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Research Report
Neuroanatomic pathway associated with attentional deficits after stroke Taro Murakamia, Seiji Hamaa,c,n, Hidehisa Yamashitab, Keiichi Onodae, Seiichiro Hibinob, Hitoshi Satob, Shuji Ogawad, Shigeto Yamawakib, Kaoru Kurisua a
Department of Neurosurgery, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan b Department of Psychiatry and Neuroscience, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan c Department of Neurosurgery, Hibino Hospital, Hiroshima, Japan d Department of Rehabilitation, Hibino Hospital, Hiroshima, Japan e Department of Neurology, Shimane University, Shimane, Japan
art i cle i nfo
ab st rac t
Article history:
We used magnetic resonance imaging (MRI) and the statistical parametric mapping (SPM)
Accepted 27 November 2013
image analysis technique to localize lesions in post-stroke patients with attention deficits. SPM can be used to combine image data from multiple participants and correlate these
Keywords:
images with other data sets. Magnetic resonance imaging acquisitions were obtained from
Statistical parametric map
115 post-stroke patients, who were systemically assessed for attention deficits using a
Attention deficit
standardized test (the Clinical Assessment for Attention; CAT) that probes various domains
Stroke
of attention. We created an SPM that displayed an association between lesion location and
Magnetic resonance imaging
attention deficit severity. The overlay plots were localized to the right hemisphere during a visual cancellation test, and were localized to the left hemisphere during other attention tests. Cortical lesion varied across specific test domain, whereas lesions from the thalamus to the basal ganglia on the dominant side were associated with performance across all attention tests/domains. Our findings are suggestive of a large-scale multimodal attentional network associated with the thalamus/basal ganglia. & 2013 Elsevier B.V. All rights reserved.
1.
Introduction
Cognitive deficits are common after stroke and have been linked to poor recovery of activities of daily living (ADLs) during rehabilitation (Barker-Collo et al., 2009; Whyte et al., 1998). Attention is widely considered to be the foundation of
other cognitive functions (Hyndman and Ashburn, 2003). Impaired attention can reduce cognitive productivity and plays a key role in the rehabilitation process after stroke (Barker-Collo et al., 2009; Whyte et al., 1998; Hyndman and Ashburn, 2003). Many previous studies of attention deficits in stroke patients have demonstrated several morphological
n Correspondence to: Department of Neurosurgery, Hibino Hospital, Tomo 7955, Numata-cho, Asaminami-ku, Hiroshima-shi, Hiroshima 731-3161, Japan. Fax: þ81 82 848 1308. E-mail address: [email protected] (S. Hama).
0006-8993/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.brainres.2013.11.029
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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changes (i.e., to the right hemisphere, including prefrontal and parietal cortices), although such findings remain controversial (Whyte et al., 1998; Naghavi and Nyberg, 2005; Cavanna and Trimble, 2006). One methodological problem with many prior studies has been an inability to localize lesions to specific brain regions across participants. Statistical parametric mapping (SPM) is an image analysis technique that can be used to combine image data from multiple participants and correlate such information with other sources of data (MacFall et al., 2001; Karnath et al., 2001; Karnath et al., 2004). Using this technique, a threedimensional rendering of patients' MRI lesion data derived from the transverse slices can be made available. Previously, we have used the SPM image analysis technique to localize lesions in patients with post-stroke depression after separating out two core symptom clusters, affective and apathetic. We demonstrated that these two core symptoms appear to be associated with different neuroanatomical pathways (Murakami et al., 2013). Based on findings such as this, the SPM method could provide a useful tool for detecting the neuroanatomical loci of post-stroke symptoms. Many structures (i.e., the brain stem, thalamus, and cortical regions) are thought to exert different influences on disparate aspects of attention processing. Conflicting findings regarding whether risk of attention deficits after a stroke is influenced by brain lesion location might have arisen because multiple vascular lesions may influence attention deficits in a coordinated fashion (Baluch and Itti, 2011). We used the SPM technique to combine data from multiple stroke patients, hypothesizing that the specific brain structure abnormalities associated with post-stroke attention deficits would differ as a function of disparate mode of attention processing.
2.
Results
2.1. Baseline structures and attention deficit frequency across patients Participants included 75 males and 40 females. Mean patient age was 66.479.89 years (range: 37–86 years). Thirty-six patients had experienced intracerebral hemorrhage, whereas 79 others had experienced cerebral infarction. The mean time lag between MRI and clinical assessment was 16.3715.0 days (range: 0–75 days; median¼ 13). Mean lesion location volume (ml) was 19.0729.3 (range: 0.3–212; median¼8.1). Attention deficits for the patient sample as determined using the CAT are described in Table 1.
2.2.
Effects of lesion location on attention deficits
Figs. 1 and 2 show the overlay plots for attention deficits observed in our sample of post-stroke patients (see Tables 2 and 3 for additional information). The number of overlapping lesions is illustrated by color-coding, with frequency of overlap increasing from red to yellow. Among patients who were impaired on the visual cancellation test, lesion overlap centered on the thalamus, basal ganglia, and cortical region,
with such lesions occurring predominantly in the right hemisphere (See Table 4). For participants experiencing difficulties on attentional tests other than visual cancellation, lesion overlap again occurred widely across the thalamus, basal ganglia and cortical regions, except that these lesions occurred primarily in the left hemisphere (See Table 4). The cortical regions involved were different for each test, although the thalamus and basal ganglia were associated with performance on all tests for the dominant hemisphere.
3.
Discussion
We used SPM to examine brain loci for attention deficits after stroke. Using a standardized test that probes various domains of attention, we found that right hemisphere abnormalities were associated with poor visual cancellation test performance, but that performance on all other attention tests was associated with left hemisphere dysfunction. Although cortical lesion differed as a function of specific attention test, lesions from the thalamus to the basal ganglia in the dominant hemisphere overlapped across all attention tests studied here.
3.1.
