Imaging function in the working brain with fmri Ravi S Menon

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1 630 Imaging function in the working brain with fmri Ravi S Menon The intrinsic flexibility of functional magnetic resonance imaging has allowed ever more innovative neuroscience applications. New acquisition and analysis techniques have contributed to improvements in detection sensitivity, as well as spatial and temporal resolution. Furthermore, by considering the dynamic evolution of the active brain areas in a network, computational models are making the first steps towards linking brain and mind. Addresses Laboratory for Functional Magnetic Resonance Research, The John P Robarts Research Institute, PO Box 5015, 100 Perth Drive, London, Ontario N6A 2B3, Canada; rmenon@irus.rrr.on.ca Current Opinion in Neurobiology 2001, 11: /01/$ see front matter 2001 Elsevier Science Ltd. All rights reserved. Abbreviations BOLD blood oxygenation level dependence CBF cerebral blood flow CBV cerebral blood volume EEG electroencephalogram fmri functional MRI MEG magnetoencephalography MRI magnetic resonance imaging NIRS near-infrared spectroscopy OIS optical imaging of intrinsic signals PET positron emission tomography Introduction Functional magnetic resonance imaging (fmri) has become the dominant technique for the study of the functional organization of the human brain during cognitive, perceptual, sensory and motor tasks, and is beginning to make inroads into clinical applications. The fmri technique relies on detecting small neural activity-induced changes in the MRI signal intensity brought about by a change in the local concentration of deoxyhemoglobin. The mechanism by which deoxyhemoglobin, found in red blood cells, effects the MRI signal intensity has been termed the blood oxygenation level dependence (BOLD) effect [1]. Ogawa et al. [1] were the first to describle BOLD in a rat model, and later showed that BOLD can be used to image human brain function by using the difference in BOLD contrast between two cognitive states. When reading any of the thousands of fmri papers in the literature, it is worth remembering that the fmri signal is a multi-phasic hemodynamic response to neural activity (see Figure 1) whose linearity, spatial properties and temporal behaviour are still not firmly established [2]. In its first applications, the fmri technique was used to map where in the brain activity occurred during a task, essentially performing mental cartography. To go beyond simple mapping, however, it is imperative to understand the following: how the fmri signal varies in space and time; how to extract biologically relevant information from these variations; and, perhaps most importantly, how the fmri activity relates to neural activity. I review here some of the recent evidence that suggests that the fmri signal is correlated with neural activity; how facets of the fmri response can be exploited for submillimeter spatial mapping and subsecond temporal mapping; and how various computational approaches to detecting and modeling the spatio-temporal fmri response are yielding insights into how the brain works. Does BOLD reflect neural activity? The initial-dip Perhaps the most astonishing fact about the rapid adoption of fmri as a tool in cognitive neuroscience is that we are still not quite sure what the measurement reflects [2]. The positron emission tomography (PET) signal also used in cognitive neuroscience exploits H 2 15 O labeled water as a tracer that reflects parenchymal blood flow. Increases in blood flow have been shown to be linearly proportional to increases in neural activity. On the other hand, the BOLD effect reflects, in some intricate combination, changes in blood flow, blood volume and blood oxygenation in the arteriole, capillary and venous vascular beds [3,4 ]. Figure 1 shows the fmri response of the primary visual cortex (V1) of a single subject. The nature and the shape of this response depend on whether the vascular bed giving rise to the signal is the capillary (desired) or venous (most likely) component. The capillary bed signal is clearly closest to the active neurons and, at higher magnetic field strengths, the small initial-dip that is sometimes observed is thought to correspond to a transient decrease in oxygenation before the occurence of the large BOLD signal increase, caused by vasodilation and concomitant flow increase [2]. Although the hyperoxygenation phase of the BOLD signal is robust and consistently observed, the transient decrease is not, nor is it observed consistently with other imaging modalities. This has led to considerable controversy over what the initial phase of the BOLD response, also observed in some optical imaging experiments, reflects. Nonetheless, there is no a priori reason to expect that the BOLD signal should be linearly related to neural activity, although the amplitude of the initial-dip may be better reflective of the strength of neural activity. Single-unit and multi-unit recordings A number of approaches have been taken to investigate the relationship of BOLD to neural activity. The most direct is to measure the spiking activity of neurons using single-unit recording and to compare it with brain activity measured using BOLD in the same areas, as has been done in the anesthetized monkey [5 ]. Although the number of neurons that are recorded from a monkey may be , there are hundreds of thousands of neurons in a single imaging voxel. An imaging voxel is the volume element determined by the MRI pixel size (typically 1 mm 1mm to 4 mm 4 mm) and the slice thickness (typically 2 mm

