FUNCTIONAL MAGNETIC RESONANCE IMAGING AND SPECTROSCOPIC IMAGING OF THE BRAIN: APPLICATION OF fMRI AND fMRS TO READING DISABILITIES AND EDUCATION.

Functional magnetic resonance imaging (fMRI) and functional magnetic resonance spectroscopy (fMRS) have been used to study adults and children with developmental reading disabilities. These individuals struggled or struggle in learning to read despite normal intelligence and sensory abilities. In contrast, individuals with acquired dyslexia had normal reading function but lost it due to disease or injury.

The purposes of this article are to (a) present a brief tutorial on fMRI and fMRS, and (b) provide an overview of the most recent findings in the use of these neuroimaging tools to study learning disabilities specific to reading (dyslexia); This information allows professionals in education and psychology to become more critical consumers of the growing body of research on functional brain imaging of dyslexia. Recent data from functional neuroimaging of the brain in children with dyslexia have demonstrated that there is a biological basis for developmental dyslexia. However, even though dyslexia is a brain-based disorder, it is treatable, as will be discussed.

Tutorial on Functional MR Imaging and Spectroscopy

Functional MRI (fMRI) and functional MR spectroscopy (fMRS) are techniques that measure different physiological parameters of neural activation (see Table 1). These functional brain-imaging techniques are very labor-intensive in terms of both acquisition and processing of the data and require a multidisciplinary team of scientists such as psychologists, MRI physicist/engineers, neuroscientists, neuroradiologists, and computer scientists. The techniques are referred to as functional (rather than structural) because participants perform tasks while they are in the magnet; as a result, analyses of the imaging permit conclusions about activation of the functioning brain rather than the neuroanatomy of the resting brain. Further, these techniques are often referred to as in vivo because they can be administered to living people. Both of these techniques are noninvasive and are based on magnetic resonance imaging, which is briefly described here. Noninvasive means in part that the subject is not exposed to ionizing radiation. In contrast, the PET technique, which is also included in Table 1, is invasive and cannot be used to study healthy children.

Table 1
Brain Imaging Techniques for Mapping Brain Function

Technique                               Contrast Mechanism

Functional MRI (fMRI)                   Blood deoxyhemogloblin levels

Positron Emission Tomography (PET) --   Intracellular glucose
FDG/O15                                 uptake/blood flow

Electroencephalography (EEG)            Electrical/magnetic signal
Magnetoencephalography (MEG)            during neuronal firing

Functional MR spectroscopy (fMRSI) --   Lactate production and
lactate                                 clearance during glycolysis/
                                        neuronal activation

MRI is a way to look inside the body (brain in this case) without using X-rays. The body contains hydrogen nuclei (protons) that can absorb and give off energy in the presence of a magnetic field. MRI scanners use a magnet, which creates a strong, steady magnetic field. This magnetic field is very homogeneous near the center of the magnet where the head is positioned for a brain scan. This field causes the protons to line up together and spin at a specific frequency, which is dependent on the strength of the magnetic field. A radiofrequency signal is transmitted into the body using a radiofrequency (RF) coil. The RF energy is absorbed by the protons and makes them move out of alignment -- similar to a spinning top when someone hits it. When the RF transmission stops, the protons gradually move back to their aligned position and release energy. Another RF coil is positioned near the body to receive this signal as energy is released from the protons. It takes some time for the protons to return to their equilibrium state (the state before they were perturbed by the RF transmission). A computer is used to control and orchestrate all scanner electronics such as RF transmission, signal reception, pulse timing, and signal delay time. The software written to control these channels is called a pulse sequence. The timing parameters inside the pulse sequence can be adjusted to produce various types of tissue contrast.

Because most of the hydrogen nuclei in the body are part of water molecules, over 90% of the signal comes from water. The signal amplitude that comes from the body is dependent on several biochemical/biophysical properties of the tissue such as protein and lipid content and the presence of paramagnetic substances such as blood deoxyhemoglobin. This sensitivity to blood oxygenation makes this proton signal ideal for functional brain imaging. The proton signal is also influenced by the mobility of tissue water, which dramatically changes in tumors or inflammation (swelling). Thus, clinicians can use this technology to assist in diagnosis of many diseases such as cancer and multiple sclerosis, as well as investigation of developmental processes such as learning to read. However, it is important to keep in mind that MRI does not measure all biological processes in the brain. Figure 1 shows an example of an MR scanner with a child on the table.

