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. Author manuscript; available in PMC: 2014 Jun 1.
Published in final edited form as: Neuropsychologia. 2012 May 23;51(7):1361–1369. doi: 10.1016/j.neuropsychologia.2012.05.017

Dorsolateral Prefrontal Contributions to Human Intelligence

Aron K Barbey 1,2,3,4,5,6, Roberto Colom 7, Jordan Grafman 8
PMCID: PMC3478435  NIHMSID: NIHMS388168  PMID: 22634247

Abstract

Although cognitive neuroscience has made remarkable progress in understanding the involvement of the prefrontal cortex in executive control functions for human intelligence, the necessity of the dorsolateral prefrontal cortex (dlPFC) for key competencies of general intelligence and executive function remains to be well established. Here we studied human brain lesion patients with dlPFC lesions to investigate whether this region is computationally necessary for performance on neuropsychological tests of general intelligence and executive function, administering the Wechsler Adult Intelligence Scale (WAIS) and subtests of the Delis Kaplan Executive Function System (D-KEFS) to three groups: dlPFC lesions (n = 19), non-dlPFC lesions (n = 152), and no brain lesions (n = 55). The key results indicate that: (1) patients with focal dlPFC damage exhibit lower scores, at the latent variable level, than controls in general intelligence (g) and executive function; (2) dlPFC patients demonstrate lower scores than controls in several executive measures; and (3) these latter differences are no longer significant when the pervasive influence of the general factor of intelligence (g) is statistically removed. The observed findings support a central role for the dlPFC in general intelligence and make specific recommendations for the interpretation and application of the WAIS and D-KEFS to the study of high-level cognition in health and disease.

Keywords: prefrontal cortex, dorsolateral prefrontal cortex, general intelligence, executive function, lesion evidence

INTRODUCTION

The search for organizing principles that govern human intelligence represents a central and enduring aim of cognitive neuroscience, with emerging research providing new insight into the neural architecture of goal-directed, intelligent behavior (see Barbey & Grafman, in press a, in press b; Barbey et al., 2009a, 2009b; Colom & Thompson, 2011; Colom, Karama, Jung, & Haier, 2010; Miller, 2000; Miller & Cohen, 2001, for reviews). Extensive functional neuroimaging evidence indicates that the dorsolateral prefrontal cortex (dlPFC) plays a central role in executive control functions for human intelligence (for meta-analytic reviews, see Owen, 1997; Owen et al., 2005; Wager & Smith, 2003; Wager et al., 2004). Fundamental questions, however, remain in the absence of definitive neuropsychological evidence to corroborate the importance of the dlPFC for higher cognition. A seminal and long-standing issue concerns whether the dlPFC is computationally necessary for key competencies of general intelligence and executive function, and, in particular, whether this region provides an integrative neural architecture for core features of human intelligence (for a recent review, see Deary et al., 2010).

Theories of intelligence and executive function have focused on the identification of a general factor, referred to as psychometric g, that has been shown to underlie performance on a broad range of cognitive tests (Spearman, 1904; 1927; for a review, see Jensen, 1998, Neisser et al., 1996, Nisbett et al., 2012). Neuroscience models deriving from Spearman’s classic theory attribute diverse functional roles to the dlPFC, positing that this cortical region provides a unified neural architecture for higher cognition (e.g., Duncan et al., 2000; Duncan, 2010). Accumulating neuroscience data support this framework, demonstrating recruitment of the dlPFC for performance on tests of general intelligence (e.g., Prabhakaran et al., 1997; Esposito et al., 1999; Duncan et al., 2000; Bishop et al., 2008) and executive function (e.g., Duncan & Owen, 2000; Duncan, 2006). Monkey electrophysiological data further indicate that cells within the dlPFC adaptively code different kinds of task-relevant information in different behavioral contexts (e.g., Duncan, 2001; Miller & Cohen, 2001), supporting the involvement of this region in a wide range of higher cognitive functions.

The alternative to Spearman’s single-factor model proposes that tests of general intelligence reflect the average or combined activity of separate cognitive processes (Thomson, 1951, see also Bartholomew, Deary & Lawn, 2009, van der Maas et al., 2006). According to this framework, general intelligence is supported by a variety of different cognitive functions that are mediated by a broadly distributed network of functionally specialized brain regions (e.g., Colom & Thompson, 2011; Colom et al., 2009; Jung & Haier, 2007; Gläscher et al., 2010, 2009). This model predicts that the dlPFC will be selectively involved in specific cognitive operations rather than providing an integrative architecture for general intelligence and executive function. An increasing number of neuropsychological studies support this framework, reporting patients with damage to prefrontal cortices who demonstrate selective deficits in general intelligence or executive function, suggesting that these domains of higher cognition recruit functionally distinct neural systems (e.g., Blair & Cipolotti, 2000; Burgess & Shallice, 1996; Eslinger & Damasio, 1985; Shallice & Burgess, 1991).

Of the neuropsychological patient studies that have examined prefrontal contributions to general intelligence (e.g., Basso et al., 1973; Bechara et al., 1994; Black, 1976; Blair & Cipolotti, 2000; Bugg et al., 2006; Burgess & Shallice, 1996; Duncan et al., 1995, 1996; Eslinger & Damasio, 1985; Gläscher et al., 2010, 2009; Isingrini & Vazou,1997; Kane & Engle, 2002; Parkin & Java, 1999; Roca et al., 2009; Shallice & Burgess, 1991; Tranel et al., 2008) and executive function (e.g., Baldo & Dronkers, 2006; D’Esposito & Postle,1999; D’Esposito et al., 2006; Muller et al., 2002; Ptito et al., 1995; Tsuchida & Fellows, 2009; Volle et al., 2008), all share one or more of the following features: diffuse (rather than focal) dlPFC lesions, lack of comparison subjects carefully matched for pre- and post-injury performance measures, and exclusive use of general intelligence or executive function tests. As a consequence, there has been no comprehensive evaluation of general intelligence and executive function in a relatively large sample of patients with damage specifically involving the dlPFC, and across a broad range of tasks and stimulus material. Furthermore, intelligence and executive function share relevant variance, which may greatly confound their contribution to the observed findings (cf., Colom et al., 2009; Haier et al., 2009). Therefore, it is critical to analyze specific variance of the constructs and measures of interest. The absence of such data represents a substantial gap in the understanding of both dlPFC function and the neural substrates of higher cognition.

The aim of the present investigation is to characterize key competencies of general intelligence and executive function in a sample of patients with focal dlPFC lesions, examining whether this region (i) provides an integrative architecture for general intelligence (g) or instead (ii) mediates a specific class of cognitive operations within a particular high-level domain (e.g., executive function, working memory, perceptual organization, processing speed).

MATERIALS AND METHODS

Participant Data

We drew brain-injured participants from the Vietnam Head Injury Study (VHIS) registry, which includes American veterans who suffered brain damage from penetrating head injuries in the Vietnam War (n = 199), as well as neurologically healthy Vietnam veterans (n = 54). The VHIS has been organized in three phases. Phase 1 (1967–1970) was the initial enrollment; Phase 2 (1981–1984) included a cognitive evaluation; and Phase 3 (2003–2006) included a more comprehensive evaluation as well as CT brain imaging. Further details regarding the VHIS participants, including methods for visualizing and quantifying brain lesions, have previously been reported (Barbey et al., 2009). Subjects were eligible for the present study if they participated in Phases 2 and 3 evaluations.

