Brain Struct Funct DOI 10.1007/s00429-013-0703-7
SHORT COMMUNICATION
The surface area of early visual cortex predicts the amplitude of the visual evoked potential Torbjørn Elvsa˚shagen • Torgeir Moberget • Erlend Bøen • Per K. Hol • Ulrik F. Malt • Stein Andersson • Lars T. Westlye
Received: 28 June 2013 / Accepted: 30 December 2013 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract The extensive and increasing use of structural neuroimaging in the neurosciences rests on the assumption of an intimate relationship between structure and function in the human brain. However, few studies have examined the relationship between advanced magnetic resonance imaging (MRI) indices of cerebral structure and conventional measures of cerebral functioning in humans. Here we examined whether MRI-based morphometric measures of early visual cortex—estimated using a probabilistic anatomical mask of primary visual cortex (V1)—can predict the amplitude of the visual evoked potential (VEP), i.e., an electroencephalogram signal that primarily reflects postsynaptic potentials in early visual cortical
areas. We found that left, right, and total V1 surface area positively predicted the VEP amplitude. In addition, we showed, using whole brain analysis of local surface areal expansion/contraction, that the association between VEP amplitude and surface area was highly specific for regions within bilateral V1. Together, these findings indicate a strong, selective relationship between MRI-based structural measures and functional properties of the human cerebral cortex.
T. Elvsa˚shagen and T. Moberget contributed equally to this work.
Introduction
T. Elvsa˚shagen (&) T. Moberget E. Bøen U. F. Malt S. Andersson Department of Psychosomatic Medicine, Oslo University Hospital, Rikshospitalet, Nydalen, Pb 4950, 0424 Oslo, Norway e-mail: [email protected] T. Elvsa˚shagen E. Bøen U. F. Malt Institute of Clinical Medicine, University of Oslo, Oslo, Norway T. Moberget S. Andersson L. T. Westlye Department of Psychology, University of Oslo, Oslo, Norway P. K. Hol The Intervention Centre, Oslo University Hospital, Oslo, Norway L. T. Westlye Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway L. T. Westlye KG Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo, Norway
Keywords Magnetic resonance imaging Visual evoked potential Visual cortex Cortical thickness Cortical surface area
The high sensitivity of magnetic resonance imaging (MRI)based brain morphometry has provided novel insights into fundamental aspects of cerebral development, neuroplasticity, and the pathophysiology of mental illnesses (Draganski et al. 2004; Hill et al. 2010; Rimol et al. 2010). However, how and to what extent brain structural properties mirror brain function remain to be clarified; this has limited our ability to interpret structural MRI findings and determine implications. In recent years, the relationships between MRI-based indices of structural and functional brain connectivity have been examined in several studies. These have shown that structural connectivity and restingstate and task-based functional connectivity are strongly interrelated in the human brain (Greicius et al. 2009; van den Heuvel et al. 2009; Cohen et al. 2008; Koch et al. 2002; Romero-Garcia et al. 2013; Honey et al. 2009). Few studies have, however, examined whether MRI-based morphometric measures, e.g., cortical surface area and
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thickness, can predict conventional measures of cerebral functioning, such as the electroencephalogram (EEG). The visual evoked potential (VEP) is an EEG signal specific for visual stimuli and primarily reflects postsynaptic potentials in the visual cortex (Luck 2005). VEP is widely used in studies of human visual system physiology and pathophysiology and is, when elicited by a reversing pattern, characterized by three major components: the N75, P100, and N145 (Di Russo et al. 2005; Tobimatsu and Celesia 2006). N75 is a small, initial negative wave that shows substantial variability within and across individuals; P100 is a prominent and reliable positive wave with minimal intraindividual variation; N145 is a large, second negative wave with several subcomponents (Luck 2005; Odom et al. 2010). Although the neural sources of the patternreversal VEP remain to be fully clarified, previous studies suggest that N75 mainly reflects postsynaptic activity in V1, whereas P100 and N145 are likely generated in both striate and extrastriate cortex (Di Russo et al. 2005; Tobimatsu and Celesia 2006; Whittingstall et al. 2007; Novitskiy et al. 2011; Fuglø et al. 2012). However, despite long and widespread use of the VEP, little is known about the relationship between the VEP and structural characteristics of visual cortex, such as surface area and thickness, in humans. Here we investigated whether morphometric measures of early visual cortex—estimated using a probabilistic surfacebased parcellation of V1 in FreeSurfer (http://surfer.nmr. mgh.harvard.edu/)—might predict the scalp-recorded pattern-reversal VEP amplitude in humans. We hypothesized that a larger surface area and increased thickness, likely reflecting a larger number of synapses, would predict larger VEP amplitude. We focussed our analyses on the P100 component of the VEP, due to its robustness and likely neural sources in early visual cortex, but also performed exploratory analyses for the N75 and N145 components. Scalp-recorded VEP amplitudes might potentially be influenced by other V1 anatomical properties than surface area and thickness, such as the amount of cortical folding and calcarine sulcus depth. Importantly, these factors might be conflated with the area measure, as surface area is positively correlated with cortical folding (Hogstrom et al. 2013). In addition, a negative relationship between cortical thickness and local gyrification has also been reported (Hogstrom et al. 2013). To address these issues, we first examined the associations between our primary morphometric measures (cortical thickness and surface area) and indices of cortical folding and calcarine sulcal depth provided by FreeSurfer. We then reran the main analyses with these indices included as additional predictors. Finally, any associations between V1 morphometry and VEP amplitude might result from regionally non-specific relationships between morphometric measures and EEG-amplitudes. Thus, to investigate regional specificity, we followed up any significant results with whole
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brain analyses. We hypothesized that the VEP amplitudes would be selectively associated with surface area and/or cortical thickness within early visual cortex.
