Neuropsychologia 65 (2014) 113–124

Contents lists available at ScienceDirect

Neuropsychologia journal homepage: www.elsevier.com/locate/neuropsychologia

Preserved mid-fusiform activation for visual words in a patient with a visual word recognition impairment Suzanne E. Welcome a,n, Adrian Pasquarella b, Xi Chen c, David R. Olson c, Marc F. Joanisse d a

Department of Psychology, University of Missouri – St. Louis, One University Boulevard, 325 Stadler Hall, St. Louis, MO 63121-4499, USA School of Education, University of Delaware, Newark, DE, USA c Department of Applied Psychology and Human Development, Ontario Institute for Studies in Education of the University of Toronto, Toronto, ON, Canada d Department of Psychology and The Brain and Mind Institute, The University of Western Ontario, London, ON, Canada b

art ic l e i nf o

a b s t r a c t

Article history: Received 3 September 2013 Received in revised form 8 October 2014 Accepted 13 October 2014 Available online 23 October 2014

Previous functional imaging studies have highlighted the role of left ventral temporal cortex in processing written word forms. We explored activation and anatomical connectivity of this region in HE, a professional writer with alexia as a result of stroke affecting primarily white matter in the left inferior temporal lobe. We used a one-back visual recognition task and functional Magnetic Resonance Imaging to elicit automatic activation to various orthographic and non-orthographic stimuli. Surprisingly, HE showed cortical activation in the left mid-fusiform area during the presentation of words and word-like stimuli, suggesting that this region's role in processing visual words is intact despite his severely impaired reading. Diffusion Tensor Imaging data further suggest that HE shows an anatomical disconnection between the ventral temporal cortex and posterior middle temporal cortex. Together, these findings suggest that activation of word-specific regions of mid-fusiform gyrus is not sufficient to yield the conscious experience of reading in the absence of activity in downstream regions of the classical reading network. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Diffusion tensor imaging (DTI) Disconnection syndrome Reading Connectivity Functional MRI

1. Introduction Skilled reading requires linking the visual system with parts of the language network that process language sounds and word meanings. Lesions can disrupt these connections, resulting in a severe reading impairment termed alexia. Pure alexia, or alexia sine agraphia, is an acquired reading disorder that typically results from lesions in or disconnections of the left ventral occipito-temporal region (Dehaene and Cohen, 2011). It is characterized by a deficit in recognizing visual words without accompanying deficits in writing, producing speech, or understanding spoken language (Montant and Behrmann, 2000). Alexia is classically considered a disconnection syndrome in which the lesion disconnects the occipital regions involved in visual processing from left temporal and parietal regions that support language processing (Dejerine, 1892; Epelbaum et al., 2008). Here, we report the case of patient HE, an author with alexia following a stroke affecting his left inferior temporal lobe (Engel, 2007). HE shows difficulty with visual word recognition in spite of relatively well-preserved writing and oral language abilities. Of interest were the neural correlates of his reading deficit, both in terms of location of word-specific activation and white matter connectivity.

n

Corresponding author. Tel.: þ 1 314 516 5383; fax: þ 1 314 516 5392. E-mail address: [email protected] (S.E. Welcome).

http://dx.doi.org/10.1016/j.neuropsychologia.2014.10.012 0028-3932/& 2014 Elsevier Ltd. All rights reserved.

To address this we used functional MRI and diffusion tensor imaging data to reveal the neural pathways underlying impaired word recognition. We investigated patterns of activation associated with processing words and other visual stimuli in HE 10 years after the stroke. The period of time that elapsed between the onset of alexia and scanning likely means that possible neural reorganization for reading is complete, allowing us to investigate the network supporting stable, but impaired reading. Of particular interest was the role of ventral temporal regions in HE's reading impairment. Neuroimaging studies of typical readers have highlighted the central role of a portion of the left mid-fusiform gyrus and occipito-temporal sulcus, termed the visual word-form area (VWFA), in visual word recognition (Cohen et al., 2000). Overall, Dehaene and Cohen (2011) have suggested that the VWFA is “attuned to reading-specific processes, and partially selective for written strings relative to other categories” (p. 254). This region is reported to be more sensitive to written words than other visual stimuli, such as checkerboards (Cohen et al., 2002), false fonts (Brunswick et al., 1999), and consonant strings (Cohen et al., 2002). Further, this region demonstrates invariance to parameters such as position, case and font (Dehaene et al., 2005; Cohen et al., 2003). It also shows consistent activation across writing systems, indicating that the same neural tissue is engaged by different scripts (Liu et al., 2008; Bolger et al., 2005). Several findings suggest that experience shapes the role of the VWFA. Activation in the VWFA is modulated by literacy (Dehaene et al., 2010). Further, portions

114

S.E. Welcome et al. / Neuropsychologia 65 (2014) 113–124

of this region show sensitivity to bigram frequency, a sensitivity that reflects experience with letter pairings (Vinckier et al., 2007). However, the exact function of the VWFA remains somewhat uncertain (Price and Devlin, 2011, 2003). Specifically, there is debate regarding the region's specificity for familiar words over other visual objects, as the ventral occipito-temporal cortex shows sensitivity to non-orthographic stimuli (Starrfelt and Gerlach, 2007). Others have demonstrated that ventral occipito-temporal cortex responds to nonvisual stimuli (Hillis et al., 2005; Vigneau et al., 2005). Thus, an alternate suggestion that the ventral occipito-temporal cortex is involved in the integration of bottom-up signals from sensory cortices and top-down signals from higher order processing regions has emerged (Price and Devlin, 2011). Regardless of the precise nature of the processing done in the VWFA, lesion data have suggested that this region can be implicated in some forms of alexia. Patients with lesions impacting the VWFA have shown behavioral profiles consistent with pure alexia, while patients with lesions in the left occipito-temporal cortex but not the VWFA showed other patterns of deficit (Cohen et al., 2003; Leff et al., 2006; Pflugshaupt et al., 2009). Damage that spared the VWFA but severed the fiber tracts leading to it also led to pure alexia (Cohen et al., 2004; Molko et al., 2002), suggesting that deafferentation of this region also yields a reading impairment. Further, surgical removal of left occipito-temporal cortex previously responsive to words led to a severe reading impairment without accompanying impairment in processing spoken language or other classes of visual stimuli (Gaillard et al., 2006). In the present study, we consider whether HE's functional activation and white matter tractography data are consistent with the notion of a dysfunctional or deafferentated VWFA, or instead suggest another potential mechanism by which ventral temporal lesions can lead to reading deficits. When patients with damage to occipito-temporal cortex process written language, they characteristically demonstrate letterby-letter (LBL) reading, an effortful and slow process that results in magnified word-length effects (Behrmann et al., 1990). While some patients with pure alexia show residual activation in the left VWFA adjacent to the lesion during covert reading of words (Cohen et al., 2003, 2004), there is evidence that the neural substrates of LBL reading differ from those that underlie typical reading. For instance, neuroimaging data suggest LBL reading is typified by greater right fusiform activation (Pyun, Sohn, Jung, & Nam, 2008; Ino et al., 2008) or broader activation in frontal regions (Ino et al., 2008; Cohen et al., 2004). It is possible that this right fusiform activation is specific to the laborious task of LBL reading. In the current study, we investigate the possibility that normal ventral-temporal activation can be evoked in fMRI using a task with brief stimulus presentation and minimal task demands, since such a task paradigm discourages LBL reading in alexia. During scanning, we used a one-back visual matching task, which requires the individual to briefly maintain a viewed item in memory to compare it to a subsequent stimulus. The one-back task does not necessitate the recognition of a given object, nor does it require the viewer to specifically access a word's orthographic, phonological or semantic coding; it simply requires the brief maintenance of a visual form for comparison to a subsequent form. That said, the automaticity of visual word recognition mea ns the task should evoke activation in portions of the ventral occipito-temporal reading network if they are available. As we report below, behavioral data suggest that in spite of significant difficulties with overt word recognition, HE was able to perform this task at a good level of accuracy. Likewise, the task also allows the comparison of activity evoked by multiple types of visual stimuli, while maintaining similar demands on working mem ory and attention. Thus, we examined both orthographic and non-orthographic stimuli including nonword letter strings, an unfamiliar orthography, visual objects and faces.