To preclude the possibility that impaired performance on general intelligence and executive function tests could be secondary to deficits in the production and/or comprehension of language, we excluded any participant who had significant impairment on a test of language production and language comprehension (defined as performance at least two standard deviations below the mean of the neurologically healthy group on the Boston Naming Test). From the remaining brain-injured veterans we selected those with damage primarily localized to the dlPFC (BA 9/46) in the left and/or right hemisphere(s) (dlPFC Lesion group; Fig. 1; n = 19). The dlPFC is located on the lateral and dorsal part of the medial convexity of the frontal lobe and comprises BA 9 and 46 and a few transitional areas: 9-8, 9–45, 46-10, and 46-45 (for a detailed description of anatomical boundaries, see Rajkowska & Goldman-Rakic, 1995a, b). In addition, we investigated a comparison group of brain-injured veterans whose damage was primarily within the PFC but involved ventral (rather than dorsal) regions (Non-dlPFC Lesion group; n = 152; Supplemental Fig. 1). Neurologically healthy veterans served as an additional comparison group (Control group; n = 54). Demographic and background cognitive function data for the three groups are presented in Supplemental Table 1. No significant between-group differences were observed with respect to basic demographic variables (age, sex, years of education), pre- and post-combat measures of cognitive function, and total percent volume loss. All patient groups were therefore well matched with respect to (1) demographic variables, (2) pre- and post-combat measures of cognitive function, and (3) lesion size. All participants understood the study procedures and gave their written informed consent, which was approved by the Institutional Review Board at the National Naval Medical Center and the National Institute of Neurological Disorders and Stroke.

Figure 1.

Figure 1

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Diagram of the lesion overlap map for the dorsolateral prefrontal patients. The color indicates the number of veterans in the dorsolateral prefrontal group (n = 19) with damage to a given voxel. The depicted sagittal slices progress from the right lateral regions (top left) to the midline and left lateral areas (bottom right).

Lesion Analysis

We acquired computed tomography (CT) data during the Phase 3 testing period. Axial CT scans without contrast were acquired at the Bethesda Naval Hospital on a General Electric Medical Systems Light Speed Plus CT scanner in helical mode. We reconstructed the images with an in-plane voxel size of 0.4 × 0.4 mm, an overlapping slice thickness of 2.5 mm and a 1-mm slice interval. We determined lesion location and volume from CT images using the Analysis of Brain Lesion (ABLe) software (Makale, et al., 2002; Solomon et al., 2007) contained in MEDx v3.44 (Medical Numerics) with enhancements to support the Automated Anatomical Labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002). We applied the AAL atlas of the human brain to obtain neuroanatomical labels for locations in 3-dimensional space. For the hypotheses about specific brain areas (dlPFC), we defined regions of interest in terms of AAL structures (Tzourio-Mazoyer et al., 2002) and Talairach coordinates (Talairach & Tournoux, 1988). As part of this process, we spatially normalized the CT image of each subject’s brain to a CT template brain image in Montreal Neurological Institute space (Collins et al., 1994). We determined the percentage of AAL structures that the lesion entailed by analyzing the overlap between the spatially normalized lesion image and the AAL atlas image. We calculated lesion volume by manually tracing the lesion in all relevant slices of the CT image and then summing the traced areas and multiplying by slice thickness. The tracing technique isolated areas of missing brain and regions affected by metallic artifacts and penetrating objects. A trained neuropsychiatrist (V.R.) carried out the manual tracing, which was then reviewed by an observer (J.G.) that was blind to the results of the neuropsychological testing. In addition, we further characterized the contribution of white matter pathways in the dlPFC patient sample, identifying that each patient group entailed damage within or adjacent to the (1) superior longitudinal fasciculus (branch 1 and 2), (2) frontal aslant tract, (3) fronto-striatal projections, (4) callosal connections, and (4) U-shaped connections between superior and middle frontal gyri (see Mori et al., 2008).

Neuropsychological Tests

We administered the Wechsler Adult Intelligence Scale, 3rd Edition (WAIS; Wechsler, 1997) and subtests of the Delis Kaplan Executive Function System (D-KEFS; Delis et al., 2001) to investigate the necessity of dlPFC for key competencies of general intelligence and executive function. The reported neuropsychological data from the WAIS and D-KEFS represent standardized scores based on the published norms in Wechsler (1997) and Delis et al. (2001), respectively.

Wechsler Adult Intelligence Scale, 3rd Edition

The WAIS embodies a four-tier hierarchy, providing a Full Scale Intelligence Index (Tier 1) derived from Verbal and Performance Intelligence Indices (Tier 2) that each consist of component operations (Tier 3) measured by intelligence subtests (Tier 4). Verbal Intelligence examines general knowledge, vocabulary, and the ability to reason using words and numbers, and is assessed by Verbal Comprehension and Working Memory subtests. Performance Intelligence examines the ability to solve problems in novel situations, independent of acquired knowledge, and is assessed by Perceptual Organization and Processing Speed subtests. Additional Performance Intelligence subtests of the WAIS that are not part of the four factor indices include Picture Arrangement and Object Assembly. Supplemental Table 2 provides a brief description of these tests (for further detail concerning the descriptions of each test, their standardization, reliability, and validity, see Wechsler, 1997).

Delis Kaplan Executive Function System

The D-KEFS consists of executive function tests that examine a broad range of high-level cognitive skills. Our analysis focused on five executive function measures that, in recent studies, have been found to be particularly sensitive to frontal lobe damage (e.g., Baldo et al., 2001; Cato et al., 2004; Delis et al., 1992; McDonald et al. 2005a, b, c). These tests include the Trail Making Test, Verbal Fluency Test, Sorting Test, Twenty Questions Test, and Tower Test. Supplemental Table 3 provides a brief description of each test (for further detail concerning the description of each test, their standardization, reliability, and validity, see Delis et al., 2001, 2007; Swanson, 2005; Homack et al., 2005).

Statistical Analysis

First, WAIS and D-KEFS measures were analyzed using a latent variable approach. A key advantage of this approach is that specific task requirements for the administered WAIS and D-KEFS subtests have less influence on the estimates of the construct relations (general intelligence and executive function). This approach also partials out measurement error for each specific measure, and therefore latent variables provide a reliable estimate of the constructs of interest. Here we computed SEM using the AMOS program (Arbuckle, 2006). Several fit indices were considered. Firstly, the χ2/DF index is frequently considered as a rule of thumb, because it corrects the high sensitivity of the chi-square statistic for large sample sizes (Jöreskog, 1993). Values showing a good fit must be around 2.0. Secondly, RMSEA is usually recommended because it is sensitive to misspecification of the model. Values between 0 and 0.05 indicate good fit; values between 0.05 and 0.08 represent acceptable errors; and values greater than 0.10 are indicative of poor fit (Byrne, 1998). Finally, comparative fit index (CFI) is also reported; acceptable values must be larger than 0.90 (Marsh et al., 1988). Verbal comprehension, perceptual organization, working memory, and processing speed measures from the WAIS were grouped in four first-order factors. Afterwards, a higher-order factor (representing the general factor of intelligence, g) predicted these four factors. Further, D-KEFS measures were collapsed in a latent executive factor. Finally, this latter factor was correlated with g.

Second, scores for the enumerated latent factors were obtained using the AMOS program (Arbuckle, 2006). The three groups of participants were systematically compared in general intelligence, verbal comprehension, perceptual organization, working memory, processing speed, and executive function.

Third, scores for verbal comprehension, perceptual organization, working memory, processing speed, and specific executive measures were computed while statistically removing the effect of the general factor of intelligence (g). This was accomplished by performing regression analyses. The three groups of participants are compared on these residual scores.