Materials and methods Subjects Thirty-nine healthy volunteers (age range 20–50 years, mean = 31.1 years, 23 females) participated in a study of VEP plasticity as described previously (Elvsa˚shagen et al. 2012). In brief, all participants were screened for somatic disorders and psychiatric illnesses by a senior psychiatrist, based on the Mini-International Neuropsychiatric Interview, DSM-IV criteria version 5.0.0 (Sheehan et al. 1998). All subjects had normal or corrected-to-normal visual acuity. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Regional Ethical Committee of South-Eastern Norway. All subjects provided written informed consent. MRI acquisition Imaging was performed with a 3 T Philips Achieva Scanner (Philips Healthcare, Eindhoven, The Netherlands) equipped with an 8-channel SENSE head coil. The pulse sequence used for volumetric analyses was a T1-weighted, 3D turbo field echo sequence (repetition time 8.4 ms, echo time 2.3 ms, field of view 256 mm 9 256 mm 9 220 mm, 1 mm isotropic resolution, acquisition time 7 min 40 s). The sequence was run twice, and the two acquisitions were combined during processing to increase the signal-to-noiseratio (SNR). MRI analysis Automated cortical surface reconstructions of T1-weighted MR images were performed with FreeSurfer version 5.1 (http://surfer.nmr.mgh.harvard.edu/). Details regarding the surface-based analysis are provided elsewhere (Dale et al. 1999; Fischl and Dale 2000; Fischl et al. 1999, 2004). Briefly, processing steps included motion correction, the removal of non-brain tissue, automated Talairach transformation, and intensity correction. Intensity and continuity information from the 3D volume were used in segmentation and deformation procedures to reconstruct a grey/white matter boundary throughout the brain (Dale et al. 1999). Cortical surfaces then underwent inflation, registration to a spherical atlas, and automatic identification of gyral and sulcal regions (Desikan et al. 2006). Reconstructed data sets were visually inspected for accuracy, and segmentation errors were manually corrected. Surface area maps of grey
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matter–white matter boundaries were computed for each individual by calculating the area of every triangle in a cortical surface tessellation. The surface area at each vertex in native space was calculated as the average of the surrounding triangles. This value was then compared with the area of the analogous points in registered space to provide an estimate of local cortical surface areal expansion/contraction; this operation was performed continuously along the cortical surface (Joyner et al. 2009). For each individual, cortical thickness maps were obtained by calculating the distance between the grey and white matter surfaces at each vertex. Before statistical analysis, cortical thickness and area maps were smoothed with a full-width, half-maximum Gaussian kernel of 20 mm. In addition to our primary morphometric measures (surface area and thickness), we estimated total intracranial volume (eTIV) as well as the local gyrification index (lGI) and average convexity at each vertex as previously described (Buckner et al. 2004; Schaer et al. 2008; Fischl et al. 1999). lGI is an estimate of the amount of cortical folding, where a larger index reflects more extensive folding (Schaer et al. 2008). Average convexity reflects the depth or height of each vertex relative to a hypothetical ‘‘mid-surface’’ between gyri and sulci and can thus be used as an indicator of sulcal depth (Fischl et al. 1999). We computed the mean average convexity over the pericalcarine probabilistic mask and used this as an indicator of calcarine sulcus depth. VEP paradigm Checkerboards (2 reversals/s; check size = 0.5°) were presented to subjects with E-Prime 1.1 (Psychology Software Tools, Sharpsburg, PA) on an LCD screen in binocular vision. Participants were seated at a fixed distance from the screen ensuring that the width and height of the stimulated visual field were kept at 30° and 19°, respectively. Participants were instructed to focus on a filled red circle (0.1°) in the centre of the screen during the experiment and were allowed to listen to music. All participants were monitored to ensure that they followed instructions and maintained attention. Pattern-reversal VEPs were elicited with checkerboard stimulation in two short blocks (20 s; 40 reversals per short block) before and six short blocks after a long block (10 min; 1,200 reversals), as described previously (Elvsa˚shagen et al. 2012). The long block generated VEPs with high SNR that was used in the analyses. EEG recording and analysis Continuous EEG was recorded using a Synamps EEG system (Neuroscan, El Paso, TX) with 15 monopolar Ag/AgCl electrodes, according to the international 10–20
system (Fp1, Fp2, F7, F3, Fz, F4, FCz, Cz, P7, Pz, P8, O1, Oz, O2). Impedances were maintained below 5 kX. Ground and reference electrodes were attached to the forehead (reference at AFz). Eye movements were recorded with bipolar electrodes placed at the sub- and supraorbital regions and at the lateral canthi of each eye. The EEG was sampled at 250 Hz with an amplifier band-pass of 0.05–100 Hz. EEG analysis was conducted with EEGLAB (Delorme and Makeig 2004), run on MATLAB 7.6.0. (The MathWorks, Natick, MA). The EEG was first high-pass filtered at 1 Hz, and then subjected to independent component analysis to isolate blink and eye movement-related artefacts; next, it was segmented into epochs, starting at 150 ms before, and continuing for 350 ms after the onset of each checkerboard reversal. Epochs that contained blinks in the initial -150 to 100 ms interval were discarded; any remaining blink or eye movement-related activity in the rest of the epochs was removed by excluding the associated independent components from the data. After identification and removal of blink and eye movement-related artefacts, epochs were shortened (-50 to 350 ms) and baselinecorrected (-50 to 0 ms); epochs were rejected when they had amplitudes exceeding ± 100 lV on any of the occipital channels (O1, Oz, O2). Finally, the epoched EEG was low-pass filtered at 30 Hz and averaged. N75, P100, and N145 peak amplitudes were obtained from the Oz channel and measured relative to the 50 ms baseline. The N75 component was defined as the most negative amplitude between 50 and 110 ms, the P100 component as the most positive amplitude between 90 and 150 ms, and the N145 component as the most negative amplitude between 130 and 190 ms. All peak amplitudes were obtained from the Oz channel (which had the maximal amplitude for all components as shown in Fig. 1b) and measured relative to the 50 ms baseline. Statistical analysis All analyses were performed on a blinded basis. Cortical surface area and thickness estimates were obtained from bilateral V1 probabilistic masks provided in FreeSurfer (Hinds et al. 2008). Data are reported as mean ± SEM. To test the hypothesis that V1 structural indices might predict the P100 amplitude, multiple linear regression analyses were performed using forced entry methods. In the analyses, P100 amplitude was used as outcome variable and age, sex, eTIV, and bilateral V1 surface area or mean thickness were entered as predictors. Separate analyses were also run for left and right V1 surface area and thickness. To investigate the possible effects of inter-individual variations in the degree of V1 cortical folding and calcarine sulcus depth, we first examined the associations between
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Fig. 1 V1 surface area, but not thickness, predicts the P100 amplitude (n = 39). a Yellow colour indicates left and right probabilistic V1 masks (FreeSurfer). b The grand average VEP is shown with a black line; grey lines represent VEPs for individual participants. The topographical maps of scalp voltages show that N75, P100, and N145 had maximal amplitudes at the Oz electrode.