Attention deficit lesion location: laterality
Attention is a basic component of human cognition and plays a critical role in the guidance of human behavior (BarkerCollo et al., 2009; Whyte et al., 1998; Hyndman and Ashburn, 2003; Naghavi and Nyberg, 2005). Many previous fMRI and PET studies have identified various brain lesions in patients with attention deficits (Whyte et al., 1998; Naghavi and Nyberg, 2005; Cavanna and Trimble, 2006). Among these, the frontal and parietal regions were most consistently involved in various aspects of attentional functioning, including spatial, divided, and selective attention, although lesion site and laterality has not differed across studies. In the present study, laterality of the damaged hemisphere also differed as a function of attention task, such that visual cancellation task performance was associated with right hemisphere lesions, whereas lesions to the left hemisphere impaired performance on all other attention tasks. The visual cancellation test is thought to predominantly assess selective attention (Vandenberghe et al., 2005; Gillebert et al., 2011). This cognitive processing is thought to closely underlie the phenomenon of neglect, which is associated with right hemisphere damage (Hyndman and Ashburn, 2003; Tomaiuolo et al., 2010). Moreover, in contrast to the left hemisphere, the right hemisphere is known for its ability to process visual-spatial information (e.g., the control of visual attention or remembering specific visual information) (Corbetta et al., 1993; Heilman and Van Den Abell, 1980; Laeng et al., 2007). These observations, along with the present results, indicate that the right hemisphere plays a pivotal role in visual information processing. On the other hand, the present study also included measures of sustained or divided attention, as well as tests of working memory functioning. Impairments in these areas were primarily associated with the left hemisphere lesions. The present results suggest that laterality of lesions
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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Table 1 – Baseline results of attention test using CAT. Test of attention
Mean score (SD)
Deficit number (rate %)
Digit span forward Digit span backward Tapping span forward Tapping span backward Visual cancellation: symbol Visual cancellation: Kana Visual cancellation: 3 SDMT Position stroop PASAT: 2seconds Memory updating: 3 CPT-SRT CPT-X CPT-AX
5.3 (1.3) 3.6 (1.3) 5.2 (1.3) 4.3 (1.4) 97.16 (6.41) 91.94 (9.930) 97.71(5.516) 29.96 (13.03) 92.38 (12.45) 35.66 (24.65) 59.53 (27.49) 91.10 (16.03) 93.84 (13.78) 88.11 (18.46)
30 60 25 68 36 40 24 61 56 65 51 55 28 31
(26.1) (52.2) (21.7) (59.1) (31.3) (34.8) (20.9) (53.0) (48.7) (56.5) (44.3) (47.8) (24.3) (27.0)
Attention deficit was determined under the cut-off value, showing number and percentage against total patients number (n ¼115).
Rt
Rt
Rt
Rt
Fig. 1 – Overlay lesion plots for post-stroke patients depicting attention deficits examined using digit span backward. (A) Statistical parametric mapping (SPM) results for lesion areas correlated with attention deficit severity (for exact coordinates, see Table 2). (B) Number of overlapping lesions is illustrated by different colors that code increasing frequencies from red to yellow. Colored regions were significant even after controlling for age, sex, stroke type, lesion volume and the time lag between MRI and clinical assessment (df ¼ 108). might influence the type of attention deficit that patients experience.
3.2.
Attentional processing network
Although cortical lesion location differed across the attention tests used here, dominant hemisphere lesions from the thalamus to the basal ganglia were associated with performance across all attention tests. Previous imaging findings suggest that both top–down and bottom–up attentional networks operate during attentional processing tasks (Salmi et al., 2007; Baluch and Itti, 2011). This attention network is thought to include various brain regions. Among them, the thalamus is notable given that both ascending signals from sensory stimuli (i.e., bottom–up processing)
and descending signals from upper level cortical regions (i.e., up–down processing) pass though the region (Baluch and Itti, 2011). There thus appears to be a general attentional network that operates independently of specific task requirements, although additional brain regions may be recruited to perform each specific task. The thalamus and basal ganglia are likely central to this attentional processing network. To date, the relationship between lesion location and resulting attention deficits has not been clearly established. On the basis of the present results, the thalamus and basal ganglia may operate as the central processing trunk of the attentional system, with additional cortical regions also operating to manage specific tasks requirements: The right hemisphere is involved in visual-spatial tasks whereas the left hemisphere underlies other attention-related tasks (e.g., those of a verbal nature).
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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Rt
Rt
Rt Rt Fig. 2 – Overlay lesion plots for post-stroke patients depicting attention deficits examined with the visual cancellation symbol test. (A) Statistical parametric mapping (SPM) results for lesion areas correlated with attention deficit severity (for exact coordinates see Table 3). (B) Number of overlapping lesions is illustrated by different colors that code increasing frequencies from red to yellow. Colored regions were significant even after controlling for age, sex, stroke type, lesion volume and time lag between MRI and clinical assessment (df ¼108). Table 2 – Statistical parametric mapping results for lesion areas correlate with attention deficit: Digit span backward test. Area
Cluster level
Left cerebrum, frontal lobe, sub-gyral Left brainstem, pons Right brainstem, pons Left cerebrum, sub-lobar, extra-Nuclear Left cerebrum, frontal lobe, sub-gyral Left cerebrum, sub-lobar, extra-nuclear Left cerebrum, sub-lobar, extra-nuclear Left brainstem, midbrain
Voxel level
p
k
p
Z
o0.001 0.044 0.025 0.020 0.027 0.006 0.006 0.027
8560 25 32 35 31 51 52 31
o0.001 o0.001 o0.001 0.011 0.012 0.014 0.017 0.032
3.37 3.32 3.32 2.30 2.25 2.19 2.12 1.85
x
y
z
30 10 10 20 18 10 22 4
27 27 27 22 0 2 7 18
36 29 29 17 33 4 24 4
Statistical parametric mapping whole brain multiple regression analysis; the name of areas of described above point to the peak of activation with each cluster. p corrected p value for spatial extent (cluster p value) and peak height (voxel level p value) of the activation: all areas exceeding the corrected clusterlevel threshold of 0.05 are displayed; k number of voxels in cluster; Z z score; x, y, z location according to the standard Talairach coordinates (in mm); df¼108.