Analysis of Variance

For the measures enumerated above, we conducted a one-way ANOVA examining the performance of dlPFC lesion patients (n = 19) with respect to non-dlPFC lesion patients (n = 152) and neurologically healthy participants (n = 55), followed by Tukey’s honestly significant difference (HSD) test to determine significant between-group differences (p < 0.01, Bonferroni corrected).

RESULTS

Structural Equation Modelling (SEM)

Figure 2 shows SEM results. The fit for this model was appropriate: χ2 (148) = 322.65; CMIN/DF = 2.2; RMSEA = 0.072, CFI = 0.91. Values depicted in Figure 2 show that, at the latent variable level, the general factor of intelligence (g) and the executive function latent factor are near perfectly related (r = 1.0). Nevertheless, it must be noted that (a) verbal comprehension, perceptual organization, working memory, and processing speed do show uniqueness, and (b) this uniqueness is also observed for the specific executive measures. Therefore, it is relevant to compare the three groups of participants both at the latent variable level and at the specific level (Colom & Thompson, 2011; Gläscher et al., 2010).

Figure 2.

Figure 2

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SEM analysis of the administered WAIS and D-KEFS measures.

It is important to have empirical evidence regarding a proper generalization of the tested SEM model to the three groups of interest. This would validate the comparison made among groups. However, testing SEM models separately for controls, non-dlPFC patients, and dlPFC patients cannot be done because of sample size limitations. Therefore, we made three additional analyses: (a) excluding dlPFC patients (n = 207), (b) excluding controls (n = 171), and (c) excluding non-dlPFC patients (n = 71). Obtained regression weights and fit indices were largely comparable to those values obtained for the complete dataset, which validates the comparisons made at the construct level.

Intelligence

Dorsolateral PFC lesion patients demonstrated significant deficits in the general factor of intelligence, g (Tables 1 and 2). This patient sample consistently obtained the lowest numeric levels of performance among groups tested on the WAIS, with significant deficits in Working Memory and Processing Speed (Table 2). The observed pattern of deficits highlights the importance of dlPFC for intelligence, supporting key competencies for working memory and mechanisms for the coordination of visual and motor representations underlying goal-directed behavior (Duncan et al., 2000; Duncan, 2010). However, when the influence of the general factor of intelligence (g) is removed from the first-order factors assessed by the WAIS, the deficits observed for the dlPFC patients in working memory and processing speed are no longer present (Tables 3 and 4). This pattern of findings suggests that the dlPFC plays a central role in the general factor of intelligence (g), rather than selectively mediating key competencies for working memory or processing speed.

Table 1.

Descriptive statistics for measures of general intelligence and executive function.

Descriptives
Group N Mean SD
latent g dlPFC 19 91.31 15.39
non-dlPFC 152 99.31 14.96
control 55 104.91 13.42
latent verbal comprehension dlPFC 19 95.28 18.82
non-dlPFC 152 99.82 14.96
control 55 102.14 13.44
latent perceptual organization dlPFC 19 93.46 15.48
non-dlPFC 152 99.32 15.23
control 55 104.14 13.24
latent working memory dlPFC 19 91.52 17.64
non-dlPFC 152 99.22 14.51
control 55 105.09 13.85
latent processing speed dlPFC 19 91.92 13.94
non-dlPFC 152 98.56 15.04
control 55 106.77 12.83
latent executive function dlPFC 19 91.31 15.39
non-dlPFC 152 99.31 14.96
control 55 104.91 13.42
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Table 2.

Inferential statistics for measures of general intelligence and executive function.

Bonferroni
(I) Group (J) Group Mean Difference (I–J) Standard Error Sig. Confidence Interval 95%
Upper Limit Lower Limit
latent g dlPFC non-dlPFC −7.99 3.56 0.08 −16.59 0.60
control −13.60 3.90 0.00 −23.00 −4.20
non-dlPFC dlPFC 7.99 3.56 0.08 −0.60 16.59
control −5.61 2.30 0.05 −11.16 −0.05
control dlPFC 13.60 3.90 0.00 4.20 23.00
non-dlPFC 5.61 2.30 0.05 0.05 11.16
latent verbal comprehension dlPFC non-dlPFC −4.53 3.64 0.64 −13.32 4.25
control −6.85 3.98 0.26 −16.46 2.75
non-dlPFC dlPFC 4.53 3.64 0.64 −4.25 13.32
control −2.32 2.35 0.98 −8.00 3.36
control dlPFC 6.85 3.98 0.26 −2.75 16.46
non-dlPFC 2.32 2.35 0.98 −3.36 8.00
latent perceptual organization dlPFC non-dlPFC −5.86 3.60 0.31 −14.54 2.82
control −10.68 3.94 0.02 −20.18 −1.19
non-dlPFC dlPFC 5.86 3.60 0.31 −2.82 14.54
control −4.82 2.33 0.12 −10.44 0.79
control dlPFC 10.68 3.94 0.02 1.19 20.18
non-dlPFC 4.82 2.33 0.12 −0.79 10.44
latent working memory dlPFC non-dlPFC −7.70 3.56 0.09 −16.29 0.89
control −13.58 3.89 0.00 −22.97 −4.18
non-dlPFC dlPFC 7.70 3.56 0.09 −0.89 16.29
control −5.87 2.30 0.03 −11.43 −0.32
control dlPFC 13.58 3.89 0.00 4.18 22.97
non-dlPFC 5.87 2.30 0.03 0.32 11.43
latent processing speed dlPFC non-dlPFC −6.65 3.52 0.18 −15.13 1.84
control −14.85 3.84 0.00 −24.12 −5.58
non-dlPFC dlPFC 6.65 3.52 0.18 −1.84 15.13
control −8.20 2.27 0.00 −13.69 −2.72
control dlPFC 14.85 3.84 0.00 5.58 24.12
non-dlPFC 8.20 2.27 0.00 2.72 13.69
latent executive function dlPFC non-dlPFC −7.99 3.56 0.08 −16.59 0.60
control −13.60 3.90 0.00 −23.00 −4.20
non-dlPFC dlPFC 7.99 3.56 0.08 −0.60 16.59
control −5.61 2.30 0.05 −11.16 −0.05
control dlPFC 13.60 3.90 0.00 4.20 23.00
non-dlPFC 5.61 2.30 0.05 0.05 11.16
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Table 3.

Descriptive statistics for measures of general intelligence and executive function with g removed.

Descriptives
Group N Mean SD
Verbal comprehension removing g dlPFC 19 102.82 20.30
non-dlPFC 152 100.52 14.77
control 55 97.59 13.44
Perceptual organization removing g dlPFC 19 103.66 10.43
non-dlPFC 152 99.89 15.74
control 55 99.05 14.24
Working memory removing g dlPFC 19 98.27 16.57
non-dlPFC 152 99.64 15.42
control 55 101.60 13.31
Processing speed removing g dlPFC 19 100.20 16.69
non-dlPFC 152 97.71 14.74
control 55 106.25 13.52
Trail making test removing latent g dlPFC 19 99.52 13.40
non-dlPFC 152 99.39 15.55
control 55 101.86 14.03
Verbal fluency test removing latent g dlPFC 19 100.11 16.44
non-dlPFC 152 100.27 14.92
control 55 99.21 14.96
Card sorting test removing latent g dlPFC 19 98.24 13.64
non-dlPFC 152 101.88 15.33
control 55 95.40 13.62
Twenty questions test removing latent g dlPFC 19 93.44 14.24
non-dlPFC 152 101.36 14.03
control 55 98.52 17.23
Tower test removing latent g dlPFC 19 102.68 17.14
non-dlPFC 152 100.06 14.74
control 55 98.91 15.11
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Table 4.

Inferential statistics for measures of general intelligence and executive function with g removed.