c, d Inter-individual variability in P100 amplitude is plotted as a function of total V1 surface area (c) and average V1 thickness (d), after regressing out effects of age, sex, and estimated total intracranial volume. Each circle represents measurements from an individual subject. Solid black lines indicate the linear regressions
these measures and our primary morphometric indices (thickness and surface area) using Pearson correlation analyses, and then reran our primary multiple regression analyses with V1 lGI and average convexity included as additional predictors. The distributions of all V1 structural measures and the VEP amplitudes did not deviate from the normal distribution, as confirmed by the Kolmogorov– Smirnov test. The statistical analyses were conducted using SPSS, version 18.0 for Windows. A two-tailed p \ 0.05 was considered statistically significant. For the whole brain analysis, we used general linear models (GLMs) to test the association between P100 amplitude and surface area in each vertex across the surface, covarying for age, sex, and eTIV. To reduce the probability of type I errors, all surface-based analyses were corrected for multiple comparisons with cluster size inference, based on Monte Carlo Z simulations, as implemented in FreeSurfer (Hagler et al. 2006; Hayasaka and Nichols 2003). Clusters were tested against an
empirical null distribution of maximum cluster size, constructed with synthesized Z distributed data across 10,000 permutations; this yields clusters that are fully corrected for multiple comparisons across the surface. The initial cluster-forming threshold employed in the present study was p \ 0.05.
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Results The mean left and right V1 surface area (Fig. 1a) was 2,324.2 ± 62.9 and 2,581.6 ± 66.8 mm2, respectively, whereas mean left and right V1 thickness was 1.87 ± 0.02 and 1.88 ± 0.02 mm. As expected, the checkerboard reversal paradigm elicited a VEP with an initial negative wave (N75), followed by a positive wave (P100), and a second negative wave (N145). These components had maximal amplitude at the Oz electrode as shown in the topographical maps of scalp voltages (Fig. 1b).
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V1 structure and P100 amplitude Multiple linear regression analysis, including age, sex, and eTIV as predictors, revealed a strong positive association between total (left ? right) V1 surface area and the P100 amplitude (n = 39; t = 3.37, p = 0.002; Fig. 1c). Furthermore, significant positive associations were observed between left and right V1 surface area and P100 amplitude (n = 39; left: t = 2.92, p = 0.006; right: t = 3.61, p = 0.001). As expected, total V1 area showed significant positive correlations with total V1 lGI (r = 0.475, p \ 0.005) and pericalcarine average convexity (r = 0.580, p \ 0.001). Importantly, however, the associations between total, left, and right V1 surface area and P100 amplitude remained significant after including V1 lGI and pericalcarine mean curvature as additional predictors in the analysis (all p values \0.05). No significant associations were found between average [(left ? right)/2], left, or right V1 cortical thickness and P100 amplitude (n = 39; average: t = - 0.08, p = 0.934; Fig. 1d; left: t = 0.25, p = 0.802; right: t = - 0.35, p = 0.731). V1 cortical thickness showed a significant negative correlation with pericalcarine average convexity (r = -0.409, p \ 0.05), but no correlation with V1 lGI (r = -0.139, p = 0.400). Including V1 lGI and pericalcarine average convexity as additional predictors in the main analysis did not markedly change the results (effects of thickness: all p values [0.52). Together, these findings indicate that probabilistically defined V1 surface area, but not thickness, can predict the scalp-recorded P100 amplitude.
Fig. 2 Whole brain analysis of the association between P100 amplitude and local surface areal expansion/contraction (n = 39). The analysis shows a highly selective, positive association (redyellow colour indicates p \ 0.05) between the P100 amplitude and local surface areal expansion/contraction in regions within the bilateral probabilistic V1 mask, while covarying for age, sex, and estimated total intracranial volume. Results were fully corrected for multiple comparisons across the cortex with cluster size correction and Monte Carlo Z simulations. Yellow lines represent the borders of left and right V1 masks as shown in Fig. 1a
No significant associations were found between N75 amplitude and V1 surface area (n = 39; all p values [0.923) or cortical thickness (n = 39; all p values[0.799). Likewise, no significant associations were found between N145 amplitude and V1 surface area (n = 39; all p values [0.309) or cortical thickness (n = 39; all p values [0.210). Thus, the structure–function relationship observed in the current study appears to be selective for the P100 component of the VEP.
Whole brain analysis Potentially, the association between V1 surface area and P100 amplitude might result from non-specific relationships between cortical surface area and EEG-amplitudes. To investigate regional specificity, we performed a whole brain analysis of local surface areal expansion/contraction, which represents an estimate of relative surface area at each vertex across the cerebral cortex (Winkler et al. 2012), while covarying for age, sex and eTIV. This analysis revealed that P100 amplitude was significantly and selectively associated with local surface areal expansion/contraction in regions within the bilateral V1 masks (n = 39; p \ 0.05, fully corrected for multiple comparisons across the cortex; Fig. 2). V1 structure and N75 and N145 amplitudes We also performed exploratory analyses investigating the relationships between V1 structure and N75 and N145 amplitudes, with age, sex, and eTIV included as predictors.
The association between the VEP and V1 surface area is not related to plasticity effects The long block (10 min; 1,200 reversals) was run in accordance with the International Society for Clinical Electrophysiology of Vision recommendations for standard pattern-reversal VEP testing (Odom et al. 2010) and elicited a VEP with a high SNR. However, because the long block induced plasticity of the VEP in a previous study (Elvsa˚shagen et al. 2012), we performed additional multiple linear regression analyses, with age, sex, and eTIV included as predictors, to determine whether the associations between V1 surface area and the P100 amplitude were related to plasticity effects. First, we found no significant association between total V1 surface area and P100 plasticity induced by the long block (n = 39; t = 0.77, p = 0.446). Second, the association between the total V1 surface area and the P100 amplitude remained significant after correcting for plasticity effects (n = 39; t = 3.20, p = 0.003). Third, a significant association was found
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between the total V1 surface area and the P100 amplitude of the two initial short blocks (which were presented before the long block; n = 39; t = 2.31, p = 0.027). Finally, significant associations were found between total V1 surface area and the averaged P100 of the first 200 reversals (n = 39; t = 3.10, p = 0.004) and the last 200 reversals (n = 39; t = 3.08, p = 0.004) of the long block. Thus, it is highly unlikely that the associations between V1 surface area and P100 amplitude were related to VEP plasticity induced by the long block.
