Machine Learning in Clinical Neuroscience

2022 ◽  
Keyword(s):  
Machine Learning ◽  
Clinical Neuroscience

scholarly journals Machine Learning for Clinical Psychology and Clinical Neuroscience

2019 ◽  
Author(s):  
Marc N Coutanche ◽  
Lauren S. Hallion
Keyword(s):  
Machine Learning ◽  
Clinical Psychology ◽  
Personal Characteristics ◽  
Computational Power ◽  
Science Data ◽  
Clinical Neuroscience ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Specific Factors ◽  
Introductory Textbooks

A rapid growth in computational power and an increasing availability of large, publicly- accessible, multimodal datasets present new opportunities for psychology and neuroscience researchers to ask novel questions, and to approach old questions in novel ways. Studies of the personal characteristics, situation-specific factors, and sociocultural contexts that result in the onset, development, maintenance, and remission of psychopathology, are particularly well-suited to benefit from machine learning methods. However, introductory textbooks for machine learning rarely tailor their guidance to the needs of psychology and neuroscience researchers. Similarly, the traditional statistical training of clinical scientists often does not incorporate these approaches. This chapter acts as an introduction to machine learning for researchers in the fields of clinical psychology and clinical neuroscience. We discuss these methods, illustrated through real and hypothetical applications in the fields of clinical psychology and clinical neuroscience. We touch on study design, selecting appropriate techniques, how (and how not) to interpret results, and more, to aid researchers who are interested in applying machine learning methods to clinical science data.

scholarly journals PredPsych: A toolbox for predictive machine learning based approach in experimental psychology research

2016 ◽  
Author(s):  
Atesh Koul ◽  
Cristina Becchio ◽  
Andrea Cavallo
Keyword(s):  
Machine Learning ◽  
Experimental Psychology ◽  
Machine Learning Algorithms ◽  
Clinical Neuroscience ◽  
Data Set ◽  
Predictive Algorithms ◽  
Research Questions ◽  
User Friendly ◽  
Behavioral Markers ◽  
Subject Motion

Recent years have seen an increased interest in machine learning based predictive methods for analysing quantitative behavioural data in experimental psychology. While these methods can achieve relatively greater sensitivity compared to conventional univariate techniques, they still lack an established and accessible software framework. The goal of this work was to build an open-source toolbox – “PredPsych” – that could make these methods readily available to all psychologists. PredPsych is a user-friendly, R toolbox based on machine learning predictive algorithms. In this paper, we present the framework of PredPsych via the analysis of a recently published multiple-subject motion capture data set. In addition, we discuss examples of possible research questions that can be addressed with the machine learning algorithms implemented in PredPsych and cannot be easily investigated with mass univariate statistical analysis. We anticipate that PredPsych will be of use to researchers with limited programming experience not only in the field of psychology, but also in that of clinical neuroscience, enabling computational assessment of putative bio-behavioral markers for both prognosis and diagnosis.

A brief overview on the contribution of machine learning in systems neuroscience

2021 ◽  
Vol 17 ◽  
Author(s):  
Graziella Orrù ◽  
Ciro Conversano ◽  
Rebecca Ciacchini ◽  
Angelo Gemignani
Keyword(s):  
Machine Learning ◽  
Cognitive Processes ◽  
Cross Validation ◽  
Neural Circuits ◽  
Clinical Medicine ◽  
Relevant Literature ◽  
Causal Analysis ◽  
Clinical Neuroscience ◽  
Systems Neuroscience ◽  
The Impact

Background: The use of Machine Learning (ML) is witnessing an exponential growth in the field of artificial intelligence (AI) and neuroscience, in particular in subdisciplines such as Systems Neuroscience (SN), as a viable alternative to the use of classical statistical techniques. The combination of this interconnection allows a more detailed study of algorithms and neural circuits that emulate core cognitive processes. ML toolbox includes algorithms that are suited to solving problems of classification, regression, clustering and anomaly detection. Objective: The aim of the present opinion was to exemplify the contribution of ML in the field of SN in three different fields: 1) cognitive modelling; 2) neuroimaging; 3) analysis of clinical datasets. Method: We gathered evidence from the relevant literature related to the interaction between neuroscience and AI and the impact of ML in SN. Results : ML is specifically suited to the analysis of large clinical neuroscience datasets. Experimental results in neuroscience are hard to replicate for a number of reasons and ML may contribute to attenuating these replicability issues via the ubiquitous use of cross-validation procedures. While ML modelling is primarily focused on prediction accuracy, one of the drawbacks in ML is the opacity of various algorithms that resist to intuitive understanding. Conclusions: Future avenues of research have already been traced and include increased interpretability of currently opaque ML models functioning and causal analysis. Causal analysis is intended to distinguish between spurious associations and cause-effect relationship and is a primary interest in both clinical medicine and basic neuroscience.

scholarly journals Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

2021 ◽  
Vol 15 ◽  
Author(s):  
Alexandre Routier ◽  
Ninon Burgos ◽  
Mauricio Díaz ◽  
Michael Bacci ◽  
Simona Bottani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Scalar Fields ◽  
Machine Learning Algorithms ◽  
Software Platform ◽  
Imaging Data ◽  
Clinical Neuroscience ◽  
Neuroimaging Data ◽  
Share Data

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning
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