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Basic concepts in fMRI. Functional magnetic resonance imaging (fMRI) is a relatively new and potentially powerful tool that can be used to study the thinking brain (Sanders & Orrison, 1995). fMRI is based on the fact that when part of the brain is used for thinking, the need for energy, nutrients (supplied from the blood), and oxygen in that area of brain increases, fMRI can be used to localize the area of activated brain within 1 cm (Hillyard, 1998).

The fMRI technique is often used to measure the differences in the MR signal during two different mental tasks such as listening to words (the "on" task) and listening to tones (the "off" task or control). Because this small change in the signal is not directly observable, a technique is used whereby activation during the "off" period is subtracted from the activation that occurs during the "on" condition. Paradigms (tasks the participant performs while in the magnet) have been fairly well established for identifying which regions of the brain are involved in simple movement tasks (e.g., finger tapping) and sensory stimulation (e.g., reaction to flashing lights; Kwong et al., 1992). Considerably more research is needed to develop reliable paradigms for assessing language and other mental functions.

Basic concepts in fMRS. In vivo functional magnetic resonance spectroscopy (fMRS) can be used to record levels of different chemicals in the brain while the subject is thinking, fMRS scanning requires the same equipment as fMRI but uses different computer software. Like fMRI, fMRS is used to detect signals from the brain using an RF detector inside a large magnet. The main difference between fMRI and fMRS is that the magnetic resonance signal in fMRI gives information about the spatial position of water of the brain. In fMRS, the signal gives information about both spatial position and chemical information of the brain.

The fMRS signal is acquired as a waveform over time, digitized, and then processed by computer software. Different chemicals have different MR signals that can be separated using a mathematical computerized processing known as the Fourier transform. For example, in ethanol three main peaks (resonances) arise from three different parts of the ethanol molecule. Known concentrations of the tissue chemicals are also compared with the amplitude of the tissue fMRS signal. Reviews of MRSI applications in clinical neuroradiology (Ross & Michaelis, 1994) show that there have been many technical advances in the last few years for acquiring and analyzing high-quality MRS signals from the human brain. Some of these advances involve pulse sequences used to acquire the data and control the timing of the different parts of the scanner such as the radiofrequency amplitude, the magnetic field gradients, and signal reception. Examples of pulse sequences are STEAM (STimulated Echo Acquisition Method), PRESS (Point RESolved Spectroscopy), and PEPSI (Proton EchoPlanar Spectroscopic Imaging). These pulse sequences are now available for single-voxel and multiple-voxel (spectroscopic imaging) acquisition.

Voxel refers to the small volume or element of tissue that is sampled by the MR technique. For single-voxel techniques only one spectrum (with its corresponding chemical measurement) is obtained from one brain region or voxel. For multiple-voxel techniques, many spectra are obtained simultaneously from many different regions or voxels of brain. These pulse sequences allow the MRS signal to be localized to specific regions of the brain, with typical volume resolution of 4-8 cc with single-voxel techniques and 1 cc volume resolution with multiple-voxel techniques. The multiple-voxel techniques have better spatial resolution than single-voxel techniques because they record signal from a larger portion of brain during each repetition of the pulse sequence. The size of the voxel and its position are controlled by the scanner operator, but there are limitations on the voxel size based on the required strength of the MR signal. Other operator-controlled variables include repetition time (TR, the time between repetitions of the basic pulse sequence) and echo time (TE, the time between the first radiofrequency [RF] pulse and the center of the echo in the spin-echo pulse sequence). Webb and colleagues (Webb et al., 1994) have developed a technique called PROBE (PROton Brain Exam) for automating spectroscopy procedures such as the adjustment and optimization of (a) RF transmit power; (b) center frequency; (c) magnetic field homogeneity; (d) water suppression pulse parameters; and (e) phasing and display of the proton spectra. PROBE recently received FDA approval for clinical application.

In summary, single-voxel techniques acquire signal from only one anatomic location while multiple-voxel techniques can be used to acquire spectroscopic information from multiple voxels of the brain and can be used to produce metabolite maps. Multiple-voxel techniques work best in studies where the exact brain region of interest is unknown.