Discussion The current findings show that MRI-estimated V1 surface area, but not thickness, can predict the P100 amplitude of the pattern-reversal VEP in humans. In addition, the whole brain analysis indicated that the association between the P100 amplitude and cortical surface area is highly specific for the primary visual cortices. To the best of our knowledge, this is the first study of the relationship between MRI-based morphometry of early visual cortex and the VEP, an index of human visual system function widely used in clinical and research settings. Possible mechanisms underlying the relationship between V1 surface area and VEP amplitude The precise mechanisms by which the surface area of early visual cortex modulates VEP amplitude remain to be clarified. One possibility is that a larger surface area reflects an increased number or width of cortical columns and, thus, a larger pool of synapses that could generate a stronger scalp-recorded VEP. A related hypothesis was recently proposed by Schwarzkopf et al. (2012): Since the external visual field is retinotopically represented across the entire V1, a given distance across the cortical mantle would represent a smaller proportion of visual space on a large V1 than on a small V1 surface. Thus, any given cortical patch should be more homogenous, and therefore, neuronal activity would be more efficiently synchronized within larger versus smaller V1 surfaces. Because scalprecorded electrophysiological indices reflect synchronized activity of neurons, an increase in local synchrony could potentially explain the observed association between V1 surface area and P100 amplitude. Alternatively, the observed association between P100 amplitude and early visual cortex surface area could be related to individual differences in the degree of cortical folding and calcarine sulcus depth. This could give rise to different orientations and distances of P100 generators relative to the recording electrode. However, we found that the relationships between P100 amplitude and V1 surface
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area remained significant after controlling for V1 lGI, i.e., an estimate of the amount of cortical folding, and pericalcarine average convexity, i.e., an indicator of calcarine sulcus depth. Thus, while we cannot exclude the possibility entirely, we regard it as unlikely that individual differences in cortical folding patterns provide a parsimonious explanation of the present results. However, studies employing dense electrode arrays and source modelling based on individual MRIs would be needed to explicitly test this hypothesis, as well as investigate the impact of other factors affecting the conductance of electromagnetic fields from their neural source(s) to scalp electrodes (e.g., skull thickness, cerebrospinal fluid, and meninges). While a detailed mechanistic explanation awaits further studies, the strong and selective association between VEP amplitude and early visual cortical structure found in the present study nonetheless suggests highly specific functional–anatomical links between electrophysiological and MRI-based measures of early sensory cortices. In line with this conjecture, Liem et al. (2012) recently reported a significant negative correlation between the auditory N1 amplitude and thickness of the auditory cortex, including the superior temporal plane. Anatomical versus functional (retinotopic) delineation of V1 The current study used a probabilistic anatomical definition of V1, while several previous studies investigating relationships between V1 surface area and behavioural measures of visual processing have delineated V1 with retinotopic mapping using functional MRI (Slotnick and Yantis 2003). Both these approaches constitute established and validated methods (Hinds et al. 2008; Schwarzkopf and Rees 2013), with their respective advantages and disadvantages. Retinotopic definitions are relatively unaffected by potential differences in the size of the visual field between individuals, because they aim to map the same portion of the visual field in each subject. Further, retinotopic mapping techniques allow a more precise delineation of V1 from the neighbouring retinotopic visual area V2, whereas probabilistic anatomical masks of V1 likely also include parts of V2. On the other hand, retinotopic mapping conflates cortical magnification with the anatomical extent of V1 (Schwarzkopf and Rees 2013). Notably, a reported association between retinotopically defined V1 surface area and the strength of optical illusions (Schwarzkopf et al. 2011), was later found to be related to central visual cortical magnification, and not to the anatomically defined V1 area (Schwarzkopf and Rees 2013). Similarly, a reported association between the peak frequency of visually induced gamma oscillations and retinotopically mapped V1 surface area (Schwarzkopf et al.
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2012), could not be replicated when V1 was defined based on anatomical features (Perry et al. 2013). The main difference between retinotopic and anatomical V1 delineations concerns anterior parts of V1: these regions represent the peripheral visual field and are often not stimulated when retinotopic mapping techniques are used (Schwarzkopf and Rees 2013; Wu et al. 2012, 2013). Notably, we observed associations between surface area and P100 amplitude also in anterior parts of V1, i.e., in areas that could easily have been missed using retinotopic mapping. This finding was unexpected, given that peripheral parts of the visual field are unlikely to have been fully stimulated by the current VEP paradigm, and needs to be confirmed in future studies. Nonetheless, we offer the following post hoc speculation. Prolonged pattern-reversal stimulation tends to induce experiences of apparent motion, and has been associated with activation of the motionsensitive area MT? in addition to several other early visual areas (e.g., V1) (Di Russo et al. 2005, 2012). Notably, the feedback connection from MT? to V1 shows an interesting retinotopic asymmetry, as projections to layer 1 are restricted to areas of V1 receiving input from more peripheral parts of the visual field (Shipp and Zeki 1989). Thus, one might speculate that the associations between P100 amplitude and the surface area of regions in peripheral V1 could to some extent be related to the anatomical connectivity between MT? and V1. We restricted our initial analyses to the probabilistic V1 mask, since other early visual functional areas are not as tightly linked to local morphological features (Benson et al. 2012). However, this fact might also have biased the whole brain analysis, since inter-individual overlap was probably better for V1 than for areas such as V2, V3, V4, and MT?. Thus, while the whole brain analysis indicated a selective relationship between P100 amplitude and probabilistically defined V1, one should be careful in attributing this effect to V1 only. Clearly, future studies using retinotopic mapping are needed to explore possible associations between VEP and additional retinotopic areas, including V2, V3, V4, and MT?, which, unlike V1, cannot easily be defined based on morphological features alone.
Conclusions In conclusion, we found that the surface area of early visual cortex was positively and selectively associated with the amplitude of the pattern-reversal VEP in humans. These findings link MRI-based cortical morphometry to electrophysiological properties of the cerebral cortex and specifically point to the importance of surface area measures. More generally, these results provide further empirical support for the longstanding assumption of an intimate
relationship between structure and function in the human brain. Acknowledgments This study was funded by the Research Council of Norway (167153/V50, 204966/F20), the South-Eastern Norway Regional Health Authority, and Oslo University Hospital— Rikshospitalet. Conflict of interest of interest.