As noted above, it was desirable to minimize working memory demands and discourage a LBL reading strategy, to better capture automatized processing of words. The one-back task was desirable in this regard as it involved a relatively short stimulus onset asynchrony and brief presentation rate and thus is unlikely to allow for LBL reading. This allowed us to assess whether greater right hemisphere involvement, previously associated with LBL reading, was also evident during this form of visual word processing. We compared HE's patterns of activation to those of two typical readers in order to identify portions of the reading network that showed atypical activation. Further, we also used diffusion tensor imaging (DTI) to specify the coherence of white matter tracts throughout the reading network, allowing us to localize differences in white matter connectivity that might relate to the behavioral and fMRI activity patterns characteristic of alexia.

2. Methods Scanning was performed in HE and two neurologically intact control participants (DO and SC). Informed consent was obtained from all participants, and the study had the approval of the University of Western Ontario Health Sciences Research Ethics Board.

2.1. Case HE HE is a right-handed man who worked as a novelist for over two decades prior to brain injury, and has also previously worked in television journalism. At the time of study, he was 79 years old. In 2001, a spontaneous intracerebral hemorrhage in the left inferior occipito-temporal region resulted in difficulty with visual word recognition while sparing oral language abilities and writing (Engel, 2007; Sacks, 2010). Examination of an MRI scan obtained 6 years post-stroke indicates a focal lesion largely or exclusively restricted to white matter adjacent to the inferior occipito-temporal cortex (Fig. 1). The volume of the lesion was approximately 16.3 cm3, and the approximate center of mess of the lesion is  30,  56,  12 (MNI coordinates). With therapy and practice, HE's reading has gradually improved, though it remains dysfluent and laborious. The Test of Word Reading Efficiency (TOWRE; Torgesen et al., 1999) is comprised of a Sight Word Efficiency subtest (a speeded measure of word reading) and a Phonemic Decoding Efficiency subtest (a speeded measure of pseudoword reading). HE scored below the range of scaled scores in Sight Word Efficiency (raw score of 31; o 0.001 percentile) and Phonemic Decoding Efficiency (raw score of 19; 1st percentile). To assess HE's reading abilities under less time pressure, the Woodcock Test of Reading Mastery-Revised (WRMT-R; Woodcock, 1998) was administered without any time constraint. The Word Identification subtest assesses accuracy in the reading of real words, and the Word Attack subtests assess phonological decoding through the accuracy of reading pseudowords. Without time pressure, HE's single-word reading scores are much improved; on the Word Identification subtest, he obtained a scaled score of 106 (65th percentile) and on the Word Attack subtest he obtained a scaled score of 101 (54th percentile). The discrepancy between timed and untimed reading performance suggests the use of a letter-by-letter (LBL) reading strategy. This was further confirmed by performance on a computer-administered lexical decision task (Pasquarella et al., 2011). When given unlimited viewing time, he was highly accurate (98%), though slow (mean RT ¼ 4063). Crucially, he showed a significant effect of word length [F(11,74) ¼5.42, p o 0.001], such that longer words resulted in a significantly longer RTs. When performing lexical decision when stimuli were presented for 500 ms, his overall performance was not significantly different from chance. However, the effect of word length on accuracy remained significant, marked by better performance on shorter words [F(11, 153)¼ 1.92, po 0.05]. To measure HE's other linguistic abilities, he completed the spelling subtest of the Wide Range Achievement Test-4 (Wilkinson and Robertson, 2006), a dictated spelling task of words of increasing length and difficulty. He obtained a scaled score of 88 (21st percentile). On the Elision subtest of the Comprehensive Test of Phonological Processing (CTOPP; Wagner et al., 1999), HE's raw score (14) corresponded to roughly the 25th percentile (the test is not normed for his age range). While these scores may indicate some difficulty with other language tasks, they also highlight the particular difficulty HE had with the speeded reading measure. While performance on the spelling task was weaker than expected, it is noteworthy that the task involves manually writing out words of increasing length, and his performance may have been impacted by his self-reported difficulty re-reading words that he has previously written (Engel, 2007), which makes it difficult to monitor for and correct spelling errors as they occur.

S.E. Welcome et al. / Neuropsychologia 65 (2014) 113–124

115

Fig. 1. Location of HE's lesion, resulting from a spontaneous intercerebral hemorrhage in 2001. (A) T1-weighted scan showing the approximate location of the lesion. (B) T1-weighted images with the lesion colored in red. (C) T2-weighted images contrasting the lesion from healthy tissue. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 2.2. Control participants Data from two neurologically healthy controls were also obtained in order to better establish the sensitivity of the imaging measures to identify neural organization on a single-subject basis. We sought to identify both an older and younger male adult with similar levels of education to HE, given the possibility that education reading experience plays some role in the nature of his difficulties. DO is a 75-year-old right-handed man with a similar educational background to HE. SC is a 29-year-old right-handed man with 18 years of education. HE is right handed and a native speaker of English with minimal proficiency in other languages. An additional four participants completed the behavioral portion of the experiment outside of the scanner. These individuals served as an age-matched control group for HE, providing a general baseline for task performance in older literate adults. These individuals (3 females, 1 male) had an average age of 70.5 years, were all strongly right-handed, and showed limited exposure to other languages.

2.3. fMRI task and stimuli Participants performed a one-back task in which they viewed sequences of visual stimuli and were instructed to press a button when the same stimulus appeared in two consecutive trials (similar to Tagamets et al. (2000) and Haxby et al. (2001)). Stimuli were projected onto a screen at the opening of the scanner bore that was viewed using a mirror placed above the head coil. During the experimental task, stimuli were presented in the center of the screen for 600 ms, followed by a fixation cross for 400 ms. Ten percent of items were repetitions of the stimulus item immediately preceding them. An additional ten percent of items were near-repetitions, chosen to be very similar to the previous stimulus (e.g., for orthographic stimuli, one letter different from the previous word). These nearrepetitions were included to maximize attention and discourage the use of a holistic visual strategy (i.e., responding based on general word shape) in favor of strategies involving processing words as linguistic units. RTs were recorded from the onset of the repeated stimulus. Only correct RTs were included in analyses. Stimuli in each block were from a set of 54 items in each of the following categories: faces (stimulus images courtesy of Michael J. Tarr, Center for the Neural Basis of Cognition, Carnegie Mellon University, http://www.tarrlab.org/), objects (Snodgrass and Vanderwart “Like” Objects; Rossion and Pourtois, 2004), Korean words written in Hangul orthography (an unfamiliar orthography for all participants), consonant strings, pronounceable nonwords, and real English words.