2 Imaging function in the working brain with fmri Menon 631 Figure 1 Multi-phasic BOLD signal. The fmri signal from the VI cortex of a single subject viewing an 8 s duration (indicated by the grey box) blue-yellow checkerboard, in which the colours interchanged at 8 Hz. The error bars (standard deviation) derive from 10 repetitions of the paradigm, one repetition every 30 s. At the 4 Tesla magnetic field this was performed at, a transient initial decrease is observed in the first couple of seconds, followed by a dramatic increase in signal intensity. Upon cessation of the stimulus, the BOLD response takes ~ 5 s to start to decay, eventually falling below the initial baseline, before final recovery. Curves such as this can be modelled in order to relate the changes to the physiological parameters such as oxygen consumption and neural activity [7,18 ]. Note the relative ratio of the initial-dip to the positive change and its relation to the error bars. Raw MRI signal (au) Current Opinion in Neurobiology to 5 mm). In a 1 mm 3 voxel, one might typically find 10 5 neurons and 10 8 synapses, which should leave no doubt that fmri measures the activity of large populations of neurons. Thus, the issue of population response versus single-unit activity needs to be considered [6,7 ], as does the issue of the effect of anesthetic on the neurovascular coupling. Neurons with similar properties tend to be spatially clustered, but the use of multiple electrode arrays is probably a requirement to characterize the population response. In-depth comparisons of the fmri response with multi-unit recordings will depend on simultaneous recording of the two signals in the awake behaving primate in the magnet. Population averages of neural activity can also be measured by electroencephalogram (EEG) recordings [8,9 ], allowing relationships between the evoked potential s features and the fmri signal to be explored. This is less direct than the unit recording methods described above, and the first order of business will be to investigate the relationship between quantitative aspects of the EEG such as the N1 and P3 and the fmri response. The N1 (also called N100) and P3 (also called P300) responses in the EEG signal correspond to negative going (N) and positive going (P) signal features with typical latencies of 100 and 300 ms respectively. Adding to this unresolved problem is the fact that EEG measurements are confounded by poor spatial specificity of the EEG relative to the fmri, although this might be improved by the use of local electrodes, as described in [8 ]. Multi-unit subdural electrodes are commonly used in human epilepsy surgery, offering some tantalizing possibilities for linking electrophysiology and fmri in awake humans. Comparison of BOLD with other imaging techniques Electrical activity is not the only way to discern brain function. The metabolic changes associated with neuronal activities and the associated upregulation in glial activity cause increases in blood flow, blood volume and blood oxygenation that can be observed with invasive optical imaging techniques (OIS), radioactive tracer techniques such as PET, non-invasive near-infrared spectroscopy (NIRS) and magnetic resonance spin-labelling techniques. Because the BOLD signal is also reflective of these parameters, there are a number of researchers attempting to relate the fmri response to the hemodynamic parameters obtained from these other techniques. For example, non-bold measures of cerebral blood flow (CBF) derived from PET [10,11] and magnetic resonance spin-labelling techniques [12,13], oxygenation and blood volume derived from NIRS and related optical techniques [14,15,16] can be compared with BOLD responses. These experiments seek to put the BOLD signal dependence on (somewhat) better understood parameters and thus a firmer footing, and are causing us to revisit interpretations of the BOLD initial-dip for example [14,15], as well as earlier OIS data. Certainly the controversy over the early transient deoxygenation in BOLD and OIS responses as a universal phenomenon in functionally activated mammalian brain has not been resolved. Another major unresolved issue is how neuronal inhibition is manifested in fmri (and PET) data [10]. There are a number of theories as to how to account for inhibition but electrophysiological data correlated with fmri are lacking. BOLD as a measure of perception Despite the fact that there is a rich tradition of modelling using the data obtained from comparison of experiments such as those discussed above, subtle variations in the model parameters allow them to agree with virtually all the disparate experimental data [17,18,19]. Certainly these models give biologically plausible answers and for the most part the data support them. However, subtleties such as the initial-dip are variously seen in the experimental data and the models. Given the number of careful measurements from respected labs cited here, the discrepancies are

