Executive functions and prefrontal cortex: a matter

Original Research Article

published: 25 January 2011 doi: 10.3389/fnsys.2011.00003

SYSTEMS NEUROSCIENCE

Executive functions and prefrontal cortex: a matter of persistence? Gareth Ball 1, Paul R. Stokes 1, Rebecca A. Rhodes 1, Subrata K. Bose 1, Iead Rezek 2, Alle-Meije Wink 1, Louis-David Lord 2, Mitul A. Mehta 3, Paul M. Grasby 1,2 and Federico E. Turkheimer 1,2* Medical Research Council Clinical Sciences Centre, Hammersmith Hospital, London, UK Division of Neuroscience and Mental Health, Imperial College London, London, UK 3 Institute of Psychiatry, King’s College London, London, UK 1 2

Edited by: Raphael Pinaud, University of Oklahoma Health Sciences Center, USA Reviewed by: Luiz Pessoa, Brown University, USA Chiang-Shan R. Li, Yale Center for Clinical Investigation, USA *Correspondence: Federico E. Turkheimer, Department of Clinical Neuroscience, Division of Neuroscience and Mental Health, Hammersmith Hospital, Cyclotron Building, Room 236, DuCane Road, London W12 0NN, UK. e-mail: federico.turkheimer@imperial. ac.uk

Executive function is thought to originates from the dynamics of frontal cortical networks. We examined the dynamic properties of the blood oxygen level dependent time-series measured with functional MRI (fMRI) within the prefrontal cortex (PFC) to test the hypothesis that temporally persistent neural activity underlies performance in three tasks of executive function. A numerical estimate of signal persistence, the Hurst exponent, postulated to represent the coherent firing of cortical networks, was determined and correlated with task performance. Increasing persistence in the lateral PFC was shown to correlate with improved performance during an n-back task. Conversely, we observed a correlation between persistence and increasing commission error – indicating a failure to inhibit a prepotent response – during a Go/No-Go task. We propose that persistence within the PFC reflects dynamic network formation and these findings underline the importance of frequency analysis of fMRI time-series in the study of executive functions. Keywords: executive function, prefrontal cortex, persistence, BOLD, Hurst exponent, networks, functional MRI

Introduction High-level cognitive functions have traditionally been localized to the anterior frontal association cortex, commonly referred to as the prefrontal cortex (PFC). Numerous studies have expanded upon this static view of executive localization, identifying functional networks activated by complex executive processes and served by extensive reciprocal connections between the PFC, posterior parietal cortex and various cortical and limbic regions (Selemon and Goldman-Rakic, 1988; Ongur and Price, 2000; Halgren et al., 2002; Honey et al., 2002; Schall et al., 2003; Perianez et al., 2004; Owen et al., 2005; Simmonds et al., 2008). The abundance of connectivity between the PFC and the rest of the brain suggests that a definition of executive function can be obtained from the dynamics of converging networks into the cortical layers of the lateral PFC (Fuster, 2001; Stevens, 2009). The persistence of these signals through time is a fundamental neurobiological substrate that enables the organization of executive actions (Durstewitz et al., 2000b; Curtis and D’Esposito, 2003). Coherent PFC neuronal firing in the gamma frequency band (30–70 Hz) has been associated with behavioral measures of executive function in humans and appears altered in pathological states (Gonzalez Andino et al., 2005; Ferrarelli et al., 2008; Haenschel et al., 2009). Although gamma frequencies are not measurable with functional MRI (fMRI), it has been demonstrated that blood oxygen level dependent (BOLD) activation is tightly correlated to the power of local-field potential oscillations in the gamma range in the cat visual cortex (Niessing et al., 2005). In humans, this relationship may be more complex (Winterer et al., 2007), but Lachaux et al. (2007) has shown experimentally that modulation of the gamma bandwidth of EEG recordings maps to BOLD signal measured

Frontiers in Systems Neuroscience

with fMRI, demonstrating that the time-envelope of these signals, convoluted with the hemodynamic response, translates into slower dynamics to which the fMRI is sensitive. The dynamic BOLD signal therefore contains frequency information that reflects coherent neuronal activity and is amenable to analysis. Indeed, a recent, simultaneous EEG–fMRI study revealed specific, load-dependent correlations between gamma band signals and BOLD activity in the dorsolateral PFC during a working memory task (Michels et al., 2010). We postulate that persistent BOLD properties, defined as longmemory processes with higher energy at low frequencies, represent synchronous neuronal firing that translates into smoother BOLD signals over time. Such properties have been investigated previously and have proven sensitive to pharmacological challenge, normal aging and pathological states of cognitive dysfunction (Maxim et al., 2005; Anderson et al., 2006; Wink et al., 2006, 2008; Suckling et al., 2008). Blood oxygen level dependent time-series have an inherent fractal nature where the self-similarity of brain activity reflects into the frequency content of the auto-correlation function that declines according to a power-law (Kitzbichler et al., 2009; Expert et al., 2010). Concurrently, the frequency properties of BOLD timeseries can be quantified by the Hurst exponent (H; Wink et al., 2006). The H is a measure of auto-correlation, or self-similarity, of a time-series within the 0–1 range describing the 1/f rate of energy distribution, where f indicates the frequency of the signal. Values of H between 0.5 and 1 indicate greater low-frequency content, e.g., greater persistence over time, a smoother time-series and longer memory. Values between 0 and 0.5 indicate greater high-frequency content and a value of 0.5 indicates a true random walk. Higher

