Information

Is there any maping between the AAL to Brodmann areas (BA)?

Is there any maping between the AAL to Brodmann areas (BA)?

I am trying to find any correspondence between BA6 (Premotor cortex) and AAL, but there are no information about it. Is there any equivalence table for Brodmann areas and other related anatomic labels?


Methods

Subjects

We recruited 63 healthy subjects (males: 31, females: 32, mean age: 37.9 years, range: 20� years) with no previous history of neurological, physical, or psychiatric illness for this study. All subjects understood the purpose of the study and provided written, informed consent prior to participation. The study protocol was approved by the Institutional Review Board of the Yeungnam university hospital (YUH-12-0421-O60).

Data Acquisition

DTI data were acquired using a 6-channel head coil on a 1.5 T Philips Gyroscan Intera (Philips, Ltd, Best, The Netherlands) with single-shot echo-planar imaging. For each of the 32 non-collinear diffusion sensitizing gradients, we acquired 67 contiguous slices parallel to the anterior commissure-posterior commissure line. Imaging parameters were as follows: acquisition matrix = 96 × 96 reconstructed to matrix = 128 × 128 field of view = 221 × 221 mm 2 repetition time (TR) = 10,726 ms echo time (TE) = 76 ms parallel imaging reduction factor (SENSE factor) = 2 echo-planar imaging (EPI) factor = 49 b = 1000 s/mm 2 number of excitations (NEX) = 1 and a slice thickness of 2.3 mm (acquired voxel size 1.73 × 1.73 × 2.3 mm 3 ).

Probabilistic Fiber Tracking

The Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL 1 ) was used for analysis of diffusion-weighted imaging data. Affine multi-scale two-dimensional registration was used for correction of head motion effect and image distortion due to the eddy current. Mean translation and rotation was observed the sub-one pixel (0.51 ± 0.47 mm). Fiber tracking was performed using a probabilistic tractography method based on a multi-fiber model, and applied in the present study utilizing tractography routines implemented in FMRIB Diffusion (5000 streamline samples, 0.5 mm step lengths, curvature thresholds = 0.2) (Behrens et al., 2003a Smith et al., 2004 Behrens et al., 2007). This fiber tracking method by multi-fiber model calculated and generated 5000 streamline samples from seed region of interest (ROI) with consideration of the both dominant and non-dominant orientation of diffusion in each voxel and showed how connects the brain regions. Therefore, it has advantage to solve the problem of the crossing fiber. Especially, cross points of the corpus callosum and corona radiata, corticospinal tract fibers and pontocerebellar fibers at pons, and superior and medial frontal gyri are known to be the crossing fiber point (Wiegell et al., 2000). For the connectivity of the SN, a seed ROI was placed on the isolated SN of the upper midbrain on the color-coded map (dorsomedially next to the cerebral peduncle of the upper midbrain) (Mori et al., 2004). For the connectivity of the VTA, a seed ROI was placed on the VTA in the upper midbrain on the color-coded map. We identified the VTA by reconstructing the adjacent structures: interpeduncular nucleus (anterior boundary), central tegmental tract (posterior (www.fmrib.ox.ac.uk/fsl) boundary), midline (medial boundary), red nucleus and SN (lateral boundary) (Mori et al., 2004 Habas and Cabanis, 2007 Blood et al., 2010 Figure 1A). Out of 5000 samples generated from the seed voxel, results for contact were visualized with the threshold at a minimum of five streamline through each voxel for analysis. Connectivity represented the percentage as all hemispheres of 63 subjects. On the other hand, we measured the size of ROI for the SN and VTA.

Figure 1. Neural connectivity of substantia nigra and ventral tegmental area. (A) The region of interest (ROI): a seed ROI for substantia nigra (SN, orange), is placed on the isolated SN of the upper midbrain on the B0 and color-coded map (dorsomedially next to the cerebral peduncle of the upper midbrain). A seed ROI for ventral tegmental area (VTA, sky-blue), is placed on the isolated VTA of the upper midbrain on the B0, and color-coded map. We use other structures to isolate the VTA such as interpeduncular nucleus (anterior boundary, red), central tegmental tract (posterior boundary, white-lined rectangular), midline (medial boundary), red nucleus (blue) and SN (lateral boundary). (B) SN/VTA: results of diffusion tensor tractography for the connectivity of SN/VTA (a: cortex level, b: upper corona radiata level, c: upper internal capsule level, d: lower internal capsule level, e: bicommissure level f: midbrain level, g: upper pons level, h: lower pons level). a to b levels: primary motor cortex, primary somatosensory cortex, premotor cortex, prefrontal cortex, corpus callosum, c to e levels: caudate nucleus, putamen, globus pallidus, nucleus accumbens, thalamus, external capsule, f to h level: red nucleus, amygdala, medial temporal lobe, temporal lobe pontine basis, pontine tegmentum, anterior lobe of cerebellum, posterior lobe of cerebellum.

Determination of Connection Between Substantia Nigra (SN), Ventral Tegmental Area (VTA) and Brain Regions

Connectivity was defined as the incidence of connection between the SN/VTA and each brain region: primary motor cortex (M1, brodmann area [BA]: 4), primary somatosensory cortex (S1, BA: 1, 2, 3), premotor cortex (PMC, BA: 6), prefrontal cortex (BA: 9, 10, 11, 12), caudate nucleus, putamen, globus pallidus, nucleus accumbens, thalamus, external capsule, red nucleus, amygdala, medial temporal lobe (BA: 27, 28, 34, 35, 36, 37), temporal lobe (superior, middle, inferior, BA: 20, 21, 22), pontine basis, pontine tegmentum, anterior lobe of cerebellum, posterior lobe of cerebellum, corpus callosum, and occipital lobe (BA: 17, 18, 19).

Statistical Analysis

SPSS software (v.15.0 SPSS, Chicago, IL) was used for statistical analysis. The Chi-square test was used for determination of the difference in connectivity between the right and left hemispheres, and between the SN and VTA. In addition, we performed an independent t-test for determination of differences in size of ROI between the SN and VTA, and between the right and left hemispheres. The significant level of the p value was set at 0.05.


Background

Autism is a neurodevelopmental disorder affecting social, cognitive, linguistic and sensorimotor abilities. These qualitative deficits are pervasive and long lasting. While genetic factors are known to be strong [1], consistent neurological markers for the disorder remain to be fully established. Behavioral markers identifying deficits in sensorimotor processing and social skills are apparent as early as one year of age [2, 3]. Sensorimotor deficits include fine motor apraxia [4–6], reduced postural control [7, 8], and impaired imitation [9]. Individuals with autism also have delays in language development [10], impaired attention [11], as well as deficits in executive cognitive processes [12, 13]. These developmental abnormalities are often discovered during the preschool years. Among the brain areas suspected to be involved in both sensorimotor and cognitive deficits are the caudate nuclei [14–16].

Several types of abnormalities of the caudate nuclei have been noted in autism. Reduced correlation of resting cerebral glucose metabolic rates between the caudate and frontal regions has been seen in children with autism [17]. Sears et al. [18] and Hollander et al. [14] found that enlargement of the caudate nuclei was associated with stereotyped behaviors in autism. Conversely, a cluster-based analysis of structural MRI scans [19] showed that reduced caudate size was correlated with greater impairment on the Childhood Autism Rating Scale (CARS), including deficits in a wide range of abilities such as body movement. While these anatomical findings appear inconsistent, they nonetheless indicate that autism may be associated with volumetric abnormalities of the caudate nuclei. Furthermore, Singh [20] noted serum antibodies to the caudate in children with autism that were not found in typical children. These antibodies may implicate the caudate nuclei in a type of autoimmune dysfunction associated with autism. Related dysfunction of the caudate nuclei may secondarily affect regions that are anatomically connected to these nuclei.

Retrograde transneural transport of the herpes simplex virus has illuminated the anatomical connectivity between caudate nuclei and other brain regions [21, 22]. These connections are widespread. The caudate nuclei primarily receive input from frontal, temporal, inferior parietal, pre-occipital, and limbic areas including the amygdala, hippocampus and parahippocampal cortex. The various connections between the caudate and other brain regions have been segregated into circuits [21, 22]. Anatomical circuits directly associated with the caudate are the associative, lateral orbitofrontal, and occulomotor circuits. The associative circuit connects the dorsolateral prefrontal cortex with the ventral caudate, and this circuit is believed to regulate executive functions in the brain by unifying cognitive processes such as attention, planning and decision-making [21, 23]. Within this circuit, the caudate nuclei may also aid in the selection of rules by which decisions and plans are made and by enhancing working memory [24–26]. The lateral orbitofrontal circuitry, believed to support set switching and inhibition, extends from Brodmann's areas 10 and 12 to the ventromedial caudate [21, 22, 27, 28]. The caudate nuclei send return projections to areas 10 and 12 via the thalamus. Evidence from human and non-human primate studies show that disruptions to the circuitry at the orbitofrontal level result in deficits in short term memory for objects and the processing of stimulus reinforcement contingencies [29, 30]. The third loop involving the caudate and the frontal eye fields (FEF) is the occulomotor circuit and is thought to be involved in saccadic eye movements [31]. The FEF, located in Brodmann's area 6 [32], project to the body of the caudate which then sends projections to the substantia nigra. Although the caudate is not considered part of the motor circuit, it has been shown to contribute to working memory in the planning and selection of motor sequences [33–36]. A disruption in the anatomical connectivity between the caudate and any region in one of the circuits is likely to be reflected in weaker functional connectivity within that circuit.

