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. Author manuscript; available in PMC: 2015 Oct 1.
Published in final edited form as: Biol Psychol. 2014 Jul 18;0:18–29. doi: 10.1016/j.biopsycho.2014.06.008

Gender differences in working memory networks: A BrainMap meta-analysis

Ashley C Hill a, Angela R Laird b, Jennifer L Robinson a,c,d
PMCID: PMC4157091  NIHMSID: NIHMS615031  PMID: 25042764

Abstract

Gender differences in psychological processes have been of great interest in a variety of fields. While the majority of research in this area has focused on specific differences in relation to test performance, this study sought to determine the underlying neurofunctional differences observed during working memory, a pivotal cognitive process shown to be predictive of academic achievement and intelligence. Using the BrainMap database, we performed a meta-analysis and applied activation likelihood estimation to our search set. Our results demonstrate consistent working memory networks across genders, but also provide evidence for gender-specific networks whereby females consistently activate more limbic (e.g., amygdala and hippocampus) and prefrontal structures (e.g., right inferior frontal gyrus), and males activate a distributed network inclusive of more parietal regions. These data provide a framework for future investigation using functional or effective connectivity methods to elucidate the underpinnings of gender differences in neural network recruitment during working memory tasks.

Keywords: gender differences, fMRI, brainmap, working memory, sex differences

Introduction

For over a century, unequal abilities between men and women, particularly within the intellectual domain, have been both intriguing and elusive. While evidence for gender differences in psychological processes have been noted across a diverse range of cognitive domains (Bradley et al., 2001; Gur et al., 2000; Koch et al., 2007; Lynn and Irwing, 2002; Ragland et al., 2000; Shaywitz et al., 1995; Volf and Razumnikova, 1999), mixed results (Stevens, 2011) have stunted progression toward an understanding of the potential basis for these differences from a strictly neurological perspective. While the majority of research in this area has focused on specific behavioral performance differences in relation to test performance, this study sought to determine the neurofunctional differences observed during working memory, a pivotal cognitive process shown to be predictive of academic achievement and intelligence (Conway et al., 2003).

Examining working memory as a whole, the observed neural activation patterns observed in functional neuroimaging studies consistently demonstrate prefrontal, temporal, and parietal involvement (Haier et al., 2005) (Baddeley, 1981; Baddeley, 1997, 2000; Baddeley and Logie, 1999; D'Esposito et al., 1998a; D'Esposito et al., 1998b; D'Esposito et al., 2000; Na et al., 2000; Prabhakaran et al., 2000; Repovs and Baddeley, 2006), posited to reflect the components of Baddeley and colleagues (2011) revised model of working memory. However, it is widely accepted that working memory operates differently when presented with verbal compared to spatial information (Reuter-Lorenz et al., 2000; Smith et al., 1996). Verbal working memory preferentially engages the left hemisphere, specifically the inferior parietal lobe, lateral frontal lobe, the supramarginal gyrus (BA 10), premotor areas, and Broca's area (Jonides et al., 1998; Schumacher et al., 1996; Smith et al., 1996; Smith et al., 1998). Spatial working memory has been associated with a more dispersed activation pattern across the hemispheres, consisting of the inferior frontal lobe, posterior parietal lobe, right occipital gyrus, right premotor area, right dorsolateral prefrontal cortex, and the extrastriate cortex in the occipital lobe (D'Esposito et al., 1998a; Jonides et al., 1993; van Asselen et al., 2006). It has long been acknowledged that working memory plays a key role in manipulating incoming information entering the cognitive system, whether the information is verbal or spatial in nature, interacting dynamically with attention and long-term memory. For this reason, working memory is an integral part of general cognitive processing with significant trickle-down effects on other critical processes. Therefore, observing gender differences among working memory networks could have robust effects in other areas of cognitive functioning.

Interestingly, when working memory is deconstructed into spatial and verbal components, evidence suggests that behavioral disparities emerge between genders (Halpern et al., 2007). Research has shown that from a behavioral performance perspective, males demonstrate greater mathematical (Lynn and Irwing, 2008), spatial (Kaufman, 2007; Lejbak et al., 2011; Masters and Sanders, 1993; Nordvik and Amponsah, 1998), and object working memory (Lejbak et al., 2011) compared to females, and females display greater verbal (including episodic memory (Lewin et al., 2001)) and writing skills than males (Bae et al., 2000; Hedges and Nowell, 1995). The discrepancy in male and female spatial ability appears to begin as early as preschool and then becomes even more significant as males and females enter adulthood (Levine et al., 1999), whereas the female superiority in verbal facets tends to appear slightly later, peaking in early adulthood (Willingham and Cole, 1997). Some researchers suggest that the male advantage in spatial ability helps set them above their female counterparts in mathematics, especially in areas like geometry, which involve the visualization of items in space (Casey et al., 1995).