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Ball et al.

fMRI persistence in the PFC

values of H and persistence have come to indicate more complex and coordinated dynamics in the underlying network activities and degradation of fractal complexity has come to indicate the desynchronization of the biological system and reflect malfunction, aging or disease (Lipsitz and Goldberger, 1992; Goldberger et al., 2002; DeKosky and Marek, 2003; Salkovic-Petrisic et al., 2009). The frequency analysis of fMRI time-series has reported interesting although sometimes discordant results. Wink et al. (2006) demonstrated a direct association between aging and scopolamine administration with the H in resting fMRI but subsequently demonstrated an association between higher persistence and faster response in a prior encoding task (Wink et al., 2008). The paradoxical results were explained by a complex multifractal analysis of the previous data, although the original result of increased H, equivalent to the persistence in the time-series, was confirmed. Aging, disease and pharmacological agents, however, alter brain vascular reactivity and perfusion (Estrada et al., 1983; Fukuyama et al., 1996; Farkas and Luiten, 2001) that will act as confounder in the BOLD frequency response. The general aim of this work was to build on the evidence described above and examine in detail the association between coherence in neuronal firing in the frontal cortices, as reflected in the average frequency content of fMRI signal and direct behavioral measures of executive function tasks in normal volunteers. Our primary hypothesis was that estimates of the H from BOLD activity, obtained using standard wavelet scalograms (Bullmore et al., 2004) from within the lateral PFC were associated with performance in three tasks of executive function (n-back, Go/No-Go, and Tower of London). Additionally, we investigated persistence in a number of associated cortical regions, commonly activated as part of an “executive” network during these tasks.

Materials and Methods Participants

Forty-six healthy, right-handed participants aged 19–59 (18 male; mean age = 35.1 years, SD = 11.6) were included. All participants were free of physical illness and had no history of psychiatric or neurological disorder as assessed using a physical examination, medical history and a structured interview (First and Pincus, 2002). The study was conducted with ethical approval from Hammersmith Hospital Research Ethics Committee, and all participants gave written, informed consent. Task Design

The tasks performed in the fMRI scanner were all programmed with E-Prime v1.1 (Psychology Software Tools, Inc., Pittsburgh, PA, USA), presented using the Integrated Functional Imaging System (IFIS-SA, Invivo, Orlando, FL, USA), and completed during the same fMRI session, in a counter-balanced order. n-back

During this working memory task, a dot was presented in one of four spatial locations, corresponding to a particular button on a response pad and arranged horizontally across the screen (Gevins and Cutillo, 1993). Participants were required to either press the button that corresponded to where the dot was currently appearing (0-back), where the dot had appeared one trial previously


(1-back) or where the dot had appeared two trials previously (2-back). Dots were presented for 500 ms followed by a blank screen for 1500 ms, with 12 dots per block. Three 0-back blocks, three 1-back blocks and three 2-back blocks, each lasting 24 s, were presented in a pseudo-random order, interspersed with two blocks of a low-level rest condition (fixation). Instructions were presented for 3 s prior to the start of each block. Each subject underwent two sets of n-back blocks during the imaging session to test the robustness of any findings. One subject failed to complete the second set of blocks and was included in the analysis using data from the first set only. Response accuracy (proportion of correct responses) and response time were used as measures of performance. Go/No-Go

This task of response inhibition consisted of ten 36-s blocks of two conditions: “Go,” requiring a response to every stimulus and “No-Go,” requiring a response to all but the “stop” stimulus. Five Go blocks and five No-Go blocks were presented in a pseudo-random order, interspersed with two blocks of a rest condition (fixation) after the third and seventh blocks. Go stimuli (the letters F, H, K, P, and S) were presented every 3 s for 500 ms during the blocks. The No-Go stimulus (the letter V) was substituted for a Go stimulus in 45% of trials in the No-Go blocks. Number of commission (responses to a No-Go signal) and omission (no response to a Go signal) errors and response time to Go signals were used as a measure of performance. Tower of London

This planning task required participants to determine the minimum number of moves to re-arrange three colored balls from a start position to a target position (Owen et al., 1996). Participants were presented with a display containing an upper “target” section that indicated the final position of the colored balls, and a lower “start” section that indicated the start position of the colored balls and were then asked to indicate the minimum number of moves via a single finger keypad response. During the “Control” block participants were requested to count the number of balls in both the start and target pockets. Four “Easy” blocks (comprising two and three move problems), four “Difficult” blocks (comprising four to six moves), and four “Control” blocks were presented in a pseudorandom order, interspersed with three blocks of a rest condition (fixation). Each block lasted 30 s and each trial was self-paced. Instructions were presented for 3 s prior to commencement of each block. Response accuracy (proportion of correct responses) and response time to correctly solved problems were used as measures of performance. Imaging Parameters