Each stimulus type was presented separately, in 20-item blocks in a random order. Each block lasted 20 s and was followed by a rest period of 10 s of fixation. Each block type was presented once during each 11.5-min experimental run, and each participant completed five experimental runs with short breaks between each.

2.4. fMRI image acquisition and processing T1 and T2 anatomical scans acquired in 2008 were used to initially visualize the lesion (Fig. 1). T1-weighted (1.2  1.016  1.06 mm3, 166  256  256 RAI) and T2weighted (0.938  0.938  3 mm3, 256  256  50 RAI) anatomical scans were acquired on a 3T GE Signa scanner. MRI imaging for fMRI and DTI tractography in the present study was performed on a 3T Siemens Tim Trio scanner equipped with a 32-channel head coil for transmit/receive. A conventional T1-weighted anatomical scan was used to select the location of 45 transverse slices to be acquired during the functional sequences, oriented to acquire the entire cerebrum (excluding portions of the cerebellum). Functional runs consisted of 276 T2n-weighted EPI volumes acquired in an axial orientation (TR¼ 2.5 s, TE ¼30 ms, flip angle ¼ 901, FOV ¼240 mm, 80  80  45 matrix, voxel size ¼ 3  3  3 mm3) using an iPAT parallel acquisition sequence (generalized auto-calibrating partially parallel acquisition [GRAPPA]; acceleration factor¼ 2). A 3D T1-weighted anatomical scan was acquired mid-session (MPRAGE; GRAPPA acceleration factor¼ 2; TR ¼ 2.3 s, TE ¼2.98 ms, FOV ¼ 256 mm). The total session took approximately 90 min. Motion correction was performed using an automatic correction procedure during scanning, registered to the first scan of each sequence. The AFNI software package (Cox, 1996) was used to correct for motion between runs by spatially realigning each run to the first volume after the acquisition of the anatomical scan using a 3D rigid body transformation (AFNI 3dvolreg). Slice timing was adjusted offline (AFNI 3dTshift, quintic interpolation). For each subject, a general linear model (GLM; AFNI 3dDeconvolve) was created. Seven GLM predictors were created (faces, scenes, objects, Hangul words, consonants strings, pronounceable nonwords, and English words). Each was modeled as a boxcar function for each block that was then convolved with a canonical hemodynamic response function. All statistical analyses were performed in native (subject) space. However, in order to report Talairach coordinates of significant findings and to provide comparisons between subjects, anatomical coregistration was performed by spatially transforming each subject's anatomical scan to standard 3D space (Talairach and Tournoux, 1988). This transformation was applied to each subject's statistical maps, which were then resampled to 1 mm3 and spatially filtered using a 5 mm FWHM Gaussian kernel.

116

S.E. Welcome et al. / Neuropsychologia 65 (2014) 113–124

2.5. Diffusion Tensor Imaging acquisition and processing Diffusion-weighted brain images were acquired at the end of the scanning session. The acquisition was done in the axial plane using a single-shot echo planar imaging sequence (60 slices with 2 mm slice thickness with no interslice gap, 64 diffusion directions with b¼ 700 s/mm2, TR ¼6.5 s, TE ¼75 ms, matrix ¼ 96  96, voxel size¼ 2.083  2.083  2 mm3, FOV ¼200 mm). Images were processed using the FSL software package (FMRIB Software Library, FMRIB, Oxford, UK; Smith et al., 2004). A brain mask for each subject was created using the Brain Extraction Tool (Smith, 2002). We performed affine coregistration (Jenkinson and Smith, 2001) to calculate the transformation matrix necessary to align the T1-weighted image and the first diffusion weighted volume. FDT (Behrens et al., 2003) was used to fit the tensor model and to compute fractional anisotropy (FA) maps. We used probabilistic tractography to track fibers from the seed mask in each individual (Behrens et al., 2003). The probabilistic approach provides information regarding the confidence with which a given white matter pathway is identified and has advantages over deterministic algorithms with regard to noise resistance and tracking near areas of low anisotropy (Behrens et al., 2007, 2003). Two cortical regions of interest in each hemisphere (the posterior portion of the middle temporal gyrus and the temporal occipital fusiform cortex) were selected from a probabilistic atlas provided with FSL (the Harvard-Oxford Cortical Structural Atlas) and served as seed regions. As will be discussed below, these regions were selected because functional data suggested a disconnection between ventral temporal cortex and the MTG and the aim of the DTI study was to examine the structural correlate of this effect. Further, the MTG is relatively distant from the site of lesion and therefore any observed differences between participants are not a necessary consequence of the stroke itself, but instead reflect altered connectivity from healthy tissue. For each voxel in each seed mask, 5000 streamlines were sent out, with a steplength of 0.5 mm and a curvature threshold of.02, and the probability of those streamlines passing through each voxel in the brain was recorded. To eliminate unlikely pathways, we thresholded the data to include voxels with a value greater than 2  10  6% of the number of streamlines sent out (following Rilling et al. (2008)). We investigated pathways in both the left hemisphere (the site of the lesion in HE) and the non-injured right hemisphere, which ensured that subject-wise differences in computed tract lengths were not due to idiosyncratic differences in an individual's overall fiber traceability.

HE's performance did not differ significantly from DO, the agematched control (tso1). In order to ensure that both DO and HE showed age-typical performance, behavioral data were collected from an additional four participants. One-sample t-tests were used to evaluate whether the mean performance of the control group was significantly different from HE's performance. These comparisons are presented in Table 2. No significant differences emerged between HE and his age-matched peers. To examine the effect of stimulus type, a repeated measures ANOVA was performed on d0 and RT measures in the group of older controls. Performance differed significantly with condition for accuracy and d0 (accuracy: F(5)¼ 8.63, p o0.05; d0 F(5)¼ 9.28, po 0.01) but not RT [F(5)¼ 0.65, p 40.10]. Pairwise comparisons demonstrated that accuracy and d0 were lower for Hangul strings than all other stimulus types. Accuracy was significantly higher for objects than consonant strings, and d0 was significantly higher for objects than for words, nonwords, and consonant strings. Similar patterns were shown by HE and SC, indicating that participants had particular difficulty recognizing repeating Hangul strings and were particularly sensitive to repeated line drawings of objects.