Despite evidence that gender differences exist in working memory, there is an equally strong case for a lack of performance differences. In recent years, as functional neuroimaging has become more commonplace, studies that do not find explicit behavioral differences have the opportunity to view more intrinsic neurofunctional patterns. Multiple studies have found that there are no significant performance differences between the genders during verbal working memory tasks, but there is evidence for neurofunctional differences (Kaufman, 2007; Lejbak et al., 2011; Speck et al., 2000), suggesting that the behavioral differences may still exist, but the studies could be underpowered, or males and females could be using different psychological strategies. Specifically, Speck and colleagues (Speck et al., 2000) observed differences in the functional networks utilized to complete a verbal working memory task, with males accessing more right hemispheric regions such as the lateral prefrontal cortex, posterior cingulate and caudate, while females utilized the left hemisphere more prominently. Females have also shown greater activation in the middle, inferior, and orbital prefrontal regions, despite similar performance to male subjects in other studies (Goldstein et al., 2005). Taken collectively, neuroimaging data support the notion that certain brain regions can function differently in males and females to produce the same behavioral responses, which appears to be the case with working memory (Goldstein et al., 2005). These results suggest that using functional neuroimaging may allow researchers to develop more accurate models of gender differences within specific cognitive domains that would allow for theories of neuroanatomical and neurofunctional differences to be tested empirically (for review, please see Halpern, et al. 2007).

From a neuroimaging perspective, recent research has shown that there are gender differences in functional connectivity during resting state (Filippi et al., 2013). Specifically, Filippi and colleagues (2013) found that women had greater intrinsic functional connectivity inclusive of the cingulate, dorsolateral prefrontal cortex, and the inferior frontal gyrus, while men demonstrated increased functional connectivity in parietal regions, characteristics that the authors attribute to potential strategy differentiation. These observed differences could help explain the disparity in performance between the genders on various cognitive tasks, as well as bringing into question the possibility of inherent neural network differences. The present study focuses on the later implication of the resting state data with regard to working memory, to see if such differences exist during working memory performance. Furthermore, because of the diversity of paradigms used to examine working memory, we chose to pursue a meta-analysis that overcomes task-dependent activation differences, allowing for a more accurate depiction of gender differences within the construct of working memory. Therefore, the present study investigated the neural underpinnings of gender differences in working memory by capitalizing on the structure of the BrainMap database (Fox et al., 2005; Fox and Lancaster, 2002; Laird et al., 2005b), a functional neuroimaging database that archives functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies with a meticulous coding scheme (Laird et al., 2009). Using meta-analysis to develop models of functional connectivity and subsequently probing differences in connectivity networks has been demonstrated to be both robust and effective (Robinson et al., 2010).

Methods

In order to ascertain the neural underpinning of working memory for males and females, the BrainMap database was queried using Sleuth version 2.2 (Fox et al., 2005; Laird et al., 2009; Laird et al., 2005b). In short, Sleuth is a free, publicly available search tool that allows users to search the BrainMap database among any of the meta-data categories contained within the database. We entered the following search criteria: 1) studies coded within the behavioral domain of cognition and paradigm class of working memory (e.g., Experiments → Behavioral Domain → Cognition → Memory – Working), 2) studies reporting activations only (e.g., Experiments → Activation → Activations Only), 3) studies using normal, healthy subjects (e.g., Experiments → Context → Normal Mapping), and 4) studies using only males or only females (e.g., two separate searches, one for each gender, were performed, Subjects → Gender → Females (or Males) Only). Resultant whole-brain coordinates of activation during working memory tasks were then downloaded (males: 44 papers, 2316 locations, 141 experiments, 127 conditions, 701 subjects; females: 15 papers, 402 locations, 36 experiments, 49 conditions, 200 subjects; to download the complete workspace files for the male and female searches, please visit http://aucanlab.com/?page_id=128). Coordinates that were not reported in Talairach space in their original publication were transformed into Talairach space by the GingerALE analysis program using the icbm2tal transform (Laird et al., 2010; Lancaster et al., 2005).

Activation likelihood estimation (ALE) meta-analysis (Eickhoff et al., 2009; Laird et al., 2005a; Turkeltaub et al., 2002) was performed on the sets of coordinates identified as activated during working memory tasks to identify regions of convergence within each search (i.e., males and females were run separately). ALE capitalizes on the nature of voxel-wise studies that are commonly reported in a standard stereotaxic space (x,y,z) by pooling 3D coordinates from like studies, and providing the probability of an event occurring at each brain voxel. The algorithm treats each coordinate of activation as a spatial probability, and ALE maps are subsequently calculated by computing the convergence of activation probabilities for every voxel. Permutation testing is then applied. Specifically, an ALE null-distribution is created by randomly assigning the same number of foci from the original analysis throughout the brain, and calculating ALE maps reiteratively after every reassignment. The original ALE scores are then compared to the random null distribution to assign p-values (Laird et al., 2005a; Turkeltaub et al., 2002). A revised ALE algorithm was proposed and subsequently implemented in the statistical toolbox GingerALE version 2.3 (Eickhoff et al., 2009). The new algorithm is statistically more robust as it treats the data using a random-effects approach, and models the uncertainty associated with a given coordinate. Furthermore, the analysis is anatomically constrained to exclude deep white matter, with the reasoning that ‘true’ activations originate in the gray matter, thus if we do not constrain the analyses, there is a potential bias in the permutation testing that creates the null-distribution by which p-values are determined (Eickhoff et al., 2009). Our analysis used the revised algorithm proposed by Eickhoff and colleagues (2009). False discovery rate (FDR) is defined as having no more than 5% false positives (i.e., if you are using an FDR corrected p-value of 0.05). In an ALE meta-analysis, FDR is dependent on the number of permutations implemented (Laird et al., 2005a). ALE maps from the present study were thresholded conservatively at an FDR-corrected p-value of 0.05 with a cluster threshold of 100mm3.

