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Bruno, C. A.

Publications and source records attributed to Bruno, C. A..

2 recordsLinked to original sources

Cognitive experience alters cortical involvement in navigation decisions

The neural correlates of decision-making have been investigated extensively, and recent work aims to identify under what conditions cortex is actually necessary for making accurate decisions. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same task, revealing past learning as a critical determinant of whether cortex is necessary for decision-making. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate decision-making in mice performing a simple navigation-based decision task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multi-area calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron-neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience sets the landscape for how future tasks are solved by the brain and is a key factor in determining whether cortical areas have a causal role in decision-making.

neuroscience↗

pMAT: An Open-Source Software Suite for the Analysis of Fiber Photometry Calcium Imaging

The combined development of new technologies for neuronal recordings and the development of novel sensors for recording both cellular activity and neurotransmitter binding has ushered in a new era for the field of neuroscience. Among these new technologies is fiber photometry, a technique wherein an implanted fiber optic is used to record signals from genetically encoded fluorescent sensors in bulk tissue. Fiber photometry has been widely adapted due to its cost-effectiveness, ability to examine the activity of neurons with specific anatomical or genetic identities, and the ability to use these highly modular systems to record from one or more sensors or brain sites in both superficial and deep-brain structures. Despite these many benefits, one major hurdle for laboratories adopting this technique is the steep learning curve associated with the analysis of fiber photometry data. This has been further complicated by a lack of standardization in analysis pipelines. In the present communication, we present pMAT, a photometry modular analysis tool that allows users to accomplish common analysis routines through the use of a graphical user interface. This tool can be deployed in MATLAB and edited by more advanced users, but is also available as an independently deployable, open-source application.

neuroscience↗