Bonferroni
(I) Group (J) Group Mean Difference (I–J) Standard Error Sig. Confidence Interval 95%
Upper Limit Lower Limit
Verbal comprehension removing g dlPFC non-dlPFC 2.30 3.65 1.00 −6.50 11.10
control 5.23 3.99 0.57 −4.40 14.85
non-dlPFC dlPFC −2.30 3.65 1.00 −11.10 6.50
control 2.93 2.36 0.65 −2.76 8.62
control dlPFC −5.23 3.99 0.57 −14.85 4.40
non-dlPFC −2.93 2.36 0.65 −8.62 2.76
Perceptual organization removing g dlPFC non-dlPFC 3.77 3.66 0.91 −5.04 12.59
control 4.61 4.00 0.75 −5.04 14.25
non-dlPFC dlPFC −3.77 3.66 0.91 −12.59 5.04
control 0.83 2.36 1.00 −4.87 6.54
control dlPFC −4.61 4.00 0.75 −14.25 5.04
non-dlPFC −0.83 2.36 1.00 −6.54 4.87
Working memory removing g dlPFC non-dlPFC −1.37 3.66 1.00 −10.20 7.45
control −3.34 4.00 1.00 −12.99 6.31
non-dlPFC dlPFC 1.37 3.66 1.00 −7.45 10.20
control −1.97 2.37 1.00 −7.67 3.74
control dlPFC 3.34 4.00 1.00 −6.31 12.99
non-dlPFC 1.97 2.37 1.00 −3.74 7.67
Processing speed removing g dlPFC non-dlPFC 2.49 3.56 1.00 −6.09 11.07
control −6.05 3.89 0.37 −15.43 3.34
non-dlPFC dlPFC −2.49 3.56 1.00 −11.07 6.09
control −8.53 2.30 0.00 −14.09 −2.98
control dlPFC 6.05 3.89 0.37 −3.34 15.43
non-dlPFC 8.53 2.30 0.00 2.98 14.09
Trail making test removing latent g dlPFC non-dlPFC 0.14 3.66 1.00 −8.69 8.96
control −2.34 4.00 1.00 −11.99 7.30
non-dlPFC dlPFC −0.14 3.66 1.00 −8.96 8.69
control −2.48 2.36 0.89 −8.18 3.23
control dlPFC 2.34 4.00 1.00 −7.30 11.99
non-dlPFC 2.48 2.36 0.89 −3.23 8.18
Verbal fluency test removing latent g dlPFC non-dlPFC −0.17 3.66 1.00 −9.01 8.67
control 0.90 4.01 1.00 −8.77 10.57
non-dlPFC dlPFC 0.17 3.66 1.00 −8.67 9.01
control 1.07 2.37 1.00 −4.65 6.78
control dlPFC −0.90 4.01 1.00 −10.57 8.77
non-dlPFC −1.07 2.37 1.00 −6.78 4.65
Card sorting test removing latent g dlPFC non-dlPFC −3.64 3.60 0.94 −12.33 5.05
control 2.84 3.94 1.00 −6.66 12.34
non-dlPFC dlPFC 3.64 3.60 0.94 −5.05 12.33
control 6.48 2.33 0.02 0.86 12.10
control dlPFC −2.84 3.94 1.00 −12.34 6.66
non-dlPFC −6.48 2.33 0.02 −12.10 −0.86
Twenty questions test removing latent g dlPFC non-dlPFC −7.91 3.62 0.09 −16.65 0.82
control −5.08 3.96 0.60 −14.63 4.48
non-dlPFC dlPFC 7.91 3.62 0.09 −0.82 16.65
control 2.83 2.34 0.68 −2.82 8.48
control dlPFC 5.08 3.96 0.60 −4.48 14.63
non-dlPFC −2.83 2.34 0.68 −8.48 2.82
Tower test removing latent g dlPFC non-dlPFC 2.62 3.66 1.00 −6.21 11.45
control 3.77 4.00 1.00 −5.89 13.42
non-dlPFC dlPFC −2.62 3.66 1.00 −11.45 6.21
control 1.14 2.37 1.00 −4.56 6.85
control dlPFC −3.77 4.00 1.00 −13.42 5.89
non-dlPFC −1.14 2.37 1.00 −6.85 4.56
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We also observed reliable deficits in the non-dlPFC comparison group on tests of processing speed (Tables 1 and 2). Although this patient group was not the focus of our investigation and was constructed as a matched comparison group, we note that the observed deficits in processing speed likely originate from damage within the orbitofrontal cortex (see Supplemental Figure 1; Barbey et al., 2011). A large body of neuroscience evidence indicates that the orbitofrontal cortex is responsible for the coordination and synthesis of visual and motor representations and appears to be important for performance on tests of processing speed (for a review, see Kringelbach, 2005).

Executive Function

Patients with dlPFC damage also showed significantly worse performance than controls on the executive function latent factor, as well as on three out of five executive measures (trail, sorting, and twenty). However, when the effect of the general factor of intelligence (g) is removed from the specific executive measures, there is no longer a significant difference between dlPFC patients and controls (Tables 3 and 4). The overall absence of impairment suggests that the dlPFC is not functionally dedicated to support specific executive processes but may instead support higher-level mechanisms for general intelligence.

DISCUSSION

The aim of the present investigation was to assess the role of the dlPFC in key competencies of general intelligence and executive function, examining whether this region (i) provides an integrative architecture for general intelligence or instead (ii) mediates a specific class of cognitive operations necessary for a particular domain of higher cognition. Using a relatively large sample of patients with dlPFC damage and a wide-ranging assessment of cognitive function, we report several main findings.

First, dlPFC lesions were reliably associated with deficits in general intelligence (g), with noteworthy impairment on measures of working memory and processing speed. These findings suggest that the dlPFC is necessary for intelligence, supporting key competencies for working memory and mechanisms for the coordination of visual and motor representations underlying goal-directed behavior. The recognized role of these processes in fluid intelligence further supports the neuroscience literature indicating that the dlPFC is particularly important for fluid aspects of intelligent behavior (see Blair, 2006, Woolgar et al., 2010).

Second, although dlPFC patients demonstrated the lowest levels of performance among groups tested on the D-KEFS, no reliable deficits were observed when the contribution of general intelligence (g) was removed. This pattern of findings suggests that the dlPFC is not functionally specialized for a specific executive function within the D-KEFS, but instead supports higher-level mechanisms for general intelligence.

Third, SEM results revealed the psychological structure of general intelligence and executive function – providing evidence that, at the latent variable level, these constructs are near perfectly correlated and further suggesting that high-level cognition is supported by a domain-general information processing architecture.

Taken together, these findings help to elucidate the cognitive and neural architecture of higher cognition in the dlPFC, supporting the view that this region provides a coordinated architecture for general intelligence rather than selectively mediating specific executive functions. This conclusion is supported by extensive neuroscience data implicating the dlPFC in general intelligence (Deary et al., 2010). Rather than provide evidence for the involvement of the dlPFC in specific executive functions, this research demonstrates substantial adaptability of function (for reviews, see Miller 2000; Miller & Cohen, 2001). The findings reported here, together with the emerging neuroscience literature, suggest that (1) the dlPFC has more than one function (Duncan et al., 2000; Duncan, 2010) and (2) functions of distinct cortical areas might overlap with one another to support an integrative architecture (Jung & Haier, 2007).