A number of fMRI studies of autism have shown atypical levels of activation in the caudate nuclei for a variety of tasks such as spatial processing [37], finger tapping [38] and face perception [39]. In each of the above mentioned studies, autism groups showed reduced caudate activation compared to control subjects. These fMRI findings only provide information on whether the caudate nuclei are involved in a task. As established above, however, participation of the caudate nuclei in sensorimotor, cognitive and executive functions reflect their role in distributed functional networks. The integrity of networks cannot be fully examined by conventional fMRI activation analyses, which are unsuited for analyzing circuitry.

In order to examine network integrity, the present study used functional connectivity magnetic resonance imaging (fcMRI). Functional connectivity measures examine the temporal covariance between spatially remote neurophysiological events [40]. Previous fcMRI studies have shown that regional signal covariance is consistent with anatomical connectivity and functional networks delineated in animal studies [41, 42]. In humans, fcMRI has been used to examine interhemispheric connectivity in the sensory [43, 44] and motor cortex [45]. Belmonte et al. [46], Just and colleagues [47] and Sporns et al. [48] have suggested that local connectivity may be relatively dense whereas long-range connectivity between brain regions may be reduced. However, the functional connectivity between basal ganglia and cerebral cortex has not been previously examined in autism. In the present study, we examine functional connectivity of the caudate nuclei in individuals with autism and healthy controls.


Know Your Brain: Primary Somatosensory Cortex

primary somatosensory cortex (in blue)

The primary somatosensory cortex is located in a ridge of cortex called the postcentral gyrus, which is found in the parietal lobe. It is situated just posterior to the central sulcus, a prominent fissure that runs down the side of the cerebral cortex. The primary somatosensory cortex consists of Brodmann's areas 3a, 3b, 1, and 2.


RESULTS

Behavioral Results

As seen from Table ​ TableI, I , there were no differences in accuracy between all sensorimotor tasks. With RTs, there was no overall difference between manual (AMViM) and vocal (AVoViVo) responses, F(1,19) = 0.90. However, responses to auditory (AM and AVo 849 ms) stimuli were overall slower than responses to visual (ViM and ViVo 675 ms) stimuli, F(1,19) = 16.06, P < 0.001. AM responses were marginally slower than ViVo responses [t(11) = 2.19, P = 0.051], and AVo responses were slower than responses in the three other tasks (see Table ​ TableI I ).

Table I

Vocal responseManual response
Visual stimulus647 msec (96.3%)712 msec (98.2%)ns (ns)
Auditory stimulus935 msec (97.9%)785 msec (96.0%)* (ns)
* (ns)ns (ns)

The diagonal comparison, using paired t‐tests, between ViVo and AM revealed no significant differences in reaction time and accuracy measures. Although the accuracy difference between ViM and AVo was not significant, responses were significantly (P < 0.05) faster in the ViM condition than they were in the AVo condition. A * denotes a significant difference, using unpaired t‐tests (P < 0.05).

Reaction time (and percentage accuracy) for sensorimotor tasks.

The slower responses to auditory tasks might be due to the presence of background noise from the scanner and/or the discriminative difficulty of the auditory stimuli. Whatever the cause for the slower RTs in auditory tasks, it should have little bearing on isolating brain regions coactivated by all tasks.

We also considered whether overall performance differed between the AVo/ViM and AM/ViVo groups. The RT difference was significant [F(1,19) = 4.64, P < 0.05] owing to faster average responses for the AM/ViVo group (716 ms) than the AVo/ViM group (824 ms). However, this difference may have been the result of a speed�uracy tradeoff as the slower AVo/ViM group (98%) responded marginally [t(19) = 2.05, P = 0.055] more accurately than the ViVo/AM group (96%).

FMRI Results

Activated regions for each sensorimotor task

SPMs for the AVo𠄏ixation, AM𠄏ixation, ViVo𠄏ixation, and ViM𠄏ixation contrasts from the blocked fMRI runs are illustrated in Figure ​ Figure1. 1 . Extensive activation foci were not only observed in primary sensory and motor cortices, but also in association areas of all cortical lobes. Although all but the ViVo tasks recruited large expanses of cortex, auditory cortex activation was more prominent in the auditory tasks, whereas visual cortex activation was more readily observed in the visual tasks. Similarly, manual responses recruited more dorsal and slightly more posterior regions of the frontal lobe than vocal responses. In addition, subcortical (thalamus and superior colliculus) activations (not shown) were also common across tasks.

Although this analysis reveals that each sensorimotor task recruits a relatively large extent of cortical and subcortical tissue, it does not identify brain regions that are either commonly activated across all four tasks or that are specific to each sensory and motor modalities. The following analyses serve these purposes.

Identification of sensory and motor areas

We sought to identify brain regions that were more activated for a particular sensory (e.g. auditory) or motor (e.g. manual) modality than for the other (i.e., visual and vocal, respectively). Regions that exhibited modality‐preferred activation in both blocked and ER runs are illustrated in Figure ​ Figure3 3 .

Not surprisingly, visual stimulation preferentially recruited Brodmann areas (BAs) 18 and 19 of the occipital lobe, whereas auditory stimuli preferentially recruited the superior temporal cortex and adjacent areas. In addition, the vocal responses preferentially recruited lateral regions of the precentral gyrus, whereas manual responses engaged more dorsal regions of the pre‐ and postcentral gyrus. Examination of the time courses from the ER runs in these areas confirms that they demonstrate preferential, but not specific responses to a given sensory or motor modality (see Fig. ​ Fig.3). 3 ). That is to say, foci showed significant activation (all Ps < 0.01) for the nonpreferred modality (e.g., auditory activation in visual cortex or manual response activation in vocal ROI). However, these nonpreferred activations were significantly weaker than the preferred modality activations (Fig. ​ (Fig.3 3 and Table ​ TableII). II ). Because these group�ined ROIs were relatively large, it is not possible to ascertain whether the present results were obtained, because these sensory or motor ROIs do not exhibit strict modality specificity or because they are composed of a mixture of multimodal and modality‐specific areas.

Table II

Average Talaraich coordinates and size of regions showing sensory‐ (auditory vs. visual) or motor‐ (manual vs. vocal) preferred activation

SideBrodmann areas x, y, zVoxels (mm 3 )Sensory/motor preference t‐value
VisualLeft18,19�, �, 317063.71**
(ViM𠄊Vo) − (AM ‐ViVo)Right18,1929, �, 414723.32**
AuditoryLeft13, 22, 40, 41, 42�, �, 12116675.67**
(AVo‐ViM) − (ViVo𠄊M)Right13, 22, 40, 41, 4252, �, 10179434.73**
ManualLeft2, 3, 4, 5, 6, 40�, �, 5555316.31**
(ViM𠄊Vo) − (ViVo𠄊M)Right441, �, 4342.90*
VocalLeft4, 6�, 𢄩, 3522026.91**
(AVo‐ViM) − (AM‐ViVo)Right4, 646, 𢄩, 3826075.63**
Right6, 4357, 𢄧, 13893.62**

The equations below the modality names describe the contrasts used to isolate the modality‐preferred activations. A * denotes a significant t‐value uncorrected (P < 0.05) and a ** denotes a significant t‐value using the Bonferroni adjustments for multiple comparisons (P < 0.006).

Identification of brain regions commonly activated across sensorimotor tasks

Group‐level conjunction analysis

We first sought to identify candidate convergence zones for all sensorimotor tasks in group𠄊veraged SPMs. To achieve this, we identified, for the block and fast𠄎R runs, voxels that were conjointly activated by all four tasks (AVo, ViM, ViVo, and AM) in a random�ts analysis. Thus, only regions that were significantly activated for all four tasks and in both designs (block and fast ER) were accepted. This analysis revealed the following regions as commonly activated across the four tasks (Fig. ​ (Fig.4 4 Table III ): a region of the medial wall comprising the bilateral SMA, pre‐SMA, and ACC the right anterior insula the posterior lateral frontal/prefrontal cortex (pLPFC) in BA 6 and 9 intersecting the inferior precentral sulcus and inferior frontal sulcus [Brass et al., 2005] a more dorsal bilateral premotor (PM) cortex region in BA6 left thalamus and a segment of the brainstem near the superior colliculus. All these brain regions showed significant peak volume activity relative to baseline in all four sensorimotor tasks (AVo, ViM, ViVo, and AM). Except for the left pLPFC, right PM, and brainstem, which responded more to the vocal than the manual tasks, these brain regions did not demonstrate sensory or motor preference (ANOVAs on the percent signal change at peak amplitude) (Table III ). Thus, these brain regions are distinct from the sensory and motor regions in that they generally respond strongly and similarly to all sensory and motor modalities tested.