Results

ALE results provide evidence for both common and gender-specific memory network utilization (please see Table 1). Common to both genders, bilateral middle frontal gyri (BA6/9), left cingulate gyrus (BA32), right precuneus (BA7/19), left inferior and superior parietal lobes (BA40,BA7, respectively), right claustrum, and left middle temporal gyrus (BA39) were found to be consistently activated during working memory performance. Gender specific networks also emerged. For females, we found that working memory tasks elicited consistent activity in regions of the limbic system such as the anterior cingulate (BA32), bilateral amygdala, and right hippocampus, in addition to an extensive prefrontal network inclusive of bilateral middle frontal gyri (BA46) and the right medial frontal gyrus (BA9). Males demonstrated a distributed gender-specific working memory network inclusive of the cerebellum, portions of the superior parietal lobe (BA7), the left insula (BA13), and bilateral thalamus (please see Figures 1 and 2).

Table 1.

Gender Differences in Working Memory Across All Working Memory Tasks

Convergent Brain Regions
Lobe Region BA Females Males ALE
x y z x y z
Frontal Right Middle Frontal Gyrus 6 26 2 52 28 −6 52 0.032 0.095
34 2 38 32 2 34 0.018 0.058
9 28 28 30 32 30 32 0.018 0.067
Left Middle Frontal Gyrus 6/9 −28 −4 50 −26 −8 54 0.030 0.083
−36 28 26 −40 26 26 0.026 0.043
Limbic Left Cingulate Gyrus 32 −4 10 42 −2 16 40 0.029 0.054
Parietal Right Precuneus 7 12 −64 48 16 −72 44 0.023 0.107
19 30 −60 40 30 −70 38 0.022 0.051
Left Superior Parietal Lobule 7 −28 −62 48 −30 −54 48 0.014 0.079
Left Inferior Parietal Lobe 40 −34 −50 36 −38 −52 38 0.028 0.072
Sub-lobar Right Claustrum 32 14 0 30 14 6 0.016 0.054
Temporal Left Middle Temporal Gyrus 39 32 −60 30 −34 −68 30 0.017 0.047
Female-Specific Network
Anterior Right Culmen 34 −56 −22 0.023
4 −36 −8 0.014
Frontal Left Precentral Gryus 4 −44 −8 40 0.019
Left Frontal Gyrus −6 6 54 0.030
Right Medial Frontal Gyrus 6 10 0 56 0.016
Left Precentral Gryus −40 2 28 0.015
Right Medial Frontal Gyrus 9 8 46 16 0.015
Right Inferior Frontal Gyrus 13 38 22 10 0.029
Left Inferior Frontal Gyrus 4 −50 28 6 0.020
Right Inferior Frontal Gyrus 52 28 12 0.024
Left Middle Frontal Gyrus 46 −42 14 20 0.023
Right Middle Frontal Gyrus 46 38 22 0.026
Right Inferior Frontal Gyrus 47 26 14 −12 0.017
Limbic Right Anterior Cingulate 32 8 36 20 0.020
Left Amygdala −22 −6 −10 0.031
Right Amygdala 22 −2 −12 0.023
Right Hippocampus 28 −14 −10 0.024
Occipital Right Cuneus 18 12 −78 28 0.018
Right Precuneus 31 20 −72 28 0.017
Parietal Left Postcentral Gyrus 2 −54 −18 28 0.018
Left Precuneus 7 −22 −66 36 0.022
31 −2 −50 30 0.017
Right Inferior Parietal Lobule 40 46 −54 40 0.023
34 −46 40 0.017
Sub-lobar Right Thalamus (Medial Dorsal Nucleus) 4 −16 4 0.031
Left Thalamus −12 −18 6 0.018
Right Caudate Head 18 24 4 0.024
Left Claustrum −30 14 8 0.016
Left Putamen (Lenitform Nucleus) −18 12 8 0.016
Temporal Left Superior Temporal Gyrus 13 −42 −46 24 0.020
Left Middle Temporal Gyrus 39 −46 −68 26 0.037
Male Specific Network
Anterior Right Cerebllum Nodule 10 −52 −28 0.060
Left Middle Frontal Gyrus 6 −48 0 38 0.072
Left Superior Frontal Gyrus 6 0 8 48 0.120
Left Medial Frontal Gyrus −4 −20 56 0.031
−8 −10 48 0.042
Right Middle Frontal Gyrus 9 42 12 40 0.048
Left Inferior Frontal Gyrus 9 −50 10 30 0.063
Left Middle Frontal Gyrus −44 14 26 0.043
Right Middle Frontal Gyrus 10 34 48 16 0.046
Midbrain Left Brainstem (Red Nucleus) 0 −20 −4 0.040
Occipital Right Cuneus 18 26 −76 20 0.065
Left Cuneus −18 −74 20 0.059
Left Middle Occipital Gyrus 19 −28 −78 20 0.042
Right Middle Occipital Gyrus 37 40 −64 10 0.031
Right Precuneus 28 −56 52 0.045
Left Precuneus 7 −14 −70 48 0.066
−6 −68 40 0.049
Right Supramarginal Gyrus 40 40 −46 36 0.030
Posterior Left Declive −32 −66 −14 0.071
Right Declive 26 −68 −16 0.065
Left Cerebellar Tonsil −32 −56 −32 0.041
−40 −58 −34 0.031
Left Declive −2 −76 −10 0.054
Right Declive 10 −68 −16 0.040
Sub-lobar Left Insula 13 −34 16 10 0.050
Right Thalamus 14 −20 16 0.067
Left Thalamus (Ventral Lateral Nucleus) −16 −16 14 0.059
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Figure 1.