3 632 New technologies Figure 2 % signal change Current Opinion in Neurobiology Population BOLD response from ocular dominance columns. The solid line is derived from the average of all the responding right eye columns in a single human subject at 4 Tesla. The dashed line is the response in all the left eye (inactive) columns. In the absence of vascular spillover, this response would not be seen, though some activity due to horizontal connections from the adjacent active columns cannot be ruled out. Details of the fmri acquisition and paradigm can be found in [27]. Again, note that the transient initial dip is barely detectable in the average across all of V1. Individual pixels do not show the initial-dip reliably at 4 Tesla, so it cannot be used to make maps of the ocular dominance columns in the manner of [25,26 ]. more likely related to the different animal and human models used to explore the coupling of the vascular effects and the BOLD signal. Even within the same animal, subtle changes in anesthesia levels can obliterate the initial-dip in BOLD experiments (SG Kim, personal communication). One way of overcoming these difficulties is to approach the problem of the meaning of the BOLD signal from a completely different slant. From the computational neuro-imaging perspective, the goal of fmri and other neuroimaging techniques is to understand how the brain accomplishes its tasks in a predictive manner. Success in this endeavor would allow us, for example, to predict whether a subject was about to make a volitional movement before the movement occurred or to predict the contrast of a grating that a subject was viewing, even if the subject could not consciously detect the target. This can be done by measuring the behaviour (e.g. movement or contrast sensitivity) and relating it to the BOLD responses in the brain networks that make such discriminations. This type of approach has had some initial success. Several recent papers show that, in fact, the BOLD response in the brain is predictive of perception measured psychophysically [20,21]. Furthermore, such measurements have been shown to shed light on deficits in the human visual system [22 ]. Thus, it may well be possible to link computation theories of vision with neuroimaging experiments that identify what the functions are of the neural substrates involved in the task. Implications for BOLD spatial resolution One of the major controversies in fmri is that of its ultimate spatial resolution [23,24]. At issue is whether the BOLD effect is confined to the site of neural activity, or whether the hemodynamic changes are more widespread than the active cortical volume. As there is not a one-toone correspondence between vascular subunits and functional subunits in the brain, the latter scenario is most likely and has been observed with OIS. One approach that has been put forward is to use the BOLD initial dip, as is done with OIS [25,26 ]. The rationale behind this choice is shown in Figure 2, where the prototypical BOLD response from ocular dominance columns corresponding to the human right eye is shown. Similar biphasic responses are seen for orientation columns in the cat [25,26 ]. Ocular dominance columns corresponding to the left and right eyes interdigitate in human V1, and are approximately 800 µm long [27]. From Figure 2, it is clear that the response from the columns that are not being stimulated by the checkerboard pattern also exhibit a positive BOLD effect, but show no initial negative dip. The fact that the positive BOLD signal occurs in the inactive columns is taken to mean that the positive BOLD signal reflects vascular spillover and therefore cannot be used for imaging columnar size structures. However, the positive BOLD signal in the inactive columns is not as large as the BOLD signal in the active columns, thus allowing the use of a differential imaging technique to make maps of the left or right eye columns [27,28]. The early negative response does not require such a differential approach, but does still require a test for significant changes from baseline. In the cat model [26 ], where the anesthetic can be manipulated to the experimenter s advantage, the early-dip has been used to make maps of orientation columns with fmri. The existence of the early negative BOLD response is controversial, as has been discussed, and its practical utility for mapping human brain function with columnar spatial specificity remains questionable. Both Figure 1 and Figure 2 show that the early negative dip represents perhaps less than 0.25% change in signal, considerably less than the available contrast differential between left and right eyes in the hyperoxygenation phase. However, there may be an optimum part of the fmri response to use, when imaging function at high spatial resolution, associated with the early hyperoxygenation phase and short stimuli [29]. Others have argued that CBF may be more tightly coupled to neural activity [30] because the perfusion signal is better localized to capillaries than the BOLD signal. Although BOLD also relies on perfusion changes, the BOLD signal exhibits sensitivity to larger veins as well, and these veins drain a large watershed. Perfusion changes can be isolated to the capillary beds with appropriate adjustments in the MRI acquisition technique.

