Incidental and Intentional Learning of Verbal Episodic Material Differentially Modifies Functional Brain Networks

Abstract

Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.

Relevance of hippocampal integrity for memory outcome after surgical treatment of mesial temporal lobe epilepsy

“…Since hippocampal cell counts cannot be determined before epilepsy surgery, presurgical biomarkers of the structural and functional integrity of the hippocampus would be appreciated that would advance individual risk-benefit evaluations before elective epilepsy surgery (e.g. high-field MRI [44,45], volumetric and FLAIR analysis [46], spectroscopy [47], relaxometry [48], diffusion tensor imaging [49], memory fMRI [50], and EEGanalyses [51,52]). …”

Brain enhancement through cognitive training: a new insight from brain connectome

“…Such difference in graph theoretical network metrics was found between rest and a task performance for the functional network derived from MEG (Bassett et al, 2006 ) and fMRI (Cao et al, 2014 ; Taya et al, 2014 ). Also, the graph theoretical metrics on fMRI functional network could discriminate between resting-state and sensory stimulation (Moussa et al, 2011 ), different cognitive load during a WM task (Ginestet and Simmons, 2011 ), different cognitive states in an emotional and a motivational task (Kinnison et al, 2012 ), and between an intentional and an incidental learning of words during neuropsychological tests (Kuhnert et al, 2013 ). The small-worldness of EEG functional network was different between during rest and mathematical thinking (Micheloyannis et al, 2009 ) and some network metrics of a combined EEG and MEG functional network was different between low- and high-memory load during a visual WM retention period (Palva et al, 2010 ).…”

Capturing time-varying brain dynamics

“…A number of studies of functional brain networks during seizures reported various local and global network characteristics to provide important clues about seizure dynamics, how they spread and terminate (see, e.g., [208,[250][251][252][253][254][255][256][257][258][259][260]). These findingsexhibiting a high similarity of topological evolution across different types of epilepsies, seizures, medication, age, gender, and other clinical featuresare of high relevance for further improving existing seizure-prevention techniques or for developing new ones, based, e.g., on behaviorally induced modifications of epileptic networks [238,261,262]. As regards the seizure-free interval, most studies investigated functional brain networks that were derived from selected EEG/iEEG epochs that ranged from some seconds to a few minutes only (see [263] for an overview), and only rarely [256,[264][265][266][267] had the evolution of an epileptic network been monitored over longer periods (days to weeks; for an example, see Fig.…”