According to this approach, neural computations should not be thought of as implemented by an individual area, but rather by the interaction among multiple areas. Specific brain regions are thought to belong to several intersecting networks based on their structural topology and functional connectivity (Passingham et al., 2002). The impact of a brain region on behavior therefore depends on its structural and functional connectivity as a member of a broader information processing network. Recent advances in network theory have shown that regions characterized by a high degree of functional connectivity are important in regulating the flow and integration of information among areas (Guimera & Nunes Amaral, 2005; Guimera et al., 2007; Sporns et al., 2007). Research indicates that the dlPFC is particularly important for linking multiple functional clusters, supporting an integrative architecture for the coordination of multiple brain systems (Sporns et al., 2007).

A growing body of evidence further indicates that this integrative architecture centrally depends on white matter fiber tracts that synthesize information across a broadly distributed neural system. A seminal model of general intelligence, the Parieto-Frontal Integration Theory (Jung & Haier, 2007), postulates central roles for cortical regions in the prefrontal (Brodmann areas 6, 9–10, 45–47), parietal (areas 7, 39–40), occipital (areas 18–19), and temporal association cortex (areas 21, 37). Recent voxel-based lesion-symptom mapping studies have sharpened our understanding of the role of white matter fiber tracts in binding these areas into an integrated system subserving g (Barbey et al., in press; Gläscher et al., 2010, 2009; see also Rudrauf et al., 2008). Barbey et al. (in press) showed that the neural architecture of g is remarkably circumscribed, concentrated within the core of white matter fiber tracts that connect ventrolateral and dorsolateral PFC with the inferior parietal cortex and that terminate in the superior parietal lobule. Converging evidence is provided by Chiang et al. (2009), who report significant correlations between integrity of the superior fronto-occipital fasciculus and neuropsychological measures of general intelligence. The observed reliance upon white matter fiber tracts suggests that general intelligence is supported by the interregional communication among many brain areas, emphasizing the central role of the dlPFC and parietal cortex (Jung & Haier, 2007).

In designing the current study, we chose to contrast dlPFC patients with a demographically matched brain-damaged comparison sample. The matching was successful and the brain-damaged comparison group was highly similar on demographic variables (age, sex, years of education), pre- and post-combat measures of cognitive function, and lesion size. The selection of patient groups based on anatomically defined lesions in the present study is distinct from previous studies that have traditionally selected patients on the basis of their behavioral profile (e.g., Shallice & Vallar, 1990). An anatomically defined approach can support stronger inferences about brain-behavior relationships by examining the causal contribution of a specific brain region to general intelligence and executive function rather than indirectly inferring these mechanisms from a particular behavioral profile (cf. Baldo & Dronkers, 2006). This design helps to isolate scientifically the causal contribution of dlPFC damage to specific higher cognitive functions. The observed pattern of significant between-group differences on general intelligence provides strong evidence that dlPFC damage leads to disproportionate defects in intelligence, relative to damage outside the dlPFC. Conversely, the reliable lack of significant between-group differences on tests of executive function (with g removed) provides key evidence that dlPFC damage, per se, does not lead to deficits in executive function.

The reported findings have significant implications for the neuropsychological assessment of brain-injured patients. From a clinical perspective, understanding general intelligence deficits in patients with dlPFC lesions may facilitate the design of appropriate assessment tools and rehabilitation strategies, with potential improvement in patients’ cognitive abilities and daily living. These data show that impairments at the level of verbal and performance IQ (Tables 1 and 2), or on specific measures of executive function (Tables 3 and 4), are not necessarily caused by dlPFC damage. Diagnostic evidence for the preserved functioning of the dlPFC instead derived from performance at the highest level, on global tests of general intelligence (g) representing key competencies for working memory and processing speed (Tables 1 and 2). These findings highlight specific tests of the WAIS that may be targeted in clinical investigations to assess the functioning of dlPFC (Blair, 2006).

It is important to emphasize in closing that the abilities measured by the WAIS and D-KEFS do not provide a comprehensive assessment of all human cognitive abilities. There are other aspects of human intelligence in addition to those abilities measured by the WAIS and D-KEFS that contribute to mental life, notably those related to social and emotional functioning (for evidence supporting the involvement of the dlPFC in emotional intelligence, see Krueger et al., 2009). We stress that the conclusions of our study speak only to the necessity of the dlPFC, not the entire network of structures that participate. Understanding the neural architecture of human intelligence and executive functions will ultimately require knowledge of the entire network, the contributions made by each of the components, and the role of white matter fiber tracts that communicate and synthesize information between them. The results reported here contribute to this emerging research program by helping to elucidate the involvement of the dlPFC, indicating that this region is necessary for global aspects of general intelligence rather than selectively mediating specific executive functions.

Supplementary Material

01

  • Investigated neural substrates of higher cognition in patients with focal brain injuries

  • Administered tests of high-level cognition, including the WAIS and D-KEFS

  • Findings elucidate the neural architecture of higher cognition within the dlPFC

Acknowledgments

We are grateful to S. Bonifant, B. Cheon, C. Ngo, A. Greathouse, V. Raymont, K. Reding, and G. Tasick for their invaluable help with the testing of participants and organization of this study. This work was supported by funding from the U.S. National Institute of Neurological Disorders and Stroke intramural research program and a project grant from the United States Army Medical Research and Material Command administered by the Henry M. Jackson Foundation (Vietnam Head Injury Study Phase III: a 30-year post-injury follow-up study, grant number DAMD17-01-1-0675). R. Colom was also supported by Grant PSI2010-20364 (Spain).