3.3.
Limitations of the present study
The present study has a number of methodological limitations. One such limitation is that patients suffering from severe comprehension deficits after a stroke could not perform the CAT assessment or give their informed consent to participate in the study. These patients therefore had to be excluded, which is problematic for the generalizability of our results. Moreover, the one-tailed probability test used in the present statistical analysis leaves open the possibility that a few highly characteristic cases influenced the analysis and led to the pattern of results observed. However, we had to utilize such a test to detect lesions that are causally associated with attention deficits: The higher the CAT subscale scores, the more lesion overlap was observed. Using a two-
tailed test would reduce power to detect such meaningful overlap. Secondly, spatial normalization is a technique that involves warping each patient's brain images to align with a standard brain (template image), and is thought to exert an influence on the observed morphological structure of lesions (particularly larger lesions). However, we automatically performed nonlinear spatial normalization using SPM8. Use of this nonlinear spatial normalization procedure would be expected to exert a minimal effect on spatial map lesion location (Brett et al., 2001; Ashburner and Friston, 1999; Ramirez et al., 2008). Lastly, this study was not able to examine or evaluate the potential effects of brain plasticity after stroke. Ongoing improvement in neurological functioning occurs as various mechanisms enable structural and functional reorganization
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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Table 3 – Statistical parametric mapping results for lesion areas correlate with attention deficit: Visual cancellation symbol test. Area
Cluster level
Right cerebrum, temporal lobe, superior temporal gyrus Right cerebrum, frontal lobe, precentral gyrus Right cerebellum, posterior lobe, tuber Left brainstem, midbrain, red nucleus Left cerebrum, limbic lobe, anterior cingulate Left cerebrum, sub-lobar, extra-nuclear Right cerebrum, limbic lobe, cingulate gyrus, Brodmann area 31 Right Cerebrum, Sub-lobar, Extra-Nuclear Right Cerebrum, Limbic Lobe, Cingulate Gyrus, Brodmann area 24 Right Cerebrum, Frontal Lobe, Sub-Gyral
Voxel level
p
k
p
Z
o0.001 0.001 o0.001 0.015 0.011 0.005 o0.001 0.002 0.003 0.030
21822 77 114 35 39 49 140 63 60 27
o0.001 o0.001 0.002 0.004 0.004 0.004 0.004 0.006 0.012 0.029
10.56 3.74 2.84 2.62 2.62 2.62 2.62 2.50 2.26 1.90
x
y
z
50 36 38 4 4 6 8 18 8 20
14 6 69 18 15 2 41 4 13 33
1 35 23 4 4 2 39 8 31 7
Statistical parametric mapping whole brain multiple regression analysis; the name of areas of described above point to the peak of activation with each cluster. p corrected p value for spatial extent (cluster p value) and peak height (voxel level p value) of the activation: all areas exceeding the corrected clusterlevel threshold of 0.05 are displayed; k number of voxels in cluster; Z z score; x, y, z location according to the standard Talairach coordinates (in mm); df¼ 108.
Table 4 – Laterality of responsible forcus of attention deficit among post-stroke patients. Voxels/Laterality of hemisphere
Dominant to left hemisphere
Dominant to right hemisphere
Digit span forward Digit span backward Tapping span forward Tapping span backward SDMT Position stroop PASAT: 2seconds Memory updating: 3 CPT-AX CPT-SRT CPT-X Visual cancellation: symbol Visual cancellation: Kana Visual cancellation: 3
Left (%)
Right (%)
Others (%)
12131 8729 6258 3737 3608 11636 4809 11971 9061 5018 11357 88 2763 48
24 0 855 477 531 813 148 467 1346 2066 2455 22189 11709 24468
0 88 0 114 37 97 0 20 132 167 165 149 125 0
(99.8) (99.0) (88.0) (86.3) (86.4) (92.7) (97.0) (96.1) (86.0) (62.2) (81.2) (0.4) (18.9) (0.2)
(0.2) (0) (12.0) (11.0) (12.7) (6.5) (3.0) (3.7) (12.3) (28.5) (17.6) (98.9) (80.2) (99.8)
(0) (1.0) (0) (2.6) (0.9) (0.8) (0) (0.2) (1.3) (2.3) (1.2) (0.7) (0.9) (0)
Table shows the voxles after separetion to the left, right hemisphere and others (cerebellar and brainstem). The percentage of each voxels against the total lesion voxels was backetted off. Upper colum indicated the voxels to be dominant in the left hemisphere, and lower colum indicated in the right hemisphere.
within the brain. Processes involved in this reorganization represent “neuroplasticity” and may continue for many months post-stroke (Ricker, 2005; Brandstater, 2005). The present study occurred during the early phase of patient recovery and a longitudinal study of much longer duration (many months to several years) could be used to evaluate neuroplasticity effects. However, the gross morphological changes that can occur many months or years post-stroke (e.g., regions of encephalomalacia) may render SPM image reconstruction less accurate with regards to the normal brain template than is the case with a study conducted sooner, such that SPM would require careful adaptation if used over a longer timeframe.
3.4.
Conclusion
We identified probable areas where brain lesions may directly relate to attention deficits, based upon a combination of image
segmentation, spatial normalization, and statistical parametric mapping. Evidence points toward the existence of a multimodal large-scale attentional network associated with thalamus/basal ganglia functioning.
4.
Experimental procedures
4.1.
Participants
The ethics committee of Hibino Hospital approved this prospective study. Written informed consent was obtained from all patients. The patients were selected from a consecutive series of 115 individuals who were admitted to Hibino Hospital less than 3 months after suffering a hemorrhagic or occlusive stroke. No patient included here had subarachnoid hemorrhage. Patients had presented for rehabilitation
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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therapy and were diagnosed using magnetic resonance imaging (MRI). Exclusion criteria were the same as those described in our previous report (Murakami et al., 2013; Hama et al., 2007).
4.2.