The authors declare that they have no conflict
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SHORT COMMUNICATION
The surface area of early visual cortex predicts the amplitude of the visual evoked potential Torbjørn Elvsa˚shagen • Torgeir Moberget • Erlend Bøen • Per K. Hol • Ulrik F. Malt • Stein Andersson • Lars T. Westlye
Received: 28 June 2013 / Accepted: 30 December 2013 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract The extensive and increasing use of structural neuroimaging in the neurosciences rests on the assumption of an intimate relationship between structure and function in the human brain. However, few studies have examined the relationship between advanced magnetic resonance imaging (MRI) indices of cerebral structure and conventional measures of cerebral functioning in humans. Here we examined whether MRI-based morphometric measures of early visual cortex—estimated using a probabilistic anatomical mask of primary visual cortex (V1)—can predict the amplitude of the visual evoked potential (VEP), i.e., an electroencephalogram signal that primarily reflects postsynaptic potentials in early visual cortical
areas. We found that left, right, and total V1 surface area positively predicted the VEP amplitude. In addition, we showed, using whole brain analysis of local surface areal expansion/contraction, that the association between VEP amplitude and surface area was highly specific for regions within bilateral V1. Together, these findings indicate a strong, selective relationship between MRI-based structural measures and functional properties of the human cerebral cortex.
T. Elvsa˚shagen and T. Moberget contributed equally to this work.
Introduction
T. Elvsa˚shagen (&) T. Moberget E. Bøen U. F. Malt S. Andersson Department of Psychosomatic Medicine, Oslo University Hospital, Rikshospitalet, Nydalen, Pb 4950, 0424 Oslo, Norway e-mail: [email protected] T. Elvsa˚shagen E. Bøen U. F. Malt Institute of Clinical Medicine, University of Oslo, Oslo, Norway T. Moberget S. Andersson L. T. Westlye Department of Psychology, University of Oslo, Oslo, Norway P. K. Hol The Intervention Centre, Oslo University Hospital, Oslo, Norway L. T. Westlye Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway L. T. Westlye KG Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo, Norway
Keywords Magnetic resonance imaging Visual evoked potential Visual cortex Cortical thickness Cortical surface area
The high sensitivity of magnetic resonance imaging (MRI)based brain morphometry has provided novel insights into fundamental aspects of cerebral development, neuroplasticity, and the pathophysiology of mental illnesses (Draganski et al. 2004; Hill et al. 2010; Rimol et al. 2010). However, how and to what extent brain structural properties mirror brain function remain to be clarified; this has limited our ability to interpret structural MRI findings and determine implications. In recent years, the relationships between MRI-based indices of structural and functional brain connectivity have been examined in several studies. These have shown that structural connectivity and restingstate and task-based functional connectivity are strongly interrelated in the human brain (Greicius et al. 2009; van den Heuvel et al. 2009; Cohen et al. 2008; Koch et al. 2002; Romero-Garcia et al. 2013; Honey et al. 2009). Few studies have, however, examined whether MRI-based morphometric measures, e.g., cortical surface area and
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thickness, can predict conventional measures of cerebral functioning, such as the electroencephalogram (EEG). The visual evoked potential (VEP) is an EEG signal specific for visual stimuli and primarily reflects postsynaptic potentials in the visual cortex (Luck 2005). VEP is widely used in studies of human visual system physiology and pathophysiology and is, when elicited by a reversing pattern, characterized by three major components: the N75, P100, and N145 (Di Russo et al. 2005; Tobimatsu and Celesia 2006). N75 is a small, initial negative wave that shows substantial variability within and across individuals; P100 is a prominent and reliable positive wave with minimal intraindividual variation; N145 is a large, second negative wave with several subcomponents (Luck 2005; Odom et al. 2010). Although the neural sources of the patternreversal VEP remain to be fully clarified, previous studies suggest that N75 mainly reflects postsynaptic activity in V1, whereas P100 and N145 are likely generated in both striate and extrastriate cortex (Di Russo et al. 2005; Tobimatsu and Celesia 2006; Whittingstall et al. 2007; Novitskiy et al. 2011; Fuglø et al. 2012). However, despite long and widespread use of the VEP, little is known about the relationship between the VEP and structural characteristics of visual cortex, such as surface area and thickness, in humans. Here we investigated whether morphometric measures of early visual cortex—estimated using a probabilistic surfacebased parcellation of V1 in FreeSurfer (http://surfer.nmr. mgh.harvard.edu/)—might predict the scalp-recorded pattern-reversal VEP amplitude in humans. We hypothesized that a larger surface area and increased thickness, likely reflecting a larger number of synapses, would predict larger VEP amplitude. We focussed our analyses on the P100 component of the VEP, due to its robustness and likely neural sources in early visual cortex, but also performed exploratory analyses for the N75 and N145 components. Scalp-recorded VEP amplitudes might potentially be influenced by other V1 anatomical properties than surface area and thickness, such as the amount of cortical folding and calcarine sulcus depth. Importantly, these factors might be conflated with the area measure, as surface area is positively correlated with cortical folding (Hogstrom et al. 2013). In addition, a negative relationship between cortical thickness and local gyrification has also been reported (Hogstrom et al. 2013). To address these issues, we first examined the associations between our primary morphometric measures (cortical thickness and surface area) and indices of cortical folding and calcarine sulcal depth provided by FreeSurfer. We then reran the main analyses with these indices included as additional predictors. Finally, any associations between V1 morphometry and VEP amplitude might result from regionally non-specific relationships between morphometric measures and EEG-amplitudes. Thus, to investigate regional specificity, we followed up any significant results with whole
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brain analyses. We hypothesized that the VEP amplitudes would be selectively associated with surface area and/or cortical thickness within early visual cortex.