4 Imaging function in the working brain with fmri Menon 633 Figure 3 Extracting relative timing from BOLD. The figure shows the brain response to two tasks that differ only in their difficulty. (a) The hypothetical process is cued by a visual presentation, which engages the parietal cortex, and results in the subject making a vocal response. The activity induced by the visual cue (black arrow) and the response button press (grey arrow) are constants that do not vary from trial to trial. The activity due to the parietal processing (dashed arrow) varies considerably between the two trials. (b) Both the width of the dashed line and the latency offsets of the solid lines can be used to measure relative timings from the BOLD response and to pin down where in the processing chain delays are incurred. Knowing the cue, computational models could predict the nature of the brain activation that would lead to the response. (a) Behavioural task Cue Response Trial 1 Trial 2 (b) Regional BOLD responses Current Opinion in Neurobiology Even in situations where the columnar structure cannot be directly imaged, it is possible to use fmri to study neural populations by adapting them and studying their subsequent recovery [31]. Methods such as these allow the properties of neuronal subpopulations to be measured even without their direct visualization, and can be accomplished with relatively high signal to noise ratio, because a change as small as a 10% change in the positive BOLD signal is generally detectable. Temporal resolution of BOLD As Figure 1 demonstrates, the BOLD response is delayed compared to the onset of neural activity. The measured BOLD response can be modeled as a convolution of the neural activity with a low-pass filter with a cutoff of ~0.1 Hz, suggesting that the spike patterns familiar to most electrophysiologists are not likely to be seen in the fmri signal. Despite this apparent limitation, there are a number of recent papers that explore the timing of events in the human brain with fmri, sometimes in conjunction with techniques such as magnetoencephalography (MEG) and EEG [32,33,34 ]. The range of timing for neural processes extends from a few milliseconds in sensory systems to days for learning networks. For many higher order cognitive tasks, the time to process information does indeed fall within the capabilities of fmri. A key issue will be whether the areas of activation found in MEG and EEG correspond to the same areas found with BOLD, given the differential sensitivities of each technique. This question itself is driving research into the relationship between BOLD and EEG [8,35]. In terms of a synthesis of the electrical and hemodynamic response maps, EEG shows the most immediate promise, as it is possible to perform EEG in the magnet. This, however, is a non-trivial engineering and post-processing challenge, because the high power radio frequency and magnetic field gradients involved in MRI produce interference many orders of magnitude larger than the EEG [36 39]. Researchers are also trying to use the BOLD effect to extract relative timings between neural substrates in the brain, motivated by early reports that this can be done with an accuracy of tens of milliseconds at 4 Tesla [24,40]. The principle behind such experiments is that the BOLD response latency is correlated with either an externally measured behavioral output, such as reaction time, or with some parametrically varied component of the task, as illustrated in Figure 3. Providing the fmri response and acquisition both meet certain criteria [41,42,43], reasonably accurate measures of serial cognitive processing in the brain can be made [44 48]. Although most timing experiments have utilized the onset or duration of the BOLD response, timing can also be studied by examining the amplitude of the BOLD response with variation of two stimuli [8 ]. Such experiments might reveal the neural substrates involved in visual masking paradigms, for example. With the vast number of cognitive processes that can be probed by amplitude and latency perturbations, it seems that fmri is well poised to make contributions to describing what happens in the brain. Simple subtraction methodologies will not suffice, and extensive computational models of the brain will be required [49 52]. Conclusions The intrinsic flexibility of MRI acquisition means that there are endless ways to exploit the metabolic and vascular changes associated with brain neural activity. The most successful of these techniques has undoubtedly been the BOLD effect, which has revolutionized cognitive