One of the chemicals that can be measured by fMRS is lactate. Lactate is thought to be metabolized in the brain as a energy substrate for neurons (one kind of brain cells) (Frahm, Kruger, Merboldt, & Kleinschmidt, 1996; Prichard, 1994; Prichard et al., 1992; Richards et al., 1997a; Richards et al., 1997b; Sappey et al., 1992; Schurr, West, & Rigor, 1988; Tsacopoulos & Magistretti, 1996). Lactate is also a by-product of glucose metabolism during brain activation. FMRS has been used to demonstrate lactate activation (increase in lactate) in normal adults during visual, auditory, and cognitive tasks (Frahm et al., 1996; Prichard et al., 1992; Richards et al., 1997a; Richards et al., 1997b; Sappey et al., 1992). In these studies, lactate was observed to increase rapidly during sensory stimulation in a regionally specific manner. Functional MR spectroscopy (fMRS) using the PEPSI technique is an approach to detecting regional brain activation during a specific mental task. fMRS is complementary to fMRI in that comparisons between two different activation tasks can be used to measure changes in brain activation in both techniques, fMRS measures the brain chemicals while fMRI measures the blood oxygen during the mental task.

Background on Language Activation in the Brain

Functional magnetic resonance imaging (fMRI) can be used to identify language-processing areas in the intact human brain (Barch, Braver, Sabb, & Noll, 2000; Bhatnagar, Mandybur, Buckingham, & Andy, 2000; Binder et al., 2000; Brockway, 2000; Burton, Small, & Blumstein, 2000; Cohen, Dehaene, Chochon, Lehericy, & Naccache, 2000; Cordes et al., 2000; Friederici, Meyer, & von Cramon, 2000a; Friederici, Opitz, & von Cramon, 2000b; Hashimoto, Homae, Nakajima, Miyashita, & Sakai, 2000; Kansaku, Yamaura, & Kitazawa, 2000; Laine, Salmelin, Helenius, & Marttila, 2000; Leung, Skudlarski, Gatenby, Peterson, & Gore, 2000; Lurito, Kareken, Lowe, Chen, & Mathews, 2000; Matsuo et al., 2000; Pugh et al., 2000a; Shah et al., 2000; Tan et al., 2000; Tomczak et al., 2000; Trauner, Wulfeck, Tallal, & Hesselink, 2000; Vikingstad, George, Johnson, & Cao, 2000; Xiong et al., 2000).

As predicted from classical models of language organization based on lesion data, cortical activation associated with language processing (using fMRI) is strongly lateralized to the left cerebral hemisphere (in right-handed people) and involves a network of regions in the frontal, temporal, and parietal lobes. Less consistent with classical models were (a) the existence of left-hemisphere temporoparietal language areas outside the traditional Wernicke area, namely, in the middle temporal, inferior temporal, fusiform, and angular gyri; (b) extensive left prefrontal language areas outside the classical Broca area; and (c) clear participation of these left frontal areas in a task emphasizing "receptive" language functions (Binder et al., 1997).

fMRS has been applied to study language function in the brain by mapping the change in certain chemicals that respond to brain activation during specific language tasks. The levels of these brain chemicals can be measured during different states of the brain, for example, when deciding if tWO words mean the same thing, if two stimuli are both real words, or if two stimuli rhyme. The chemical levels change in the brain because of a process known as metabolism.

Here is a brief explanation of what the brain does during brain activation (also see Figure 2). When a person first starts thinking (about language, for example), there is an increase in electrical activity in the region(s) of the brain used for that task. This electrical activity uses up energy, which, in turn, leads to increased utilization of nutrients (glucose, oxygen, etc.) and an increase in blood flow. As these nutrients are pulled into the brain, chemical reactions take place (metabolism) so energy can be extracted. In addition, a chemical called lactate, which is a by-product that forms as energy, is used up. So, in summary, when a person starts performing a language task, the brain responds by using up energy and producing chemical changes in the language centers of the brain (Broca's and Wernicke's areas, see Figure 3). Fortunately, some of these chemicals (know as metabolites) can be viewed noninvasively by MR spectroscopy.

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Other Imaging Technologies

fMRI and fMRS are not the only imaging technologies used to study dyslexia. Positron emission tomography (PET) is a brain-imaging technique in which radioactive substances such as carbon-11, fluorine-18, oxygen-15 and nitrogen-13 are injected into the body (Krasuski, Horwitz, & Rumsey, 1996). The PET scanner uses a ring of detectors to measure and localize the radioactive signal from the body. Certain radioactive substances can be used to monitor brain activation because the activated regions selectively pull in the radioactive substances associated with increased blood flow and metabolism. However, this technique is considered invasive and cannot be used in typical children because the radioactive risk is too great.