Footnotes

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References

  1. Baldo JV, Dronkers NF. The role of inferior parietal and inferior frontal cortex in working memory. Neuropsychology. 2006;20:529–538. doi: 10.1037/0894-4105.20.5.529. [DOI] [PubMed] [Google Scholar]
  2. Baldo JV, Shimamura AP, Delis DC, Kramer J, Kaplan E. Verbal and design fluency in patients with frontal lobe lesions. Journal of the International Neuropsychological Society. 2001;7:586–596. doi: 10.1017/s1355617701755063. [DOI] [PubMed] [Google Scholar]
  3. Barbey AK, Colom R, Solomon J, Krueger F, Forbes C, Grafman J. An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain. 135:1154–1164. doi: 10.1093/brain/aws021. in press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Barbey AK, Grafman J. The prefrontal cortex and goal-directed social behavior. In: Decety J, Cacioppo J, editors. The Handbook of Social Neuroscience. Oxford University Press; in press a. [Google Scholar]
  5. Barbey AK, Grafman J. Wiley Interdisciplinary Reviews: Cognitive Science. An integrative cognitive neuroscience theory for social reasoning and moral judgment. in press b. [DOI] [PubMed] [Google Scholar]
  6. Barbey AK, Krueger F, Grafman J. Structured event complexes and mental models for counterfactual inference. In: Bar M, editor. Predictions in the Brain: Using our Past to Prepare for the Future. Oxford University Press; in press. [Google Scholar]
  7. Barbey AK, Koenigs M, Grafman J. Orbitofrontal contributions to human working memory. Cerebral Cortex. 2011;21:789–795. doi: 10.1093/cercor/bhq153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Barbey AK, Krueger F, Grafman J. An evolutionarily adaptive neural architecture for social reasoning. Trends in Neuroscience. 2009a;32:603–610. doi: 10.1016/j.tins.2009.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Barbey AK, Krueger F, Grafman J. Structured event complexes in the prefrontal cortex support counterfactual representations for future planning. Philosophical Transactions of the Royal Society of London: Biological Sciences. 2009b;364:1291–1300. doi: 10.1098/rstb.2008.0315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bartholomew DJ, Deary IJ, Lawn M. A New Lease of Life for Thomson’s Bonds Model of Intelligence. Psychological Review. 2009;116:567–579. doi: 10.1037/a0016262. [DOI] [PubMed] [Google Scholar]
  11. Basso A, De Renzi E, Faglioni P, Scotti G, Spinnler H. Neuropsychological evidence for the existence of cerebral areas critical to the performance of intelligence tasks. Brain. 1973;96:715–728. doi: 10.1093/brain/96.4.715. [DOI] [PubMed] [Google Scholar]
  12. Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994;50:7–15. doi: 10.1016/0010-0277(94)90018-3. [DOI] [PubMed] [Google Scholar]
  13. Bishop SJ, Fossella J, Croucher CJ, Duncan J. COMT val158met genotype affects neural mechanisms supporting fluid intelligence. Cerebral Cortex. 2008;18:2132–2140. doi: 10.1093/cercor/bhm240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Black FW. Cognitive deficits in patients with unilateral war-related frontal lobe lesions. Journal of Clinical Psychology. 1976;32:366–372. doi: 10.1002/1097-4679(197604)32:2<366::aid-jclp2270320234>3.0.co;2-f. [DOI] [PubMed] [Google Scholar]
  15. Blair C. How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability. Behavioral and Brain Sciences. 2006;29:109–125. doi: 10.1017/S0140525X06009034. [DOI] [PubMed] [Google Scholar]
  16. Blair RJR, Cipolotti L. Impaired social response reversal: A case of “acquired sociopathy”. Brain. 2000;123:1122–1141. doi: 10.1093/brain/123.6.1122. [DOI] [PubMed] [Google Scholar]
  17. Bugg JM, Zook NA, DeLosh EL, Davalos DB, Davis HP. Age differences in fluid intelligence: Contributions of general slowing and frontal decline. Brain and Cognition. 2006;62:9–16. doi: 10.1016/j.bandc.2006.02.006. [DOI] [PubMed] [Google Scholar]
  18. Burgess PW, Shallice T. Response suppression, initiation and strategy use following frontal lobe lesions. Neuropsychologia. 1996;34:263–272. doi: 10.1016/0028-3932(95)00104-2. [DOI] [PubMed] [Google Scholar]
  19. Cato MA, Delis DC, Abildskov TJ, Bigler E. Assessing the elusive cognitive deficits associ- ated with ventromedial prefrontal damage: A case of a modern-day Phineas Cage. Journal of the International Neuropsychological Society. 2004;10:453–465. doi: 10.1017/S1355617704103123. [DOI] [PubMed] [Google Scholar]
  20. Chiang MC, et al. Genetics of brain fiber architecture and intellectual performance. Journal of Neuroscience. 2009;29:2212–2224. doi: 10.1523/JNEUROSCI.4184-08.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Collins DL, Neelin P, Peters TM, Evans AC, Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography. 1994;18:192–205. [PubMed] [Google Scholar]
  22. Colom R, Jung RE, Haier RJ. Distributed brain sites for the g-factor of intelligence. Neuroimage. 2006a;31:1359–1365. doi: 10.1016/j.neuroimage.2006.01.006. [DOI] [PubMed] [Google Scholar]
  23. Colom R, Jung RE, Haier RJ. Finding the g-factor in brain structure using the method of correlated vectors. Intelligence. 2006b;34:561–570. [Google Scholar]
  24. Colom R, Haier RJ, Head K, Álvarez-Linera J, Quiroga M, Shih PC, Jung RE. Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model. Intelligence. 2009;37:124–135. [Google Scholar]
  25. Colom R, Karama S, Jung RE, Haier RJ. Human intelligence and brain networks. Dialogues in Clinical Neuroscience. 2010;12:489–501. doi: 10.31887/DCNS.2010.12.4/rcolom. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Colom R, Thompson PM. Understanding Human Intelligence by Imaging the Brain. In: Chamorro-Premuzic T, von Stumm S, Furnham A, editors. Handbook of individual differences. London: Wiley-Blackwell; 2011. [Google Scholar]
  27. D’Esposito M, Postle BR. The dependence of span and delayed-response performance on prefrontal cortex. Neuropsychologia. 1999;37:89–101. doi: 10.1016/s0028-3932(99)00021-4. [DOI] [PubMed] [Google Scholar]
  28. D’Esposito M, Cooney JW, Gazzaley A, Gibbs SEB, Postle BR. Is the prefrontal cortex necessary for delay task performance? Evidence from lesion and fMRI data. Journal of the International Neuropsychological Society. 2006;12:248–260. doi: 10.1017/S1355617706060322. [DOI] [PubMed] [Google Scholar]
  29. Deary IJ, Penke L, Johnson W. The neuroscience of human intelligence differences. Nature Reviews Neuroscience. 2010;11:201–211. doi: 10.1038/nrn2793. [DOI] [PubMed] [Google Scholar]
  30. Delis DC, Kaplan E, Kramer JH. Delis Kaplan Executive Function System. San Antonio TX: The Psychological Corporation; 2001. [Google Scholar]
  31. Delis DC, Lansing A, Houston WS, Wetter S, Han SD, Jacobson M, Holdnack J, Kramer J. Creativity lost. Journal of Psychoeducational Assessment. 2007;25:29–40. [Google Scholar]
  32. Delis DC, Squire LR, Bihrle AM, Massman PJ. Componential analysis of problem-solving ability: Performance of patients with frontal lobe damage and amnesic patients on a new sorting test. Neuropsychologia. 1992;30:683–697. doi: 10.1016/0028-3932(92)90039-o. [DOI] [PubMed] [Google Scholar]
  33. Duncan J. The multiple-demand (MD) system of the primate brain: mental programs for intelligence behaviour. Trends in Cognitive Science. 2010;14:172–179. doi: 10.1016/j.tics.2010.01.004. [DOI] [PubMed] [Google Scholar]
  34. Duncan J. Brain mechanisms of attention. Quarterly Journal of Experimental Psychology. 2006;59:2–27. doi: 10.1080/17470210500260674. [DOI] [PubMed] [Google Scholar]