Brain regions exhibiting coactivation across all four sensorimotor tasks (AVo, AM, ViVo, and ViM) in both the block and fast𠄎vent runs [q(FDR) < 0.05] in group𠄊veraged SPMs. The timecourses are from the fast𠄎vent‐related runs. (a) Medial regions (b) Lateral ROIs (c) Time courses in subcortical structures (cerebellum, brainstem, and thalamus).

Table III

Average Talaraich coordinates and size of brain regions showing conjunction of task activation

x,y,zVoxels (mm 3 )Sensory preference t‐valueMotor preference t‐value
Left pLPFC (BAs 6,9)�, 0, 368241.644.54**
Right pLPFC (BAs 6,9)48,2,231721.201.61
Left PM (BA 6)�, 𢄥, 48451.460.24
Right PM (BA 6)41, 𢄣, 45750.672.94*
Right anterior insula37,8,81051.151.50
Medial wall (BAs 6, 24, 32)𢄢, 4, 5112571.641.33
Brainstem𢄢, �, 03651.112.57*
Left thalamus�, �, 108301.881.33

Group�ined ROIs with random effects analysis. A * denotes a significant t‐value (P < 0.05), and ** denotes a significant t‐value using a Bonferroni adjustment for 12 comparisons (P < 0.004). The medial wall ROI consists of pre‐SMA, SMA, and anterior cingulate. A right precentral gyrus ROI (BAs 3,4) was removed from further analysis due to its close proximity between auditory and vocal ROIs. pLPFC, posterior lateral prefrontal cortex PM, premotor.

Although these results are consistent with the notion that these regions may constitute neural convergence zones for all sensorimotor tasks, they may also result from the blurring of adjacent foci displaying sensory‐ and/or motor‐specific activation in this standardized group𠄊veraged data. To address this issue, we carried out an analysis on individual subject's nonstandardized data.

Individual 2D analysis

Within each convergence ROI, using in‐plane 2D images, we assessed whether the majority of subjects exhibited at least one voxel of conjunction and/or task‐specific activity (see Methods section and Table ​ TableIV). IV ). Given that this brain region is thought to be functionally heterogeneous (Picard and Strick, 2001), the frontal medial wall ROI was divided into a ventral ACC ROI and a dorsal pre‐SMA/SMA ROI. It was also divided according to the left and right hemisphere. Regions that demonstrated conjunction‐related activation for all conditions in most subjects consisted of the right anterior insula, bilateral pLPFC, bilateral pre‐SMA/SMA, bilateral ACC, and the left thalamus. The PM cortex demonstrated conjunction‐related activation in only the AVo/ViM tasks, whereas the brainstem did not exhibit robust conjunction activity. In addition, the pLPFC and the pre‐SMA/SMA showed task‐specific activation in at least one of the four sensorimotor tasks (AVo, ViM, AM, and ViVo).

Table IV

Number of participants demonstrating conjunction and/or task‐specific activation

SideROIAM/ViVoAVo/ViM
ConjunctionAMViVoConjunctionAVoViM
LeftThalamus6*4210*11
Brainstem201510
pLPFC7*438*6*3
PM5357*32
Pre‐SMA/SMA9*47*9*7*3
Anterior Cingulate8*5311*35
RightBrainstem301320
Anterior Insula6*437*22
pLPFC6*247*7*3
PM5348*55
Pre‐SMA/SMA10*6*510*33
Anterior Cingulate7*559*03

The total number of participants per cell is 12 (i.e., only subjects with sagittal prescriptions were analyzed in 2D). Cells with asterisks (*) have six or more participants, indicating that there is 99.72% confidence, using the Bonferroni corrected Clopper–Pearson test (see Methods section), that there is at least one participant with conjunction or task‐specific activation. SP, superior–posterior pLPFC, posterior lateral prefrontal cortex PM, premotor SMA, supplementary motor area.

Thus, several of the conjunction ROIs defined at the group level also demonstrated robust conjunction activity in 2D analysis of single subjects. However, some of these ROIs, namely the pLPFC and pre‐SMA/SMA, also showed task‐specific activation. The latter finding can be interpreted in at least two ways. First, the conjunction activity observed both at the group and individual levels could be an artifact of the hemodynamic blurring of distinct single‐task activation foci. Alternatively, the conjunction ROIs may include distinct subregions demonstrating true task‐specific and conjunction‐related activity. Importantly, although the pLPFC showed both conjunction activity and task‐specific activity, the latter was limited to a vocal task (AVo see Table ​ TableIV). IV ). These vocal‐specific voxels were not only posterior to the conjunction voxels in the individual subject 2D analysis, they were also immediately anterior to the vocal motor ROI (cf. Figs. ​ Figs.3 3 and ​ and4), 4 ), suggesting that they belong to the vocal motor/premotor cortex. Thus, the posterior extent of the pLPFC ROI may have been tainted by vocal‐specific activity from the adjacent vocal motor cortex. This finding, together with the fact that no significant manual‐specific activation was observed in pLPFC, suggests that this brain region contains genuine conjunction‐related activity. By contrast, the pre‐SMA/SMA showed significant task‐specific activity in both vocal and manual tasks, casting doubt as to whether this brain region truly contains conjunction foci.

Replication of individual subject analysis

For three of the ROIs that exhibited conjunction activity at the group level, the individual‐level analysis revealed a more complex pattern of activation. Specifically, examination of individual subjects' activity profile in pLPFC and pre‐SMA/SMA showed both conjunction‐related and task‐specific activations, whereas the PM ROI showed conjunction‐related activity in the AVo/ViM, but not in the AM/ViVo, pairing. To determine whether the complex activation patterns in these brain regions are robust, we rescanned four of the participants who showed conjunction and task‐specific activations in these three ROIs in a fast ER experiment that was identical to the first scanning session. The conjunction and task‐specific voxels were separately identified in each session of each subject and assessed for overlap of activation across sessions using a conjunction analysis that required voxels to be significantly activated in each of the two sessions (see Methods section).

The analysis revealed that both conjunction and task‐specific activations replicated in three or all the subjects for the pre‐SMA/SMA and pLPFC, whereas the replication for task‐specific activation held for about half the subjects in PM cortex (Table ​ (TableV). V ). Figure ​ Figure5 5 illustrates the replication of activation across sessions for single‐task activity in the pre‐SMA/SMA and for conjunction‐related activity in the pLPFC. Thus, the replication analysis provides additional support for the presence of genuine conjunction‐related activity in pLPFC and of task‐specific activity in pre‐SMA/SMA. By contrast, the pattern of activation in PM proved to be less consistent.

Replication of activation patterns across fMRI sessions in a representative subject. Left, 2D SPMs and right, time courses. (a) Replication of task‐specific (AVo/ViM) activation across sessions in pre‐SMA/SMA. (b) Replication of conjunction activation across fMRI sessions in pLPFC. White voxels were those that demonstrated replicable coactivation for the AVo/ViM tasks across sessions. Maps were thresholded at q(FDR) < 0.22 for an omnibus q(FDR) < 0.05 across sessions.

Table V

Number of participants exhibiting replicable conjunction and task‐specific activation

SideROIConjunction replicationsSingle‐task replications
LeftpLPFC4/44/4
RightpLPFC4/43/4
LeftPM3/31/3
RightPM3/42/4
LeftPre‐SMA/SMA4/43/4
RightPre‐SMA/SMA4/43/3

Three participants performed the AVo/ViM tasks and the other participant performed the AM/ViVo tasks. The numerator indicates the number of subjects who demonstrated intersession replicability of activation (i.e., significant conjunction or task‐specific activation in Sessions 1 and 2), whereas the denominator indicates the number of subjects who showed conjunction or task‐specific activation in Session 1 (as activation in session 1 is a prerequisite for assessing reliability). Note that one participant did not demonstrate any activity in left PM in Session 1, and another did not demonstrate single‐task activity in right pre‐SMA/SMA in Session 1, so the denominator in those cases is three, rather than four. pLPFC, posterior lateral prefrontal cortex PM, premotor SMA, supplementary motor area.

Summary

The results of the group𠄊verage and individual‐level analyses specifically highlight the anterior insula, pLPFC, ACC, and thalamus as candidate neural substrates subserving response selection irrespective of the sensory and motor modalities. The pre‐SMA/SMA is another candidate brain region, but the presence of task‐related activity neighboring the conjunction activity does not allow us to rule out the possibility that the conjunction activation is a result of overlap of the hemodynamic spread of task‐specific activity. A similar issue limits the interpretability of the PM activation, especially considering that the conjunction activation for one set of tasks (i.e. AM/ViVo) failed to demonstrate a sufficient number of participants with conjunctive activation.