Materials and methods Subjects Thirty-nine healthy volunteers (age range 20–50 years, mean = 31.1 years, 23 females) participated in a study of VEP plasticity as described previously (Elvsa˚shagen et al. 2012). In brief, all participants were screened for somatic disorders and psychiatric illnesses by a senior psychiatrist, based on the Mini-International Neuropsychiatric Interview, DSM-IV criteria version 5.0.0 (Sheehan et al. 1998). All subjects had normal or corrected-to-normal visual acuity. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Regional Ethical Committee of South-Eastern Norway. All subjects provided written informed consent. MRI acquisition Imaging was performed with a 3 T Philips Achieva Scanner (Philips Healthcare, Eindhoven, The Netherlands) equipped with an 8-channel SENSE head coil. The pulse sequence used for volumetric analyses was a T1-weighted, 3D turbo field echo sequence (repetition time 8.4 ms, echo time 2.3 ms, field of view 256 mm 9 256 mm 9 220 mm, 1 mm isotropic resolution, acquisition time 7 min 40 s). The sequence was run twice, and the two acquisitions were combined during processing to increase the signal-to-noiseratio (SNR). MRI analysis Automated cortical surface reconstructions of T1-weighted MR images were performed with FreeSurfer version 5.1 (http://surfer.nmr.mgh.harvard.edu/). Details regarding the surface-based analysis are provided elsewhere (Dale et al. 1999; Fischl and Dale 2000; Fischl et al. 1999, 2004). Briefly, processing steps included motion correction, the removal of non-brain tissue, automated Talairach transformation, and intensity correction. Intensity and continuity information from the 3D volume were used in segmentation and deformation procedures to reconstruct a grey/white matter boundary throughout the brain (Dale et al. 1999). Cortical surfaces then underwent inflation, registration to a spherical atlas, and automatic identification of gyral and sulcal regions (Desikan et al. 2006). Reconstructed data sets were visually inspected for accuracy, and segmentation errors were manually corrected. Surface area maps of grey
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matter–white matter boundaries were computed for each individual by calculating the area of every triangle in a cortical surface tessellation. The surface area at each vertex in native space was calculated as the average of the surrounding triangles. This value was then compared with the area of the analogous points in registered space to provide an estimate of local cortical surface areal expansion/contraction; this operation was performed continuously along the cortical surface (Joyner et al. 2009). For each individual, cortical thickness maps were obtained by calculating the distance between the grey and white matter surfaces at each vertex. Before statistical analysis, cortical thickness and area maps were smoothed with a full-width, half-maximum Gaussian kernel of 20 mm. In addition to our primary morphometric measures (surface area and thickness), we estimated total intracranial volume (eTIV) as well as the local gyrification index (lGI) and average convexity at each vertex as previously described (Buckner et al. 2004; Schaer et al. 2008; Fischl et al. 1999). lGI is an estimate of the amount of cortical folding, where a larger index reflects more extensive folding (Schaer et al. 2008). Average convexity reflects the depth or height of each vertex relative to a hypothetical ‘‘mid-surface’’ between gyri and sulci and can thus be used as an indicator of sulcal depth (Fischl et al. 1999). We computed the mean average convexity over the pericalcarine probabilistic mask and used this as an indicator of calcarine sulcus depth. VEP paradigm Checkerboards (2 reversals/s; check size = 0.5°) were presented to subjects with E-Prime 1.1 (Psychology Software Tools, Sharpsburg, PA) on an LCD screen in binocular vision. Participants were seated at a fixed distance from the screen ensuring that the width and height of the stimulated visual field were kept at 30° and 19°, respectively. Participants were instructed to focus on a filled red circle (0.1°) in the centre of the screen during the experiment and were allowed to listen to music. All participants were monitored to ensure that they followed instructions and maintained attention. Pattern-reversal VEPs were elicited with checkerboard stimulation in two short blocks (20 s; 40 reversals per short block) before and six short blocks after a long block (10 min; 1,200 reversals), as described previously (Elvsa˚shagen et al. 2012). The long block generated VEPs with high SNR that was used in the analyses. EEG recording and analysis Continuous EEG was recorded using a Synamps EEG system (Neuroscan, El Paso, TX) with 15 monopolar Ag/AgCl electrodes, according to the international 10–20
system (Fp1, Fp2, F7, F3, Fz, F4, FCz, Cz, P7, Pz, P8, O1, Oz, O2). Impedances were maintained below 5 kX. Ground and reference electrodes were attached to the forehead (reference at AFz). Eye movements were recorded with bipolar electrodes placed at the sub- and supraorbital regions and at the lateral canthi of each eye. The EEG was sampled at 250 Hz with an amplifier band-pass of 0.05–100 Hz. EEG analysis was conducted with EEGLAB (Delorme and Makeig 2004), run on MATLAB 7.6.0. (The MathWorks, Natick, MA). The EEG was first high-pass filtered at 1 Hz, and then subjected to independent component analysis to isolate blink and eye movement-related artefacts; next, it was segmented into epochs, starting at 150 ms before, and continuing for 350 ms after the onset of each checkerboard reversal. Epochs that contained blinks in the initial -150 to 100 ms interval were discarded; any remaining blink or eye movement-related activity in the rest of the epochs was removed by excluding the associated independent components from the data. After identification and removal of blink and eye movement-related artefacts, epochs were shortened (-50 to 350 ms) and baselinecorrected (-50 to 0 ms); epochs were rejected when they had amplitudes exceeding ± 100 lV on any of the occipital channels (O1, Oz, O2). Finally, the epoched EEG was low-pass filtered at 30 Hz and averaged. N75, P100, and N145 peak amplitudes were obtained from the Oz channel and measured relative to the 50 ms baseline. The N75 component was defined as the most negative amplitude between 50 and 110 ms, the P100 component as the most positive amplitude between 90 and 150 ms, and the N145 component as the most negative amplitude between 130 and 190 ms. All peak amplitudes were obtained from the Oz channel (which had the maximal amplitude for all components as shown in Fig. 1b) and measured relative to the 50 ms baseline. Statistical analysis All analyses were performed on a blinded basis. Cortical surface area and thickness estimates were obtained from bilateral V1 probabilistic masks provided in FreeSurfer (Hinds et al. 2008). Data are reported as mean ± SEM. To test the hypothesis that V1 structural indices might predict the P100 amplitude, multiple linear regression analyses were performed using forced entry methods. In the analyses, P100 amplitude was used as outcome variable and age, sex, eTIV, and bilateral V1 surface area or mean thickness were entered as predictors. Separate analyses were also run for left and right V1 surface area and thickness. To investigate the possible effects of inter-individual variations in the degree of V1 cortical folding and calcarine sulcus depth, we first examined the associations between
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Fig. 1 V1 surface area, but not thickness, predicts the P100 amplitude (n = 39). a Yellow colour indicates left and right probabilistic V1 masks (FreeSurfer). b The grand average VEP is shown with a black line; grey lines represent VEPs for individual participants. The topographical maps of scalp voltages show that N75, P100, and N145 had maximal amplitudes at the Oz electrode.