Table 2 Results of one-sample t-test and Descriptive Statistics Comparing HE to agematched controls. Measure

Condition

M

SD

Comparison value (HE)

t (5)

Accuracy

Words Nonwords Consonant strings Korean strings Objects Faces

93.90 93.95 92.85 89.00 95.80 92.80

1.59 1.59 1.34 2.47 2.59 3.19

92.75 92.50 92.00 89.50 94.00 95.00

1.66 2.03 1.42 0.45 1.55 1.54

d0

Words Nonwords Consonant strings Korean strings Objects Faces

2.29 2.42 1.98 1.30 4.20 2.98

0.41 0.38 0.36 0.64 1.44 1.22

1.95 2.03 1.81 1.47 4.05 2.54

1.83 2.35 1.09 0.61 0.24 0.81

RT

Words Nonwords Consonant strings Korean strings Objects Faces

529 514 521 508 501 519

24.5 19.4 37.3 17.0 28.2 31.0

527 530 527 491 501 513

0.18 0.59 0.38 2.30 0.02 0.41

3. Results 3.1. Behavior HE and control participants DO and SC were able to perform the one-back task both quickly and accurately (Table 1). An ANOVA demonstrated that reaction time and accuracy significantly differed between participants (RT: F(2, 15)¼ 24.0, po0.0001; d0 : F(2, 15)¼55.9, p40.0001 ). However, post-hoc tests revealed that these differences were driven by subject SC, who differed from both HE and control participant SC (all ts45 pso0.0001). This was likely due to SC's relatively younger age compared to HE and control participant DO. Table 1 Behavioral performance on the one-back task. Participant

Accuracy

Hits

Misses

False alarms

Correct rejections

d0

RT

HE

Words Nonwords Consonants Korean Objects Faces

92.8 92.5 92.0 89.5 94.0 95.0

39 15 18 9 4 6

41 27 23 11 6 4

17 3 9 10 0 1

703 345 350 170 90 89

1.95 2.03 1.81 1.47 4.05 2.54

527 530 527 491 516 513

(51) (42) (39) (47) (59) (84)

DO

Words Nonwords Consonants Korean Objects Faces

92.3 92.0 92.5 90.5 95.0 92.0

23 12 15 5 5 2

57 30 26 15 5 8

5 2 4 4 0 0

715 356 355 176 90 90

1.90 1.97 1.94 1.33 4.30 3.46

512 549 567 486 518 537

(55) (38) (34) (186) (86) (28)

SC

Words Nonwords Consonants Korean Objects Faces

99.0 99.0 99.0 96.0 100 100

77 37 37 5 9 10

3 3 3 15 1 0

2 1 0 4 0 0

718 359 360 176 90 90

4.55 4.25 5.44 2.68 4.34 5.39

411 410 422 478 326 380

(22) (31) (7) (38) (21) (23)

S.E. Welcome et al. / Neuropsychologia 65 (2014) 113–124

3.2. fMRI results 3.2.1. Words versus fixation In order to localize the network involved in reading familiar English words, we contrasted English words with the interstimulus fixation period (Fig. 2). Both control participants showed widespread activation for this contrast. The largest clusters of activation were in the left and right inferior occipito-temporal regions. HE showed a largely similar spatial pattern of activation. While the spatial extent of activation was similar across participants, HE showed less statistically reliable activation within the occipito-temporal regions of both hemispheres. In each of the three participants, activation extended near the average VWFA coordinates reported by Cohen et al. (2000) (Talairach coordinates: x ¼ 43, y ¼ 54, z ¼  12). We note that, in all three participants, the peak of activation and the center of mass of these clusters were located posterior to these coordinates. All three participants showed additional large clusters of activation in the left inferior frontal gyrus and other portions of the temporal lobe bilaterally. This is consistent with the assumption that passive viewing of words will tend to activate brain regions commonly associated with a “reading network” (Sandak et al., 2012). In order to examine the response specificity of these regions showing activation during the viewing of words, we obtained beta weights corresponding to the degree of activation in these regions for words and the other classes of stimuli. This analysis suggested that while these regions showed activation to words, they also were activated during the viewing of other complex visual stimuli, including faces, objects, and stimuli in a non-familiar orthography (Fig. 2). While ventral portions of the reading network showed similar patterns of activation in HE and the control participants, other regions did not. In both control participants, words elicited activation near the superior temporal sulcus bilaterally (Fig. 3). This middle temporal activation corresponds to the “lexico-semantic reading route” in the model presented in Epelbaum et al. (2008). However, HE showed no clusters of significant activation in the left or right

117

middle temporal gyri. Thus, in HE, ventral temporal activation did not feed into this portion of the lexico-semantic reading route. In order to address the possibility that HE showed word-related activation in the MTG that would become apparent with a less stringent threshold, HE's data were examined using various statistical thresholds. Even with very liberal thresholds (po0.05), no significant activation was seen in HE's left MTG during the viewing of words, supporting our view that this null result is not due to our use of an unnecessarily stringent statistical threshold.

3.2.2. Words versus objects In order to explore patterns of activation more specifically associated with reading English words, rather than processing complex visual objects in general, we contrasted activation shown during the processing of English words with activation shown during the processing of colored line drawings of objects (Fig. 4). Line drawings, like words, have associated semantic and phonological codes. However, identification of words clearly places different demands on the visual system than identification of objects, due at least in part to critical differences in the visual properties of orthographic stimuli (Szwed et al., 2011). HE and control participants showed regions of greater activation to words than objects in inferior occipito-temporal regions (Fig. 4, red circles). For all participants, this activation was greater for not only familiar English words, but also nonwords and consonant strings, and to some degree, Hangul script. All three participants also showed regions in which activation to objects was greater than activation to words. One cluster of activation in the right hemisphere (Fig. 4, blue arrows) was greater for faces and objects than orthographic stimuli (consonant strings, nonwords, and words) for all participants. Thus, in all participants, different portions of ventral temporal/occipital cortex show different patterns of activation for different types of stimuli. Similar to words versus fixation, contrasting words with objects yielded activation in the left middle temporal gyri of both

Fig. 2. Ventral occipital and temporal regions showing significantly more activation during the 1-back task with familiar English words than during the inter-stimulus fixation period. Data are plotted separately for two control participants SC and DO, and patient HE. Graphs show standardized fMRI signal levels (GLM beta weights) from a ventral temporal ROI in the left and right hemispheres corresponding to the degree of activation for each class of stimuli.