Comparison of Sensorimotor Pairings

In a final analysis, we considered whether the AVo/ViM and ViVo/AM pairings differentially affected brain activations. It has been argued that some sensorimotor mappings are more naturally compatible than others [Hazeltine and Ruthruff, 2006 McCleod and Posner, 1984 Stelzel et al., 2006 Wickens, 1984] and that this compatibility effect may differentially recruit the (left) prefrontal cortex [Stelzel et al., 2006]. Specifically, the AVo/ViM mapping may be less demanding on limited resources than the ViVo/AM pairing given that in the former pairing the input and outputs may be preferentially linked: we tend to respond verbally to auditory input and often make manual movements toward visual objects. However, our behavioral results are inconsistent with this hypothesis, as subjects were generally faster in the AM/ViVo pairing than in the AVo/ViM pairing [t(19) = 2.15, P < 0.05]. Furthermore, a between‐group comparison of these two sensorimotor mappings ([ViVo + AM] − [AVo + ViM]) revealed no activation differences at q(FDR) < 0.05 or even with an uncorrected threshold of P < 0.001. Thus, even if sensorimotor pairings differ in their compatibilities, it does not appear that these compatibility differences are expressed by differential activation of brain tissue. These results apply, at least, under conditions in which the sensorimotor tasks are performed separately. However, it is possible that different results could be obtained under dual‐task situations [Stelzel et al., 2006].


Program Highlights

As a student in the BA in P sychology degree program, you will study the science of behavior and mental processes while develop ing strong academic and social skills including critical thinking, collaboration, oral and written expression, interpersonal effectiveness, and social maturity. You will also learn to analytically review psychological literature and apply theory to practice by conducting your own psycholog y research projects.

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Conclusions

While our data is preliminary, it suggests that perception of non-consciously perceived stimuli activates anterior cingulate cortex (ACC) and insular cortex, to form a basis for conscious perception. Activation of primary visual areas by non-consciously perceived stimuli is perhaps driven by a bias for these studies to use images of emotional faces, and so more fMRI studies are needed to compare subliminal and supraliminal presentation of other types of stimuli in different modalities. After further fMRI studies comparing the neural correlates of subliminal versus supraliminal stimulation, meaningful conclusions are more likely to be drawn about brain systems involved in unconscious perception.


Methods

Participants

Eleven native speakers of Japanese (11 men, mean age 20.8 years, range 20� years) participated in this study. All participants were right-handed, as confirmed by a modified version of the Edinburgh Handedness Inventory [35], and were free of any neurological or hearing impairments. The participants fulfilled the following two criteria: they had no professional musical education or training (mean 3.1 years of music lessons other than music education classes at primary and secondary school), and they were 𠇌ommon listeners” (i.e., not “music lovers,” who tend to listen to one specific type of music only their time spent listening to music per day was 1.1 (SD =𠂠.74) hours.

Ethics statement

This study is approved by the ethical committee of the Tokyo Metropolitan Institute of Gerontology. All participants gave written informed consent to participate in this study.

Auditory stimuli

Twenty-four highly familiar songs and twenty-four unfamiliar songs were selected to serve as stimuli based on the results of our preparatory study of familiarity ratings of Japanese children's and traditional songs [34]. The mean familiarity rating on our five-point scale (1 = unfamiliar, 5 = highly familiar) was 4.69 for the familiar songs and 1.19 for the unfamiliar songs. The beginnings of the familiar songs were rated as equally familiar even when the lyrics or the melody was presented in isolation. Additionally, 30 songs with intermediate familiarity were used during a training session. A total of 234 sound stimuli were prepared.

In the first type of sound stimuli (song), original lyrics were sung to the original melody. In the second type of sound stimuli (lyrics), the original lyrics were sung using the original rhythm, but on a single pitch (G3, 196 Hz). In the third type of sound stimuli (melody), the syllable “la” was sung to the original melody and using the original rhythm. All stimuli were generated by the VOCALOID voice-synthesizing software (YAMAHA, Inc., Tokyo, Japan) in order to make the three types of sound stimuli as similar as possible in terms of acoustical features, such as intensity, voice timbre, prosody, and duration. As a result, the three types of sound stimuli had exactly the same temporal features, such as tempo, rhythm, and duration of notes ( Figure 1 ). None of the stimuli contained instrumental or choral accompaniment. The auditory stimuli were digital music files with 16-bit depth, 44,100-Hz sampling rate, and mean loudness of 75.1 dB SPL. The warning stimulus was a pure tone (sine wave, 500-Hz frequency, 500-ms duration). All sound stimuli were presented using E-Prime (Psychology Software Tools, Inc., Pittsburgh, USA).

We checked the clearness of and participants' feelings of discomfort with the synthetic voices compared to natural human voices in the pre-experimental study, which tested 12 participants (12 men, mean age =�, SD =𠂢). The results showed that the clearness of and participants' feelings of discomfort with the synthetic voices were not significantly different from values obtained using human voice sounds (t =𠂡.02, pϠ.13, paired t-test).

Procedure

Prior to each PET scanning session, the participant completed a short practice session in which five sound stimuli were presented. During PET scanning, there were two decision tasks: a familiarity decision (FamD) and a sound-type decision (which was a perceptive control task [Control]). There were three sound type conditions in which FamD task were made labeled as Song, Lyrics, and Melody. For each sound type condition, 24 stimuli (12 familiar songs and 12 unfamiliar songs) were presented via in-ear headphones each sound type. The participants were instructed to decide whether a song excerpt was known to have been acquired in his/her life or not as quickly and accurately as possible by pressing one of two buttons: the first button, pressed with the right index finger, signified that the participant was familiar with the song, and the second button, pressed with the right middle finger, signified that the participant was unfamiliar with the song. During each trial, a warning stimulus was presented for 500 ms, followed by a silence interval (500 ms), then the target auditory stimulus (3000 ms), then silence (500 ms), followed by the next trial. The auditory stimulus was stopped immediately after a response button was pressed by the participant in order to prevent additional processing. Reaction time (RT) was measured as the interval between target stimulus onset and participant response. The designation of a song as familiar signifies that the participant had learned it well during his/her life.

The Control (sound-type decision) task was designed to recruit multiple processes, such as listening, monitoring variations in phoneme and pitch, working memory, decisional processing, and motor processing. The participants were asked to decide whether an excerpt that they heard belonged to the complete-song type (song) or to the other types (lyrics-in-isolation type [lyrics] and melody-in-isolation type [melody]) as quickly and accurately as possible by pressing one of two buttons: the first button, pressed by the right index finger, signified the song type, and the second button, pressed with the right middle finger, signified that the excerpt was one of the other types. The three types of sound stimuli were presented in a random order. Identical stimuli were used in both the FamD and the control tasks so as to balance the acoustical and perceptional processes.

The three FamD conditions (Song, Lyrics, and Melody) and the Control condition were presented to participants in a counterbalanced order. Then, each of four conditions was performed twice. RT and button selection data were recorded using a response box placed under the participant's right hand, which was linked to a computer running the E-Prime software package. In a post-scan test, participants were asked to rate the familiarity of the 78 songs presented as song, lyrics, and melody stimuli types, including the stimuli used during the PET session, using a 5-point scale.

Data acquisition

Regional cerebral blood flow (rCBF) was measured via PET scanning using 15 O-labeled water. A SET 2400W scanner (Shimadzu Inc., Kyoto, Japan), operated in three-dimensional mode, acquiring a 128󗄨흐 in matrix with a 2൲൳.125-mm voxel size. Each participant was scanned eight times to measure the distribution of 15 O-labeled water with a 10-min inter-scan interval to allow for decay. Each scan was started upon the appearance of radioactivity in the brain after an intravenous bolus injection of 180 MBq of 15 O-labeled water. Each scan lasted 60 s. The activity measured during this period was summed and used as a measure of rCBF. A transmission scan was obtained using a 68 Ga/ 68 Ge source for attenuation correction prior to participant scanning. Each experimental condition was started 15 s before data acquisition and continued until the completion of the scan. Participants were scanned while lying supine with their eyes closed in a darkened, quiet room. T1-weighted structural MRI scans were also obtained for each participant on a 1.5-T GE Signa system (SPGR: TR =� ms, TE =𠂦 ms, matrix =�󗉖󗄥 voxels) for anatomical reference and in order to screen for any asymptomatic brain disorders.

Behavioral data analysis

For each of the three types of auditory sound stimuli, the degree of familiarity measured in the post-scan task was calculated. Mean familiarities were analyzed by means of paired t-tests. For each participant and for each experimental condition, mean RTs were calculated. Accuracy was calculated based on the results of the post-scan familiarity rating task. We performed a repeated-measures ANOVA on RTs and accuracy. Behavioral data was analyzed by SPSS 17.0 (SPSS Inc., Chicago, USA). Post hoc, Bonferroni-corrected, paired t-tests were used to test for differences between conditions.