c, d Inter-individual variability in P100 amplitude is plotted as a function of total V1 surface area (c) and average V1 thickness (d), after regressing out effects of age, sex, and estimated total intracranial volume. Each circle represents measurements from an individual subject. Solid black lines indicate the linear regressions
these measures and our primary morphometric indices (thickness and surface area) using Pearson correlation analyses, and then reran our primary multiple regression analyses with V1 lGI and average convexity included as additional predictors. The distributions of all V1 structural measures and the VEP amplitudes did not deviate from the normal distribution, as confirmed by the Kolmogorov– Smirnov test. The statistical analyses were conducted using SPSS, version 18.0 for Windows. A two-tailed p \ 0.05 was considered statistically significant. For the whole brain analysis, we used general linear models (GLMs) to test the association between P100 amplitude and surface area in each vertex across the surface, covarying for age, sex, and eTIV. To reduce the probability of type I errors, all surface-based analyses were corrected for multiple comparisons with cluster size inference, based on Monte Carlo Z simulations, as implemented in FreeSurfer (Hagler et al. 2006; Hayasaka and Nichols 2003). Clusters were tested against an
empirical null distribution of maximum cluster size, constructed with synthesized Z distributed data across 10,000 permutations; this yields clusters that are fully corrected for multiple comparisons across the surface. The initial cluster-forming threshold employed in the present study was p \ 0.05.
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Results The mean left and right V1 surface area (Fig. 1a) was 2,324.2 ± 62.9 and 2,581.6 ± 66.8 mm2, respectively, whereas mean left and right V1 thickness was 1.87 ± 0.02 and 1.88 ± 0.02 mm. As expected, the checkerboard reversal paradigm elicited a VEP with an initial negative wave (N75), followed by a positive wave (P100), and a second negative wave (N145). These components had maximal amplitude at the Oz electrode as shown in the topographical maps of scalp voltages (Fig. 1b).
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V1 structure and P100 amplitude Multiple linear regression analysis, including age, sex, and eTIV as predictors, revealed a strong positive association between total (left ? right) V1 surface area and the P100 amplitude (n = 39; t = 3.37, p = 0.002; Fig. 1c). Furthermore, significant positive associations were observed between left and right V1 surface area and P100 amplitude (n = 39; left: t = 2.92, p = 0.006; right: t = 3.61, p = 0.001). As expected, total V1 area showed significant positive correlations with total V1 lGI (r = 0.475, p \ 0.005) and pericalcarine average convexity (r = 0.580, p \ 0.001). Importantly, however, the associations between total, left, and right V1 surface area and P100 amplitude remained significant after including V1 lGI and pericalcarine mean curvature as additional predictors in the analysis (all p values \0.05). No significant associations were found between average [(left ? right)/2], left, or right V1 cortical thickness and P100 amplitude (n = 39; average: t = - 0.08, p = 0.934; Fig. 1d; left: t = 0.25, p = 0.802; right: t = - 0.35, p = 0.731). V1 cortical thickness showed a significant negative correlation with pericalcarine average convexity (r = -0.409, p \ 0.05), but no correlation with V1 lGI (r = -0.139, p = 0.400). Including V1 lGI and pericalcarine average convexity as additional predictors in the main analysis did not markedly change the results (effects of thickness: all p values [0.52). Together, these findings indicate that probabilistically defined V1 surface area, but not thickness, can predict the scalp-recorded P100 amplitude.
Fig. 2 Whole brain analysis of the association between P100 amplitude and local surface areal expansion/contraction (n = 39). The analysis shows a highly selective, positive association (redyellow colour indicates p \ 0.05) between the P100 amplitude and local surface areal expansion/contraction in regions within the bilateral probabilistic V1 mask, while covarying for age, sex, and estimated total intracranial volume. Results were fully corrected for multiple comparisons across the cortex with cluster size correction and Monte Carlo Z simulations. Yellow lines represent the borders of left and right V1 masks as shown in Fig. 1a
No significant associations were found between N75 amplitude and V1 surface area (n = 39; all p values [0.923) or cortical thickness (n = 39; all p values[0.799). Likewise, no significant associations were found between N145 amplitude and V1 surface area (n = 39; all p values [0.309) or cortical thickness (n = 39; all p values [0.210). Thus, the structure–function relationship observed in the current study appears to be selective for the P100 component of the VEP.
Whole brain analysis Potentially, the association between V1 surface area and P100 amplitude might result from non-specific relationships between cortical surface area and EEG-amplitudes. To investigate regional specificity, we performed a whole brain analysis of local surface areal expansion/contraction, which represents an estimate of relative surface area at each vertex across the cerebral cortex (Winkler et al. 2012), while covarying for age, sex and eTIV. This analysis revealed that P100 amplitude was significantly and selectively associated with local surface areal expansion/contraction in regions within the bilateral V1 masks (n = 39; p \ 0.05, fully corrected for multiple comparisons across the cortex; Fig. 2). V1 structure and N75 and N145 amplitudes We also performed exploratory analyses investigating the relationships between V1 structure and N75 and N145 amplitudes, with age, sex, and eTIV included as predictors.
The association between the VEP and V1 surface area is not related to plasticity effects The long block (10 min; 1,200 reversals) was run in accordance with the International Society for Clinical Electrophysiology of Vision recommendations for standard pattern-reversal VEP testing (Odom et al. 2010) and elicited a VEP with a high SNR. However, because the long block induced plasticity of the VEP in a previous study (Elvsa˚shagen et al. 2012), we performed additional multiple linear regression analyses, with age, sex, and eTIV included as predictors, to determine whether the associations between V1 surface area and the P100 amplitude were related to plasticity effects. First, we found no significant association between total V1 surface area and P100 plasticity induced by the long block (n = 39; t = 0.77, p = 0.446). Second, the association between the total V1 surface area and the P100 amplitude remained significant after correcting for plasticity effects (n = 39; t = 3.20, p = 0.003). Third, a significant association was found
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between the total V1 surface area and the P100 amplitude of the two initial short blocks (which were presented before the long block; n = 39; t = 2.31, p = 0.027). Finally, significant associations were found between total V1 surface area and the averaged P100 of the first 200 reversals (n = 39; t = 3.10, p = 0.004) and the last 200 reversals (n = 39; t = 3.08, p = 0.004) of the long block. Thus, it is highly unlikely that the associations between V1 surface area and P100 amplitude were related to VEP plasticity induced by the long block.