Assessment of attention deficits
Attention deficits were systematically assessed using the Clinical Assessment for Attention (CAT), developed and standardized by the Japan Society for Higher Brain Dysfunction (Takeda et al., 2011; Japan Society for Higher Brain Dysfunction, 2006). The CAT assesses attention with respect to reaction times, vigilance, short-term storage capacity, mental tracking (i.e., working memory), and complex attention (Lezak et al., 2004). The CAT assessment battery includes digit and visual tapping spans, a visual cancellation task, an auditory detection task (Mizuno, 1991), a symbol digit modalities test (SDMT) (Smith, 1968), a memory-updating test (Morris and Jones, 1990), a paced auditory serial addition test (PASAT) (Gronwall and Wrightson, 1974), a position Stroop test (Sohlberg and Mateer, 1987), and a continuous performance test (CPT) (Beck et al., 1956). Completion rates for the auditory detection task, memory-updating test-4, and PASAT1 were low due to auditory disturbances and calculation difficulties. These test results were excluded from further analysis. Specific CAT assessment details are as follows. The digit span test consists of both digits forward and backward tasks and was administered to participants based upon procedures used for administration of the Wechsler intelligence tests. The visual tapping span test is also known as the Corsi block-tapping task. In this task, participants are asked to copy tapping patterns forward and backward, following the examiner's taps in the same prearranged sequence. The visual cancellation task is a simple test measuring processing accuracy. Participants use a pencil to cross out a target stimulus dispersed within rows of randomly placed interfering stimuli displayed on a sheet. Two sets of the stimulus sequence were used, Arabic numerals and kanaletters. The test was scored based upon the ratio of correct answers to the total number of stimuli. The SDMT is a visuographic task that preserves the substitution format of Wechsler's Digit Symbol Coding test but reverses the presentation of the material, so that symbols are printed and the patient writes out the numbers. Ninety seconds were allowed for each trial, each of which includes 110 items. Completion ratios (i.e., the number of correct answers compared to the number of items) were computed. The position Stroop test uses a Japanese–kanji version of the High-Middle-Low format developed by Sohlberg (Sohlberg and Mateer, 1987). Participants were asked to verbally report the kanji positions (high, mid, or low) instead of their meanings. One hundred and fourteen items were used in the trials, which were presented in six rows. Correct response ratios were assessed. The PASAT requires participants to add 60 pairs of randomized digits, such that each is added to the digit immediately preceding it. The digits were presented either every 1 or 2 s. The ratio of correct answers was assessed. The memory-updating test requires participants to attend to digit strings of unknown length and to then recall the three or four most recently presented digits. During the test,
participants are required to remember the first three or four items presented and then, if there were more than three or four items in a list, to update the contents of their memory by dropping the oldest item and adding the most recent to the string. Finally, the CPT is a computerized vigilance test that presents stimuli for brief durations, providing reaction times as well as accuracy data. In the simple version, participants respond to the digit “7” when it appears briefly in the center of the screen at random intervals. In the X version, digits appeared in a random order, and participants were asked to respond to every “7”. In the more difficult (AX) version, participants were asked to respond to “7” only if it followed “3”. Each participant's ratio of correct answers was calculated for each test. Japanese cut-off points for each CAT test have been previously reported (Kimura et al., 2003). Consistent with prior reports, attention deficits as evidenced by performance on each CAT test were determined with reference to a cut-off point of less than 1.0 SD from the normal value, for the corresponding Japanese age group.
4.3.
Lesion analysis
We obtained brain images including a T2-weighted sequence using a 1.5-T MRI scanner (Sigma EXCITE XL, version 11.0; GE Healthcare), within one month after admission (around the same time as the psychological assessment took place). The MR protocol included a fast spi-echo pulse sequence. Imaging was performed at 1.5 T on a commercial MRI system (GE Signa, GE Medical Systems, Milwaukee) using the standard quadrature head coil. Brain lesions were identified using axial images of T2 weighted MRI scans of each patient. First, images were converted from dicom to analyze format and were then spatially normalized to the Montreal Neurological Institute (MNI) brain template. At that time, the image was re-sliced with a voxel size of 2 2 2 mm3. These conversions and normalizations (non-linear normalization) were performed using the Statistical Parametric Mapping, Version 8 (SPM8, Wellcome Department of Cognitive Neurology) statistical package (Wright et al., 1995; Ashburner and Friston, 2004). The normalized T2 image for each patient was mapped using MRIcron software (http://www.cabiatl.com/mricro/mricron/ index.html), and the lesions were drawn manually. Inter-rater reliability for individual lesion volumes was assessed using the κ coefficient, which was 0.817 (two neurosurgeons took part in this estimation process, examin ing 40 cases). The voxels judged as lesions were assigned to 1 and all others to 0. The lesion images were saved in the MRIcron volume of interest (VOI) format. Patient VOI images were used in the group analysis. Finally, whole-brain multiple regression analysis was performed using SPM8. The independent variables used in this study were total scores on the measures described above (scores for each CAT task), with other factors such as age, sex, and stroke type (hemorrhage or infarction), time lag between MRI and clinical assessment, and lesion volume being entered as covariates. We used a one-tailed test and
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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T-contrast to estimate cluster level. The statistical criteria were set at po0.05 for voxel levels and a cluster size of 420. To exclude the possibility that lesions were artificially morphed during the normalization process, we first mapped lesions manually and then performed normalization of the MRI images, inter-rater reliabilities for the two methods (1st method: Initial normalization followed by lesion mapping, 2nd method: Initial lesion mapping followed by normalization) were assessed using the κ coefficient, which was 0.98 (comparison performed with 20 cases). The first method is most commonly used for SPM analysis and was therefore selected as the primary approach used in the present study.
Conflict of interest We confirm that there are no known conflicts of interest associated with this publication. There has been no significant financial support for this work that could have influenced its outcome.
Acknowledgments The present work was supported, in part, by a Grant-in-Aid from the Japanese Ministry of Health, Labor and Welfare, Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan and the Tsuchiya Foundation.
Appendix A.
Supporting information
Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.brainres. 2013.11.029.