Imaging analysis

PET images were analyzed using the Statistical Parametric Mapping software package (SPM8, Wellcome Department of Cognitive Neurology, London, UK), implemented in MATLAB 7.5.0 (Mathworks Inc., Massachusetts, USA). During preprocessing, PET data were realigned, spatially transformed into standard Montreal Neurological Institute stereotactic space (MNI, voxel size 2൲൲ mm), and smoothed with a 12-mm Gaussian filter. Each scan was scaled to a mean global activity of 50 ml/100 g/min. We used a threshold of 80% of the whole brain mean as the cutoff point for designation of voxels as containing gray matter, and covariates were centered around their means before inclusion in the design matrices. An analysis of covariance (ANCOVA), with global activity as a confounding covariate, was performed on a voxel-by-voxel basis. The results, expressed in SPM as t-statistics (SPM ), were then transformed onto a standard normal distribution (SPM ). All statistical thresholds were set at pπ.005, uncorrected at the voxel level, with an extent threshold requiring a cluster size of more than 20 voxels.

First, using t-tests, we created SPM contrasts subtracting the Control condition from each of the three FamD conditions: Song𠄼, Lyrics𠄼 and Melody𠄼. Then, we performed conjunction analyses to classify the activations in each FamD condition in terms of whether they were also activated in either of the other two conditions ( Figure 2 ). For instance, areas of activation in the contrasts (Song𠄼, Lyrics𠄼 and Melody𠄼) may be classified into seven categories: (1) commonly activated in all three FamD conditions (Song𠄼 ∩ Lyrics𠄼 ∩ Melody𠄼, Figure 2, a ), (2) commonly activated by Song and Lyrics (Song𠄼 ∩ Lyrics𠄼, Figure 2, b ), (3) commonly activated by Song and Melody (Song𠄼 ∩ Melody𠄼, Figure 2, c ), (4) commonly activated by Lyrics and Melody (Lyrics𠄼 ∩ Melody𠄼, Figure 2, d ), (5) specifically activated by Song (Song–Lyrics ∩ Song–Melody ∩ Song𠄼, Figure 2, e ), (6) specifically activated by Lyrics (Lyrics–Song ∩ Lyrics–Melody ∩ Lyrics𠄼, Figure 2, f ), and (7) specifically activated by Melody (Melody–Song ∩ Melody–Lyrics ∩ Melody𠄼, Figure 2, g ).


PointToTD operates on a single coordinate and has three search options: (1) Structural Probability Maps, (2) Talairach label, (3) Talairach labels within a cube range and (4) Talairach gray matter labels. The program is run as follows:

java -cp talairach.jar org.talairach.PointToTD 1, 15, 10, 12

In this example, the Structrual Probability Map results for (15,10,12) would be returned. The cube range search uses 5mm (or +/-2mm) as its default. To set a different cube size, use "3:<cubesize>". Sizes of 3, 5, 7, 9 and 11 are accepted. For example:

java -cp talairach.jar org.talairach.PointToTD 3:7, 15, 10, 8

That search would return the nearby 343 labels (for a 7x7x7 region).


DISCUSSION

To our knowledge, this is the first study integrating cytoarchitectonically defined probabilities with MRI-based signal intensities to assess sex differences in gray matter within BA 44 and BA 45 in a sizable cohort. The approach employed differs from classic landmark-based ROI studies in that it does not depend on macroanatomic landmarks (sulci, gyri, etc.) that may be highly variable across individuals or even entirely missing. Our findings of more gray matter in BA 44 and BA 45 in females compared with males seem to be in close agreement with some (albeit not all) previous studies addressing sex differences in the human brain, as further discussed below. In addition, we elucidate how different analysis methods may impact study outcomes, and we conclude by elaborating on the potential functional relevance and possible determinants of the observed effects.

Correspondence to Prior Findings

Our results indicating a sexual dimorphism with respect to gray matter in BA 44/45 are in good correspondence with the outcomes of other in vivo studies (Schlaepfer et al., 1995 Good et al., 2001a Luders et al., 2005, 2006 Im et al., 2006 Sowell et al., 2007 ). More specifically, an ROI analysis that investigated sex differences in frontal, temporal, and inferior parietal gray matter indicated larger volumes in females than in males in frontal areas related to language processing (Schlaepfer et al., 1995 ). Furthermore, vertexwise investigations across the entire brain revealed a larger thickness of the cerebral cortex in females than in males within a region of the inferior frontal gyrus that matches BA 44/45, where effects were observed either in the left hemisphere only (Im et al., 2006 ) or in both hemispheres (Luders et al., 2006 Sowell et al., 2007 ). Similarly, voxelwise investigations across the entire brain exposed larger gray matter volumes or higher gray matter concentrations in females than in males within BA 44/45 in both hemispheres (Good et al., 2001a Luders et al., 2005 ). Last but not least, our findings also correspond well with outcomes from a post mortem study (Harasty et al., 1997 ) that was specifically designed to examine language-associated cortical regions, including the inferior frontal gyrus (which includes Broca's area in the dominant hemisphere). In that study, the cortical volume fraction of the region that includes Broca's area was 20.4% larger in female brains than in male brains. Interestingly, the original post mortem study (Amunts et al., 1999 ) that provided the cytoarchitectonic tissue probabilities for our present analysis did not detect sex differences in BA 44/45. However, given the extremely small sample (five males/five females) in that study, the lack of a sexual dimorphism might be due to missing statistical power.

Our results indicate a lack of sexual dimorphism with respect to gray matter asymmetry in BA 44/45 and as such are in line with prior findings that do not support a sex difference in region-specific asymmetry (Good et al., 2001a Watkins et al., 2001 Luders et al., 2004 Takao et al., 2011 Savic, 2014 ). Furthermore, they seem to agree with conclusions drawn based on a meta-analysis (Frost et al., 1999 Sommer et al., 2004 ) negating that women have more bilateral language representation (and thus presumably less asymmetry) than men, as also critically reviewed elsewhere (Wallentin, 2009 ). Nevertheless, other studies have reported less lateralized language processing in females (Shaywitz et al., 1995 ) or more left-asymmetric gray matter in males (Gur et al., 1999 ). However, different methodological approaches might account for the discrepancies across studies, as discussed further below.

Possible Impact of Methodological Approaches

Different methodological approaches measure different cerebral attributes that may not be readily comparable. Thus, varying findings in the literature with respect to a sexual dimorphism in BA 44/45 are not unexpected. For example, in structural magnetic resonance images, the manual delineation of an ROI covering BA 44 and BA 45 requires detailed tracing guidelines. Consequently, this makes the resulting ROI highly dependent on the applied protocol (i.e., in terms of its preciseness, correctness, and reproducibility) as well as on the precision and consistency of the rater. In case of BA 44/45, things are even more challenging not only are visible macroanatomic landmarks variable across individuals, but they also show only little (if any) spatial correspondence with the cytoarchitectonic boundaries (Amunts et al., 1999, 2007 Zilles and Amunts, 2010 ). Different studies may therefore include different regions pertaining to BA 44/45. The current approach circumvents these issues altogether by considering probabilistic a priori information on cytoarchitecture, which allows reliably capturing the boundaries of the ROI with high consistency across all subjects. Similarly to the current approach, vertex- or voxelwise techniques are not dependent on delineation protocols, raters, or judgment calls. However, these approaches come with an inherent and substantial multiple-comparisons problem that may render them less sensitive. Last but not least, regardless of the applied measurement, another factor might have contributed to the discrepancy in findings across studies: On average, male brains are larger than female brains (Luders and Toga, 2010 Gong et al., 2011 Giedd et al., 2012 Sacher et al., 2013 ). Thus, without proper corrections for individual brain size, relatively more gray matter in BA 44/45 in female brains might remain undetected (i.e., if comparing only absolute volumes). Importantly, the present results indicating more gray matter in BA 44/45 in female brains were derived from data that were corrected for differences in overall brain size.

Potential Functional Relevance and Determinants

On a microstructural level, studies by Rabinowicz et al. ( 1999, 2002 ) revealed a higher cell density in BA 44/45 for males. However, the authors also reported a larger soma and a thicker cortex as well as more neuropil per neuron in females, even if the latter observation was not specifically restricted to BA 44 and BA 45 (Rabinowicz et al., 2002 ). Thus, the currently observed larger gray matter volumes in females may be explained by more neuropil and increased dendritic arborization, perhaps providing better language-related skills and advantages in language processing because of the higher numbers of synapses and/or connections per neuron. This, in turn, would be in strong agreement with the aforementioned language-related superiority in females (Benton and Hamsher, 1976 Murray et al., 1990 Spreen and Strauss, 1991 Halpern, 1992 Morisset et al., 1995 Kimura, 1999 Bauer et al., 2002 Lutchmaya et al., 2002 Roulstone et al., 2002 ). In fact, various studies have reported an association between gray matter volume and verbal fluency as well as successful verbal memory strategies (Newman et al., 2007 Kirchhoff et al., 2014 Roehrich-Gascon et al., 2015 ). However, because no language measures (or microanatomical data) have been obtained for the current sample, this proposal remains merely conjecture.