5 634 New technologies neuroscience. The BOLD effect is relatively easy to measure on conventional MRI equipment, has excellent spatial and temporal resolution properties and allows considerable flexibility in paradigm design. The next few years will see corroborative research performed on primates with single-unit recording, optical imaging and fmri to put the technique on a firm mechanistic basis. It is also likely that for demanding cognitive studies at high spatial or temporal resolution, there will be a push towards higher magnetic fields, as the evidence for their suitability for human use emerges [53]. Certainly a large body of work done over the past decade at 4 Tesla has motivated the installation of a large number of 3 and 4 Tesla machines in the past few years. Improvements in MRI system performance that are being motivated by cardiac imaging and other applications where moving organs are present will also pay big dividends for fmri research. Much like fmri was unexpected a decade ago, it is hard to predict whether a different neuroimaging technique will come along and supplant it. In the absence of such a paradigm shift, however, it looks as if the exponential increase in fmri papers will continue. Update Two papers with important implications for BOLD fmri have appeared recently. In the first of these, Logothetis et al. [54 ] demonstrate the simultaneous recording of neural activity and BOLD signals in an anesthetized monkey viewing checkerboard patterns. Studies using this type of technology will be key to uniting the fmri literature on humans with decades of neurophysiology on non-human primates, as well as providing new targets for electrophysiologists to place their electrodes in the monkey brain. Caveats on the somewhat strong conclusions of this paper are raised in another recent commentary [55]. In another paper that deals with the localization of BOLD to columnar structures, Duong et al. [56 ] demonstrate that the perfusion signal is precisely restricted to the neural site of activity in orientation columns, and that the extended point spread function reported with optical imaging and the hyperoxygenation phase of BOLD is, in fact, likely due to the effect of large draining veins. Acknowledgements The author would like to thank the Canadian Institutes of Health Research, the National Institutes of Health and the Canada Research Chairs program for their support of the research from his lab that appears in this article. References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest 1. Ogawa S, Lee TM, Kay AR, Tank DW: Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci USA 1990, 87: Ogawa S, Menon RS, Kim SG, Ugurbil K: On the characteristics of functional magnetic resonance imaging of the brain. Annu Rev Biophys Biomol Struct 1998, 27: Lee SP, Duong TQ, Yang G, Iadecola C, Kim SG: Relative changes of cerebral arterial and venous blood volumes during increased cerebral blood flow: implications for BOLD fmri. Magn Reson Med 2001, 45: Duong TQ, Kim SG: In vivo MR measurements of regional arterial and venous blood volume fractions in intact rat brain. Magn Reson Med 2000, 43: This paper describes a clever measurement of the relative vascular volume occupied by the arterial and venous sides in the rat, using the relaxation time dependence of 19 F on blood oxygenation. 5. Disbrow EA, Slutsky DA, Roberts TP, Krubitzer LA: Functional MRI at 1.5 Tesla: a comparison of the blood oxygenation level-dependent signal and electrophysiology. Proc Natl Acad Sci USA 2000, 97: The first comparison of fmri and electrophysiology in anesthetized macaque monkeys. The authors find a 55% concordance between the two measures, and that the variance primarily arises from the direction of the vasculature in the brain, reinforcing the need to suppress large vessel BOLD effects in some manner. 6. op de Beeck H, Wagemans J, Vogels R: Can neuroimaging really tell us what the human brain is doing? The relevance of indirect measures of population activity. Acta Psychol (Amst) 2001, 107: This review addresses the visual neuroscience literature and the extent to which population measures of brain activity such as fmri can shed light on the properties of single neurons. 