  35. Duncan J, Burgess P, Emslie H. Fluid intelligence after frontal lobe lesions. Neuropsychologia. 1995;33:261–268. doi: 10.1016/0028-3932(94)00124-8. [DOI] [PubMed] [Google Scholar]
  36. Duncan J, Emslie H, Williams P, Johnson R, Freer C. Intelligence and the frontal lobe: The organization of goal-directed behavior. Cognitive Psychology. 1996;30:257–303. doi: 10.1006/cogp.1996.0008. [DOI] [PubMed] [Google Scholar]
  37. Duncan J, Owen AM. Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences. 2000;23:475–483. doi: 10.1016/s0166-2236(00)01633-7. [DOI] [PubMed] [Google Scholar]
  38. Duncan J, Seitz RJ, Kolodny J, Bor D, Herzog H, Ahmed A, Newell FN, Emslie H. A neural basis for general intelligence. Science. 2000;289:457–460. doi: 10.1126/science.289.5478.457. [DOI] [PubMed] [Google Scholar]
  39. Eslinger PJ, Damasio AR. Severe disturbance of higher cognition after bifrontal lobe ablation: Patient EVR. Neurology. 1985;35:1731–1741. doi: 10.1212/wnl.35.12.1731. [DOI] [PubMed] [Google Scholar]
  40. Esposito G, Kirkby BS, Van Horn JD, Ellmore TM, Berman KF. Context- dependent, neural system-specific neurophysiological concomitants of ageing: mapping PET correlates during cognitive activation. Brain. 1999;122:963–79. doi: 10.1093/brain/122.5.963. [DOI] [PubMed] [Google Scholar]
  41. Gläscher J, Tranel D, Paul LK, Rudrauf D, Rorden C, Hornaday A, Grabowski T, Damasio H, Adolphs R. Lesion mapping of cognitive abilities linked to intelligence. Neuron. 2009;61:681–691. doi: 10.1016/j.neuron.2009.01.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Gläscher J, Rudrauf D, Colom R, Paul LK, Tranel D, Damasio H, Adolphs R. Distributed neural systems for general intelligence revealed by lesion mapping. Proceedings of the National Academy of Sciences of the USA. 2010;107:4705–4709. doi: 10.1073/pnas.0910397107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Gray JR, Chabris CF, Braver TS. Neural mechanisms of general fluid intelligence. Nature Neuroscience. 2003;6:316–321. doi: 10.1038/nn1014. [DOI] [PubMed] [Google Scholar]
  44. Guimera R, Nunes Amaral LA. Functional cartography of complex metabolic networks. Nature. 2005;433:895–900. doi: 10.1038/nature03288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Guimera R, Sales-Pardo M, Amaral LAN. Classes of complex networks defined by role-to-role connectivity profiles. Nature Phys. 2007;3:63–69. doi: 10.1038/nphys489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Haier RJ. Neuro-intelligence, neuro-metrics and the next phase of brain imaging studies. Intelligence. 2009;37:121–123. [Google Scholar]
  47. Haier RJ, Colom R, Schroeder D, Condon C, Tang C, Eaves E, Head K. Gray matter and intelligence factors: Is there a neuro-g? Intelligence. 2009;37:136–144. [Google Scholar]
  48. Haier RJ, Jung RE, Yeo RA, Head K, Alkire MT. Structural brain variation and general intelligence. Neuroimage. 2004;23:425–433. doi: 10.1016/j.neuroimage.2004.04.025. [DOI] [PubMed] [Google Scholar]
  49. Haier RJ, Jung RE, Yeo RA, Head K, Alkire MT. The neuroanatomy of general intelligence: sex matters. Neuroimage. 2005;25:320–327. doi: 10.1016/j.neuroimage.2004.11.019. [DOI] [PubMed] [Google Scholar]
  50. Heaton RK, Marcotte TD. Clinical neuropsychological tests and assessment techniques. In: Boller F, Grafman J, editors. Handbook of Neuropsychology. 2. Vol. 2. Amsterdam: Elsevier; 2000. pp. 27–52. [Google Scholar]
  51. Heaton RK, Miller SW, Taylor MJ, Grant I. Revised comprehensive norms for an expanded Halstead-Reitan battery: Demographically adjusted neuropsychological norms for African American and caucasian adults. Lutz, FL: PAR; 2004. [Google Scholar]
  52. Homack S, Lee D, Riccio CA. Delis-Kaplan Executive Function System (Test Review) Journal of Clinical & Experimental Neuropsychology. 2005;27:599–609. doi: 10.1080/13803390490918444. [DOI] [PubMed] [Google Scholar]
  53. Isingrini M, Vazou F. Relation between fluid intelligence and frontal lobe in older adults. International Journal of Aging and Human Development. 1997;45:99–109. doi: 10.2190/WHWX-YNVB-079V-2L74. [DOI] [PubMed] [Google Scholar]
  54. Jensen AR. The g factor: The science of mental ability. Praeger; 1998. [Google Scholar]
  55. Jung RE, Haier RJ. The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence. Behavioral and Brain Sciences. 2007;30:135–154. doi: 10.1017/S0140525X07001185. [DOI] [PubMed] [Google Scholar]
  56. Jung RE, Haier RJ, Yeo RA, Rowland LM, Petropoulos H, Levine AS, Sibbitt WL, Brooks WM. Sex differences in N-acetylaspartate correlates of general intelligence: an 1H-MRS study of normal human brain. Neuroimage. 2005;26:965–972. doi: 10.1016/j.neuroimage.2005.02.039. [DOI] [PubMed] [Google Scholar]
  57. Kane MJ, Engle RW. The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic Bulletin Review. 2002;9:637–671. doi: 10.3758/bf03196323. [DOI] [PubMed] [Google Scholar]
  58. Koechlin E, Ody C, Kouneiher F. The architecture of cognitive control in the human prefrontal cortex. Science. 2003;302:1181–1185. doi: 10.1126/science.1088545. [DOI] [PubMed] [Google Scholar]
  59. Koenigs M, Barbey AK, Postle B, Grafman J. Superior parietal cortex is critical for the manipulation of information in working memory. Journal of Neuroscience. 2009;47:14980–14986. doi: 10.1523/JNEUROSCI.3706-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Kringelbach ML. The human orbitofrontal cortex: Linking reward to hedonic experience. Nature Reviews Neuroscience. 2005;6:691–702. doi: 10.1038/nrn1747. [DOI] [PubMed] [Google Scholar]
  61. Krueger F, Barbey AK, McCabe K, Strenziok M, Zamboni G, Solomon J, Raymont V, Grafman J. The neural bases of key competencies of emotional intelligence: Brain lesion evidence. Proceedings of the National Academy of Sciences of the USA. 2009;106:22486–22491. doi: 10.1073/pnas.0912568106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Makale M, Solomon J, Patronas NJ, Danek A, Butman JA, Grafman J. Quantification of brain lesions using interactive automated software. Behavioral Research Methods: Instruments and Computers. 2002;34:6–18. doi: 10.3758/bf03195419. [DOI] [PubMed] [Google Scholar]
  63. McDonald CR, Delis DC, Norman MA, Tecoma ES, Iragui VJ. Discriminating patients with frontal lobe epilepsy and temporal lobe epilepsy: Utility of a multi-level design fluency test. Neuropsychology. 2005a;19:806–813. doi: 10.1037/0894-4105.19.6.806. [DOI] [PubMed] [Google Scholar]
  64. McDonald CR, Delis DC, Norman MA, Tecoma ES, Iragui-Madozi VI. Is impairment in set-shifting specific to frontal-lobe dysfunction? Evidence from patients with frontal-lobe or temporal-lobe epilepsy. Journal of the International Neuropsychological Society. 2005b;11:477–481. [PubMed] [Google Scholar]
  65. McDonald CR, Delis DC, Norman MA, Wetter SR, Tecoma ES, Iragui VJ. Response inhibition and set-shifting in patients with frontal-lobe epilepsy or temporal-lobe epilepsy. Epilepsy and Behavior. 2005c;7:438–446. doi: 10.1016/j.yebeh.2005.05.005. [DOI] [PubMed] [Google Scholar]
  66. Miller EK. The prefrontal cortex and cognitive control. Nature Reviews Neuroscience. 2000;1:59–65. doi: 10.1038/35036228. [DOI] [PubMed] [Google Scholar]
  67. Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annual Review of Neuroscience. 2001;24:167–202. doi: 10.1146/annurev.neuro.24.1.167. [DOI] [PubMed] [Google Scholar]
  68. Muller NG, Machado L, Knight RT. Contributions of subregions of the prefrontal cortex to working memory: evidence from brain lesions in humans. Journal of Cognitive Neuroscience. 2002;14:673–686. doi: 10.1162/08989290260138582. [DOI] [PubMed] [Google Scholar]