Figure 1

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Mosaic view of working memory networks in males (blue) and females (red). Brain regions recruited by both genders during working memory tasks are depicted by yellow. Maps were thresholded at p < 0.05, FDR-corrected.

Figure 2.

Figure 2

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3D rendering of the working memory networks in males and females.

Post-hoc Decomposition of Working Memory

Our initial findings revealed neural network recruitment differences in working memory, such that females demonstrated more limbic activation. Because of the disparate search set sizes, and to ensure our data were driven by cognitively coded papers, we did post-hoc analyses examining the two most prevalent working memory tasks: the n-back and the delayed match to sample (DMTS) task. For these searches, we followed the above procedure, but in addition to the search criteria of ‘Experiments → Behavioral Domain → Cognition → Memory – Working’, we also included Experiments → Paradigm Class → Delayed Match to Sample (or n-back)’. This allowed us to narrow our search to only those studies implementing n-back or DMTS tasks within the behavioral domain of ‘Cognition’. The DMTS and n-back search specific to females yielded 15 papers, 195 subjects, 45 experiments, 53 conditions, and 484 locations. The male workspace consisted of 30 papers, 397 subjects, 76 experiments, 89 conditions, and 757 locations. ALE was implemented as described above. Maps were thresholded at an FDR-corrected p-value of 0.05, with a cluster threshold of 100mm3. We also performed a quantitative contrast of the resultant ALE maps to objectively determine the differences between male and female networks in a statistically sound manner using the GingerALE program within the BrainMap environment. To do this, GingerALE performs a subtraction of one ALE image from the other. Similar to a traditional ALE analysis, GingerALE creates simulated data by pooling the coordinates from the original datasets and randomly dividing them into two new groupings of the same size as the original datasets, then subtracting these new pairings (i.e., permutations are used to create a null distribution of which the real-data is then compared). The resultant images are converted to z-score maps.

Our results largely mirror the results obtained from including all working memory studies, with females demonstrating more activation throughout the limbic and prefrontal regions, including bilateral amygdalae and cingulate regions, and males activating more parietal areas, such as the inferior and superior parietal lobe and the precuneus (please see Tables, 2, 3, and 4). The quantitative assessment of gender differences on the resultant ALE maps from the post-hoc analysis corroborated with evidence from visual assessment. Specifically, the females showed greater activation of limbic structures inclusive of the amygdalae, in addition to frontal regions such as the left medial and superior frontal gyri and the right middle and inferior gyri. Males demonstrated greater activation consistently in the left precuneus and superior parietal lobule, as well as the right insula (please see Table 5 and Figure 3, Panel B).

Table 2.