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Research Report
Neuroanatomic pathway associated with attentional deficits after stroke Taro Murakamia, Seiji Hamaa,c,n, Hidehisa Yamashitab, Keiichi Onodae, Seiichiro Hibinob, Hitoshi Satob, Shuji Ogawad, Shigeto Yamawakib, Kaoru Kurisua a
Department of Neurosurgery, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan b Department of Psychiatry and Neuroscience, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan c Department of Neurosurgery, Hibino Hospital, Hiroshima, Japan d Department of Rehabilitation, Hibino Hospital, Hiroshima, Japan e Department of Neurology, Shimane University, Shimane, Japan
art i cle i nfo
ab st rac t
Article history:
We used magnetic resonance imaging (MRI) and the statistical parametric mapping (SPM)
Accepted 27 November 2013
image analysis technique to localize lesions in post-stroke patients with attention deficits. SPM can be used to combine image data from multiple participants and correlate these
Keywords:
images with other data sets. Magnetic resonance imaging acquisitions were obtained from
Statistical parametric map
115 post-stroke patients, who were systemically assessed for attention deficits using a
Attention deficit
standardized test (the Clinical Assessment for Attention; CAT) that probes various domains
Stroke
of attention. We created an SPM that displayed an association between lesion location and
Magnetic resonance imaging
attention deficit severity. The overlay plots were localized to the right hemisphere during a visual cancellation test, and were localized to the left hemisphere during other attention tests. Cortical lesion varied across specific test domain, whereas lesions from the thalamus to the basal ganglia on the dominant side were associated with performance across all attention tests/domains. Our findings are suggestive of a large-scale multimodal attentional network associated with the thalamus/basal ganglia. & 2013 Elsevier B.V. All rights reserved.
1.
Introduction
Cognitive deficits are common after stroke and have been linked to poor recovery of activities of daily living (ADLs) during rehabilitation (Barker-Collo et al., 2009; Whyte et al., 1998). Attention is widely considered to be the foundation of
other cognitive functions (Hyndman and Ashburn, 2003). Impaired attention can reduce cognitive productivity and plays a key role in the rehabilitation process after stroke (Barker-Collo et al., 2009; Whyte et al., 1998; Hyndman and Ashburn, 2003). Many previous studies of attention deficits in stroke patients have demonstrated several morphological
n Correspondence to: Department of Neurosurgery, Hibino Hospital, Tomo 7955, Numata-cho, Asaminami-ku, Hiroshima-shi, Hiroshima 731-3161, Japan. Fax: þ81 82 848 1308. E-mail address: [email protected] (S. Hama).
0006-8993/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.brainres.2013.11.029
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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changes (i.e., to the right hemisphere, including prefrontal and parietal cortices), although such findings remain controversial (Whyte et al., 1998; Naghavi and Nyberg, 2005; Cavanna and Trimble, 2006). One methodological problem with many prior studies has been an inability to localize lesions to specific brain regions across participants. Statistical parametric mapping (SPM) is an image analysis technique that can be used to combine image data from multiple participants and correlate such information with other sources of data (MacFall et al., 2001; Karnath et al., 2001; Karnath et al., 2004). Using this technique, a threedimensional rendering of patients' MRI lesion data derived from the transverse slices can be made available. Previously, we have used the SPM image analysis technique to localize lesions in patients with post-stroke depression after separating out two core symptom clusters, affective and apathetic. We demonstrated that these two core symptoms appear to be associated with different neuroanatomical pathways (Murakami et al., 2013). Based on findings such as this, the SPM method could provide a useful tool for detecting the neuroanatomical loci of post-stroke symptoms. Many structures (i.e., the brain stem, thalamus, and cortical regions) are thought to exert different influences on disparate aspects of attention processing. Conflicting findings regarding whether risk of attention deficits after a stroke is influenced by brain lesion location might have arisen because multiple vascular lesions may influence attention deficits in a coordinated fashion (Baluch and Itti, 2011). We used the SPM technique to combine data from multiple stroke patients, hypothesizing that the specific brain structure abnormalities associated with post-stroke attention deficits would differ as a function of disparate mode of attention processing.
2.
Results
2.1. Baseline structures and attention deficit frequency across patients Participants included 75 males and 40 females. Mean patient age was 66.479.89 years (range: 37–86 years). Thirty-six patients had experienced intracerebral hemorrhage, whereas 79 others had experienced cerebral infarction. The mean time lag between MRI and clinical assessment was 16.3715.0 days (range: 0–75 days; median¼ 13). Mean lesion location volume (ml) was 19.0729.3 (range: 0.3–212; median¼8.1). Attention deficits for the patient sample as determined using the CAT are described in Table 1.
2.2.
Effects of lesion location on attention deficits
Figs. 1 and 2 show the overlay plots for attention deficits observed in our sample of post-stroke patients (see Tables 2 and 3 for additional information). The number of overlapping lesions is illustrated by color-coding, with frequency of overlap increasing from red to yellow. Among patients who were impaired on the visual cancellation test, lesion overlap centered on the thalamus, basal ganglia, and cortical region,
with such lesions occurring predominantly in the right hemisphere (See Table 4). For participants experiencing difficulties on attentional tests other than visual cancellation, lesion overlap again occurred widely across the thalamus, basal ganglia and cortical regions, except that these lesions occurred primarily in the left hemisphere (See Table 4). The cortical regions involved were different for each test, although the thalamus and basal ganglia were associated with performance on all tests for the dominant hemisphere.
3.
Discussion
We used SPM to examine brain loci for attention deficits after stroke. Using a standardized test that probes various domains of attention, we found that right hemisphere abnormalities were associated with poor visual cancellation test performance, but that performance on all other attention tests was associated with left hemisphere dysfunction. Although cortical lesion differed as a function of specific attention test, lesions from the thalamus to the basal ganglia in the dominant hemisphere overlapped across all attention tests studied here.
3.1.