7. Rees G, Friston K, Koch C: A direct quantitative relationship between the functional properties of human and macaque V5. Nat Neurosci 2000, 3: These authors provide an explicit quantitative estimate for the interspecies comparison of single-neuron activity and BOLD population responses.they did this by comparing the dependence of the fmri responses in human V5 in response to increased motion coherence with the slopes of single-unit firing rates in monkey V5. This contrasts with [54 ], where single-unit activity was not found to be a good predictor of the BOLD response. Given that the experimental evidence is stronger than a theoretical, though well reasoned link, this paper demonstrates that simultaneous measurements in an area need to be made to properly interpret the fmri signal. 8. Ogawa S, Lee TM, Stepnoski R, Chen W, Zhu XH, Ugurbil K: An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds. Proc Natl Acad Sci USA 2000, 97: Although the results presented here do not agree with the human visual masking psychophysical literature or the human EEG literature, the authors present a method in which amplitude modulation of the BOLD signal can be used to discern fast events such as interhemispheric transfer of information. The authors demonstrate an important point by showing that the BOLD signal can be modulated by altering the timing between two successive stimuli. The amplitude of the BOLD signal is easily measured with precision, and the timing between any two stimuli can also be altered with a great deal of accuracy. Thus, indirect measurements of the timing of neuronal interactions can be made with millisecond precision. 9. Kashikura K, Kershaw J, Yamamoto S, Zhang X, Matsuura T, Kanno I: Temporal characteristics of event-related BOLD response and visual-evoked potentials from checkerboard stimulation of human V1: a comparison between different control features. Magn Reson Med 2001, 45: This paper attempts to relate the P3 and N1 components in visual-evoked potentials with the BOLD response, suggesting that the magnitude and extent of the fmri signal may give rise to the different electrophysiological signatures. 10. Tagamets MA, Horwitz B: Interpreting PET and fmri measures of functional neural activity: the effects of synaptic inhibition on cortical activation in human imaging studies. Brain Res Bull 2001, 54: Lowe MJ, Lurito JT, Mathews VP, Phillips MD, Hutchins GD: Quantitative comparison of functional contrast from BOLDweighted spin-echo and gradient-echo echo-planar imaging at 1.5 Tesla and H 15 2 O PET in the whole brain. J Cereb Blood Flow Metab 2000, 20: Li TQ, Haefelin TN, Chan B, Kastrup A, Jonsson T, Glover GH, Moseley ME: Assessment of hemodynamic response during focal neural activity in human using bolus tracking, arterial spin labeling and BOLD techniques. Neuroimage 2000, 12: The authors find that when comparing the experimentally measured cerebral blood volume (CBV) with the calculated CBV using the ubiquitous Grubb s power-law relation, the use of the power-law relationship results in systematic underestimates of CBV. This is important because most researchers use Grubb s relation to convert CBF into CBV or vice versa. See also [14 ].

6 Imaging function in the working brain with fmri Menon Yang Y, Engelien W, Pan H, Xu S, Silbersweig DA, Stern E: A CBF-based event-related brain activation paradigm: characterization of impulse-response function and comparison to BOLD. Neuroimage 2000, 12: Jones M, Berwick J, Johnston D, Mayhew J: Concurrent optical imaging spectroscopy and laser-doppler flowmetry: the relationship between blood flow, oxygenation, and volume in rodent barrel cortex. Neuroimage 2001, 13: This paper uses OIS and laser Doppler to examine the hemodynamic changes upon stimulation in the rat somatosensory cortex. The OIS spectroscopy shows that stimulation produces a biphasic early increase in deoxyhemoglobin, followed by a decrease below baseline, similar to some fmri data. The authors find Grubb s relation to hold, unlike in [12 ]. 15. Lindauer U, Royl G, Leithner C, Kuhl M, Gold L, Gethmann J, Kohl-Bareis M, Villringer A, Dirnagl U: No evidence for early decrease in blood oxygenation in rat whisker cortex in response to functional activation. Neuroimage 2001, 13: Wobst P, Wenzel R, Kohl M, Obrig H, Villringer A: Linear aspects of changes in deoxygenated hemoglobin concentration and cytochrome oxidase oxidation during brain activation. Neuroimage 2001, 13: Kershaw J, Kashikura K, Zhang X, Abe S, Kanno I: Bayesian technique for investigating linearity in event-related BOLD fmri. Magn Reson Med 2001, 45: Friston KJ, Mechelli A, Turner R, Price CJ: Nonlinear responses in fmri: the Balloon model, Volterra kernels, and other hemodynamics. Neuroimage 2000, 12: In this paper, the authors combine the system identification and model-based approaches to ask whether the Balloon model is sufficient to account for the nonlinear behaviors observed in real fmri time series. They conclude that the Balloon model is more than adequate. 