involving discriminating forward vs. reverse sequence of events, i.e., information mainly temporal in nature. Further studies revealed that this superiority required the asymmetry in the profile of the postsynaptic potential (PSP), and that temporal sequence information was converted to biased spatial distribution of synaptic weight modifications during learning. Thus, the intrinsic PSP asymmetry is a mechanistic basis for the high efficiency of spike timing-based computation for processing temporal information.

Visualizing advances in the future of primate neuroscience research.

Future neuroscience and biomedical projects involving non-human primates (NHPs) remain essential in our endeavors to understand the complexities and functioning of the mammalian central nervous system. In so doing, the NHP neuroscience researcher must be allowed to incorporate state-of-the-art technologies, including the use of novel viral vectors, gene therapy and transgenic approaches to answer continuing and emerging research questions that can only be addressed in NHP research models. This perspective piece captures these emerging technologies and some specific research questions they can address. At the same time, we highlight some current caveats to global NHP research and collaborations including the lack of common ethical and regulatory frameworks for NHP research, the limitations involving animal transportation and exports, and the ongoing influence of activist groups opposed to NHP research.

Neural Mechanism Underlying Task-Specific Enhancement of Motor Learning by Concurrent Transcranial Direct Current Stimulation.

The optimal protocol for neuromodulation by transcranial direct current stimulation (tDCS) remains unclear. Using the rotarod paradigm, we found that mouse motor learning was enhanced by anodal tDCS (3.2 mA/cm2) during but not before or after the performance of a task. Dual-task experiments showed that motor learning enhancement was specific to the task accompanied by anodal tDCS. Studies using a mouse model of stroke induced by middle cerebral artery occlusion showed that concurrent anodal tDCS restored motor learning capability in a task-specific manner. Transcranial in vivo Ca2+ imaging further showed that anodal tDCS elevated and cathodal tDCS suppressed neuronal activity in the primary motor cortex (M1). Anodal tDCS specifically promoted the activity of task-related M1 neurons during task performance, suggesting that elevated Hebbian synaptic potentiation in task-activated circuits accounts for the motor learning enhancement. Thus, application of tDCS concurrent with the targeted behavioral dysfunction could be an effective approach to treating brain disorders.

Transcriptome, connectome and neuromodulation of the primate brain.

Single-cell transcriptomic analysis has facilitated cell type identification in the brain and mapping of cell type-specific connectomes, helping to elucidate neural circuits underlying brain functions and to treat brain disorders by neuromodulation. Yet, we lack a consensual definition of neuronal types/subtypes and clear distinction between cause and effect within interconnected networks.

Distinct neuron populations for simple and compound calls in the primary auditory cortex of awake marmosets.

Marmosets are highly social non-human primates that live in families. They exhibit rich vocalization, but the neural basis underlying this complex vocal communication is largely unknown. Here we report the existence of specific neuron populations in marmoset A1 that respond selectively to distinct simple or compound calls made by conspecific marmosets. These neurons were spatially dispersed within A1 but distinct from those responsive to pure tones. Call-selective responses were markedly diminished when individual domains of the call were deleted or the domain sequence was altered, indicating the importance of the global rather than local spectral-temporal properties of the sound. Compound call-selective responses also disappeared when the sequence of the two simple-call components was reversed or their interval was extended beyond 1 s. Light anesthesia largely abolished call-selective responses. Our findings demonstrate extensive inhibitory and facilitatory interactions among call-evoked responses, and provide the basis for further study of circuit mechanisms underlying vocal communication in awake non-human primates.

Self-backpropagation of synaptic modifications elevates the efficiency of spiking and artificial neural networks.

Many synaptic plasticity rules found in natural circuits have not been incorporated into artificial neural networks (ANNs). We showed that incorporating a nonlocal feature of synaptic plasticity found in natural neural networks, whereby synaptic modification at output synapses of a neuron backpropagates to its input synapses made by upstream neurons, markedly reduced the computational cost without affecting the accuracy of spiking neural networks (SNNs) and ANNs in supervised learning for three benchmark tasks. For SNNs, synaptic modification at output neurons generated by spike timing­dependent plasticity was allowed to self-propagate to limited upstream synapses. For ANNs, modified synaptic weights via conventional backpropagation algorithm at output neurons self-backpropagated to limited upstream synapses. Such self-propagating plasticity may produce coordinated synaptic modifications across neuronal layers that reduce computational cost.