Gender Differences in DMTS and N-back Working Memory Tasks

Convergent Brain Regions
Lobe Region BA Females Males ALE Female ALE Male
x y z x y z
Anterior Right Culmen 2 −50 −20 6 −42 −20 0.009 0.012
Frontal Left Middle Frontal Gyrus −26 −4 50 −26 −8 56 0.022 0.051
Left Precentral Gyrus 6 −40 0 28 −44 0 30 0.035 0.051
Right Precentral Gyrus 42 2 28 32 0 34 0.010 0.049
Right Sub-Gyral 26 2 52 20 −6 56 0.029 0.047
Left Inferior Frontal Gyrus −56 12 24 −52 10 30 0.011 0.034
Right Medial Frontal Gyrus 9 8 48 16 8 50 16 0.016 0.014
Right Middle Frontal Gyrus 28 34 24 32 30 32 0.013 0.039
48 16 34 48 16 36 0.009 0.021
Left Middle Frontal Gyrus −38 44 16 −42 50 4 0.012 0.013
Right Middle/Superior Frontal Gyrus 10 38 48 20 36 46 16 0.014 0.026
Left Inferior Frontal Gyrus 45 −50 28 6 −52 18 4 0.021 0.013
Left Extra-Nuclear/Inferior Frontal Gyrus 47 −30 18 −10 −32 20 −8 0.014 0.017
Limbic Left Cingulate Gyrus 31 0 −50 26 −2 −50 28 0.020 0.016
32 −4 10 42 −12 6 40 0.032 0.024
Occipital Left Lingual Gyrus 18 −20 −78 −8 −14 −82 −10 0.008 0.012
Right Cuneus 26 −68 18 26 −76 20 0.011 0.057
Parietal Left Postcentral Gyrus 3 −54 −18 26 −50 −18 38 0.015 0.011
Left Precuneus 7 −20 −64 38 −14 −70 48 0.020 0.041
Right Precuneus 12 −64 48 18 −70 46 0.019 0.064
19 30 −60 40 32 −66 38 0.022 0.025
Right Superior Parietal Lobule 7 38 −58 52 28 −58 54 0.009 0.032
Right Inferior Parietal Lobule 40 46 −54 40 44 −50 40 0.023 0.019
Posterior Left Cerebellar Tonsil −36 −52 −44 −36 −56 −44 0.010 0.011
Right Cerebellar Tonsil 24 −58 −44 28 −58 −36 0.014 0.012
Right Declive 26 −70 −16 26 −68 −16 0.015 0.058
Sub-lobar Left Insula 13 −36 18 8 −34 16 10 0.024 0.025
−32 20 2 0.023
Right Claustrum 32 14 0 32 12 4 0.019 0.029
Left Caudate Body −6 0 10 −6 2 18 0.011 0.011
Temporal Left Superior Temporal Gyrus 22 −44 −34 −2 −46 −36 0 0.019 0.011
Left Fusiform Gyrus 37 −40 −54 −18 −42 −44 −12 0.027 0.010
Right Superior Temporal Gyrus 38 44 20 −18 42 20 −18 0.009 0.011
Left Middle Temporal Gyrus 39 −32 −60 30 −34 −68 30 0.014 0.018
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Table 3.

Female-Specific Network in DMTS and N-back Working Memory Tasks

Lobe Region BA x y z ALE
Anterior Right Pyramis 2 −64 −26 0.017
4 −42 −22 0.009
Right Culmen 10 −36 −20 0.009
34 −56 −22 0.027
Frontal Right Precentral Gyrus 4 32 −18 48 0.009
6 24 −14 46 0.008
Left Precentral Gyrus 6 −62 0 14 0.016
6 −44 −8 40 0.021
Left Middle Frontal Gyrus 6 −22 14 56 0.011
Left Superior Frontal Gyrus 6 −6 6 54 0.039
Right Medial Frontal Gyrus 6 10 0 56 0.010
Right Middle Frontal Gyrus 6 16 14 58 0.011
6 38 0 40 0.016
Left Middle Frontal Gyrus 8 −34 16 42 0.013
Left Medial Frontal Gyrus 8 −10 40 40 0.008
Left Inferior Frontal Gyrus 9 −54 4 22 0.014
Left Middle Frontal Gyrus 9 −52 14 32 0.010
9 −36 28 26 0.026
Left Medial Frontal Gyrus 9 −4 48 26 0.016
10 −16 48 6 0.009
Left Middle Frontal Gyrus 11 −20 48 −8 0.010
Right Middle Frontal Gyrus 11 24 48 −10 0.009
Right Inferior Frontal Gyrus 13 34 10 −12 0.015
Right Medial Frontal Gyrus 25 2 14 −16 0.015
Right Inferior Frontal Gyrus 44 42 16 10 0.013
Left Inferior Frontal Gyrus 45 −42 16 16 0.021
Right Middle Frontal Gyrus 46 46 38 22 0.026
Right Inferior Frontal Gyrus 46 52 28 12 0.024
47 26 14 −10 0.012
Left Inferior Frontal Gyrus 47 −40 28 0 0.012
Limbic Left Anterior Cingulate 25 0 0 −6 0.015
Left Posterior Cingulate 31 −10 −54 18 0.012
Right Cingulate Gyrus 31 4 −30 36 0.014
Left Amygdala −22 −6 −12 0.030
Right Amygdala 22 −2 −12 0.025
Right Hippocampus 28 −14 −12 0.025
Midbrain Left Substania Nigra −8 −20 −8 0.016
Occipital Left Cuneus 18 −8 −80 20 0.012
Left Middle Temporal Gyrus 19 −40 −60 16 0.009
Right Middle Occipital Gyrus 19 30 −80 22 0.012
Left Precuneus 31 −8 −60 26 0.008
Parietal Right Superior Parietal Lobule 7 36 −66 48 0.008
Left Angular Gyrus 39 −46 −66 28 0.015
Right Angular Gyrus 39 54 −64 32 0.010
Left Inferior Parietal Lobule 40 −52 −54 44 0.017
Left Inferior Parietal Lobule 40 −34 −50 36 0.026
Right Inferior Parietal Lobule 40 34 −48 40 0.019
Right Inferior Parietal Lobule 40 60 −32 30 0.009
Posterior Right Declive 32 −64 −12 0.015
Sub-lobar Left Insula 13 −42 −28 24 0.011
Right Insula 13 36 20 18 0.009
13 40 −12 −2 0.015
Left Amygdala −24 −10 −10 0.029
Left Thalamus −12 −18 6 0.024
Right Thalamus (Medial Dorsal Nucleus) 4 −16 4 0.031
Right Lateral Globus Pallidus 12 2 4 0.009
Right Caudate Head 18 24 4 0.024
Right Caudate Body 20 −2 20 0.008
Right Lateral Globus Pallidus 22 −12 2 0.019
Right Thalamus (Pulvinar) 26 −30 6 0.016
Temporal Right Fusiform Gyrus 20 46 −6 −20 0.009
Right Middle Temporal Gyrus 20 58 −42 −10 0.009
21 56 −14 −6 0.013
Left Middle Temporal Gyrus 21 −54 −12 −6 0.017
22 −48 −46 2 0.014
38 −42 4 −8 0.008
Left Superior Temporal Gyrus 38 −38 8 −14 0.009
38 −36 4 −14 0.009
Right Angular Gyrus 39 46 −74 30 0.010
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Table 4.