Attention deficit lesion location: laterality
Attention is a basic component of human cognition and plays a critical role in the guidance of human behavior (BarkerCollo et al., 2009; Whyte et al., 1998; Hyndman and Ashburn, 2003; Naghavi and Nyberg, 2005). Many previous fMRI and PET studies have identified various brain lesions in patients with attention deficits (Whyte et al., 1998; Naghavi and Nyberg, 2005; Cavanna and Trimble, 2006). Among these, the frontal and parietal regions were most consistently involved in various aspects of attentional functioning, including spatial, divided, and selective attention, although lesion site and laterality has not differed across studies. In the present study, laterality of the damaged hemisphere also differed as a function of attention task, such that visual cancellation task performance was associated with right hemisphere lesions, whereas lesions to the left hemisphere impaired performance on all other attention tasks. The visual cancellation test is thought to predominantly assess selective attention (Vandenberghe et al., 2005; Gillebert et al., 2011). This cognitive processing is thought to closely underlie the phenomenon of neglect, which is associated with right hemisphere damage (Hyndman and Ashburn, 2003; Tomaiuolo et al., 2010). Moreover, in contrast to the left hemisphere, the right hemisphere is known for its ability to process visual-spatial information (e.g., the control of visual attention or remembering specific visual information) (Corbetta et al., 1993; Heilman and Van Den Abell, 1980; Laeng et al., 2007). These observations, along with the present results, indicate that the right hemisphere plays a pivotal role in visual information processing. On the other hand, the present study also included measures of sustained or divided attention, as well as tests of working memory functioning. Impairments in these areas were primarily associated with the left hemisphere lesions. The present results suggest that laterality of lesions
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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Table 1 – Baseline results of attention test using CAT. Test of attention
Mean score (SD)
Deficit number (rate %)
Digit span forward Digit span backward Tapping span forward Tapping span backward Visual cancellation: symbol Visual cancellation: Kana Visual cancellation: 3 SDMT Position stroop PASAT: 2seconds Memory updating: 3 CPT-SRT CPT-X CPT-AX
5.3 (1.3) 3.6 (1.3) 5.2 (1.3) 4.3 (1.4) 97.16 (6.41) 91.94 (9.930) 97.71(5.516) 29.96 (13.03) 92.38 (12.45) 35.66 (24.65) 59.53 (27.49) 91.10 (16.03) 93.84 (13.78) 88.11 (18.46)
30 60 25 68 36 40 24 61 56 65 51 55 28 31
(26.1) (52.2) (21.7) (59.1) (31.3) (34.8) (20.9) (53.0) (48.7) (56.5) (44.3) (47.8) (24.3) (27.0)
Attention deficit was determined under the cut-off value, showing number and percentage against total patients number (n ¼115).
Rt
Rt
Rt
Rt
Fig. 1 – Overlay lesion plots for post-stroke patients depicting attention deficits examined using digit span backward. (A) Statistical parametric mapping (SPM) results for lesion areas correlated with attention deficit severity (for exact coordinates, see Table 2). (B) Number of overlapping lesions is illustrated by different colors that code increasing frequencies from red to yellow. Colored regions were significant even after controlling for age, sex, stroke type, lesion volume and the time lag between MRI and clinical assessment (df ¼ 108). might influence the type of attention deficit that patients experience.
3.2.
Attentional processing network
Although cortical lesion location differed across the attention tests used here, dominant hemisphere lesions from the thalamus to the basal ganglia were associated with performance across all attention tests. Previous imaging findings suggest that both top–down and bottom–up attentional networks operate during attentional processing tasks (Salmi et al., 2007; Baluch and Itti, 2011). This attention network is thought to include various brain regions. Among them, the thalamus is notable given that both ascending signals from sensory stimuli (i.e., bottom–up processing)
and descending signals from upper level cortical regions (i.e., up–down processing) pass though the region (Baluch and Itti, 2011). There thus appears to be a general attentional network that operates independently of specific task requirements, although additional brain regions may be recruited to perform each specific task. The thalamus and basal ganglia are likely central to this attentional processing network. To date, the relationship between lesion location and resulting attention deficits has not been clearly established. On the basis of the present results, the thalamus and basal ganglia may operate as the central processing trunk of the attentional system, with additional cortical regions also operating to manage specific tasks requirements: The right hemisphere is involved in visual-spatial tasks whereas the left hemisphere underlies other attention-related tasks (e.g., those of a verbal nature).
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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Rt
Rt
Rt Rt Fig. 2 – Overlay lesion plots for post-stroke patients depicting attention deficits examined with the visual cancellation symbol test. (A) Statistical parametric mapping (SPM) results for lesion areas correlated with attention deficit severity (for exact coordinates see Table 3). (B) Number of overlapping lesions is illustrated by different colors that code increasing frequencies from red to yellow. Colored regions were significant even after controlling for age, sex, stroke type, lesion volume and time lag between MRI and clinical assessment (df ¼108). Table 2 – Statistical parametric mapping results for lesion areas correlate with attention deficit: Digit span backward test. Area
Cluster level
Left cerebrum, frontal lobe, sub-gyral Left brainstem, pons Right brainstem, pons Left cerebrum, sub-lobar, extra-Nuclear Left cerebrum, frontal lobe, sub-gyral Left cerebrum, sub-lobar, extra-nuclear Left cerebrum, sub-lobar, extra-nuclear Left brainstem, midbrain
Voxel level
p
k
p
Z
o0.001 0.044 0.025 0.020 0.027 0.006 0.006 0.027
8560 25 32 35 31 51 52 31
o0.001 o0.001 o0.001 0.011 0.012 0.014 0.017 0.032
3.37 3.32 3.32 2.30 2.25 2.19 2.12 1.85
x
y
z
30 10 10 20 18 10 22 4
27 27 27 22 0 2 7 18
36 29 29 17 33 4 24 4
Statistical parametric mapping whole brain multiple regression analysis; the name of areas of described above point to the peak of activation with each cluster. p corrected p value for spatial extent (cluster p value) and peak height (voxel level p value) of the activation: all areas exceeding the corrected clusterlevel threshold of 0.05 are displayed; k number of voxels in cluster; Z z score; x, y, z location according to the standard Talairach coordinates (in mm); df¼108.
3.3.
