The study used machine learning to predict The American Spinal Injury Association Impairment Scale (AIS) scores for newly injured spinal cord injury patients at hospital discharge time from hospital admission data. Additionally, machine learning was used to analyze the best model for feature importance to validate the criticality of the AIS score and highlight relevant demographic details. The data used for training machine learning models was from the National Spinal Cord Injury Statistical Center (NSCISC) database of U.S. spinal cord injury patient details. Eighteen real features were used from 417 provided features, which mapped to 53 machine learning features after processing. Eight models were tuned on the dataset to predict AIS scores, and Shapely analysis was performed to extract the most important of the 53 features. Patients within the NSCISC database who sustained injuries were between 1972 and 2016 after data cleaning ( n  = 20,790). Outcomes were test set multiclass accuracy and aggregated Shap...

Wnt signaling is crucial for synapse and cognitive function. Indeed, deficient Wnt signaling is causally related to increased expression of DKK1, an endogenous negative Wnt regulator, and synapse loss, both of which likely contribute to cognitive decline in Alzheimer’s disease (AD). Increasingly, AD research efforts have probed the neuroinflammatory role of microglia, the resident immune cells of the CNS, which have furthermore been shown to be modulated by Wnt signaling. The DKK1 homolog DKK2 has been previously identified as an activated response and/or disease-associated microglia (DAM/ARM) gene in a mouse model of AD. Here, we performed a detailed analysis of DKK2 in mouse models of neurodegeneration, and in human AD brain. In APP/PS1 and APPNL-G-F AD mouse model brains as well as in SOD1G93A ALS mouse model spinal cords, but not in control littermates, we demonstrated significant microgliosis and microglial Dkk2 mRNA upregulation in a disease-stage-dependent manner. In the AD models, these DAM/ARM Dkk...

The ability to interrogate specific representations in the brain, determining how, and where, difference sources of information are instantiated can provide invaluable insight into neural functioning. Pattern component modeling (PCM) is a recent analytic technique for human neuroimaging that allows the decomposition of representational patterns in brain into contributing subcomponents. In the current study, we present a novel PCM variant that tracks the contribution of prespecified representational patterns to brain representation across areas, thus allowing hypothesis-guided employment of the technique. We apply this technique to investigate the contributions of hedonic and nonhedonic information to the neural representation of tactile experience. We applied aversive pressure (AP) and appetitive brush (AB) to stimulate distinct peripheral nerve pathways for tactile information (C-/CT-fibers, respectively) while patients underwent functional magnetic resonance imaging (fMRI) scanning. We performed represen...

The ventral lateral geniculate nucleus (vLGN) is a retinorecipient region of thalamus that contributes to a number of complex visual behaviors. Retinal axons that target vLGN terminate exclusively in the external subdivision (vLGNe), which is also transcriptionally and cytoarchitectonically distinct from the internal subdivision (vLGNi). While recent studies shed light on the cell types and efferent projections of vLGNe and vLGNi, we have a crude understanding of the source and nature of the excitatory inputs driving postsynaptic activity in these regions. Here, we address this by conducting in vitro whole-cell recordings in acutely prepared thalamic slices and using electrical and optical stimulation techniques to examine the postsynaptic excitatory activity evoked by the activation of retinal or cortical layer V input onto neurons in vLGNe and vLGNi. Activation of retinal afferents by electrical stimulation of optic tract or optical stimulation of retinal terminals resulted in robust driver-like excitato...

The direct and indirect pathways of the basal ganglia (BG) have been suggested to learn mainly from positive and negative feedbacks, respectively. Since these pathways unevenly receive inputs from different cortical neuron types and/or regions, they may preferentially use different state/action representations. We explored whether such a combined use of different representations, coupled with different learning rates from positive and negative reward prediction errors (RPEs), has computational benefits. We modeled animal as an agent equipped with two learning systems, each of which adopted individual representation (IR) or successor representation (SR) of states. With varying the combination of IR or SR and also the learning rates from positive and negative RPEs in each system, we examined how the agent performed in a dynamic reward navigation task. We found that combination of SR-based system learning mainly from positive RPEs and IR-based system learning mainly from negative RPEs could achieve a good per...

