Elsevier

Brain and Cognition

Volume 86, April 2014, Pages 75-81
Brain and Cognition

Unraveling Brain Functional Connectivity of encoding and retrieval in the context of education

https://doi.org/10.1016/j.bandc.2014.01.018Get rights and content

Highlights

  • Negative correlation of default mode network (DMN) with encoding and retrieval.

  • Inhibition of DMN was more in CE for both encoding and retrieval.

  • Associative brain areas for retrieval are greater than encoding.

  • Dorso-lateral pre frontal region (DLPFR) related with encoding and retrieval.

  • Education influences performance and functional brain connectivity.

Abstract

Human memory is an enigmatic component of cognition which many researchers have attempted to comprehend. Accumulating studies on functional connectivity see brain as a complex dynamic unit with positively and negatively correlated networks in perfect coherence during a task. We aimed to examine coherence of network connectivity during visual memory encoding and retrieval in the context of education. School Educated (SE) and College Educated (CE) healthy volunteers (n = 60) were recruited and assessed for visual encoding and retrieval. Functional connectivity using seed to voxel based connectivity analysis of the posterior cingulate cortex (PCC) was evaluated. We noticed that there were reciprocal dynamic changes in both dorsolateral prefrontal cortex (DLPFC) region and PCC regions during working memory encoding and retrieval. In agreement with the previous studies, there were more positively correlated regions during retrieval compared to encoding. The default mode network (DMN) networks showed greater negative correlations during more attentive task of visual encoding. In tune with the recent studies on cognitive reserve we also found that number of years of education was a significant factor influencing working memory connectivity. SE had higher positive correlation to DLPFC region and lower negative correlation to DMN in comparison with CE during encoding and retrieval.

Introduction

The importance of memory cannot be understated since our very survival depends on our ability to remember who we are, who others are, our past experiences, what is dangerous, what is safe, etc. Atkinson–Shiffrin model (Bjork & Whitten, 1974) remains the most popular model for studying memory which describes memory as a sequence of three stages, from sensory to short-term to long term memory, rather than as a unitary process. All aspects of memory, can be viewed as the activation of network memory, that is, the increased firing of the cortical neurons making up a memory network (Joaquı́n M. Fuster, 1997). Networks remain open-ended throughout life, subject to expansion and recombination by new experience. The networks of perceptual and motor memory appear hierarchically organized on a foundation of phyletic memory-that is, primary sensory and motor cortex (Joaquı́n M. Fuster, 1997). Short-term memory is represented in a network involving the prefrontal and posterior sensory cortices. The prefrontal cortex controls subsidiary posterior cortices, enabling retention of short-lived information relevant to ongoing goal-directed behavior. Long-term declarative memory is also represented in the cerebral cortex in a domain-specific fashion. For instance, in the inferior temporal cortex, which is involved in object recognition, some neurons encode stimulus repetition, while others learn to encode an elemental semantic-like association between visual images. (T. Fukushima, 2003). The process of encoding varies with the levels of shallow and deep processing (Craik & Tulving, 1975). The retrieval is influenced by cues, encoding specificity, state-dependent learning and transfer-appropriate processing. Neurobiology of memory involves connection, cognition, compartmentalization, and consolidation (Milner, Squire, and Kandel, 1998; Eichenbaum, 2011).

With the developments in the field of functional MRI (fMRI) a very high level of integrative real time understanding of working memory is now possible as we are able to see brain in real time as functionally defined set of networks that transiently interact to perform a particular neural function. Accumulating studies on functional connectivity now see brain as a complex dynamic unit with positively and negatively correlated networks in perfect coherence during a task (Chanraud et al., 2011, Esposito et al., 2009, Fox et al., 2009, Hampson et al., 2006, Mayer et al., 2010, Meda et al., 2009, Sala-Llonch et al., 2012, Van den Bosch et al., 2012). Task-positive networks are regions that are simultaneously activated during active cognitive processing. Frontoparietal network is a prominent task positive working memory network (Champod and Petrides, 2010, Deiber et al., 1997, Iidaka et al., 2006, LaBar et al., 1999). Dorsolateral prefrontal cortex is considered as hub for many networks especially in the context memory and education (Champod and Petrides, 2007, Collette et al., 1999, Gerton et al., 2004, Sun et al., 2005). Task-negative networks (resting-related networks) are those which are active during passive or stimulus-independent thought, and are subsequently deactivated during active processing. One of the well documented resting related networks is DMN. This network comprises a set of highly functionally connected regions such as medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), lateral and medial temporal lobes and posterior inferior parietal lobule (Greicius et al., 2009, Raichle et al., 2001, Uddin et al., 2009; Fransson, 2005, Damoiseaux et al., 2006, Fox and Raichle, 2007, Buckner et al., 2008, Johnson et al., 2012). The negative correlation between regions in the DMN during a cognitive task suggests that the circuit interferes with or distracts from cognitive processing and facilitates cognitive performance. In prior studies, a negative correlation between working memory performance and task-irrelevant mental processing was reported (Chanraud et al., 2011, Esposito et al., 2009, Fox et al., 2005, Fox et al., 2009, Hampson et al., 2006, Mayer et al., 2010, Meda et al., 2009, Michels et al., 2010, Sala-Llonch et al., 2012, Van den Bosch et al., 2012). A recent study observed and found that not only there is negative correlation between the DMN and cognitive tasks but the degree is proportionally related with the demands of the cognitive function (Sala-Llonch et al., 2012). Further study confirmed that the relative extent of the anterior and posterior midline spots within the DMN was negatively correlated in the PCC with the level of task difficulty. The study concluded that the working-memory function is related to a spatial re-configuration of the DMN functional connectivity which could function as a novel predictor of the working-memory efficiency (Esposito et al., 2009). From the perspective of development, Van den Bosch et al. studied the brain connectivity during verbal working memory in children and adolescents. They noted children have higher task-related connectivity at lower loads, but they tend to equalize with the adolescents with higher loads and non-load related network involving the orbital frontal and anterior cingulate cortices showed less connectivity in children (Van den Bosch et al., 2012). In a study done by Champod and Petrides (2007), it was noted that the PCC is centrally involved in manipulation processes, whereas activation of the DLFC is related to the monitoring of the information that is being manipulated and their relative contribution to working memory. Brain deactivation patterns during working memory and visual attention tasks was noted in task fMRI (Tomasi, Ernst, Caparelli, & Chang, 2006). Mayer et al. noted task induced brain deactivation in DMN region in visual working memory and attention task. They reported task-induced deactivations within regions of the DMN with a segregation of areas that were additively deactivated by an increase in the demands on both attention and WM. Attention selective deactivations appeared in the left ventrolateral and medial prefrontal cortex and the left lateral temporal cortex. Conversely, WM selective deactivations were reported in the right hemisphere including the medial-parietal, the lateral temporo-parietal, and the medial prefrontal cortex. Moreover they also reported, during WM encoding deactivated regions showed task specific functional connectivity. Their findings demonstrate that task induced deactivations within parts of the DMN depend on the specific characteristics of the cognitive process (Mayer et al., 2010). Recently an EEG-fMRI study found that low frequency band (theta and alpha) activity negatively correlated with the BOLD signal during the retention phase of a WM task. They found negative BOLD signal correlations with lower frequency of EEG (theta band) in the MPFC, posterior parietal cortex (PPC), and cingulate cortex (ACC and PCC). For alpha1, positive correlations with the BOLD signal were found in ACC, MPFC, and PCC; negative correlations were observed in DLPFC, PPC, and inferior frontal gyrus (IFG). Beta and gamma showed positive correlations with BOLD in DLPFC, MPFC and IFG (Michels et al., 2010).