Discussion The current findings show that MRI-estimated V1 surface area, but not thickness, can predict the P100 amplitude of the pattern-reversal VEP in humans. In addition, the whole brain analysis indicated that the association between the P100 amplitude and cortical surface area is highly specific for the primary visual cortices. To the best of our knowledge, this is the first study of the relationship between MRI-based morphometry of early visual cortex and the VEP, an index of human visual system function widely used in clinical and research settings. Possible mechanisms underlying the relationship between V1 surface area and VEP amplitude The precise mechanisms by which the surface area of early visual cortex modulates VEP amplitude remain to be clarified. One possibility is that a larger surface area reflects an increased number or width of cortical columns and, thus, a larger pool of synapses that could generate a stronger scalp-recorded VEP. A related hypothesis was recently proposed by Schwarzkopf et al. (2012): Since the external visual field is retinotopically represented across the entire V1, a given distance across the cortical mantle would represent a smaller proportion of visual space on a large V1 than on a small V1 surface. Thus, any given cortical patch should be more homogenous, and therefore, neuronal activity would be more efficiently synchronized within larger versus smaller V1 surfaces. Because scalprecorded electrophysiological indices reflect synchronized activity of neurons, an increase in local synchrony could potentially explain the observed association between V1 surface area and P100 amplitude. Alternatively, the observed association between P100 amplitude and early visual cortex surface area could be related to individual differences in the degree of cortical folding and calcarine sulcus depth. This could give rise to different orientations and distances of P100 generators relative to the recording electrode. However, we found that the relationships between P100 amplitude and V1 surface
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area remained significant after controlling for V1 lGI, i.e., an estimate of the amount of cortical folding, and pericalcarine average convexity, i.e., an indicator of calcarine sulcus depth. Thus, while we cannot exclude the possibility entirely, we regard it as unlikely that individual differences in cortical folding patterns provide a parsimonious explanation of the present results. However, studies employing dense electrode arrays and source modelling based on individual MRIs would be needed to explicitly test this hypothesis, as well as investigate the impact of other factors affecting the conductance of electromagnetic fields from their neural source(s) to scalp electrodes (e.g., skull thickness, cerebrospinal fluid, and meninges). While a detailed mechanistic explanation awaits further studies, the strong and selective association between VEP amplitude and early visual cortical structure found in the present study nonetheless suggests highly specific functional–anatomical links between electrophysiological and MRI-based measures of early sensory cortices. In line with this conjecture, Liem et al. (2012) recently reported a significant negative correlation between the auditory N1 amplitude and thickness of the auditory cortex, including the superior temporal plane. Anatomical versus functional (retinotopic) delineation of V1 The current study used a probabilistic anatomical definition of V1, while several previous studies investigating relationships between V1 surface area and behavioural measures of visual processing have delineated V1 with retinotopic mapping using functional MRI (Slotnick and Yantis 2003). Both these approaches constitute established and validated methods (Hinds et al. 2008; Schwarzkopf and Rees 2013), with their respective advantages and disadvantages. Retinotopic definitions are relatively unaffected by potential differences in the size of the visual field between individuals, because they aim to map the same portion of the visual field in each subject. Further, retinotopic mapping techniques allow a more precise delineation of V1 from the neighbouring retinotopic visual area V2, whereas probabilistic anatomical masks of V1 likely also include parts of V2. On the other hand, retinotopic mapping conflates cortical magnification with the anatomical extent of V1 (Schwarzkopf and Rees 2013). Notably, a reported association between retinotopically defined V1 surface area and the strength of optical illusions (Schwarzkopf et al. 2011), was later found to be related to central visual cortical magnification, and not to the anatomically defined V1 area (Schwarzkopf and Rees 2013). Similarly, a reported association between the peak frequency of visually induced gamma oscillations and retinotopically mapped V1 surface area (Schwarzkopf et al.
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2012), could not be replicated when V1 was defined based on anatomical features (Perry et al. 2013). The main difference between retinotopic and anatomical V1 delineations concerns anterior parts of V1: these regions represent the peripheral visual field and are often not stimulated when retinotopic mapping techniques are used (Schwarzkopf and Rees 2013; Wu et al. 2012, 2013). Notably, we observed associations between surface area and P100 amplitude also in anterior parts of V1, i.e., in areas that could easily have been missed using retinotopic mapping. This finding was unexpected, given that peripheral parts of the visual field are unlikely to have been fully stimulated by the current VEP paradigm, and needs to be confirmed in future studies. Nonetheless, we offer the following post hoc speculation. Prolonged pattern-reversal stimulation tends to induce experiences of apparent motion, and has been associated with activation of the motionsensitive area MT? in addition to several other early visual areas (e.g., V1) (Di Russo et al. 2005, 2012). Notably, the feedback connection from MT? to V1 shows an interesting retinotopic asymmetry, as projections to layer 1 are restricted to areas of V1 receiving input from more peripheral parts of the visual field (Shipp and Zeki 1989). Thus, one might speculate that the associations between P100 amplitude and the surface area of regions in peripheral V1 could to some extent be related to the anatomical connectivity between MT? and V1. We restricted our initial analyses to the probabilistic V1 mask, since other early visual functional areas are not as tightly linked to local morphological features (Benson et al. 2012). However, this fact might also have biased the whole brain analysis, since inter-individual overlap was probably better for V1 than for areas such as V2, V3, V4, and MT?. Thus, while the whole brain analysis indicated a selective relationship between P100 amplitude and probabilistically defined V1, one should be careful in attributing this effect to V1 only. Clearly, future studies using retinotopic mapping are needed to explore possible associations between VEP and additional retinotopic areas, including V2, V3, V4, and MT?, which, unlike V1, cannot easily be defined based on morphological features alone.
Conclusions In conclusion, we found that the surface area of early visual cortex was positively and selectively associated with the amplitude of the pattern-reversal VEP in humans. These findings link MRI-based cortical morphometry to electrophysiological properties of the cerebral cortex and specifically point to the importance of surface area measures. More generally, these results provide further empirical support for the longstanding assumption of an intimate
relationship between structure and function in the human brain. Acknowledgments This study was funded by the Research Council of Norway (167153/V50, 204966/F20), the South-Eastern Norway Regional Health Authority, and Oslo University Hospital— Rikshospitalet. Conflict of interest of interest.
The authors declare that they have no conflict
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