Structural and functional differences between male and female brains may be driven by sex hormones and/or genes (Carruth et al., 2002 De Vries et al., 2002 Arnold and Burgoyne, 2004 Arnold and Chen, 2009 ). For example, a reduced verbal fluency was reported in females with congenital adrenal hyperplasia (Malouf et al., 2006 ), suggesting that increased levels of androgens negatively affect performance in language tasks. The assumption of a link among language performance, local gray matter in BA 44/45, and testosterone is further supported by recent findings (Hahn et al., 2016 ) in which high-dose administration of testosterone in female-to-male transsexuals led to a decrease in gray matter volume in Broca's area. The (innate) sex differences in behavior (i.e., earlier language production and more language output in females) might have further enhanced the local gray matter of BA 44/45, similarly to that reported from studies directed at examining use-dependent brain plasticity (Maguire et al., 2000 Draganski et al., 2004, 2006 Bezzola et al., 2011 Woollett and Maguire, 2011 ). In addition, the lifetime of gender-differentiated experience (including differential nurturing and socialization) may shape the sex differences in brain structure and function, as discussed in detail elsewhere (Eliot, 2011 ) and as such perhaps also contributed to the observed effect in BA 44/45. Clearly, further studies combining gray matter estimates with detailed individual information on genes, hormones, and language skills and behavior are needed to characterize these proposed links better. Moreover, additional insights with respect to the modulating impact of life experience may come from studies that analyze participants by gender role rather than just biological sex (Eliot, 2011 ).


PointToTD operates on a single coordinate and has three search options: (1) Structural Probability Maps, (2) Talairach label, (3) Talairach labels within a cube range and (4) Talairach gray matter labels. The program is run as follows:

java -cp talairach.jar org.talairach.PointToTD 1, 15, 10, 12

In this example, the Structrual Probability Map results for (15,10,12) would be returned. The cube range search uses 5mm (or +/-2mm) as its default. To set a different cube size, use "3:<cubesize>". Sizes of 3, 5, 7, 9 and 11 are accepted. For example:

java -cp talairach.jar org.talairach.PointToTD 3:7, 15, 10, 8

That search would return the nearby 343 labels (for a 7x7x7 region).


Methods

Subjects

We recruited 63 healthy subjects (males: 31, females: 32, mean age: 37.9 years, range: 20� years) with no previous history of neurological, physical, or psychiatric illness for this study. All subjects understood the purpose of the study and provided written, informed consent prior to participation. The study protocol was approved by the Institutional Review Board of the Yeungnam university hospital (YUH-12-0421-O60).

Data Acquisition

DTI data were acquired using a 6-channel head coil on a 1.5 T Philips Gyroscan Intera (Philips, Ltd, Best, The Netherlands) with single-shot echo-planar imaging. For each of the 32 non-collinear diffusion sensitizing gradients, we acquired 67 contiguous slices parallel to the anterior commissure-posterior commissure line. Imaging parameters were as follows: acquisition matrix = 96 × 96 reconstructed to matrix = 128 × 128 field of view = 221 × 221 mm 2 repetition time (TR) = 10,726 ms echo time (TE) = 76 ms parallel imaging reduction factor (SENSE factor) = 2 echo-planar imaging (EPI) factor = 49 b = 1000 s/mm 2 number of excitations (NEX) = 1 and a slice thickness of 2.3 mm (acquired voxel size 1.73 × 1.73 × 2.3 mm 3 ).

Probabilistic Fiber Tracking

The Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL 1 ) was used for analysis of diffusion-weighted imaging data. Affine multi-scale two-dimensional registration was used for correction of head motion effect and image distortion due to the eddy current. Mean translation and rotation was observed the sub-one pixel (0.51 ± 0.47 mm). Fiber tracking was performed using a probabilistic tractography method based on a multi-fiber model, and applied in the present study utilizing tractography routines implemented in FMRIB Diffusion (5000 streamline samples, 0.5 mm step lengths, curvature thresholds = 0.2) (Behrens et al., 2003a Smith et al., 2004 Behrens et al., 2007). This fiber tracking method by multi-fiber model calculated and generated 5000 streamline samples from seed region of interest (ROI) with consideration of the both dominant and non-dominant orientation of diffusion in each voxel and showed how connects the brain regions. Therefore, it has advantage to solve the problem of the crossing fiber. Especially, cross points of the corpus callosum and corona radiata, corticospinal tract fibers and pontocerebellar fibers at pons, and superior and medial frontal gyri are known to be the crossing fiber point (Wiegell et al., 2000). For the connectivity of the SN, a seed ROI was placed on the isolated SN of the upper midbrain on the color-coded map (dorsomedially next to the cerebral peduncle of the upper midbrain) (Mori et al., 2004). For the connectivity of the VTA, a seed ROI was placed on the VTA in the upper midbrain on the color-coded map. We identified the VTA by reconstructing the adjacent structures: interpeduncular nucleus (anterior boundary), central tegmental tract (posterior (www.fmrib.ox.ac.uk/fsl) boundary), midline (medial boundary), red nucleus and SN (lateral boundary) (Mori et al., 2004 Habas and Cabanis, 2007 Blood et al., 2010 Figure 1A). Out of 5000 samples generated from the seed voxel, results for contact were visualized with the threshold at a minimum of five streamline through each voxel for analysis. Connectivity represented the percentage as all hemispheres of 63 subjects. On the other hand, we measured the size of ROI for the SN and VTA.

Figure 1. Neural connectivity of substantia nigra and ventral tegmental area. (A) The region of interest (ROI): a seed ROI for substantia nigra (SN, orange), is placed on the isolated SN of the upper midbrain on the B0 and color-coded map (dorsomedially next to the cerebral peduncle of the upper midbrain). A seed ROI for ventral tegmental area (VTA, sky-blue), is placed on the isolated VTA of the upper midbrain on the B0, and color-coded map. We use other structures to isolate the VTA such as interpeduncular nucleus (anterior boundary, red), central tegmental tract (posterior boundary, white-lined rectangular), midline (medial boundary), red nucleus (blue) and SN (lateral boundary). (B) SN/VTA: results of diffusion tensor tractography for the connectivity of SN/VTA (a: cortex level, b: upper corona radiata level, c: upper internal capsule level, d: lower internal capsule level, e: bicommissure level f: midbrain level, g: upper pons level, h: lower pons level). a to b levels: primary motor cortex, primary somatosensory cortex, premotor cortex, prefrontal cortex, corpus callosum, c to e levels: caudate nucleus, putamen, globus pallidus, nucleus accumbens, thalamus, external capsule, f to h level: red nucleus, amygdala, medial temporal lobe, temporal lobe pontine basis, pontine tegmentum, anterior lobe of cerebellum, posterior lobe of cerebellum.

Determination of Connection Between Substantia Nigra (SN), Ventral Tegmental Area (VTA) and Brain Regions

Connectivity was defined as the incidence of connection between the SN/VTA and each brain region: primary motor cortex (M1, brodmann area [BA]: 4), primary somatosensory cortex (S1, BA: 1, 2, 3), premotor cortex (PMC, BA: 6), prefrontal cortex (BA: 9, 10, 11, 12), caudate nucleus, putamen, globus pallidus, nucleus accumbens, thalamus, external capsule, red nucleus, amygdala, medial temporal lobe (BA: 27, 28, 34, 35, 36, 37), temporal lobe (superior, middle, inferior, BA: 20, 21, 22), pontine basis, pontine tegmentum, anterior lobe of cerebellum, posterior lobe of cerebellum, corpus callosum, and occipital lobe (BA: 17, 18, 19).

Statistical Analysis

SPSS software (v.15.0 SPSS, Chicago, IL) was used for statistical analysis. The Chi-square test was used for determination of the difference in connectivity between the right and left hemispheres, and between the SN and VTA. In addition, we performed an independent t-test for determination of differences in size of ROI between the SN and VTA, and between the right and left hemispheres. The significant level of the p value was set at 0.05.


Conclusions

While our data is preliminary, it suggests that perception of non-consciously perceived stimuli activates anterior cingulate cortex (ACC) and insular cortex, to form a basis for conscious perception. Activation of primary visual areas by non-consciously perceived stimuli is perhaps driven by a bias for these studies to use images of emotional faces, and so more fMRI studies are needed to compare subliminal and supraliminal presentation of other types of stimuli in different modalities. After further fMRI studies comparing the neural correlates of subliminal versus supraliminal stimulation, meaningful conclusions are more likely to be drawn about brain systems involved in unconscious perception.


Program Highlights

As a student in the BA in P sychology degree program, you will study the science of behavior and mental processes while develop ing strong academic and social skills including critical thinking, collaboration, oral and written expression, interpersonal effectiveness, and social maturity. You will also learn to analytically review psychological literature and apply theory to practice by conducting your own psycholog y research projects.

Our faculty of professionals brings years of experience in the psychology field into the classroom, giving you the edge of practical and applicable learning. By adding an internship to your main campus academic experience in a human services agency, school setting, or research university, you may gain valuable hands-on experience to better prepare for the real world.

Coursework emphasizes these core areas: personality and psychopathology child and adolescent development the relationship between the nervous system and behavior societal and culture influence on human interaction and the relationship between psychology and the law .