Electroencephalography (EEG) and magnetoencephalography (MEG) are two techniques for measuring brain activation that have excellent temporal resolution (about 1 millisecond) but poor spatial localization compared with fMRI. EEG and MEG can be used to measure brain activation and the amplitude of neural activation during mental processing because specific regions of brain have increased electrical and magnetic activity during activation. In EEG, electrodes (19 to 128 different positions) are placed on the scalp with a conductive gel so electrical signals can be recorded. In MEG, an array of highly sensitive magnetic field detectors ([greater than] 100 positions) is placed around the head so magnetic fields can be recorded from the brain. Brain activation can be measured using an EEG/MEG technique called event-related potentials (ERP), which is based on averaging the electric/magnetic signal while repeating the stimulus. The EEG/MEG signal is time-locked or synchronized to the stimulus onset. These techniques do not expose the subject to radiation and are not considered hazardous. The EEG and MEG techniques are especially useful because brain events can be studied that rapidly change from one brain region to the next during language processing. Using this technique, Simos, Breier, Fletcher, Bergman and Pepanicolaon (2000) showed that both dyslexics and controls initially processed written words in the inferior temporal regions, but thereafter the dyslexics activated right tempoparietal areas whereas the controls activated left tempoparietal areas.

Diffusion tensor magnetic resonance imaging (DT-MRI) (Alexander, Hasan, Kindlmann; Parker, & Tsuruda, 2000; Assaf & Cohen, 2000; Bammer et al., 2000; Basser, Pajevic, Pierpaoli, Duda, & Aldroubi, 2000; Beaulieu et al., 1999; Conturo et al., 1999; Klingberg et al., 2000; Klingberg, Vaidya, Gabrieli, Moseley, & Hedehus, 1999; Pajevic & Pierpaoli, 2000; Poupon et al., 2000; Ulug, Moore, Bojko, & Zimmerman, 1999) is a technique used to study the microstructural integrity of white matter that relates to the functional connectivity of neurons. Klingberg et al. (2000) used DT-MRI to show that subjects with reading difficulty exhibited decreased diffusion anisotropy bilaterally in temporoparietal white matter and demonstrated the specificity of group differences between poor readers and control subjects in the microstructural characteristics measured by diffusion tensor imaging (DTI). The anisotropy reflects microstructure of white matter tracts, which may contribute to reading ability by determining the strength of communication between cortical areas involved in visual, auditory, and language processing (Klingberg et al., 2000).

Functional Imaging Studies of Dyslexia

A growing number of investigations have found regional associations between neurophysiological abnormalities and developmental dyslexia (in adults or children) using different imaging modalities: positron emission tomography, PET (Gross-Glenn et al., 1991; Lubs et al., 1988; Paulesu et al., 1996; Rumsey et al., 1994; Rumsey et al., 1997; Tallal, Merzenich, Miller, & Jenkins, 1998); fMRI (Demb, Boynton, & Heeger, 1998; Eden et al., 1996; Georgiewa et al., 1999; Shaywitz et al., 1998; Vanni, Uusitalo, Kiesila, & Hari, 1997); and fMRS (Rae et al., 1998; Richards et al., 1999; Richardson, Cox, Sargentoni, & Puri, 1997). Table 2 lists selected references and notes the brain regions where there was a significant difference between dyslexic controls in adults and children.

Table 2
Neuroimaging Differences Between Dyslexics and Normal Readers

                     Brain Region Where
Reference            Difference Was Found

Adult Studies
Shaywitz (1998)      "Wernicke's area, angular gyrus, striate cortex"
                     Broca's area (inferior frontal gyrus)
Demb (1998)          visual cortex
Vanni (1997)         visual cortex
Eden (1996)          visual cortex
Robichon (2000a)     corpus callosum size
Robichon (2000b)     frontal and parietal areas size
Green (1999)         infrasylvian surface size
Rumsey (1999)        "angular gyrus, inferior parietal"
Brunswick (1999)     "Wernicke's area, left cerebellum,
                     left thalamus, striatum"
McCrory (2000)       "superior temporal, post-central, cerebellum"
Nicolson (1999)      "right cerebellar cortex, left cingulate gyrus"
Rumsey (1994)        right frontotemporal region
Hagman (1992)        medial temporal lobe
Gross-Glenn (1991)   prefrontal cortex and occipital lobe
Paulesu (1996)       insula
Lubs (1988)          insula
Pugh (2000b)         angular gyrus and functional connectivity
Salmelin (2000)      temporal processing in left superior
                     temporal cortex