Limitations of the present study
The present study has a number of methodological limitations. One such limitation is that patients suffering from severe comprehension deficits after a stroke could not perform the CAT assessment or give their informed consent to participate in the study. These patients therefore had to be excluded, which is problematic for the generalizability of our results. Moreover, the one-tailed probability test used in the present statistical analysis leaves open the possibility that a few highly characteristic cases influenced the analysis and led to the pattern of results observed. However, we had to utilize such a test to detect lesions that are causally associated with attention deficits: The higher the CAT subscale scores, the more lesion overlap was observed. Using a two-
tailed test would reduce power to detect such meaningful overlap. Secondly, spatial normalization is a technique that involves warping each patient's brain images to align with a standard brain (template image), and is thought to exert an influence on the observed morphological structure of lesions (particularly larger lesions). However, we automatically performed nonlinear spatial normalization using SPM8. Use of this nonlinear spatial normalization procedure would be expected to exert a minimal effect on spatial map lesion location (Brett et al., 2001; Ashburner and Friston, 1999; Ramirez et al., 2008). Lastly, this study was not able to examine or evaluate the potential effects of brain plasticity after stroke. Ongoing improvement in neurological functioning occurs as various mechanisms enable structural and functional reorganization
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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Table 3 – Statistical parametric mapping results for lesion areas correlate with attention deficit: Visual cancellation symbol test. Area
Cluster level
Right cerebrum, temporal lobe, superior temporal gyrus Right cerebrum, frontal lobe, precentral gyrus Right cerebellum, posterior lobe, tuber Left brainstem, midbrain, red nucleus Left cerebrum, limbic lobe, anterior cingulate Left cerebrum, sub-lobar, extra-nuclear Right cerebrum, limbic lobe, cingulate gyrus, Brodmann area 31 Right Cerebrum, Sub-lobar, Extra-Nuclear Right Cerebrum, Limbic Lobe, Cingulate Gyrus, Brodmann area 24 Right Cerebrum, Frontal Lobe, Sub-Gyral
Voxel level
p
k
p
Z
o0.001 0.001 o0.001 0.015 0.011 0.005 o0.001 0.002 0.003 0.030
21822 77 114 35 39 49 140 63 60 27
o0.001 o0.001 0.002 0.004 0.004 0.004 0.004 0.006 0.012 0.029
10.56 3.74 2.84 2.62 2.62 2.62 2.62 2.50 2.26 1.90
x
y
z
50 36 38 4 4 6 8 18 8 20
14 6 69 18 15 2 41 4 13 33
1 35 23 4 4 2 39 8 31 7
Statistical parametric mapping whole brain multiple regression analysis; the name of areas of described above point to the peak of activation with each cluster. p corrected p value for spatial extent (cluster p value) and peak height (voxel level p value) of the activation: all areas exceeding the corrected clusterlevel threshold of 0.05 are displayed; k number of voxels in cluster; Z z score; x, y, z location according to the standard Talairach coordinates (in mm); df¼ 108.
Table 4 – Laterality of responsible forcus of attention deficit among post-stroke patients. Voxels/Laterality of hemisphere
Dominant to left hemisphere
Dominant to right hemisphere
Digit span forward Digit span backward Tapping span forward Tapping span backward SDMT Position stroop PASAT: 2seconds Memory updating: 3 CPT-AX CPT-SRT CPT-X Visual cancellation: symbol Visual cancellation: Kana Visual cancellation: 3
Left (%)
Right (%)
Others (%)
12131 8729 6258 3737 3608 11636 4809 11971 9061 5018 11357 88 2763 48
24 0 855 477 531 813 148 467 1346 2066 2455 22189 11709 24468
0 88 0 114 37 97 0 20 132 167 165 149 125 0
(99.8) (99.0) (88.0) (86.3) (86.4) (92.7) (97.0) (96.1) (86.0) (62.2) (81.2) (0.4) (18.9) (0.2)
(0.2) (0) (12.0) (11.0) (12.7) (6.5) (3.0) (3.7) (12.3) (28.5) (17.6) (98.9) (80.2) (99.8)
(0) (1.0) (0) (2.6) (0.9) (0.8) (0) (0.2) (1.3) (2.3) (1.2) (0.7) (0.9) (0)
Table shows the voxles after separetion to the left, right hemisphere and others (cerebellar and brainstem). The percentage of each voxels against the total lesion voxels was backetted off. Upper colum indicated the voxels to be dominant in the left hemisphere, and lower colum indicated in the right hemisphere.
within the brain. Processes involved in this reorganization represent “neuroplasticity” and may continue for many months post-stroke (Ricker, 2005; Brandstater, 2005). The present study occurred during the early phase of patient recovery and a longitudinal study of much longer duration (many months to several years) could be used to evaluate neuroplasticity effects. However, the gross morphological changes that can occur many months or years post-stroke (e.g., regions of encephalomalacia) may render SPM image reconstruction less accurate with regards to the normal brain template than is the case with a study conducted sooner, such that SPM would require careful adaptation if used over a longer timeframe.
3.4.
Conclusion
We identified probable areas where brain lesions may directly relate to attention deficits, based upon a combination of image
segmentation, spatial normalization, and statistical parametric mapping. Evidence points toward the existence of a multimodal large-scale attentional network associated with thalamus/basal ganglia functioning.
4.
Experimental procedures
4.1.
Participants
The ethics committee of Hibino Hospital approved this prospective study. Written informed consent was obtained from all patients. The patients were selected from a consecutive series of 115 individuals who were admitted to Hibino Hospital less than 3 months after suffering a hemorrhagic or occlusive stroke. No patient included here had subarachnoid hemorrhage. Patients had presented for rehabilitation
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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therapy and were diagnosed using magnetic resonance imaging (MRI). Exclusion criteria were the same as those described in our previous report (Murakami et al., 2013; Hama et al., 2007).
4.2.