Electrophysiological studies with behaving non-human primates (NHP) often require the separation of animals from their social group as well as partial movement restraint to perform well controlled experiments. When the research goal per se does not mandate constraining the animals’ movements there are often still experimental needs imposed by tethered data acquisition. Recent technological advances meanwhile allow wireless neurophysiological recordings at high band-width in limited-size enclosures. Here, we demonstrate wireless neural recordings at single unit resolution from unrestrained Rhesus macaques while they performed self-paced, structured visuomotor tasks on our custom-built, stand-alone touchscreen system (XBI) in their home environment. We were able to successfully characterize neural tuning to task parameters, such as visuo-spatial selectivity during movement planning and execution, as expected from existing findings obtained via setup-based neurophysiology recordings. We conclude that when mov...

Accurate and efficient quantification of animal behavior facilitates the understanding of the brain. An emerging approach within machine learning (ML) field is to combine multiple ML-based algorithms to quantify animal behavior. These so-called hybrid models have emerged because of limitations associated with supervised (e.g., random forest, RF) and unsupervised (e.g., hidden Markov model, HMM) ML models. For example, RF models lack temporal information across video frames, and HMM latent states are often difficult to interpret. We sought to develop a hybrid model, and did so in the context of a study of mouse risk assessment behavior. We utilized DeepLabCut to estimate the positions of mouse body parts. Positional features were calculated using DeepLabCut outputs and were used to train RF and HMM models with equal number of states, separately. The per-frame predictions from RF and HMM models were then passed to a second HMM model layer ("reHMM"). The outputs of the reHMM layer showed improved interpretabi...

Postsynaptic scaffolding proteins function as central organization hubs, ensuring the synaptic localization of neurotransmitter receptors, trans-synaptic adhesion proteins, and signaling molecules. Gephyrin is the major postsynaptic scaffolding protein at glycinergic and a subset of GABAergic inhibitory synapses. In contrast to cells outside the CNS, where one gephyrin isoform is predominantly expressed, neurons express different splice variants. In this study, we characterized the expression and scaffolding of neuronal gephyrin isoforms differing in the inclusion of the C4 cassettes located in the central C-domain. In hippocampal and cortical neuronal populations, gephyrin P1, lacking additional cassettes, is the most abundantly expressed isoform. In addition, alternative splicing generated isoforms carrying predominantly C4a, and minor amounts of C4c or C4d cassettes. We detected no striking difference in C4 isoform expression between different neuron types and a single neuron can likely express all C4 i...

The tuning properties of neurons in the visual system can be contextually modulated by the statistics of the area surrounding their receptive field, particularly when the surround contains natural features. However, stimuli presented in specific egocentric locations may have greater behavioural relevance, raising the possibility that the extent of contextual modulation may vary with position in visual space. To explore this possibility we utilised the small size and optical transparency of the larval zebrafish to describe the form and spatial arrangement of contextually modulated cells throughout an entire tectal hemisphere. We found that the spatial tuning of tectal neurons to a prey-like stimulus sharpens when the stimulus is presented against a background with the statistics of complex natural scenes, relative to a featureless background. These neurons are confined to a spatially restricted region of the tectum and have receptive fields centred within a region of visual space in which the presence of pr...

Objective: To use machine learning to predict AIS scores for newly injured SCI patients at hospital discharge time from hospital admission data. Additionally, to analyze the best model for feature importance in order to validate the criticality of AIS score and highlight relevant demographic details. Design: Data used for training machine learning models was from the NSCISC database of United States SCI patient details. 18 real features were used from 417 provided ones, which mapped to 53 machine learning features after processing. 8 models were tuned on the dataset to predict AIS scores and Shapely analysis was performed to extract the most important of the 53 features. Participants: Patients within the NSCISC database who sustained injuries between 1972 and 2016 after data cleaning (n = 20,790). Outcome Measures: Test set multi-class and aggregated Shapely score magnitudes. Results: Ridge Classifier was the best performer with 73.6% test set accuracy. AIS scores and neurologic category at admission t...