Child Studies
Georgiewa (1999)     Broca's area (IFG) and left
                     inferior temporal region
Simos (2000)         left temporoparietal

Richards (1999)      left frontal region
Hynd (1990)          insula
Pennington (1999)    insula
Leonard (1993)       duplication of Heschl's gyrus

                     Imaging
Reference            Technique        Mental Task

Adult Studies
Shaywitz (1998)      fMRI             phonological
Demb (1998)          fMRI             motion-detection task
Vanni (1997)         MEG              motion-detection task
Eden (1996)          fMRI             motion-detection task
Robichon (2000a)     Structural MRI
Robichon (2000b)     Structural MRI
Green (1999)         Structural MRI
Rumsey (1999)        rCBF-PET         reading task
Brunswick (1999)     fMRI             phonological
McCrory (2000)       PET              word-repetition task
Nicolson (1999)      PET              motor skill task
Rumsey (1994)        PET              tone task
Hagman (1992)        PET              auditory syllable-discrimination
Gross-Glenn (1991)   PET              oral reading
Paulesu (1996)       PET              phonological
Lubs (1988)          PET              oral reading
Pugh (2000b)         fMRI             "letter, phonological, semantic"
Salmelin (2000)      MEG              semantic processing

Child Studies
Georgiewa (1999)     fMRI             phonological and nonword
Simos (2000)         MEG              phonological processing
                                      (visual words)
Richards (1999)      fMRS             phonological (auditory words)
Hynd (1990)          Structural MRI
Pennington (1999)    Structural MRI
Leonard (1993)       Structural MRI

Note: Only the first author for reference is noted.

PET and regional cerebral blood flow (rCBF) studies. PET ([sup.18]F-fluorodeoxyglucose]) studies indicate that adult dyslexics have focal increases in glucose metabolism in the prefrontal cortex (Gross-Glenn et al., 1991) and medial temporal lobe (Hagman et al., 1992), suggesting either inefficient processing or activation of compensatory pathways (Rumsey, 1996). High-resolution rCBF studies reviewed by Ingvar (1983) showed that in normal adults during silent reading, the following regions were activated in the left hemisphere: primary visual area, paravisual areas, the frontal eye fields, the lower frontal regions, including Broca's area, and the premotor frontal region. But during oral reading additional regions were activated such as the Rolandic mouth area and the auditory and para-auditory areas.

Using PET measures of regional cerebral blood flow (rCBF), Rumsey et al. (1999) have identified the left angular gyrus as the most probable site of a functional lesion in dyslexia and suggested that greater reliance on this region normally facilitates reading, but impairs reading in dyslexia. Rumsey et al. (1997) have also observed altered patterns of activation (reduced activation, unusual deactivation) in dyslexic men in mid- to posterior temporal cortex bilaterally and in inferior parietal cortex, predominantly on the left, during both pronunciation and linguistic decision making. In contrast, dyslexic men demonstrated essentially normal activation of the left inferior frontal cortex during both phonological and orthographic linguistic decision making (Rumsey et al., 1997).

MEG studies. Simos et al. (2000) used magnetic source imaging (MEG-MSI) to describe spatiotemporal brain activation profiles during word reading in dyslexic and normal children. Dyslexic children's activation profiles during the printed word-recognition task consistently featured activation of the left basal temporal cortices (includes the inferior temporal gyrus and possibly the fusiform gyrus) followed by activation of the right temporoparietal areas (including the angular gyrus). Nonimpaired readers showed predominant activation of left basal followed by left temporoparietal activation. From these results, the researchers hypothesized that reading difficulties in developmental dyslexia are associated with an aberrant pattern of functional connectivity between brain areas normally involved in reading, namely, the ventral visual association cortex and temporoparietal areas in the left hemisphere (Simos, Breier, Fletcher, Bergman, & Papanicolaou, 2000). Salmelin, Helenius, and Service (2000) have published a review of a series of magnetoencephalographic (MEG) experiments aimed at identifying cortical areas and time windows relevant to or even critical for fluent reading.

