Functional brain microstate predicts the outcome in a visuospatial working memory task

Muthukrishnan, Suriya Prakash; Ahuja, Navdeep; Mehta, Nishank; Sharma, Ratna

doi:10.1016/j.bbr.2016.08.020

Cited by 26 publications

(18 citation statements)

References 76 publications

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“…Further, we demonstrated that the general state of awareness, as reflected in the ongoing dynamics of pre-stimulus EEG microstates, are indeed informative of the capacity of a subject to respond to a stimulus during drowsiness at an individual trial level. This finding echoes similar evidence from the literature, where pre-stimulus microstate properties predict perception of weak stimuli (Britz et al 2014 ), accuracy of working memory (Muthukrishnan et al 2016 ) and perceptual shifts between bistable stimuli (Britz et al 2009 ). Again, the special significance of microstate D during unresponsiveness was visible from its increased ability to predict the likelihood of a response, in comparison with microstates A-C.…”

Section: Discussionsupporting

confidence: 90%

Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness

2018

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As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes. While participants performed an auditory semantic classification task, we allowed them to become drowsy and unresponsive. As they stopped responding to the stimuli, we report the breakdown of alpha networks and the emergence of theta connectivity. Further, we show that the temporal dynamics of all canonical EEG microstates slow down during unresponsiveness. We identify a specific microstate (D) whose occurrence and duration are prominently increased during this period. Employing machine learning, we show that the temporal properties of microstate D, particularly its prolonged duration, predicts the response likelihood to individual stimuli. Finally, we find a novel relationship between microstates and brain networks as we show that microstate D uniquely indexes significantly stronger theta connectivity during unresponsiveness. Our findings demonstrate that the transition to unconsciousness is not linear, but rather consists of an interplay between transient brain networks reflecting different degrees of sleep depth. Electronic supplementary material The online version of this article (10.1007/s10548-018-0689-9) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 90%

Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness

2018

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“…In the context of EEG and MEG data, the pattern of power or amplitude in a set of sensors or a set of reconstructed sources is frequently referred to as a microstate (von Wegner et al, 2016). The composition and dynamics of these microstates have shown interesting cognitive and clinical utility, predicting working memory (Muthukrishnan et al, 2016) and disease (Geschwind et al, 2016). Yet, while patterns of activation are acknowledged as an important representation of a brain or cognitive state, little is known about how these states evolve into one another.…”

Section: Discussionmentioning

confidence: 99%

Brain state flexibility accompanies motor-skill acquisition

et al. 2018

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Learning requires the traversal of inherently distinct cognitive states to produce behavioral adaptation. Yet, tools to explicitly measure these states with non-invasive imaging - and to assess their dynamics during learning - remain limited. Here, we describe an approach based on a distinct application of graph theory in which points in time are represented by network nodes, and similarities in brain states between two different time points are represented as network edges. We use a graph-based clustering technique to identify clusters of time points representing canonical brain states, and to assess the manner in which the brain moves from one state to another as learning progresses. We observe the presence of two primary states characterized by either high activation in sensorimotor cortex or high activation in a frontal-subcortical system. Flexible switching among these primary states and other less common states becomes more frequent as learning progresses, and is inversely correlated with individual differences in learning rate. These results are consistent with the notion that the development of automaticity is associated with a greater freedom to use cognitive resources for other processes. Taken together, our work offers new insights into the constrained, low dimensional nature of brain dynamics characteristic of early learning, which give way to less constrained, high-dimensional dynamics in later learning.

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“…The VSWM task scores were the number of errors committed in load 1, load 2, load 3, and total number of errors committed in all the loads. Search time was the time taken for matching second picture of the pair with first picture, beginning from the first mouse click to second click (Muthukrishnan et al, ; Suriya‐Prakash & Sharma, ). The scores were normalized for each subject.…”

Section: Methodsmentioning

confidence: 99%

“…Very few studies have investigated the VSWM in schizophrenia with increasing memory loads (Johnson et al, 2006). Therefore, a VSWM task that involved simultaneous maintenance, manipulation, retrieval, and recoding of encoded information with increasing memory loads simulating day-to-day activities has been used in the current study (Muthukrishnan, Ahuja, Mehta, & Sharma, 2016;Suriya-Prakash & Sharma, 2015).…”

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confidence: 99%

A novel method for assessing patients with schizophrenia and their first‐degree relatives by increasing cognitive load of visuo‐spatial working memory

Soni

Muthukrishnan

Sood

et al. 2018

Asia-Pacific Psychiatry

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Functional brain microstate predicts the outcome in a visuospatial working memory task

Cited by 26 publications

References 76 publications

Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness

Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness

Brain state flexibility accompanies motor-skill acquisition

A novel method for assessing patients with schizophrenia and their first‐degree relatives by increasing cognitive load of visuo‐spatial working memory

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