Male-Specific Network in DMTS and N-back Working Memory Tasks

Lobe Region BA x y z ALE
Anterior Right Cerebellar Lingual 2 −42 −8 0.022
Right Nodule 10 −52 −28 0.051
Right Culmen 12 −60 −2 0.013
Frontal Left Middle Frontal Gyrus 6 −46 0 38 0.053
Left Medial Frontal Gyrus 6 −8 −10 48 0.015
6 −4 −20 56 0.027
Left Superior Frontal Gyrus 6 0 8 48 0.065
Right Middle Frontal Gyrus 6 28 −6 54 0.042
Left Superior Frontal Gyrus 10 −38 50 18 0.012
Left Precentral Gyrus 44 −52 6 10 0.010
Left Inferior Frontal Gyrus 46 −42 30 10 0.014
Left Middle Frontal Gyrus 46 −42 18 26 0.027
Left Inferior Frontal Gyrus 47 −48 18 −6 0.013
Limbic Left Posterior Cingulate 23 −4 −56 20 0.014
29 0 −42 22 0.018
Midbrain Left Red Nucleus 0 −20 −6 0.029
Occipital Left Cuneus 17 −6 −78 14 0.013
Right Lingual Gyrus 17 10 −88 −4 0.016
Left Cuneus 18 −18 −82 28 0.011
Left Middle Occipital Gyrus 19 −28 −78 18 0.023
Left Lingual Gyrus 19 −18 −60 −4 0.012
Right Middle Occipital Gyrus 19 38 −64 10 0.023
Left Inferior Temporal Gyrus 37 −44 −64 −2 0.011
Parietal Left Postcentral Gyrus 3 −40 −26 56 0.015
Left Superior Parietal Lobule 7 −30 −54 46 0.052
7 4 −52 60 0.011
Right Precuneus 7 6 −70 42 0.027
7 8 −50 44 0.015
7 28 −44 42 0.011
Left Precuneus 7 −4 −68 36 0.027
19 −10 −84 44 0.010
Left Inferior Parietal Lobule 40 −36 −52 36 0.034
Posterior Left Cerebellar Tonsil −42 −58 −32 0.019
−34 −68 −14 0.052
Left Declive −26 −84 −16 0.013
−12 −68 −18 0.025
−2 −76 −10 0.042
Right Uvula 6 −66 −34 0.015
Right Declive 10 −68 −16 0.040
Sub-lobar Left Insula 13 −40 0 14 0.010
Right Insula 13 36 −24 22 0.024
Left Caudate Body −16 −2 16 0.014
Left Thalamus (Ventral Lateral Nucleus) −16 −16 12 0.048
Right Caudate Body 8 4 10 0.020
Right Thalamus (Lateral Dorsal Nucleus) 12 −20 16 0.052
Left Cerebellum −2 −82 −24 0.013
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Table 5.