19. Miller KL, Luh WM, Liu TT, Martinez A, Obata T, Wong EC, Frank LR, Buxton RB: Nonlinear temporal dynamics of the cerebral blood flow response. Hum Brain Mapp 2001, 13: Polonsky A, Blake R, Braun J, Heeger DJ: Neuronal activity in human primary visual cortex correlates with perception during binocular rivalry. Nat Neurosci 2000, 3: Heeger DJ: Linking visual perception with human brain activity. Curr Opin Neurobiol 1999, 9: Goodyear BG, Nicolle DA, Humphrey GK, Menon RS: BOLD fmri response of early visual areas to perceived contrast in human amblyopia. J Neurophysiol 2000, 84: The authors demonstrate that in both normal subjects and those with amblyopia, the BOLD response can be modeled and used to predict the psychophysically measured contrast sensitivity function. 23. Zarahn E: Spatial localization and resolution of BOLD fmri. Curr Opin Neurobiol 2001, 11: Menon RS, Kim SG: Spatial and temporal limits in cognitive neuroimaging with fmri. Trends Cogn Sci 1999, 3: Duong TQ, Kim DS, Ugurbil K, Kim SG: Spatiotemporal dynamics of the BOLD fmri signals: toward mapping submillimeter cortical columns using the early negative response. Magn Reson Med 2000, 44: Kim DS, Duong TQ, Kim SG: High-resolution mapping of iso-ori- entation columns by fmri. Nat Neurosci 2000, 3: Kim et al. use the initial-dip to demonstrate orientation columns in anesthetized cats. They also explore the localization of the hyperoxygenation phase, in [25], and conclude that it cannot be used for columnar mapping. However, Menon and Goodyear [27] demonstrate otherwise for the case of human ocular dominance columns. They suggest that short stimuli must be used to get appropriate differential sensitivity using the hyperoxygenation phanse of the BOLD response. 27. Menon RS, Goodyear BG: Submillimeter functional localization in human striate cortex using BOLD contrast at 4 Tesla: implications for the vascular point-spread function. Magn Reson Med 1999, 41: Dechent P, Frahm J: Direct mapping of ocular dominance columns in human primary visual cortex. Neuroreport 2000, 11: Krause T, Kurth R, Ruben J, Schwiemann J, Villringer K, Deuchert M, Moosmann M, Brandt S, Wolf K, Curio G, Villringer A: Representational overlap of adjacent fingers in multiple areas of human primary somatosensory cortex depends on electrical stimulus intensity: an fmri study. Brain Res 2001, 899: Luh WM, Wong EC, Bandettini PA, Ward BD, Hyde JS: Comparison of simultaneously measured perfusion and BOLD signal increases during brain activation with T(1)-based tissue identification. Magn Reson Med 2000, 44: Grill-Spector K, Malach R: fmri-adaptation: a tool for studying the functional properties of human cortical neurons. Acta Psychol (Amst) 2001, 107: Disbrow E, Roberts T, Poeppel D, Krubitzer L: Evidence for interhemispheric processing of inputs from the hands in human S2 and PV. J Neurophysiol 2001, 85: Bonmassar G, Schwartz DP, Liu AK, Kwong KK, Dale AM, Belliveau JW: Spatiotemporal brain imaging of visual-evoked activity using interleaved EEG and fmri recordings. Neuroimage 2001, 13: Dale AM, Halgren E: Spatiotemporal mapping of brain activity by integration of multiple imaging modalities. Curr Opin Neurobiol 2001, 11: A review of the progress in integrating fmri, EEG and MEG studies on the same brain. The authors discuss issues of the inverse problem and the concordence between MEG and fmri derived maps. 35. Kashikura K, Kershaw J, Yamamoto S, Zhang X, Matsuura T, Kanno I: Temporal characteristics of event-related BOLD response and visual-evoked potentials from checkerboard stimulation of human V1: a comparison between different control features. Magn Reson Med 2001, 45: Lazeyras F, Zimine I, Blanke O, Perrig SH, Seeck M: Functional MRI with simultaneous EEG recording: feasibility and application to motor and visual activation. J Magn Reson Imaging 2001, 13: Goldman RI, Stern JM, Engel J Jr, Cohen MS: Acquiring simultaneous EEG and functional MRI. Clin Neurophysiol 2000, 111: Kruggel F, Wiggins CJ, Herrmann CS, von Cramon DY: Recording of the event-related potentials during functional MRI at 3.0 Tesla field strength. Magn Reson Med 2000, 44: Allen PJ, Josephs O, Turner R: A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 2000, 12: Menon RS, Luknowsky DC, Gati JS: Mental chronometry using latency-resolved