Connectivity analysis could be either task independent (resting state) based connectivity or task based functional connectivity. Task based functional connectivity could be superior in understanding dynamic changes involved in network in relation to a task (Greicius et al., 2003, Mayer et al., 2010, Wang et al., 2013).

Cognitive reserve (CR) hypothesis emphasizes inter-individual differences in the effective recruitment of neural networks and cognitive processes to compensate for age related effects or pathology. Under the reserve hypothesis (Stern, 2002), education is being considered as one of the main proxy of reserve, although other factors (i.e. intelligence quotient (IQ), occupation, social and physical activities, complex mental activities) may also be considered (Satz et al., 2011). Functional studies during memory tasks showed functional reorganization of brain networks (compensation) in healthy elders with higher education compared to young individuals (Scarmeas et al., 2003), and more efficient or optimal patterns of brain activation in elders with higher reserve proxies compared to elders with lower reserve proxies (Bosch et al., 2010; Solé-Padullés et al., 2009). According to C. Bastin et al., 2012, for tasks of low to moderate difficulty, high cognitive reserve will take the form of reduced – more efficient activation of the network for an equivalent or even greater success in the task and for high-demanding tasks, individuals with high cognitive reserve will have a greater capacity, so that they can show greater increase in network activation to cope with increasing task difficulty.

With the insights gained from these studies we aimed to understand the coherence of positively and negatively correlated networks during a working memory task as we strongly believe that brain is better understood as a balanced network resonating in harmony during task performance. Education being a main proxy for cognitive reserve we also aimed at understanding the effect of education on the harmony of these networks.

Section snippets

Sample

Written informed consent was sought from all the participants as per the institute Ethics Committee. Sixty right handed educated healthy volunteers from both the genders (32 females and 28 males) in the age range of 18–40 years were recruited and segregated into two groups of 30 each as school educated (SE < 10 years of education) and college educated (CE > 10 years of education) on the basis of education acquired in years. School educated participants’ inability to pursue further years of education

Preprocessing

The functional and structural MRI pre analysis was performed using statistical parametric mapping (Friston, Ashburner, Kiebel, Nichols, & Penny, 2011) (SPM8; Welcome Department of Cognitive Neurology, London). The data was realigned for motion correction by registration to the mean image. The images were then normalized to the Montreal Neurological Institute (MNI) space. Finally images were smoothed with a Gaussian kernel of 8 mm at full-width half maximum. In addition to these steps, the

Behavioral results

Performance scores (d prime) and reaction times (RTs) at the time of retrieval were noted for both the CE and SE group. All the subjects performed well (greater than 80% correct response) during the scanning session across the group. A Mann–Whitney U test was used to evaluate their difference in behavioral preferences. CE showed significantly higher performance scores and shorter RTs in retrieval. d Prime values are significantly different between the CE (3.045 ± 0.4917, median = 3.16) and SE (1.443

Discussion

Brain regions simultaneously activated during any cognitive process are functionally connected, forming large-scale networks. These functional networks can be examined during task performance and rest states and such analysis is being widely used. The aim of our study was to examine brain networks connectivity for encoding and retrieval in the context of education. Sixty healthy young school educated and college educated subjects were selected to study the visual encoding and retrieval using

Acknowledgments

This research work was funded by the Department of Science and Technology. We thank our radiographers, (Dept. Neuroimaging and Interventional Radiology (NIIR), NIMHANS, India) for their technical support and data collection. Special thanks to Mr. Deepak Ulal (Senior Technician, Dept. of Clinical Psychology) and Ms. Saranya VR (Radiographer, Dept. NIIR).

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