Assessment of attention deficits
Attention deficits were systematically assessed using the Clinical Assessment for Attention (CAT), developed and standardized by the Japan Society for Higher Brain Dysfunction (Takeda et al., 2011; Japan Society for Higher Brain Dysfunction, 2006). The CAT assesses attention with respect to reaction times, vigilance, short-term storage capacity, mental tracking (i.e., working memory), and complex attention (Lezak et al., 2004). The CAT assessment battery includes digit and visual tapping spans, a visual cancellation task, an auditory detection task (Mizuno, 1991), a symbol digit modalities test (SDMT) (Smith, 1968), a memory-updating test (Morris and Jones, 1990), a paced auditory serial addition test (PASAT) (Gronwall and Wrightson, 1974), a position Stroop test (Sohlberg and Mateer, 1987), and a continuous performance test (CPT) (Beck et al., 1956). Completion rates for the auditory detection task, memory-updating test-4, and PASAT1 were low due to auditory disturbances and calculation difficulties. These test results were excluded from further analysis. Specific CAT assessment details are as follows. The digit span test consists of both digits forward and backward tasks and was administered to participants based upon procedures used for administration of the Wechsler intelligence tests. The visual tapping span test is also known as the Corsi block-tapping task. In this task, participants are asked to copy tapping patterns forward and backward, following the examiner's taps in the same prearranged sequence. The visual cancellation task is a simple test measuring processing accuracy. Participants use a pencil to cross out a target stimulus dispersed within rows of randomly placed interfering stimuli displayed on a sheet. Two sets of the stimulus sequence were used, Arabic numerals and kanaletters. The test was scored based upon the ratio of correct answers to the total number of stimuli. The SDMT is a visuographic task that preserves the substitution format of Wechsler's Digit Symbol Coding test but reverses the presentation of the material, so that symbols are printed and the patient writes out the numbers. Ninety seconds were allowed for each trial, each of which includes 110 items. Completion ratios (i.e., the number of correct answers compared to the number of items) were computed. The position Stroop test uses a Japanese–kanji version of the High-Middle-Low format developed by Sohlberg (Sohlberg and Mateer, 1987). Participants were asked to verbally report the kanji positions (high, mid, or low) instead of their meanings. One hundred and fourteen items were used in the trials, which were presented in six rows. Correct response ratios were assessed. The PASAT requires participants to add 60 pairs of randomized digits, such that each is added to the digit immediately preceding it. The digits were presented either every 1 or 2 s. The ratio of correct answers was assessed. The memory-updating test requires participants to attend to digit strings of unknown length and to then recall the three or four most recently presented digits. During the test,
participants are required to remember the first three or four items presented and then, if there were more than three or four items in a list, to update the contents of their memory by dropping the oldest item and adding the most recent to the string. Finally, the CPT is a computerized vigilance test that presents stimuli for brief durations, providing reaction times as well as accuracy data. In the simple version, participants respond to the digit “7” when it appears briefly in the center of the screen at random intervals. In the X version, digits appeared in a random order, and participants were asked to respond to every “7”. In the more difficult (AX) version, participants were asked to respond to “7” only if it followed “3”. Each participant's ratio of correct answers was calculated for each test. Japanese cut-off points for each CAT test have been previously reported (Kimura et al., 2003). Consistent with prior reports, attention deficits as evidenced by performance on each CAT test were determined with reference to a cut-off point of less than 1.0 SD from the normal value, for the corresponding Japanese age group.
4.3.
Lesion analysis
We obtained brain images including a T2-weighted sequence using a 1.5-T MRI scanner (Sigma EXCITE XL, version 11.0; GE Healthcare), within one month after admission (around the same time as the psychological assessment took place). The MR protocol included a fast spi-echo pulse sequence. Imaging was performed at 1.5 T on a commercial MRI system (GE Signa, GE Medical Systems, Milwaukee) using the standard quadrature head coil. Brain lesions were identified using axial images of T2 weighted MRI scans of each patient. First, images were converted from dicom to analyze format and were then spatially normalized to the Montreal Neurological Institute (MNI) brain template. At that time, the image was re-sliced with a voxel size of 2 2 2 mm3. These conversions and normalizations (non-linear normalization) were performed using the Statistical Parametric Mapping, Version 8 (SPM8, Wellcome Department of Cognitive Neurology) statistical package (Wright et al., 1995; Ashburner and Friston, 2004). The normalized T2 image for each patient was mapped using MRIcron software (http://www.cabiatl.com/mricro/mricron/ index.html), and the lesions were drawn manually. Inter-rater reliability for individual lesion volumes was assessed using the κ coefficient, which was 0.817 (two neurosurgeons took part in this estimation process, examin ing 40 cases). The voxels judged as lesions were assigned to 1 and all others to 0. The lesion images were saved in the MRIcron volume of interest (VOI) format. Patient VOI images were used in the group analysis. Finally, whole-brain multiple regression analysis was performed using SPM8. The independent variables used in this study were total scores on the measures described above (scores for each CAT task), with other factors such as age, sex, and stroke type (hemorrhage or infarction), time lag between MRI and clinical assessment, and lesion volume being entered as covariates. We used a one-tailed test and
Please cite this article as: Murakami, T., et al., Neuroanatomic pathway associated with attentional deficits after stroke. Brain Research (2013), http://dx.doi.org/10.1016/j.brainres.2013.11.029
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T-contrast to estimate cluster level. The statistical criteria were set at po0.05 for voxel levels and a cluster size of 420. To exclude the possibility that lesions were artificially morphed during the normalization process, we first mapped lesions manually and then performed normalization of the MRI images, inter-rater reliabilities for the two methods (1st method: Initial normalization followed by lesion mapping, 2nd method: Initial lesion mapping followed by normalization) were assessed using the κ coefficient, which was 0.98 (comparison performed with 20 cases). The first method is most commonly used for SPM analysis and was therefore selected as the primary approach used in the present study.
Conflict of interest We confirm that there are no known conflicts of interest associated with this publication. There has been no significant financial support for this work that could have influenced its outcome.
Acknowledgments The present work was supported, in part, by a Grant-in-Aid from the Japanese Ministry of Health, Labor and Welfare, Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan and the Tsuchiya Foundation.
Appendix A.
Supporting information
Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.brainres. 2013.11.029.
r e f e r e n c e s
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