functional MRI. Proc Natl Acad Sci USA 1998, 95: Saad ZS, Ropella KM, Cox RW, DeYoe EA: Analysis and use of fmri response delays. Hum Brain Mapp 2001, 13: Miezin FM, Maccotta L, Ollinger JM, Petersen SE, Buckner RL: Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing. Neuroimage 2000, 11: In agreement with Menon et al. [40], but with considerable other data on variances, the authors find that the within-region stability of the BOLD response is sufficient to allow offsets in the timing of the response to be detected that are under a second. The magnetic field strength and signalto-noise ratio clearly differ from [40], suggesting that the great contrast-tonoise ratio offered by higher field MRI scanners may be important in establishing timing with any degree of precision, when using the shapes and widths of the BOLD signal. 43. Friman O, Cedefamn J, Lundberg P, Borga M, Knutsson H: Detection of neural activity in functional MRI using canonical correlation analysis. Magn Reson Med 2001, 45: Schmid A, Rees G, Frith C, Barnes G: An fmri study of anticipation and learning of smooth pursuit eye movements in humans. Neuroreport 2001, 12: Weilke F, Spiegel S, Boecker H, von Einsiedel HG, Conrad B, Schwaiger M, Erhard P: Time-resolved fmri of activation patterns in M1 and SMA during complex voluntary movement. J Neurophysiol 2001, 85: Rao SM, Mayer AR, Harrington DL: The evolution of brain activation during temporal processing. Nat Neurosci 2001, 4: Zarahn E: Testing for neural responses during temporal components of trials with BOLD fmri. Neuroimage 2000, 11: Calhoun V, Adali T, Kraut M, Pearlson G: A weighted least-squares algorithm for estimation and visualization of relative latencies in event-related functional MRI. Magn Reson Med 2000, 44:

7 636 New technologies 49. Ng VW, Bullmore ET, de Zubicaray GI, Cooper A, Suckling J, Williams SC: Identifying rate-limiting nodes in large-scale cortical networks for visuospatial processing: an illustration using fmri. J Cogn Neurosci 2001, 13: Konishi S, Donaldson DI, Buckner RL: Transient activation during block transition. Neuroimage 2001, 13: Chawla D, Lumer ED, Friston KJ: Relating macroscopic measures of brain activity to fast, dynamic neuronal interactions. Neural Comput 2000, 12: Horwitz B, Tagamets MA, McIntosh AR: Neural modeling, functional brain imaging, and cognition. Trends Cogn Sci 1999, 3: Yacoub E, Shmuel A, Pfeuffer J, van de Moortele PF, Adriany G, Andersen P, Vaughan JT, Merkle H, Ugurbil K, Hu X: Imaging brain function in humans at 7 Tesla. Magn Reson Med 2001, 45: Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A: Neurophysiological investigation of the basis of the fmri signal. Nature 2001, 412: A tremendous technological achievement that opens the door to further studies on the relationship between fmri measured activity and conventional neurophysiological explorations of monkey cortex. The authors show that the local field potential is a slightly better predictor of the BOLD response than the single-unit or multi-unit activity of neurons, and draw the conclusion that the BOLD contrast mechanism reflects the input and intracortical processing of a given area, rather than its spiking output. An important point, missed by many readers, is that the sensitivity of BOLD to changes in neural activity is perhaps tenfold less than the local field potential. Thus, maps made using the BOLD signal are really the tip of the iceberg of neural activity and should be treated with some caution. 55. Bandettini PA, Ungerleider LG: From neuron to BOLD: new connections. Nat Neurosci 2001, 4: Duong TQ, Kim D-S, Ugurbil K, Kim S-G: Cerebral blood flow response is localized at sub-millimeter columnar resolution. Proc Natl Acad Sci USA 2001, 98: Earlier papers, using the positive phase of BOLD [27] or OIS, suggested a vascular spillover into adjacent inactivated cortex, which meant that cortical columnar structures could only be identified using differential mapping techniques. This paper shows that pure perfusion signals can be localized to the capillary bed and appear to be completely co-registered with the site of neural activity. They also suggest the use of spin-echo fmri as an alternative approach worthy of investigation. Another method that preserves the rapid temporal resolution of standard BOLD approaches is also to appear shortly, but has not been compared with the CBF approach yet [57]. 57. Menon RS: Post-acquisition suppression of large vessel BOLD signals in high-resolution fmri. Magn Reson Med 2001, in press.