118

S.E. Welcome et al. / Neuropsychologia 65 (2014) 113–124

Fig. 3. Middle temporal regions showing significantly more activation during the 1-back task with familiar English words than during the inter-stimulus fixation period. Data are plotted separately for two in control participants SC and DO, and patient HE. Green circles indicate the region in the middle temporal gyrus activated in SC and DO, but not HE. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4. Regions showing significant differences during the 1-back task with familiar English words and with line drawings of objects. Hot colors indicate greater activation during the viewing of words than objects, cool colors indicate greater activation during the viewing of objects than words. Graphs show standardized fMRI signal levels (GLM beta weights) for each class of stimuli from Region 1, circled in red, and Region 2, indicated with blue arrows. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

S.E. Welcome et al. / Neuropsychologia 65 (2014) 113–124

control participants (Fig. 5). In these individuals, this region showed activation to consonant strings, nonwords, and familiar words, suggesting that activation is specific to stimuli composed of letters. One plausible interpretation is that control participants were attempting to access phonological information when presented with strings composed of familiar letters. No readingspecific activation was seen in HE's left middle temporal gyrus, however. The line drawings used in this study differed from words in some basic visual properties. For example, the words were black and white, while the objects were colored, and it is well known that low-level visual features influence activation in ventral temporal cortex (Dehaene and Cohen, 2011). To address this potential confound, we performed a more stringent, but statistically weaker, test that contrasted activation during English words with activation during Hangul strings (see Supplementary Fig. S1). Since both English and Hangul are orthographic stimuli, they likely share many visual characteristics common to all writing systems but not other objects (Szwed et al., 2011). However, the English words were both more familiar and pronounceable for our participants. These analyses revealed that all participants showed specific activation in ventral temporal cortex in response to Hangul strings. As in the object/word contrast, both control participants, but not HE, showed regions of English-word-related activation in the MTG. 3.3. Diffusion Tensor Imaging DTI was used to further examine the anatomical basis of the functional disconnection between ventral temporal areas and posterior MTG. First, we used probabilistic tractography to examine white matter connectivity from the fusiform cortex in both left and right hemispheres. As depicted in Fig. 6, in all participants, right hemisphere white matter pathways extended superior and anterior of the seed region, connecting the fusiform cortex with the rest of the

119

language network. These pathways included portions of the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus, as identified by the Johns Hopkins University white-matter tractography atlas (Wakana et al., 2004). Similar pathways were also seen in the left hemisphere of both control participants. However, the results were appreciably different in HE, where pathways extending from fusiform cortex did not reach superior and anterior regions. To quantify this difference, we examined the volume of tracts extending from the fusiform seed region into superior regions (superior to z¼6 in MNI space). These data are presented in Table 3. It should be noted that the volume of HE's fiber tracts in both the right and left hemispheres is somewhat reduced relative to both controls. Given our sample size, it is difficult to know whether his tract volume is within the typical range. However, it likely reflects his premorbid state given that his infarct is isolated to the left hemisphere. More importantly though, HE showed a marked asymmetry in left versus right tract volume, such that he showed a smaller fiber tract in the left hemisphere, a pattern that was not evident in either control participant. Next, we used probabilistic tractography to examine white matter connectivity from the MTG in both left and right hemispheres. This region's location is relatively distant from the location of HE's lesion. Thus, any observed differences in tractography from this unaffected region are not a necessary consequence of the lesion itself, but instead will reflect the interruption of white matter projections extending from it. As depicted in Fig. 7, all participants' right hemisphere white matter pathways, including the inferior longitudinal fasciculus, connect the posterior portion of the right MTG with ventral temporal/occipital cortex. Similar pathways are also seen in the left hemisphere of both control participants. However the results were appreciably different in HE, where pathways extending from the posterior portion of the left MTG failed to reach ventral temporal/occipital cortex. To quantify this difference, we examined the volume of tracts extending from the posterior MTG seed region

Fig. 5. Regions showing significant differences during the 1-back task with familiar English words and with line drawings of objects. Graphs show standardized fMRI activation levels (GLM beta weights) for each class of stimuli from the middle temporal region circled in red. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

120

S.E. Welcome et al. / Neuropsychologia 65 (2014) 113–124

Fig. 6. Results of probabilistic tractography examining connectivity from the fusiform cortex in the left and right hemispheres. Tracts in the control participants are shown in blue and green, while tracts in HE are shown in red. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Table 3 Volume (mm3) of tracts extending from the fusiform seed region to zo 6. Participant

Left

Right

SC DO HE

39,376 39,896 12,472

41,648 34,376 20,272

into posterior inferior regions (between  20 and 6 in the zdimension, yo  70). These data are presented in Table 4. As in the fusiform seed region, HE's tracts are smaller in both hemispheres. Also like the fusiform seed region, HE showed an asymmetry not evident in control participants marked by a less extensive tract in his left hemisphere compared to right hemisphere. Together, these findings suggest an anatomical basis for the observation that HE's typical ventral temporal/occipital fMRI activation is not associated with typical MTG activation.

4. Discussion We used a one-back task to investigate automatic processing of various orthographic and non-orthographic stimuli in HE, a patient with a visual word recognition deficit following stroke. HE's performance on the one-back task was markedly different from his performance on the lexical decision task despite similar rates of stimulus presentation for the two tasks. Specifically, lexical decision times were very long, generally less accurate than expected in typical readers, and showed a pronounced word length effect, a pattern that is typical of reading in alexia. In contrast, HE demonstrated similar performance on the one-back task to DO and other participants similar in age. HE, therefore, shows the ability to hold a visual word in memory and compare it to a subsequent word under conditions in which he would not be

able to accurately name the word, or classify it as a word or nonword. One possible interpretation of this pattern of results is that while lexical decision requires overt recognition of a word, the one-back task does not; instead it can be accomplished through a simple visual comparison of words without accessing their lexical or sublexical characteristics. The one-back task was specifically chosen to minimize the demands on the viewer to access an orthographic, phonological, or semantic code for the item, and could be performed without any knowledge of the identity of a specific stimulus. Thus, the possibility of making matching decisions regarding stimuli without accessing information about their lexical status may explain HE's performance. A similar pattern of results was shown in a previous study of a patient with severe alexia who performed at chance on a lexical decision task, but could nonetheless identify the language in which words were printed quickly and accurately (Di Pietro et al., 2012). We used fMRI to examine the neural substrates of automatic activation to visual stimuli in HE, a patient with alexia. HE showed a relatively typical pattern of activation in ventral temporal/ occipital areas that appear to correspond to VWFA. While HE's ventral temporal activation to words appeared largely typical, he failed to show typical activation in a left middle temporal area that was recruited by both control participants during the viewing of familiar English words. DTI data suggest that white matter pathways between the ventral temporal cortex and posterior middle temporal cortex were disrupted in HE. As we discuss below, this appears to reflect a disconnection of regions subserving a visual analysis of written words from those regions responsible for accessing words' semantic and/or phonological forms. Contrary to our initial expectations, HE showed relatively typical activation of peri-lesional ventral-temporal cortex during the automatic processing engaged in the one-back repetition detection task despite his severe difficulties with explicit word recognition. Like our control participants, HE showed selective activation to different categories of visual stimuli in ventral cortex. One region showed

S.E. Welcome et al. / Neuropsychologia 65 (2014) 113–124

121

Fig. 7. Results of probabilistic tractography examining connectivity from the MTG in the left and right hemispheres. Tracts in the control participants are shown in blue and green, while tracts in HE are shown in red. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Table 4 Volume (mm3) of tracts extending from the posterior middle temporal gyrus seed region to yo  70, z  20–6. Participant