You’ll analyze major concepts, theories, and empirical findings in psychology in order to explain and solve problems related to human behavior and mental processes. You’ll cover the application of various scientific research methods, such as research design and data analysis. You’ll explore the factors, experiential and biological, that shape mental processes and behaviors. You’ll learn how to apply ethical standards in evaluating psychology research and practice. And you’ll about diverse sociocultural experiences and how they influence behavior and mental processes.


Methods

Participants

Eleven native speakers of Japanese (11 men, mean age 20.8 years, range 20� years) participated in this study. All participants were right-handed, as confirmed by a modified version of the Edinburgh Handedness Inventory [35], and were free of any neurological or hearing impairments. The participants fulfilled the following two criteria: they had no professional musical education or training (mean 3.1 years of music lessons other than music education classes at primary and secondary school), and they were 𠇌ommon listeners” (i.e., not “music lovers,” who tend to listen to one specific type of music only their time spent listening to music per day was 1.1 (SD =𠂠.74) hours.

Ethics statement

This study is approved by the ethical committee of the Tokyo Metropolitan Institute of Gerontology. All participants gave written informed consent to participate in this study.

Auditory stimuli

Twenty-four highly familiar songs and twenty-four unfamiliar songs were selected to serve as stimuli based on the results of our preparatory study of familiarity ratings of Japanese children's and traditional songs [34]. The mean familiarity rating on our five-point scale (1 = unfamiliar, 5 = highly familiar) was 4.69 for the familiar songs and 1.19 for the unfamiliar songs. The beginnings of the familiar songs were rated as equally familiar even when the lyrics or the melody was presented in isolation. Additionally, 30 songs with intermediate familiarity were used during a training session. A total of 234 sound stimuli were prepared.

In the first type of sound stimuli (song), original lyrics were sung to the original melody. In the second type of sound stimuli (lyrics), the original lyrics were sung using the original rhythm, but on a single pitch (G3, 196 Hz). In the third type of sound stimuli (melody), the syllable “la” was sung to the original melody and using the original rhythm. All stimuli were generated by the VOCALOID voice-synthesizing software (YAMAHA, Inc., Tokyo, Japan) in order to make the three types of sound stimuli as similar as possible in terms of acoustical features, such as intensity, voice timbre, prosody, and duration. As a result, the three types of sound stimuli had exactly the same temporal features, such as tempo, rhythm, and duration of notes ( Figure 1 ). None of the stimuli contained instrumental or choral accompaniment. The auditory stimuli were digital music files with 16-bit depth, 44,100-Hz sampling rate, and mean loudness of 75.1 dB SPL. The warning stimulus was a pure tone (sine wave, 500-Hz frequency, 500-ms duration). All sound stimuli were presented using E-Prime (Psychology Software Tools, Inc., Pittsburgh, USA).

We checked the clearness of and participants' feelings of discomfort with the synthetic voices compared to natural human voices in the pre-experimental study, which tested 12 participants (12 men, mean age =�, SD =𠂢). The results showed that the clearness of and participants' feelings of discomfort with the synthetic voices were not significantly different from values obtained using human voice sounds (t =𠂡.02, pϠ.13, paired t-test).

Procedure

Prior to each PET scanning session, the participant completed a short practice session in which five sound stimuli were presented. During PET scanning, there were two decision tasks: a familiarity decision (FamD) and a sound-type decision (which was a perceptive control task [Control]). There were three sound type conditions in which FamD task were made labeled as Song, Lyrics, and Melody. For each sound type condition, 24 stimuli (12 familiar songs and 12 unfamiliar songs) were presented via in-ear headphones each sound type. The participants were instructed to decide whether a song excerpt was known to have been acquired in his/her life or not as quickly and accurately as possible by pressing one of two buttons: the first button, pressed with the right index finger, signified that the participant was familiar with the song, and the second button, pressed with the right middle finger, signified that the participant was unfamiliar with the song. During each trial, a warning stimulus was presented for 500 ms, followed by a silence interval (500 ms), then the target auditory stimulus (3000 ms), then silence (500 ms), followed by the next trial. The auditory stimulus was stopped immediately after a response button was pressed by the participant in order to prevent additional processing. Reaction time (RT) was measured as the interval between target stimulus onset and participant response. The designation of a song as familiar signifies that the participant had learned it well during his/her life.

The Control (sound-type decision) task was designed to recruit multiple processes, such as listening, monitoring variations in phoneme and pitch, working memory, decisional processing, and motor processing. The participants were asked to decide whether an excerpt that they heard belonged to the complete-song type (song) or to the other types (lyrics-in-isolation type [lyrics] and melody-in-isolation type [melody]) as quickly and accurately as possible by pressing one of two buttons: the first button, pressed by the right index finger, signified the song type, and the second button, pressed with the right middle finger, signified that the excerpt was one of the other types. The three types of sound stimuli were presented in a random order. Identical stimuli were used in both the FamD and the control tasks so as to balance the acoustical and perceptional processes.

The three FamD conditions (Song, Lyrics, and Melody) and the Control condition were presented to participants in a counterbalanced order. Then, each of four conditions was performed twice. RT and button selection data were recorded using a response box placed under the participant's right hand, which was linked to a computer running the E-Prime software package. In a post-scan test, participants were asked to rate the familiarity of the 78 songs presented as song, lyrics, and melody stimuli types, including the stimuli used during the PET session, using a 5-point scale.

Data acquisition

Regional cerebral blood flow (rCBF) was measured via PET scanning using 15 O-labeled water. A SET 2400W scanner (Shimadzu Inc., Kyoto, Japan), operated in three-dimensional mode, acquiring a 128󗄨흐 in matrix with a 2൲൳.125-mm voxel size. Each participant was scanned eight times to measure the distribution of 15 O-labeled water with a 10-min inter-scan interval to allow for decay. Each scan was started upon the appearance of radioactivity in the brain after an intravenous bolus injection of 180 MBq of 15 O-labeled water. Each scan lasted 60 s. The activity measured during this period was summed and used as a measure of rCBF. A transmission scan was obtained using a 68 Ga/ 68 Ge source for attenuation correction prior to participant scanning. Each experimental condition was started 15 s before data acquisition and continued until the completion of the scan. Participants were scanned while lying supine with their eyes closed in a darkened, quiet room. T1-weighted structural MRI scans were also obtained for each participant on a 1.5-T GE Signa system (SPGR: TR =� ms, TE =𠂦 ms, matrix =�󗉖󗄥 voxels) for anatomical reference and in order to screen for any asymptomatic brain disorders.

Behavioral data analysis

For each of the three types of auditory sound stimuli, the degree of familiarity measured in the post-scan task was calculated. Mean familiarities were analyzed by means of paired t-tests. For each participant and for each experimental condition, mean RTs were calculated. Accuracy was calculated based on the results of the post-scan familiarity rating task. We performed a repeated-measures ANOVA on RTs and accuracy. Behavioral data was analyzed by SPSS 17.0 (SPSS Inc., Chicago, USA). Post hoc, Bonferroni-corrected, paired t-tests were used to test for differences between conditions.

Imaging analysis

PET images were analyzed using the Statistical Parametric Mapping software package (SPM8, Wellcome Department of Cognitive Neurology, London, UK), implemented in MATLAB 7.5.0 (Mathworks Inc., Massachusetts, USA). During preprocessing, PET data were realigned, spatially transformed into standard Montreal Neurological Institute stereotactic space (MNI, voxel size 2൲൲ mm), and smoothed with a 12-mm Gaussian filter. Each scan was scaled to a mean global activity of 50 ml/100 g/min. We used a threshold of 80% of the whole brain mean as the cutoff point for designation of voxels as containing gray matter, and covariates were centered around their means before inclusion in the design matrices. An analysis of covariance (ANCOVA), with global activity as a confounding covariate, was performed on a voxel-by-voxel basis. The results, expressed in SPM as t-statistics (SPM ), were then transformed onto a standard normal distribution (SPM ). All statistical thresholds were set at pπ.005, uncorrected at the voxel level, with an extent threshold requiring a cluster size of more than 20 voxels.

First, using t-tests, we created SPM contrasts subtracting the Control condition from each of the three FamD conditions: Song𠄼, Lyrics𠄼 and Melody𠄼. Then, we performed conjunction analyses to classify the activations in each FamD condition in terms of whether they were also activated in either of the other two conditions ( Figure 2 ). For instance, areas of activation in the contrasts (Song𠄼, Lyrics𠄼 and Melody𠄼) may be classified into seven categories: (1) commonly activated in all three FamD conditions (Song𠄼 ∩ Lyrics𠄼 ∩ Melody𠄼, Figure 2, a ), (2) commonly activated by Song and Lyrics (Song𠄼 ∩ Lyrics𠄼, Figure 2, b ), (3) commonly activated by Song and Melody (Song𠄼 ∩ Melody𠄼, Figure 2, c ), (4) commonly activated by Lyrics and Melody (Lyrics𠄼 ∩ Melody𠄼, Figure 2, d ), (5) specifically activated by Song (Song–Lyrics ∩ Song–Melody ∩ Song𠄼, Figure 2, e ), (6) specifically activated by Lyrics (Lyrics–Song ∩ Lyrics–Melody ∩ Lyrics𠄼, Figure 2, f ), and (7) specifically activated by Melody (Melody–Song ∩ Melody–Lyrics ∩ Melody𠄼, Figure 2, g ).