fMRI studies. Pugh et al. (2000b) have reported fMRI evidence that there is dysfunction at posterior brain regions centered in and around the angular gyrus in the left hemisphere. They also observed a disruption in the functional connectivity in the language-dominant left hemisphere (but not in the right hemisphere) confined to tasks that make explicit demands on phonological assembly. Binder et al. reported that several left-hemisphere areas, including the superior temporal sulcus, middle temporal gyrus, angular gyrus and lateral frontal lobe, showed stronger activation during the word conditions than the tone conditions in normal human subjects. However, this was not true of the planum temporale (PT), which responded equally to tones and words during passive listening and more strongly to tones during active listening. The PT is likely to be involved in early auditory processing, while specifically linguistic functions are mediated by multimodal association areas distributed elsewhere in the left hemisphere (Binder, Frost, Hammeke, Rao, & Cox, 1996).

Using fMRI, Shaywitz et al. (1998) measured brain activation patterns and found that the dyslexic adult readers and controls differed in that the former showed relative underactivation in posterior regions (Wernicke's area, the angular gyrus, and striate cortex) and relative overactivation in an anterior region (inferior frontal gyms). The authors concluded that these brain activation patterns provide evidence of an imperfectly functioning system for segmenting words into their phonological constituents. However, the contrasting anterior and posterior patterns cannot be accounted for by the same explanation -- that dyslexic readers are using greater effort. Georgiewa et al. (1999) observed differences in patterns of activation of dyslexic and normally reading children in Broca's area and the left inferior temporal region for both nonword reading and phonological transformation tasks. Together, these results are consistent with the conclusion that dyslexics and typical readers have brain-based differences in phonological processing, which may provide a neural signature for dyslexia (Shaywitz et al., 1998).

There is also evidence that dyslexics demonstrate anomalies of the physiological system involved in the fast visual-processing (magnocellular) system but not the slow visual-processing (parvocellular) system. Thus, Eden et al. (1996) found psychophysical and activation evidence to indicate an anomaly in the magnocellular visual subsystem in dyslexic subjects. They reported that in all dyslexics, presentation of moving stimuli failed to produce the same task-related functional activation in area V5/MT (part of the magnocellular visual subsystem) observed in controls. In contrast, presentation of stationary patterns resulted in equivalent activations in V1/V2 and extrastriate cortex in both groups. However, Demb et al. (1997) showed that dyslexics demonstrated reduced activity compared with controls both in the primary visual cortex and the MT visual area. Along this same line, Best and Demb (1999) found that dyslexic subjects with a magnocellular deficit do not always have abnormal symmetry of the planum temporale. Finally, using MEG, Vanni et al. (1997) reported activation of V5 in both dyslexic and controls but a trend for longer latencies in the dyslexics. Both high- and low-contrast stimuli activated the V5 region in dyslexics.

fMRI has also detected differences between dyslexics and able readers on tasks that do not involve visual stimuli or visually presented reading tasks. For example, Corina et al. (2001) used fMRI to compare dyslexics and controls during auditory phonological, lexical access, and tone tasks. The phonological task required children to attend to phonology and ignore semantics, whereas the lexical access task required them to attend to semantics while ignoring phonology. When the tone task was analyzed separately, it did not appear to differentiate the dyslexics and controls, consistent with an fMRS study using PEPSI (Richards et al., 1999).

When the two groups (dyslexics and controls) were compared on two auditory language tasks (phonological and lexical access) and two hemispheres (right and left), four regions -- middle frontal gyrus, inferior temporal gyrus, precentral gyrus, and orbital frontal cortex -- had a significant interaction between auditory language task and group and the last three had a significant three-way interaction with task, group, and hemisphere. A significant underactivation of the left insula was also noted, which may reflect dyslexics' problems in articulatory coding (Dronkers, 1996), phonological decisions (Rumsey et al., 1997), or rapid automatic naming (RAN) (Semrud-Clikeman, Hynd, Novey, & Eliopulos, 1992). Observed underactivation in inferior temporal gyrus may reflect dyslexics' problems in lexical representation.

Thus, dyslexics differed from controls on both of these tasks but in different brain regions, suggesting that they have difficulty in coordinating phonological and semantic .codes of auditory language, which in turn makes it difficult for them to learn to translate visual language into spoken language. One model of the reading system differentiates posterior ventral and dorsal circuits and dorsal connections to a left anterior phonological output system (Eden & Zeffiro, 1998). Results for child dyslexics on the two auditory language tasks implicate both of these pathways. PET studies with adult developmental dyslexics also showed abnormalities in the dorsal system during an auditory rhyme task (Rumsey et al., 1992).