Left

Right

SC DO HE

199,128 159,296 40,432

157,152 138,160 60,568

similar response properties to control participants, with orthographic stimuli yielding less activation than picture stimuli. A more posterior occipital region showed the opposite effect, marked by greater activation to words than other complex visual stimuli in HE and in both control participants. While similar areas of ventral temporal cortex were active across participants, there are additional factors to note. First, in some contrasts, HE showed less reliable differences in activation to different classes of stimuli than DO. Thus, while different portions of his ventral temporal cortex showed stronger activation to different stimuli, these effects were different in magnitude from DO. This could reflect underlying differences in the responsiveness of these brain regions in HE, such that regardless of the stimulus, HE's ventral temporal cortex is less reliably active. An alternate explanation is that the ventral temporal regions in control participants show top-down activation from phonological/semantic areas (e.g., MTG). In HE, anatomical and functional connections with regions involved in phonological and semantic analyses might be severed, and top-down activation reduced, resulting in less reliable activation in ventral temporal regions. Second, control SC was

markedly younger than either HE or DO, and his behavioral performance was superior to that of all of the older controls. Despite this age difference, SC's imaging data revealed a pattern very similar to DO, and thus it appears that he engaged similar neural networks to achieve a different level of performance. Of particular interest in our study was the finding that HE's left ventral temporal activation for words reflect automatic but disconnected bottom-up visual word processing. This account differs somewhat from how similar activation has been previously described in patients with alexia. Cohen et al. (2004) suggest that post-morbid activation in VWFA that they have observed result from top-down processes exerting their influence through preserved connections with other language areas. That is, the region is in fact disconnected from bottom-up visual information, but still receives top-down modulation from amodal cortical regions being activated via a decompositional (phonological) reading route. Such a finding would be in keeping with prior functional imaging studies that find VWFA activation during auditory word recognition in neurologically intact individuals (Booth et al., 2002; Kherif et al., 2011), which is explained as resulting from a top-down and automatic activation of orthographic codes. In HE, however, we have reason to believe that the situation is different, and that ventral temporal activation is the result of intact bottom-up visual pathways rather than indirectly via top-down activation. The one-back paradigm we used involved relatively brief (600 ms) stimulus presentation, a rate at which HE reported not being able to identify words accurately. Indeed, HE performed at chance level on a lexical decision task at a similar presentation rate of 500 ms. Further, word-related activation in middle temporal regions, which we observed in the controls, was absent in

122

S.E. Welcome et al. / Neuropsychologia 65 (2014) 113–124

HE. DTI tractography data also suggest that the white matter pathway connecting ventral and middle temporal regions was disrupted, further supporting our view that this cortical region was disconnected both functionally and anatomically from regions that can exert a top-down influence. Instead, we suggest that ventral/temporal activation in this patient reflects the automatic engagement of this region in processing complex visual stimuli. His alexia, then, results not from underactivation in ventral temporal regions, but disconnection of these regions from portions of the reading network subserving phonological and lexical/ semantic processes. These findings parallel reports from prosopagnosic patients in whom the fusiform face area activates typically, but fails to connect anatomically with more anterior regions (Thomas et al., 2009). An interesting consequence of this model is how it relates different aspects of lexical processing to the conscious experience of visual word recognition. Although HE demonstrated apparently normal and automatic activation in left ventral temporal regions, the inability to feed this activation forward to the rest of the reading network resulted in significantly impaired word recognition. Instead, HE only demonstrates overt recognition of words following slow and effortful LBL reading. Previous studies have suggested that enhanced activation in the right hemisphere homolog of the VWFA supports LBL reading in pure alexia (Pyun et al., 2008; Ino et al., 2008; Cohen et al., 2004). In contrast to those studies, we failed to see strong evidence for right hemisphere compensation in HE. Instead, it appears that the rapid word presentation rate used in this fMRI task inhibited LBL reading strategies. While we saw right VWFA activation when contrasting words with fixation in HE, the general response properties of this region did not differ from an age- and experience-matched control, showing greater activation for pictures than orthographic stimuli. However, in response to the automatic activation provoked by brief visual presentation of words, HE demonstrates no evidence of right hemispheric specialization. 4.1. Limitations Some observations lend some caution to how we interpret the present findings. The first observation concerns the volume of white matter in HE's right temporal lobe. We noted that although the pathway of white matter tracts HE's right hemisphere was similar to those of the two control subjects, the overall tract volume was appreciably smaller. Since the extent of his infarct is small and limited to the left hemisphere, it seems more likely that this reflects his pre-morbid state rather than being the result of his stroke. This unusual pre-morbid state could, in turn, reflect a greater than usual pre-stroke left-hemisphere language lateralization in HE due to his relatively extensive experience with reading and writing, yielding less developed white matter pathways in the right hemisphere. Unfortunately, no premorbid data are available to test that possibility. Another possibility is simply that this reflects a normal variability in white matter tract volume in the population. Either way, we argue that the interruption in left hemisphere white matter integrity was the proximal cause of his reading loss following stroke. The second concern is whether HE should indeed be classified as having pure alexia. Prior reports do describe him as such (e.g., Sacks, 2010), and he does report having preserved writing skills, marked by his ongoing productivity as a professional novelist. Likewise, testing revealed appreciable speeded reading difficulties (scoring below the measureable scale on a standardized test) and signs of LBL reading marked by poorer performance in untimed reading and longer lexical decision times for longer words. That said, the available data suggest other abilities are also somewhat

below average in HE. He did show some difficulty on measures of spelling and phonological processing, indicating some weaknesses in that regard, though it is notable that his performance on those tests was far better than on speeded measures of reading. As a result of these findings, we focus on his reading difficulties and the extent to which they are related to activation and connectivity in occipito-temporal cortex. It remains an open question how his stroke is related to his other non-reading difficulties. Finally, it is possible that HE also has difficulty recognizing nonorthographic visual objects. For instance, HE showed weaker than expected fMRI activation for faces in ventral temporal cortex. This finding parallels a behavioral report demonstrating that other patients with alexia demonstrated subtle deficits in face recognition (Behrmann and Plaut, 2012). Indeed, recent studies of pure alexia have challenged the notion that this syndrome impacts solely the processing of written language, as some individuals with pure alexia have been shown to have difficulty with processing other types of complex visual stimuli (e.g., Behrmann et al., 1998; Starrfelt et al., 2010; Behrmann and Plaut, 2012) and with other language tasks (Swick et al., 2004). That said, regardless of other possible areas of deficit, HE nonetheless displays substantial reading difficulties despite partially intact neural responses to words, and that is the focus of the present report. 4.2. Conclusion The idea that some forms of alexia result from disconnection is not new (Dejerine, 1892). However the locus of disconnection in HE is different from that reported in prior studies. Lesion data (Damasio and Damasio, 1983) and DTI data (Epelbaum et al., 2008) suggest that disruption of fiber tracts adjacent to VWFA, without accompanying damage to the VWFA itself, can lead to reading difficulties. However, the prevailing “disconnection” model of pure alexia suggests this syndrome occurs as the result of a failure of this region to receive afferent connections from visual cortex. The present data suggest that a functional disconnection between the output of occipito-temporal cortex and regions supporting higherup processes is also sufficient to disrupt typical reading. These prior reports from patients with pure alexia have focused on disconnection of visual regions in occipital cortex from ventral temporal regions engaged specifically by orthographic stimuli (Damasio and Damasio, 1983; Epelbaum et al., 2008). Functional and diffusion tensor data from HE suggest instead that his form of alexia results from the disconnection of ventral temporal regions from middle temporal regions engaged by lexico-semantic analysis. Thus, anatomical and functional data suggest that communication between ventral temporal regions and the rest of the reading network is necessary to support the conscious experience of word recognition. In this sense, HE represents a case in which intact ventraltemporal regions are disconnected from other portions of the reading network. One reason may be the specific location of his lesion. Many instances of alexia result from left occipito-temporal damage that also extends into the splenium of the corpus callosum (Montant and Behrmann, 2000), which interrupts the visual word knowledge input from ventral temporal portions of both hemispheres. With this site of lesion, even if the left VWFA were to become disconnected from higher-up regions, connections across the corpus callosum from right-hemisphere ventral temporal regions could also be used to recognize words. In contrast, HE's lesion is completely restricted to the left hemisphere, and has spared connections from visual areas to VWFA. Thus, in HE's case, left hemisphere disconnection was sufficient to impair his reading. One explanation for this is HE represents an outlier in his premorbid state, due to his fairly advanced degree of literacy. In addition to being a prolific novelist, HE also served as a print

