A machine learning approach to classify working memory load from optical neuroimaging data

2021 ◽  
Author(s):  
Manob Jyoti Saikia ◽  
Shiba Kuanar ◽  
Debanjan Borthakur ◽  
Maria Vinti ◽  
Thupten Tendhar
Keyword(s):  
Machine Learning ◽  
Working Memory ◽  
Memory Load ◽  
Learning Approach ◽  
Working Memory Load ◽  
Machine Learning Approach ◽  
Neuroimaging Data

scholarly journals Connectomic Underpinnings of Working Memory Deficits in Schizophrenia: Evidence From a replication fMRI study

2020 ◽  
Vol 46 (4) ◽  
pp. 916-926 ◽  
Author(s):  
Jie Yang ◽  
Weidan Pu ◽  
Guowei Wu ◽  
Eric Chen ◽  
Edwin Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Working Memory ◽  
Negative Symptoms ◽  
Clinical Symptoms ◽  
Linear Regression Analysis ◽  
Symptom Burden ◽  
Learning Approach ◽  
Brain Connectome ◽  
Graph Properties ◽  
Machine Learning Approach

Abstract Background Working memory (WM) deficit is a key feature of schizophrenia that relates to a generalized neural inefficiency of extensive brain areas. To date, it remains unknown how these distributed regions are systemically organized at the connectome level and how the disruption of such organization brings about the WM impairment seen in schizophrenia. Methods We used graph theory to examine the neural efficiency of the functional connectome in different granularity in 155 patients with schizophrenia and 96 healthy controls during a WM task. These analyses were repeated in another independent dataset (81 patients and 54 controls). Linear regression analysis was used to test associations of altered graph properties, clinical symptoms, and WM accuracy in patients. A machine-learning approach was adopted to study the ability of multivariate connectome features from one dataset to discriminate patients from controls in the second dataset. Results Small-worldness of the whole-brain connectome was significantly increased in schizophrenia during the WM task; this increase is related to better (though subpar) WM accuracy in patients with more severe negative symptom burden. There was a shift in the degree distribution to a more homogeneous form in patients. The machine-learning approach classified a new set of patients from controls with 84.3% true-positivity rate for schizophrenia and 71.6% overall accuracy. Conclusions We demonstrate a putative mechanistic link between connectome topology, hub redistribution, and impaired n-back performance in schizophrenia. The task-dependent modulation of the connectome relates to, but remains inefficient in, improving the performance above par in the presence of severe negative symptoms.

scholarly journals Unity and diversity in working memory load: Evidence for the separability of the executive functions updating and inhibition using machine learning

2018 ◽  
Vol 139 ◽  
pp. 163-172 ◽  
Author(s):  
Tanja Krumpe ◽  
Christian Scharinger ◽  
Wolfgang Rosenstiel ◽  
Peter Gerjets ◽  
Martin Spüler
Keyword(s):  
Machine Learning ◽  
Working Memory ◽  
Executive Functions ◽  
Memory Load ◽  
Working Memory Load

scholarly journals Working memory load-dependent changes in cortical network connectivity estimated by machine learning

NeuroImage ◽  
2020 ◽  
Vol 217 ◽  
pp. 116895
Author(s):  
Hamdi Eryilmaz ◽  
Kevin F. Dowling ◽  
Dylan E. Hughes ◽  
Anais Rodriguez-Thompson ◽  
Alexandra Tanner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Working Memory ◽  
Memory Load ◽  
Network Connectivity ◽  
Cortical Network ◽  
Working Memory Load

scholarly journals Interpreting neural decoding models using grouped model reliance

2019 ◽  
Author(s):  
Simon Valentin ◽  
Maximilian Harkotte ◽  
Tzvetan Popov
Keyword(s):  
Machine Learning ◽  
Working Memory ◽  
Memory Load ◽  
Memory Task ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Neural Decoding ◽  
Working Memory Load ◽  
Eeg Data

AbstractThe application of machine learning algorithms for decoding psychological constructs based on neural data is becoming increasingly popular. However, there is a need for methods that allow to interpret trained decoding models, as a step towards bridging the gap between theory-driven cognitive neuroscience and data-driven decoding approaches. The present study demonstrates grouped model reliance as a model-agnostic permutation-based approach to this problem. Grouped model reliance indicates the extent to which a trained model relies on conceptually related groups of variables, such as frequency bands or regions of interest in electroencephalographic (EEG) data. As a case study to demonstrate the method, random forest and support vector machine models were trained on within-participant single-trial EEG data from a Sternberg working memory task. Participants were asked to memorize a sequence of digits (0–9), varying randomly in length between one, four and seven digits, where EEG recordings for working memory load estimation were taken from a 3-second retention interval. Present results confirm previous findings in so far, as both random forest and support vector machine models relied on alpha-band activity in most subjects. However, as revealed by further analyses, patterns in frequency and particularly topography varied considerably between individuals, pointing to more pronounced inter-individual differences than reported previously.Author summaryModern machine learning algorithms currently receive considerable attention for their predictive power in neural decoding applications. However, there is a need for methods that make such predictive models interpretable. In the present work, we address the problem of assessing which aspects of the input data a trained model relies upon to make predictions. We demonstrate the use of grouped model-reliance as a generally applicable method for interpreting neural decoding models. Illustrating the method on a case study, we employed an experimental design in which a comparably small number of participants (10) completed a large number of trials (972) over multiple electroencephalography (EEG) recording sessions from a Sternberg working memory task. Trained decoding models consistently relied on alpha frequency activity, which is in line with existing research on the relationship between neural oscillations and working memory. However, our analyses also indicate large inter-individual variability with respect to the relation between activity patterns and working memory load in frequency and topography. Taken together, we argue that grouped model reliance provides a useful tool to better understand the workings of (sometimes otherwise black-box) decoding models.

Visual Search Facilitation in Repeated Displays Depends on Visuospatial Working Memory

2012 ◽  
Vol 59 (1) ◽  
pp. 47-54 ◽  
Author(s):  
Angela A. Manginelli ◽  
Franziska Geringswald ◽  
Stefan Pollmann
Keyword(s):  
Working Memory ◽  
Visual Search ◽  
Control Experiment ◽  
Memory Load ◽  
Contextual Cueing ◽  
Long Term Memory ◽  
Visuospatial Working Memory ◽  
Working Memory Load ◽  
Memory Resources

When distractor configurations are repeated over time, visual search becomes more efficient, even if participants are unaware of the repetition. This contextual cueing is a form of incidental, implicit learning. One might therefore expect that contextual cueing does not (or only minimally) rely on working memory resources. This, however, is debated in the literature. We investigated contextual cueing under either a visuospatial or a nonspatial (color) visual working memory load. We found that contextual cueing was disrupted by the concurrent visuospatial, but not by the color working memory load. A control experiment ruled out that unspecific attentional factors of the dual-task situation disrupted contextual cueing. Visuospatial working memory may be needed to match current display items with long-term memory traces of previously learned displays.

A high working memory load eliminates the numerical distance effect

2010 ◽  
Author(s):  
Erin A. Maloney ◽  
Evan F. Risko ◽  
Derek Besner ◽  
Jonathan A. Fugelsang
Keyword(s):  
Working Memory ◽  
Memory Load ◽  
Distance Effect ◽  
Numerical Distance ◽  
Working Memory Load ◽  
Numerical Distance Effect
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