Gender Differences in DMTS and N-back Working Memory Tasks

Females > Males
Lobe Region BA x y z Z-Score
Anterior Right Culmen 30 −56 −24 3.01
Frontal Left Medial Frontal Gyrus 6 −13 10 53 3.35
−8 6 56 3.09
Left Superior Frontal Gyrus −10 12 58 3.29
Right Inferior Frontal Gyrus 45 50 22 11.14 3.72
54 26 14 3.43
Right Middle Frontal Gyrus 46 46 32 24 3.29
50 32 18 3.09
Limbic 28 26 −20 −10 3.43
Right Parahippocampal Gyrus 20 −3.6 −9.2 3.12
34 21 −12 −16 2.66
Left Uncus −22.6 −0.53 −13.57 3.89
Left Amygdala −16.67 −4 −18 3.43
−16 −8 −10 3.35
25 −3 −11.5 2.85
Right Amygdala 19.5 −9.5 −12 2.83
18 −4 −16 2.82
Right Hippocampus 32 −10 −14 2.70
Sub-lobar Left Insula 13 −42 −6 −6 3.09
Left Thalamus −2 −11 2 2.97
Right Claustrum 36.86 −12.86 −0.29 3.72
Right Lateral Globus Pallidus 25.6 −14 −4.8 3.24
Right Medial Globus Pallidus 18.67 −4.67 −8 2.79
30 −18 −8 3.54
Right Putamen 29 −15 −6 3.35
28 −8 −8 3.19
Right Thalamus 6 −8 2 2.82
Temporal Left Sub-Gyral 21 −44 −6 −10 3.24
Left Superior Temporal Gyrus 22 −50.5 −8.75 −4.25 3.72
−46 −11 −4 3.35
Males > Females
Frontal −12.8 −17.4 55.6 3.89
Left Medial Frontal Gyrus −4 −24 59 3.72
−4.8 −17.2 58.4 3.29
6 0 −14 56 2.85
Left Middle Frontal Gyrus −19 −7 60 3.16
Left Precentral Gyrus −28 −14 62 2.99
Right Sub-Gyral 24 −10 54 3.29
Parietal Left Precuneus −26 −56 54 3.04
Left Superior Parietal Lobule 7 −30 −61 45 2.95
−26 −62 54 2.93
Sub-lobar Right Insula 13 36 −22 25 3.04
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Figure 3.

Figure 3

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A) 3D rendering of networks involved in n-back and DMTS tasks, thresholded at p < 0.05, FDR-corrected. B) 3D rendering from the contrast analysis of the resultant ALE maps from panel A, thresholded at z > 2.3.

Discussion

Despite over a century of scientific inquiry, little progress has been made in addressing the substrates of gender differences, specifically as they relate to working memory. Using a novel approach, we used the BrainMap database to probe neurofunctional differences in working memory. Our results provide evidence for differential network recruitment by males and females undergoing working memory tasks. The results are consistent with previous literature suggesting that males utilize more spatial processing related networks (i.e., parietal regions) than females, and females tend to recruit more prefrontal regions (Haier et al., 2005), suggesting that men and women may use different strategies to solve complex problems (Haier et al., 2005).

The congruent areas of activation are not surprising as they are the anatomical structures most associated with working memory processes. Across studies, there has been consistent activation patterns seen in the frontal, temporal, and parietal regions (Baddeley, 1981; Baddeley, 1997, 2000; Baddeley and Logie, 1999; D'Esposito et al., 1998a; D'Esposito et al., 1998b; D'Esposito et al., 2000; Na et al., 2000; Prabhakaran et al., 2000; Repovs and Baddeley, 2006). Baddeley and Hitch's revised theory of working memory (2000) can be used to explain the observed activation patterns. In their theory, working memory was composed of four interconnecting systems: 1) the phonological loop, responsible for the storage and maintenance of speech-based information, 2) the visuospatial sketchpad, which stores and maintains visual and spatial information, 3) the central executive, responsible for controlling and integrating the information from the prior systems while also manipulating the information within working memory, and lastly, the most recently added component, 4) the episodic buffer, which assists with the binding of information to create episodes (Baddeley, 2000; Repovs and Baddeley, 2006). These systems are not mutually exclusive, but rather are thought to have overlapping neural components inclusive of the regions we identified as convergent in our dataset. The prefrontal cortex has been found to reliably activate during working memory tasks, which can be related back to the role of the central executive as well as the episodic buffer. Research has shown that the prefrontal cortex is critical in the maintenance and integration of verbal and spatial information (Prabhakaran et al., 2000), one of the primary roles of the central executive and a feature of the episodic buffer. Solidifying this, research has demonstrated that tasks employing the episodic buffer reliably activate the right prefrontal cortex (Repovs and Baddeley, 2006). The activation seen in areas associated with language can be interpreted as a function of the phonological loop due to their importance in linguistic processing. Furthermore, activation observed in both the inferior and superior parietal cortices may be related to the visuospatial sketchpad due to their known pertinence in the integration of visual information and spatial cognition (please see Na et al., 2000 for a review).

Our data demonstrates consistency with the working memory literature, but also highlights differences that should be examined more thoroughly in future research. Differences in neurophysiology (i.e., cerebral glucose metabolism, cerebral blood flow) during rest have been observed between genders (Davidson et al., 1976; Gur et al., 1995; Ray et al., 1976). Given that our results are based on functional neuroimaging results, which are tightly correlated with these physiological measurements, it is not surprising that differences in neural network recruitment exist during an active state as well. It is possible that the differences observed during rest ‘prime’ the brain to utilize certain networks preferentially. Given the strong limbic activation in the female dataset, it is also possible that females have more limbic contributions to working memory processing than males, a theory that should be investigated further using more advanced analysis techniques such as effective and functional connectivity.