S.E. Welcome et al. / Neuropsychologia 65 (2014) 113–124

journalist and a writer-in-residence at a large university in the decades prior to his stroke. One possibility is that enhanced experience with print in very highly literate individuals leads to a high level of consolidation of visual word knowledge within left ventral temporal cortex. Thus, although visual word-specific activation has been observed in both left and right ventral temporal regions in many individuals (Rauschecker et al., 2012), there is also evidence that the degree of the left-lateralization and differentiation of orthographic versus non-orthographic stimuli in ventral-temporal regions increases with degree of literacy (Dehaene et al., 2010). In that sense HE represents the extreme end of this distribution: a strictly left hemisphere lesion adjacent to VWFA was sufficient to interrupt automatic reading processes because of the high degree of consolidation of reading processes within left hemisphere compared to other individuals. Overall, these results speak to the role of the ventral temporal cortex in reading. While HE showed a typical degree of activation in the left ventral temporal cortex and a typical response profile, his reading remains quite impaired and he lacked word-related activation in other portions of the reading network. These results are particularly noteworthy because the activation resulted from briefly presented words and is thus not likely to result from topdown influences. Connectivity data further support the notion that in HE, unlike in many other patients with alexia, ventral temporal cortex has normal connections with visual regions but is disconnected from middle temporal regions involved in lexical and semantic processing. This suggests that activation of wordspecific regions of mid-fusiform gyrus, while perhaps necessary for reading, is not sufficient to yield the conscious experience of reading. Instead, conscious word recognition likely requires coordinated activation throughout the reading network, and it is the lack of appropriate activation in other portions of the language network that explain HE's alexia.

Acknowledgments This research was supported by operating grants from the Natural Sciences and Engineering Research Council of Canada and the Canadian Institutes of Health Research.

Appendix A. Supplementary information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.neuropsychologia. 2014.10.012. References Behrens, T.E.J., Berg, H.J., Jbabdi, S., Rushworth, M.F.S., Woolrich, M.W., 2007. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34 (1), 144–155. Behrens, T.E.J., Johansen-Berg, H., Woolrich, M.W., Smith, S.M., Wheeler-Kingshott, C.A.M., Boulby, P.A., Matthews, P.M., 2003. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6 (7), 750–757. Behrens, T.E.J., Woolrich, M.W., Jenkinson, M., Johansen-Berg, H., Nunes, R.G., Clare, S., Smith, S.M., 2003. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50 (5), 1077–1088. Behrmann, M., Black, S.E., Bub, D., 1990. The evolution of pure alexia: a longitudinal study of recovery. Brain and Lang. 39 (3), 405–427. Behrmann, M., Nelson, J., Sekuler, E.B., 1998. Visual complexity in letter-by-letter reading: pure alexia is not pure. Neuropsychologia 36 (11), 1115–1132. Behrmann, M., Plaut, D.C., 2012. Bilateral hemispheric processing of words and faces: evidence from word impairments in prosopagnosia and face impairments in pure alexia. Cereb. Cortex 24 (4), 1102–1118. Bolger, D.J., Perfetti, C.A., Schneider, W., 2005. Cross-cultural effect on the brain revisited: universal structures plus writing system variation. Hum. Brain Mapp. 25 (1), 92–104.

