Amyotrophic lateral sclerosis and frontotemporal dementia (ALS-FTD) represents a spectrum of neurodegenerative disease with clinical presentations ranging from progressive paralysis to cognitive impairment. Approximately 15% of ALS-FTD patients initially presenting with motor symptoms also receive a diagnosis of dementia, but a majority of these patients demonstrate some level of cognitive impairment over the course of disease. Identifying molecular pathways that contribute to the development of cognitive deficits in ALS-FTD has thus far been limited by the quality of clinical information and postmortem tissue preservation as well as available technologies. This thesis aims to investigate early stages of cognitive involvement in ALS-FTD using postmortem tissues from a cohort of non-demented ALS patients who have had cognitive and pathological phenotyping. Spatially resolved transcriptome profiling of prefrontal cortex tissues from this cohort identifies dysregulated pathways in non-motor regions, contributing to our understanding of molecular perturbations underlying cognitive impairment in ALS-FTD.

Neural coding is essential for understanding how the activity of individual neurons or ensembles of neurons relates to cognitive processing of the world. Neurons can code for multiple variables simultaneously and neuroscientists are interested in classifying neurons based on the variables they represent. Building a model identification paradigm to identify neurons in terms of their coding properties is essential to understanding how the brain processes information. Statistical paradigms are capable of methodologically determining the factors influencing neural observations and assessing the quality of the resulting models to characterize and classify individual neurons. However, as neural recording technologies develop to produce data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analysis; however, they require huge training data sets, and model assessment and interpretation are more challenging than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to evaluate our approaches, we apply them to data from a population of neurons in rat hippocampus and prefrontal cortex (PFC), to characterize how spatial learning and memory processes are represented in these areas. The data consist of local field potentials (LFP) and spiking data simultaneously recorded from the CA1 region of hippocampus and the PFC of a male Long Evans rat performing a spatial alternation task on a W-shaped track. We have examined this data in three separate but related projects. In one project, we build an improved class of statistical models for neural activity by expanding a common set of basis functions to increase the statistical power of the resulting models. In the second project, we identify the individual neurons in hippocampus and PFC and classify them based on their coding properties by using statistical model identification methods. We found that a substantial proportion of hippocampus and PFC cells are spatially selective, with position and velocity coding, and rhythmic firing properties. These methods identified clear differences between hippocampal and prefrontal populations, and allowed us to classify the coding properties of the full population of neurons in these two regions. For the third project, we develop a supervised machine learning classifier based on convolutional neural networks (CNNs), which use classification results from statistical models and additional simulated data as ground truth signals for training. This integration of statistical and ML approaches allows for statistically principled and computationally efficient classification of the coding properties of general neural populations.

The Lateral Prefrontal Cortex (LPFC) and the Dorsal Premotor cortex (PMd) are two cortical structures that are involved in cognitive processes such as motor planning and decision-making. The LPFC is extensively connected to sensory, somatosensory, and motor cortices that help it control several cognitive functions [for review, see: (Tanji & Hoshi, 2008)]. Similarly, the PMd can integrate information from the prefrontal and motor cortex, acting as a link, in action planning and decision making [for review, see: (Hoshi & Tanji, 2007)]. Therefore, it is important to study the cortical pathways between these areas because of their common role in processing and selecting relevant information in tasks requiring decision-making. Using neural tract-tracing, immunolabeling and microscopy in rhesus monkeys (M. mulatta), we assessed the distribution and layer-specific organization of projection neurons from LPFC area 46 and PMd area 6 directed to the LPFC area 9. Our study revealed that projection neurons to area 9 were found originating from upper (L2-3) and deep (L5-6) layers of both areas, but with a slight upper layer bias. We found that the LPFC area 46 had a higher density of projection neurons directed to LPFC area 9 compared to the PMd area 6. Additionally, our data also revealed laminar differences in the perisomatic parvalbumin (PV) inhibitory inputs onto area 9 projection neurons, which were dependent on area of origin. Within ventral LPFC area 46, perisomatic PV+ inhibitory inputs onto upper layer projection neurons to area 9 was greater than those onto deep layer projection neurons. The opposite pattern was found for PMd area 6DR, where perisomatic PV+ inhibition onto deep layer projection neurons to area 9 was greater than those onto upper layer neurons. These findings provide additional insights into the layer-specific organization of prefrontal and premotor pathways that play an important role in action planning and decision-making.

Huntington Disease (HD) is a fatal neurodegenerative disorder that is characterized by motor, cognitive, and psychiatric symptoms. Although HD onset is determined by motor symptoms, psychiatric symptoms, like depression and aggression, can develop earlier, have a larger impact on quality of life, and are understudied due to stigma. Our lab has observed hyper aggression in our humanized HD mouse model (Hu97/18) compared to our knock-in HD mouse model (Q175FDN). We characterized these differences and found that the Hu97/18 mice overreact in neutral situations, behaving as if they are in threatening situations. We are now using this novel model of HD-related aggression to study its neurological basis. Increased reactive aggression has been linked to stress levels and the prefrontal cortex (PFC) due to its role in emotional regulation. This study seeks to determine if HD-related aggression is associated with increased stress levels and changes in the PFC. Our cortisol study shows that the Hu97/18 mice display significantly higher cortisol levels than baseline, suggesting a link between systemic stress and heightened aggression. Additionally, quantified PFC volumes show a moderate relationship between PFC volume and aggression in wild-type (WT) mice that is lost in the Hu97/18 mice. This data will help elucidate factors that modulate aggression in HD and may identify therapies with high potential to alleviate this devastating symptom in patients.

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