Taken together, the fMRI studies suggest that the differences between dyslexics and controls are unlikely to be localized to one brain region but are more likely to occur throughout certain neural pathways. Pugh et al. (2000a) differentiated between a ventral and a dorsal pathway (with connections to left anterior regions) in the reading system, hypothesizing that dyslexics may differ from controls in both.

A frequently asked question is whether fMRI is developed to the point where it could be used in clinical diagnosis. Results of fMRI and other imaging modalities are ususally reported for groups rather than individuals; results need to be reliable across individual brains within groups to apply this technology to clinical diagnosis. The significant group effect for insula (controls always more activated) found by Corina et al. (2001) is of interest for two reasons. First, several other reports based on group data have shown structural differences between dyslexics and controls (Hynd, Semrud-Clikeman, Lorys, Novey, and Eliopulos, 1990; Pennington et al., 1999) and functional differences between dyslexics and controls (Paulesu et al., 1996) in the insula. Second, the Corina et al. data also show reliability across individual subjects in insula on the lexical access task within the dyslexic group (6 of 7 had no activation) and within the control group (7 of 8 had activation). In addition, the data show reliability across subjects within groups for both the phonological and lexical access tasks in inferior temporal gyrus, where another fMRI study with children (Georgiewa et al., 1999) found differences between dyslexics and controls. Clearly, more research is needed before the reliability of fMRI or any of the imaging techniques is sufficient to be used for diagnostic purposes.

fMRS studies. The purpose of the first study (Richards et al., 1999) was to compare regional changes in brain lactate using fast fMRS (PEPSI) between well-characterized dyslexic children and control children (age- and IQ-matched children who are good readers, ages 9-12, all right-handed boys) during auditory language activation. The boys differed only in reading ability; they did not differ in verbal IQ or age. Brain lactate metabolism was measured during four different tasks (three auditory language tasks and one nonlanguage auditory tone task) in dyslexic boys (n = 6) and in control boys (n = 7). PEPSI (proton echo-planar spectroscopic imaging, 1 [cm.sup.3] voxel resolution) was used to acquire the images. The same stimuli (pairs of real and/or pseudowords) were used for the three auditory language tasks -- phonological, lexical access, and baseline passive-listening tasks. Functional PEPSI data acquired during passive listening to the stimuli were subtracted from data acquired during the other two tasks in which the boys made phonological judgments (Do the words/nonwords rhyme?) or lexical access judgments (Are the words/nonwords real words?). Functional data acquired during scanner noise was subtracted from the tone judgment tasks (Are the tones the same pitch?). The area under the N-acetyl aspartate (NAA) and lactate peaks was measured to calculate the lactate/NAA ratio in each voxel.

Dyslexic boys showed a greater area of brain lactate elevation (2.33 [+ or -] SE 0.843 voxels) than the control group (0.57 [+ or -] SE 0.30 voxels) during a phonological task in the left anterior quadrant (ANOVA, p = .05). This result is consistent with Shaywitz's finding of overactivation in the left anterior brain region (Shaywitz et al., 1998). No significant differences were observed in the lexical access or nonlanguage task. Figure 4 shows an example of a control image and a dyslexic image. Richards et al. (1999) concluded that dyslexic and control children differ in brain lactate when performing a phonological judgment task, but do not differ in nonlanguage auditory tasks. The authors hypothesized that this finding was related to dyslexics being less efficient at phonological processing and thus producing more lactate during the metabolic processes supporting the phonological judgments.

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The purpose of the second study (Richards et al., 2000b), which was a follow-up to the first, was to measure the effect of a phonologically driven treatment for dyslexia on brain lactate response on oral language tasks during functional MR spectroscopy (using the PEPSI technique). Brain lactate metabolism was measured at two different time points (one year apart) during four different tasks (three oral language tasks and one auditory nonlanguage task) in dyslexic boys (n = 8) and in control boys (n = 7) using the same PEPSI neuroimaging technique. Between the first and second imaging session, the dyslexic boys participated in an instructional intervention that provided a phonologically driven treatment in the context of a reading/science workshop (Berninger, 2000). The same four tasks were given and the same baseline subtractions were used as described in the first fMRS study.