  69. Neisser U, Boodoo G, Bouchard TJ, Boykin AW, Brody N, Ceci SJ, Halpern DF, Loehlin JC, Perloff R, Sternberg RJ, Urbina S. Intelligence: Knowns and unknowns. American Psychologist. 1996;51:77–101. [Google Scholar]
  70. Nisbett RE, Aronson J, Blair C, Dickens W, Flynn J, Halpern DF, Turkheimer E. Intelligence: New Findings and Theoretical Developments. American Psychologist. 2012 doi: 10.1037/a0026699. [DOI] [PubMed] [Google Scholar]
  71. Owen AM, Evans AC, Petrides M. Evidence for a two-stage model of spatial working memory processing within the lateral frontal cortex: A positron emission tomography study. Cerebal Cortex. 1996;6:31–38. doi: 10.1093/cercor/6.1.31. [DOI] [PubMed] [Google Scholar]
  72. Owen AM. The functional organization of working memory processes within human lateral prefrontal cortex: The contribution of functional neuroimaging. European Journal of Neuroscience. 1997;9:1329–1339. doi: 10.1111/j.1460-9568.1997.tb01487.x. [DOI] [PubMed] [Google Scholar]
  73. Owen AM, McMillan KM, Laird AR, Bullmore E. N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Human Brain Mapping. 2005;25:46–59. doi: 10.1002/hbm.20131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Passingham RE, Stephan KE, Kotter R. The anatomical basis of functional localization in the cortex. Nature Rev Neurosci. 2002;3:606–616. doi: 10.1038/nrn893. [DOI] [PubMed] [Google Scholar]
  75. Parkin AJ, Java RI. Deterioration in frontal lobe function in normal aging: Influences of fluid intelligence versus perceptual speed. Neuropsychology. 1999;13:539–545. doi: 10.1037//0894-4105.13.4.539. [DOI] [PubMed] [Google Scholar]
  76. Petrides M. Frontal lobes and memory. In: Boller F, Grafman J, editors. Handbook of Neuropsychology. 2. Vol. 2. Amsterdam: Elsevier; 2000. pp. 67–84. [Google Scholar]
  77. Petrides M. Lateral prefrontal cortex: architectonic and functional organization. Philosophical Transactions of the Royal Society: Biological Sciences. 2005;360:781–795. doi: 10.1098/rstb.2005.1631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Prabhakaran V, Smith JAL, Desmond JE, Glover GH, Gabrieli JDE. Neural substrates of fluid reasoning: An fMRI study of neocortical activation during performance of the Raven’s Progressive Matrices Test. Cognitive Psychology. 1997;33:43–63. doi: 10.1006/cogp.1997.0659. [DOI] [PubMed] [Google Scholar]
  79. Ptito A, Crane J, Leonard G, Amsel R, Caramanos Z. Visual-spatial localization by patients with frontal lobe lesions invading or sparing area 46. Neuroreport. 1995;6:45–48. doi: 10.1097/00001756-199509000-00018. [DOI] [PubMed] [Google Scholar]
  80. Roca M, Parr A, Thompson R, Woolgar A, Torralva T, Antoun N, Manes F, Duncan J. Executive function and fluid intelligence after frontal lobe lesions. Brain. 2009;133:234–247. doi: 10.1093/brain/awp269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Rudrauf D, Mehta S, Grabowski TJ. Disconnection’s renaissance takes shape: Formal incorporation in group-level lesion studies. Cortex. 2008;44:1084–1096. doi: 10.1016/j.cortex.2008.05.005. [DOI] [PubMed] [Google Scholar]
  82. Schmid J, Leiman JM. The development of hierarchical factor solutions. Psychometrika. 1957;22:53–61. [Google Scholar]
  83. Shallice T, Burgess PW. Deficits in strategy application following frontal lobe damage in man. Brain. 1991;114:727–741. doi: 10.1093/brain/114.2.727. [DOI] [PubMed] [Google Scholar]
  84. Shallice T, Vallar G. The impairment of auditory-verbal short-term storage. In: Vallar G, Shallice T, editors. Neuropsychological Impairments of Short-Term Memory. Cambridge University Press; Cambridge: 1990. pp. 11–53. [Google Scholar]
  85. Solomon J, Raymont V, Braun A, Butman JA, Grafman J. User-friendly software for the analysis of brain lesions ABLe. Computer Methods and Programs in Biomedicine. 2007;86:245–254. doi: 10.1016/j.cmpb.2007.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Spearman C. General intelligence, objectively determined and measured. American Journal of Psychology. 1904;15:201–93. [Google Scholar]
  87. Spearman C. The abilities of man: Their nature and measurement. Macmillan; 1927. [Google Scholar]
  88. Sporns O, Honey CJ, Kotter R. Identification and classification of hubs in brain networks. PLoS ONE. 2007;2:e1049. doi: 10.1371/journal.pone.0001049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Swanson J. The Delis-Kaplan Executive Function System (Test Review) Canadian Journal of School Psychology. 2005;20:117. [Google Scholar]
  90. Talairach J, Tournoux P. Co-planar Stereotaxic Atlas of the Human Brain 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Stuttgart Germany and New York: Thieme; 1988. [Google Scholar]
  91. Taylor MJ, Heaton RK. Sensitivity and specificity of WAIS-III/WMS-III demographically corrected factor scores in neuropsychological assessment. Journal of International Neuropsychological Society. 7:867–874. [PubMed] [Google Scholar]
  92. Thomson GH. The factorial analysis of human ability. 5. London: University of London Press; 1951. [Google Scholar]
  93. Tranel D, Manzel K, Anderson SW. Is the prefrontal cortex important for fluid intelligence? A neuropsychological study using matrix reasoning. Clinical Neuropsychology. 2008;22:242–261. doi: 10.1080/13854040701218410. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Tsuchida A, Fellows LK. Lesion evidence that two distinct regions within prefrontal cortex are critical for n-back performance in humans. Journal of Cognitive Neuroscience. 2009;12:2263–2275. doi: 10.1162/jocn.2008.21172. [DOI] [PubMed] [Google Scholar]
  95. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–289. doi: 10.1006/nimg.2001.0978. [DOI] [PubMed] [Google Scholar]
  96. van der Maas HL, Dolan CV, Grasman RP, Wicherts JM, Huizenga HM, Raijmakers ME. A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review. 2006;113:842–861. doi: 10.1037/0033-295X.113.4.842. [DOI] [PubMed] [Google Scholar]
  97. Volle E, Kinkingnehun S, Pochon J, Mondon K, de Schotten MT, Seassau M, Duffau H, Samson Y, Dubois B, Levy R. The functional architecture of the left posterior and lateral prefrontal cortex in humans. Cerebral Cortex. 2008;10:1093–1103. doi: 10.1093/cercor/bhn010. [DOI] [PubMed] [Google Scholar]
  98. Wager TD, Smith EE. Neuroimaging studies of working memory: A meta-analysis. Cognitive and Affective Behavioral Neuroscience. 2003;3:255–274. doi: 10.3758/cabn.3.4.255. [DOI] [PubMed] [Google Scholar]
  99. Wager TD, Jonides J, Reading S. Neuroimaging studies of shifting attention: A meta-analysis. Neuroimage. 2004;22:1679–1693. doi: 10.1016/j.neuroimage.2004.03.052. [DOI] [PubMed] [Google Scholar]
  100. Wechsler D. Wechsler adult intelligence test administration and scoring manual. San Antonio TX: The Psychological Corporation; 1997. [Google Scholar]
  101. Wood JN, Grafman J. Human prefrontal cortex: processing and representational perspectives. Nature Reviews Neuroscience. 2003;4:139–147. doi: 10.1038/nrn1033. [DOI] [PubMed] [Google Scholar]
  102. Woolgar A, Parr A, Cusak R, Thompson R, Nimmo-Smith I, Torralva T, Roca M, Antoun N, Manes F, Duncan J. Fluid intelligence loss linked to restricted regions of damage within frontal and parietal cortex. Proceedings of the National Academy of Science. 2010;107:14899–14902. doi: 10.1073/pnas.1007928107. [DOI] [PMC free article] [PubMed] [Google Scholar]

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