123

Booth, J.R., Burman, D.D., Meyer, J.R., Gitelman, D.R., Parrish, T.B., Mesulam, M.M., 2002. Functional anatomy of intra-and cross-modal lexical tasks. NeuroImage 16 (1), 7. Brunswick, N., McCrory, E., Price, C.J., Frith, C.D., Frith, U., 1999. Explicit and implicit processing of words and pseudowords by adult developmental dyslexics: a search for Wernicke's Wortschatz? Brain 122 (10), 1901–1917. Cohen, L., Dehaene, S., Naccache, L., Lehericy, S., Dehaene-Lambertz, G., Henaff, M.A., Michel, F., 2000. The visual word form area: spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. Brain 123 (2), 291–307. Cohen, L., Henry, C., Dehaene, S., Martinaud, O., Lehéricy, S., Lemer, C., Ferrieux, S., 2004. The pathophysiology of letter-by-letter reading. Neuropsychologia 42 (13), 1768–1780. Cohen, L., Lehéricy, S., Chochon, F., Lemer, C., Rivaud, S., Dehaene, S., 2002. Language-specific tuning of visual cortex? Functional properties of the Visual Word Form Area. Brain 125 (5), 1054–1069. Cohen, L., Martinaud, O., Lemer, C., Lehericy, S., Samson, Y., Obadia, M., Dehaene, S., 2003. Visual word recognition in the left and right hemispheres: anatomical and functional correlates of peripheral alexias. Cereb. Cortex 13 (12), 1313–1333. Cox, R.W., 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29 (3), 162–173. Damasio, A.R., Damasio, H., 1983. The anatomic basis of pure alexia. Neurology 33 (12), 1573–1583. Dejerine, J., 1892. Contribution à l'étude anatomo-pathologique et clinique des différentes variétés de cécité verbale. Mém. Soc. Biol. 4, 61–90. Dehaene, S., Cohen, L., 2011. The unique role of the visual word form area in reading. Trends Cogn. Sci. 15 (6), 254–262. Dehaene, S., Cohen, L., Signal, M., Vickier, F., 2005. The neural code for written words: a proposal. Trends Cogn. Sci. 9 (7), 335–341. Dehaene, S., Pegado, F., Braga, L.W., Ventura, P., Nunes Filho, G., Jobert, A., Cohen, L., 2010. How learning to read changes the cortical networks for vision and language. Science 330 (6009), 1359–1364. Di Pietro, M., Ptak, R., Schnider, A., 2012. Preserved visual language identification despite severe alexia. Neuropsychologia 50 (7), 1327–1334. Engel, H., 2007. The Man Who Forgot How to Read: A Memoir. St. Martin's Press, New York, NY. Epelbaum, S., Pinel, P., Gaillard, R., Delmaire, C., Perrin, M., Dupont, S., Cohen, L., 2008. Pure alexia as a disconnection syndrome: new diffusion imaging evidence for an old concept. Cortex 44 (8), 962–974. Gaillard, R., Naccache, L., Pinel, P., Clémenceau, S., Volle, E., Hasboun, D., Cohen, L., 2006. Direct intracranial, fMRI, and lesion evidence for the causal role of left inferotemporal cortex in reading. Neuron 50 (2), 191–204. Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P., 2001. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293 (5539), 2425–2430. Hillis, A.E., Newhart, M., Heidler, J., Barker, P., Herskovits, E., Degaonkar, M., 2005. The roles of the “visual word form area” in reading. NeuroImage 24 (2), 548–559. Ino, T., Tokumoto, K., Usami, K., Kimura, T., Hashimoto, Y., Fukuyama, H., 2008. Longitudinal fMRI study of reading in a patient with letter-by-letter reading. Cortex 44 (7), 773–781. Jenkinson, M., Smith, S.M., 2001. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5 (2), 143–156. Kherif, F., Josse, G., Price, C.J., 2011. Automatic top-down processing explains common left occipito-temporal responses to visual words and objects. Cereb. Cortex 21 (1), 103–114. Leff, A.P., Spitsyna, G., Plant, G.T., Wise, R.J.S., 2006. Structural anatomy of pure and hemianopic alexia. J. Neurol. Neurosurg. Psychiatry 77 (9), 1004–1007. Liu, C., Zhang, W.T., Tang, Y.Y., Mai, X.Q., Chen, H.C., Tardif, T., Luo, Y.J., 2008. The visual word form area: evidence from an fMRI study of implicit processing of Chinese characters. NeuroImage 40 (3), 1350–1361. Molko, N., Cohen, L., Mangin, J.F., Chochon, F., Lehericy, S., Le Bihan, D., Dehaene, S., 2002. Visualizing the neural bases of a disconnection syndrome with diffusion tensor imaging. J. Cogn. Neurosci. 14 (4), 629–636. Montant, M., Behrmann, M., 2000. Pure alexia. Neurocase 6 (4), 265–294. Pasquarella, A., Welcome, S.E., Chen, X., Joanisse, M.F., Olson, D., 2011. When a Writer Can't Read: A Case Study of Pure Alexia. Canadian Psychological Association, Toronto, ON. Pflugshaupt, T., Gutbrod, K., Wurtz, P., von Wartburg, R., Nyffeler, T., de Haan, B., Mueri, R.M., 2009. About the role of visual field defects in pure alexia. Brain 132 (7), 1907–1917. Price, C.J., Devlin, J.T., 2003. The myth of the visual word form area. NeuroImage 19 (3), 473–481. Price, C.J., Devlin, J.T., 2011. The interactive account of ventral occipitotemporal contributions to reading. Trends Cogn. Sci. 15 (6), 246–253. Pyun, S.B., Sohn, H.J., Jung, J.B., Nam, K., 2008. Differential reorganization of fusiform gyrus in two types of alexia after stroke. Neurocase 13 (5–6), 417–425. Rauschecker, A.M., Bowen, R.F., Parvizi, J., Wandell, B.A., 2012. Position sensitivity in the visual word form area. Proc. Natl. Acad. Sci. 109 (24), E1568–E1577. Rilling, J.K., Glasser, M.F., Preuss, T.M., Ma, X., Zhao, T., Hu, X., Behrens, T.E., 2008. The evolution of the arcuate fasciculus revealed with comparative DTI. Nat. Neurosci. 11 (4), 426–428. Rossion, B., Pourtois, G., 2004. Revisiting Snodgrass and Vanderwart's object pictorial set: the role of surface detail in basic-level object recognition. Perception 33 (2), 217–236.

124

S.E. Welcome et al. / Neuropsychologia 65 (2014) 113–124

Sacks, O., 2010. A Man of Letters. The Mind's Eye. Alfred A. Knopf, New York, NY. Sandak, R., Frost, S.J., Rueckl, J.G., Landi, N., Mencl, W.E., Katz, L., & Pugh, K.R., 2012. In: M. Spivey, M. Joanisse, & K. McRae (Eds.), How does the brain read words? The Cambridge Handbook of Psycholinguistics, 218. Smith, S.M., 2002. Fast robust automated brain extraction. Hum. Brain Mapp. 17 (3), 143–155. Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., JohansenBerg, H., Matthews, P.M., 2004. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23, S208–S219. Starrfelt, R., Habekost, T., Gerlach, C., 2010. Visual processing in pure alexia: a case study. Cortex 46 (2), 242–255. Starrfelt, R., Gerlach, C., 2007. The visual what for area: words and pictures in the left fusiform gyrus. NeuroImage 35 (1), 334–342. Swick, D., Miller, K.M., Larsen, J., 2004. Auditory repetition priming is impaired in pure alexic patients. Brain Lang. 89 (3), 543–553. Szwed, M., Dehaene, S., Kleinschmidt, A., Eger, E., Valabrègue, R., Amadon, A., Cohen, L., 2011. Specialization for written words over objects in the visual cortex. NeuroImage 56 (1), 330–344. Tagamets, M.A., Novick, J.M., Chalmers, M.L., Friedman, R.B., 2000. A parametric approach to orthographic processing in the brain: an fMRI study. J. Cogn. Neurosci. 12 (2), 281–297.

Talairach, J., & Tournoux, P., 1988. Co-planar stereotaxic atlas of the human brain. 3-Dimensional Proportional System: An Approach to Cerebral Imaging, Thieme, New York. Thomas, C., Avidan, G., Humphreys, K., Jung, K.J., Gao, F., Behrmann, M., 2009. Reduced structural connectivity in ventral visual cortex in congenital prosopagnosia. Nat. Neurosci. 12 (1), 29–31. Torgesen, J.K., Wagner, R.K., Rashotte, C.A., 1999. Test of Word Reading Efficiency. Pro-ed, Austin, Texas. Vigneau, M., Jobard, G., Mazoyer, B., Tzourio-Mazoyer, N., 2005. Word and nonword reading: what role for the Visual Word Form Area? Neuroimage 27 (3), 694–705. Vinckier, F., Dehaene, S., Jobert, A., Dubus, J.P., Sigman, M., Cohen, L., 2007. Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Neuron 55 (1), 143–156. Wakana, S., Jiang, H., Nagae-Poetscher, L.M., Van Zijl, P.C., Mori, S., 2004. Fiber tractbased atlas of human white matter anatomy 1. Radiology 230 (1), 77–87. Wagner, R.K., Torgesen, J.K., Rashotte, C.A., 1999. Comprehensive Test of Phonological Processing: CTOPP. Pro-ed, Austin, TX. Wilkinson, G.S., Robertson, G.J., 2006. Wide Range Achievement Test (WRAT4). Psychological Assessment Resources, Lutz. Woodcock, R.W., 1998. Woodcock Reading Mastery Tests, Revised. American Guidance Service, Circle Pines, MN.