Functional MRI was performed with a Philips Intera 3 Tesla MRI scanner. Functional T2*-weighted images were acquired using gradient-echo echoplanar imaging, with an automated higher order shim procedure (SENSE factor 2; TE 30 ms; TR 3000 ms; flip angle 90°; FOV 280 mm; voxel dimensions 2.2 mm × 2.2 mm × 2.75 mm). Images were acquired in 48 contiguous 2.75 mm axial slices per brain volume. The total number of brain volumes acquired for each of these tasks was 218, 154, and

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fMRI persistence in the PFC

169. The first five volumes of each scan were discarded to account for T1 equilibration effects. Functional images were acquired during two runs of 5 min 27 s (n-back), a single run of 7 min 42 s (Go/No-Go) and one of 8 min 27 s (Tower of London). A highresolution T1-weighted TFE structural scan was also acquired for each participant for subsequent high-resolution image registration (TE 4.6 ms; TR 9.7 ms; Flip angle 8°; FOV 240 mm; voxel dimensions 0.94 mm × 0.94 mm × 1.2 mm). Image Analysis

Initial processing of functional data sets was carried out using FSL (FMRIB’s Software Library, http://www.fmrib.ox.ac.uk/fsl; Smith et al., 2004). Each 4D functional volume was motion corrected, brain-extracted, spatially smoothed with a Gaussian kernel of FWHM 5 mm and grand-mean intensity normalized. Given the importance of the PFC to executive function, mean BOLD time-series collected during each task were extracted from three bilateral regions of interest (ROI), defined by the Harvard– Oxford Cortical Structural Atlas and chosen to represent the lateral PFC (middle frontal gyrus; inferior frontal gyrus pars opercularis, and pars triangularis; Table 1, shown in Figure 1A) to test the primary hypothesis that executive function task performance is associated with persistence in the BOLD activity within the lateral PFC. Many studies have described functionally connected networks comprising several common structures and engaged by executive task demands regardless of the specific task involved (Menon et al., 2001; Schall et al., 2003; Owen et al., 2005; Seeley et al., 2007; Stevens et al., 2007, 2009; Simmonds et al., 2008). In addition to the primary ROI defined above, BOLD time-series were also extracted from several ROI previously identified as part of the executive networks Table 1 | Center of gravity (COG) in MNI coordinates of cortical regions of interest. Lateral prefrontal cortex

COG

x

Middle frontal gyrus

Left

Right

Inferior frontal gyrus

Left

pars opercularis

Right

Inferior frontal gyrus

Left

pars triangularis

Right

y

z

Left

Right

Parahippocampal gyrus

Left

Right

Superior parietal cortex

Left

Right

19.0

41.4

38.8

19.2

42.1

Numerical Estimates of Persistence

–50.1

14.4

16.5

51.3

15.5

16.5

For some class of models, persistence (P) of a time-series can be defined as the ratio between the energy at low vs. high frequencies. By this definition, a signal that persists longer has longer memory and displays a larger numerical ratio than a signal with lower persistence. The non-linear and complex organization of the human brain and its internal dynamics are reflected in output signals at the microscopic, mesoscopic and macroscopic level that are generally fractal and exhibit decay in energy that is proportional to 1/f. For a complete review on the topic see Werner (2010). Numerical estimates of persistence in fractal time-series can be determined using the H, a number defined in the [0–1] range where values of H = 0.5 indicate a purely random process, values in the [0–0.5] interval indicate antipersistent, mean-reverting behavior and values in the [0.5–1] range indicate persistent/longmemory processes. The estimation of H is far from trivial and requires an understanding of the generating process (see review

–48.1

28.4

9.0

50.0

28.3

8.2

–36.7

1.7

–0.1

37.7

3.2

–0.6

–23.1

–8.9

–30.2 –30.5

23.3

–7.7

–29.2

–48.9

57.5

29.2

–47.6

58.8

Anterior cingulate cortex

0.7

19.3

24.4

Posterior cingulate cortex

0.8

–37.1

30.1

Cuneus

1.5

–78.2

27.4

Precuneus

0.9

–58.7

38.1

Medial frontal cortex

0.1

43.7

–17.0


(anterior and posterior cingulate cortex, cuneus, precuneus, medial frontal cortex, parahippocampal gyrus, insula, and superior parietal cortex, Figure 1B and Table 1) to investigate BOLD signal persistence outside of the PFC. Regional time-series were obtained by registering standard space ROI masks to the filtered functional datasets via individual high-resolution T1-weighted structural images with a 12 degrees of freedom affine registration using FSL’s linear registration tool (FLIRT) and then averaging the pixel time-data within the nativespace masks. In addition, group average BOLD activation maps were generated using FSL’s FEAT. After pre-processing, data were entered into a GLM to determine where BOLD activation occurred during each task condition (Contrasts: n-back: 0-back > Rest; 1-back > Rest; 2-back > Rest; Go/No-Go: Go > Rest; No-Go > Rest; Tower of London: Easy > Rest; Difficult > Rest) on average across the group. Z-statistic images were thresholded at Z = 2.3 and clustercorrected across the whole brain using family-wise error (FWE) with a significance threshold of p