A temporal classification method based on behavior time series data in patients with behavioral variant of frontotemporal dementia and apathy.

Background Apathy is a common behavioral syndrome that occurs across neurological and psychiatric disorders. An influential theoretical framework defined apathy as the quantitative reduction of self-generated voluntary and purposeful behaviors. There is evidence in the literature of the multidimensional nature of apathy with cognitive, behavioral, and emotional dimensions. To date, apathy has been assessed using various scales and questionnaires. Alternative objective and ecological measurements of apathy are needed. New method We used the ECOCAPTURE protocol and an ethological approach to investigate behavior in bvFTD patients under ecological conditions (a waiting room) while they freely explored a novel environment. Data were collected by behavioral coding from 7-minute video using an ethogram and transformed into behavior time series data. We present an approach considering behavioral kinetics to assess behavior. We aimed to construct a new behavior analysis method, called ECOCAPTURE kinetics, using temporal classification for behavior time series data analysis. To develop our classifier, we retained a nonelastic Euclidian metric, combined with a convolutional approach. Results We applied the ECOCAPTURE kinetics method to a cohort of 20 bvFTD patients and 18 healthy controls. We showed that bvFTD patients can be classified according to their behavioral kinetics into three groups. Each subgroup was characterized by specific behavior disorders and neuropsychological profile. Comparison with Existing Method(s) The ECOCAPTURE kinetics method is different from those of the classical approach of measuring behavior, producing time budgets, frequency of behavior occurrences, or kinematic diagrams. Conclusions This approach can be extended to any behavioral study encoding time.

ProbeInterface: A Unified Framework for Probe Handling in Extracellular Electrophysiology.

Recording neuronal activity with penetrating extracellular multi-channel electrode arrays, more commonly known as neural probes, is one of the most widespread approaches to probe neuronal activity. Despite a plethora of available extracellular probe designs, the time-consuming process of mapping of electrode channel order and relative geometries, as required by spike-sorting software is invariably left to the end-user. Consequently, this manual process is prone to mis-mapping mistakes, which in turn lead to undesirable spike-sorting errors and inefficiencies. Here, we introduce ProbeInterface, an open-source project that aims to unify neural probe metadata descriptions by removing the manual step of probe mapping prior to spike-sorting for the analysis of extracellular neural recordings. ProbeInterface is first of all a Python API, which enables users to create and visualize probes and probe groups at any required complexity level. Second, ProbeInterface facilitates the generation of comprehensive wiring description in a reproducible fashion for any specific data-acquisition setup, which usually involves the use of a recording probe, a headstage, adapters, and an acquisition system. Third, we collaborate with probe manufacturers to compile an open library of available probes, which can be downloaded at run time using our Python API. Finally, with ProbeInterface we define a file format for probe handling which includes all necessary information for a FAIR probe description and is compatible with and complementary to other open standards in neuroscience.

Experimental Pipeline (Expipe): A Lightweight Data Management Platform to Simplify the Steps From Experiment to Data Analysis.

As experimental neuroscience is moving toward more integrative approaches, with a variety of acquisition techniques covering multiple spatiotemporal scales, data management is becoming increasingly challenging for neuroscience laboratories. Often, datasets are too large to practically be stored on a laptop or a workstation. The ability to query metadata collections without retrieving complete datasets is therefore critical to efficiently perform new analyses and explore the data. At the same time, new experimental paradigms lead to constantly changing specifications for the metadata to be stored. Despite this, there is currently a serious lack of agile software tools for data management in neuroscience laboratories. To meet this need, we have developed Expipe, a lightweight data management framework that simplifies the steps from experiment to data analysis. Expipe provides the functionality to store and organize experimental data and metadata for easy retrieval in exploration and analysis throughout the experimental pipeline. It is flexible in terms of defining the metadata to store and aims to solve the storage and retrieval challenges of data/metadata due to ever changing experimental pipelines. Due to its simplicity and lightweight design, we envision Expipe as an easy-to-use data management solution for experimental laboratories, that can improve provenance, reproducibility, and sharing of scientific projects.