Data from this study and previous research supports the notion that males and females rely on different brain networks to perform the same function, with the implications must notable in the academic realm. Halpern and colleagues (2007) suggest that we can use this knowledge to teach female and male students ways to solve problems that correspond to their most efficient cognitive process (i.e. verbal versus visuospatial solution strategies) to allow more flexibility in their problem solving and positively impact performance overall. Furthermore, a trickle down effect of understanding the neural differences underlying working memory processes between genders may lead to advancements in unbiased test design, particularly with regard to popular standardized tests such as the GRE and SAT, which have been criticized for having gender-biased questions. Such considerations may alleviate the gender discrepancy observed in academics.

Working memory is utilized during many complex cognitive functions, and the knowledge of gender differences could bring into question preferential strategy use, and unlock methods that would eliminate the gender gap. Due to working memory's pivotal role across a diverse set of cognitive functions, there is a possibility of neurofunctional differences during processing, and if this is the case, research addressing these differences will yield greater insight into gender specific cognitive function and expand the literature on gender differences in these constructs. Furthermore, with the robust and sensitive cognitive neuroscience tools, we may delineate the neurophysiological basis of the differences.

Possible limitations on the present study are those that are shared among meta-analysis based methods. We were unable to control for specific attributes of the participants that could add possible confounds to the overall data such as handedness and where the female participants were in their menstrual cycle, both of which have been shown to impact imaging data. There were also more males than females in the studies included in our meta-analysis. In this study, we did not select working memory tasks based on their content either (i.e., verbal versus spatial). Research has shown that different working memory tasks utilize different brain networks, so depending on the tasks used in the experiments some differences could be related to proportions of specific tasks used (Na et al., 2000) in each workspace. We examined the behavioral domains and paradigms within each of our search sets (Figure 3). As noted in the figure, only a very small percentage of data were coded as emotion, perception, interoception, or action (73% of the female dataset and 76% of the male dataset were coded as cognition). The majority of both data sets were drawn from classic working memory paradigms (84% of paradigms in the female dataset and 56% in the male dataset were either delayed match to sample or n-back paradigms). In the deconstruction analysis that we carried out post-hoc, we limited our search to only those tasks that were coded as n-back or DMTS, and coded under the behavioral domain of ‘Cognition’. These additional analyses did not change our initial findings, thus, we believe our sample is robust and likely offsets the possibility of the above confounds.

Future studies should attempt to have an even gender distribution to control for any effects caused by the greater depth of the male workspace. As shown in Figure 4, the male dataset also had a more diverse profile of working memory paradigms compared to the female workspace. However, we do note that our post-hoc analysis that just examined n-back and DMTS cognitive tasks still demonstrated gender differences. Therefore, future studies should focus on increasing the number of verbal and spatial working memory papers to further deconstruct the observed differences. Additionally, future neuroimaging studies should use the models presented in this paper to look at functional and effective connectivity differences during working memory tasks. Using this strategy, we may be able to probe the strategic differences and their effects on the neurofunctional networks subservient to working memory. These differences may exist even when activation patterns don't demonstrate differences between genders.

Figure 4.

Figure 4

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Behavioral domain (top panels, shown in pie graph form) and paradigm breakdown (bottom panel) of the male and female workspaces. Because of the disparate workspace sizes, all values are shown as percentages within each gender-specific workspace, respectively.

Although gender differences are socially and scientifically important to understand, few studies have addressed their potential neurophysiological basis. Addressing these issues could lead to advances in our understanding of the underlying neural networks that may be responsible for gender differences in working memory, potentially leading to tailored developmental cognitive programs or novel strategy development that could reduce the gender gap that is thought to exist in some areas of cognition (Irwing and Lynn, 2005, 2006; Lynn and Irwing, 2002). It also provides a foundation to further investigate brain based gender differences and the implications they have for all areas of cognition (Davidson et al., 1976; Gur et al., 1995). To our knowledge, this is the first study addressing neural network differences in working memory using meta-analytic modeling, a powerful and robust technique that capitalizes on the advantages of archived functional neuroimaging studies (Laird et al., 2005c; Minzenberg et al., 2009). Here, we have provided a preliminary model of neurofunctional gender-specific working memory networks. Further research directions could use this model to ascertain why and how males and females use different neural networks during working memory tasks, or could attempt to assess when these neurofunctional differences first appear in development as well as the possible stimuli influencing the emergence of these observed difference.

Highlights.

  • - Our results provide evidence for gender-specific working memory networks.

  • - Females activate more limbic structures such as the amygdala and hippocampus.

  • - Males activate a distributed network inclusive of more parietal regions.

  • - Our data provide a foundation for future network analyses.

Acknowledgements

This work was supported by NIMH R01-MH074457 (PI: PTF AND ARL). A Collaborative Use Agreement exists between JLR and the BrainMap® Database.

Footnotes

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