After treatment, dyslexics' brain lactate elevation was not significantly different from that of controls in the left anterior quadrant during the same phonological task. Behaviorally, the dyslexic children improved in phonological aspects of reading but not in all aspects of reading. Although the dyslexics and controls did not differ on the lexical access task in the left posterior region prior to phonologically driven treatment, following treatment there was a difference during the lexical access task, suggesting that a brain signature remained. There could have been a lexical access task difference before treatment but the very large variability among the boys may have masked the effect. Figure 5 shows brain imaging data from a dyslexic child demonstrating the effect of the treatment.

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Studies comparing fMRI and fMRS. In the third study, brain activation was measured during three different tasks (phonological, lexical, tone judgment) in eight dyslexic and eight control boys (age- and VIQ-matched right-handed boys) using fMRI and fMRS (Richards et al., 2000a). The age range for both groups was 10-13 years old. fMRI and fMRS were acquired on a GE signa 1.5T system using a custom-made RF head coil (Hayes & Mathis, 1996). During PEPSI (fMRS), the children were asked to listen to aurally presented pairs of words, nonwords, or tones at a rate of one stimulus pair every 4 seconds. During fMRI subjects listened to the same pairs of words (30 seconds on/main task) or tone stimuli (30 seconds off/control task) for a total of three on-off cycles. During the phonological task, subjects listened to the word pairs and judged whether they rhymed or did not rhyme. During the lexical access task, they listened to the same word and/or nonword pairs and judged whether both words were real. Finally, during the tone task, subjects judged whether the first tone was higher than the second. In contrast to the first two studies, the tone task was used for baseline subtraction from the phonological and lexical access tasks because of the subjects' difficulty in rapidly changing between an active and passive listening task in fMRI.

There was a significant positive chi-squared association between fMRI and PEPSI in the left temporal region during the lexical access task for the controls (Fisher's exact test, p = 0.018) (Serafini et al., 2000), but, for the dyslexics, in this same region during the same task, there was a significant negative chi-squared association between fMRI and PEPSI (Richards et al., 2000a). Concerning the comparison between fMRS (PEPSI) and fMRI, there appears to be a greater correspondence between the two techniques in normal controls than in dyslexics (Richards et al., 2000a).

Based on previous work (Frahm et al., 1996; Menon & Gati, 1997), there is evidence that fMRI and lactate fMRS are tightly coupled during part of the temporal evolution of brain activation, but there is also an uncoupling during the later part of brain activation and processing, which may help to explain the disparity we are seeing between fMRI and lactate fMRS in dyslexics. The combining of fMRI and fMRS may be a way to study the vertical organization of language processing on the basis of cortical (obtained from fMRI) and subcortical (obtained from fMRS) information (Serafini et al., 2000).

Progress is being made on identifying brain regions where dyslexics and controls differ. However, current knowledge is incomplete because regions of difference often depend on the exact parameters of image acquisition and task used and because the field lacks a full understanding of the connectivity of brain regions. A primary deficit may occur in a neural network well before the regions downstream where functional deficits are also observed. At this stage of research, it is reasonable to conclude that there is converging evidence suggesting brain-based differences in the way dyslexics and matched controls process written words and related oral language processes.

Conclusions

The neuroimaging studies discussed in this article have established that there are significant differences between normal and dyslexic adults/children in the way their brains are activated on specific aural or written language tasks. There is also evidence that these differences may weaken with appropriate treatment. Because most children respond positively at a behavioral level to instructional interventions, many educators have rejected the notion that reading problems are brain-based. However, functional brain studies have demonstrated that dyslexia is a brain-based disorder, even though it responds to instructional treatment. Although it is still premature from a scientific perspective to use brain scans as a diagnostic tool for dyslexia or to generate lesson plans or teaching techniques for the classroom, these neuroimaging findings shed light on brain mechanisms involved with dyslexia at a developmental stage when dyslexia is amenable to treatment. Neuroimaging techniques are most likely to be educationally relevant and useful if basic research on reading acquisition in cognitive science and psycholinguistics and applied research on reading instruction are carefully integrated with neuroimaging studies.

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NOTES

Grant No. P 50 33812 from the U.S. National Institute of Child Health and Human Development (NICHD) supported preparation of this article. The author thanks Alicia Richards for the photograph used in Figure 1.

Please address correspondence to: Todd L. Richards, Department of Radiology, Box 357115, University of Washington, Seattle, WA 98195. Phone [phone], fax [phone], e-mail [e-mail].

TODD L. RICHARDS, Ph.D., is professor, University of Washington.