DISCUSSION

To our knowledge, this is the first study integrating cytoarchitectonically defined probabilities with MRI-based signal intensities to assess sex differences in gray matter within BA 44 and BA 45 in a sizable cohort. The approach employed differs from classic landmark-based ROI studies in that it does not depend on macroanatomic landmarks (sulci, gyri, etc.) that may be highly variable across individuals or even entirely missing. Our findings of more gray matter in BA 44 and BA 45 in females compared with males seem to be in close agreement with some (albeit not all) previous studies addressing sex differences in the human brain, as further discussed below. In addition, we elucidate how different analysis methods may impact study outcomes, and we conclude by elaborating on the potential functional relevance and possible determinants of the observed effects.

Correspondence to Prior Findings

Our results indicating a sexual dimorphism with respect to gray matter in BA 44/45 are in good correspondence with the outcomes of other in vivo studies (Schlaepfer et al., 1995 Good et al., 2001a Luders et al., 2005, 2006 Im et al., 2006 Sowell et al., 2007 ). More specifically, an ROI analysis that investigated sex differences in frontal, temporal, and inferior parietal gray matter indicated larger volumes in females than in males in frontal areas related to language processing (Schlaepfer et al., 1995 ). Furthermore, vertexwise investigations across the entire brain revealed a larger thickness of the cerebral cortex in females than in males within a region of the inferior frontal gyrus that matches BA 44/45, where effects were observed either in the left hemisphere only (Im et al., 2006 ) or in both hemispheres (Luders et al., 2006 Sowell et al., 2007 ). Similarly, voxelwise investigations across the entire brain exposed larger gray matter volumes or higher gray matter concentrations in females than in males within BA 44/45 in both hemispheres (Good et al., 2001a Luders et al., 2005 ). Last but not least, our findings also correspond well with outcomes from a post mortem study (Harasty et al., 1997 ) that was specifically designed to examine language-associated cortical regions, including the inferior frontal gyrus (which includes Broca's area in the dominant hemisphere). In that study, the cortical volume fraction of the region that includes Broca's area was 20.4% larger in female brains than in male brains. Interestingly, the original post mortem study (Amunts et al., 1999 ) that provided the cytoarchitectonic tissue probabilities for our present analysis did not detect sex differences in BA 44/45. However, given the extremely small sample (five males/five females) in that study, the lack of a sexual dimorphism might be due to missing statistical power.

Our results indicate a lack of sexual dimorphism with respect to gray matter asymmetry in BA 44/45 and as such are in line with prior findings that do not support a sex difference in region-specific asymmetry (Good et al., 2001a Watkins et al., 2001 Luders et al., 2004 Takao et al., 2011 Savic, 2014 ). Furthermore, they seem to agree with conclusions drawn based on a meta-analysis (Frost et al., 1999 Sommer et al., 2004 ) negating that women have more bilateral language representation (and thus presumably less asymmetry) than men, as also critically reviewed elsewhere (Wallentin, 2009 ). Nevertheless, other studies have reported less lateralized language processing in females (Shaywitz et al., 1995 ) or more left-asymmetric gray matter in males (Gur et al., 1999 ). However, different methodological approaches might account for the discrepancies across studies, as discussed further below.

Possible Impact of Methodological Approaches

Different methodological approaches measure different cerebral attributes that may not be readily comparable. Thus, varying findings in the literature with respect to a sexual dimorphism in BA 44/45 are not unexpected. For example, in structural magnetic resonance images, the manual delineation of an ROI covering BA 44 and BA 45 requires detailed tracing guidelines. Consequently, this makes the resulting ROI highly dependent on the applied protocol (i.e., in terms of its preciseness, correctness, and reproducibility) as well as on the precision and consistency of the rater. In case of BA 44/45, things are even more challenging not only are visible macroanatomic landmarks variable across individuals, but they also show only little (if any) spatial correspondence with the cytoarchitectonic boundaries (Amunts et al., 1999, 2007 Zilles and Amunts, 2010 ). Different studies may therefore include different regions pertaining to BA 44/45. The current approach circumvents these issues altogether by considering probabilistic a priori information on cytoarchitecture, which allows reliably capturing the boundaries of the ROI with high consistency across all subjects. Similarly to the current approach, vertex- or voxelwise techniques are not dependent on delineation protocols, raters, or judgment calls. However, these approaches come with an inherent and substantial multiple-comparisons problem that may render them less sensitive. Last but not least, regardless of the applied measurement, another factor might have contributed to the discrepancy in findings across studies: On average, male brains are larger than female brains (Luders and Toga, 2010 Gong et al., 2011 Giedd et al., 2012 Sacher et al., 2013 ). Thus, without proper corrections for individual brain size, relatively more gray matter in BA 44/45 in female brains might remain undetected (i.e., if comparing only absolute volumes). Importantly, the present results indicating more gray matter in BA 44/45 in female brains were derived from data that were corrected for differences in overall brain size.

Potential Functional Relevance and Determinants

On a microstructural level, studies by Rabinowicz et al. ( 1999, 2002 ) revealed a higher cell density in BA 44/45 for males. However, the authors also reported a larger soma and a thicker cortex as well as more neuropil per neuron in females, even if the latter observation was not specifically restricted to BA 44 and BA 45 (Rabinowicz et al., 2002 ). Thus, the currently observed larger gray matter volumes in females may be explained by more neuropil and increased dendritic arborization, perhaps providing better language-related skills and advantages in language processing because of the higher numbers of synapses and/or connections per neuron. This, in turn, would be in strong agreement with the aforementioned language-related superiority in females (Benton and Hamsher, 1976 Murray et al., 1990 Spreen and Strauss, 1991 Halpern, 1992 Morisset et al., 1995 Kimura, 1999 Bauer et al., 2002 Lutchmaya et al., 2002 Roulstone et al., 2002 ). In fact, various studies have reported an association between gray matter volume and verbal fluency as well as successful verbal memory strategies (Newman et al., 2007 Kirchhoff et al., 2014 Roehrich-Gascon et al., 2015 ). However, because no language measures (or microanatomical data) have been obtained for the current sample, this proposal remains merely conjecture.

Structural and functional differences between male and female brains may be driven by sex hormones and/or genes (Carruth et al., 2002 De Vries et al., 2002 Arnold and Burgoyne, 2004 Arnold and Chen, 2009 ). For example, a reduced verbal fluency was reported in females with congenital adrenal hyperplasia (Malouf et al., 2006 ), suggesting that increased levels of androgens negatively affect performance in language tasks. The assumption of a link among language performance, local gray matter in BA 44/45, and testosterone is further supported by recent findings (Hahn et al., 2016 ) in which high-dose administration of testosterone in female-to-male transsexuals led to a decrease in gray matter volume in Broca's area. The (innate) sex differences in behavior (i.e., earlier language production and more language output in females) might have further enhanced the local gray matter of BA 44/45, similarly to that reported from studies directed at examining use-dependent brain plasticity (Maguire et al., 2000 Draganski et al., 2004, 2006 Bezzola et al., 2011 Woollett and Maguire, 2011 ). In addition, the lifetime of gender-differentiated experience (including differential nurturing and socialization) may shape the sex differences in brain structure and function, as discussed in detail elsewhere (Eliot, 2011 ) and as such perhaps also contributed to the observed effect in BA 44/45. Clearly, further studies combining gray matter estimates with detailed individual information on genes, hormones, and language skills and behavior are needed to characterize these proposed links better. Moreover, additional insights with respect to the modulating impact of life experience may come from studies that analyze participants by gender role rather than just biological sex (Eliot, 2011 ).


Background

Autism is a neurodevelopmental disorder affecting social, cognitive, linguistic and sensorimotor abilities. These qualitative deficits are pervasive and long lasting. While genetic factors are known to be strong [1], consistent neurological markers for the disorder remain to be fully established. Behavioral markers identifying deficits in sensorimotor processing and social skills are apparent as early as one year of age [2, 3]. Sensorimotor deficits include fine motor apraxia [4–6], reduced postural control [7, 8], and impaired imitation [9]. Individuals with autism also have delays in language development [10], impaired attention [11], as well as deficits in executive cognitive processes [12, 13]. These developmental abnormalities are often discovered during the preschool years. Among the brain areas suspected to be involved in both sensorimotor and cognitive deficits are the caudate nuclei [14–16].

Several types of abnormalities of the caudate nuclei have been noted in autism. Reduced correlation of resting cerebral glucose metabolic rates between the caudate and frontal regions has been seen in children with autism [17]. Sears et al. [18] and Hollander et al. [14] found that enlargement of the caudate nuclei was associated with stereotyped behaviors in autism. Conversely, a cluster-based analysis of structural MRI scans [19] showed that reduced caudate size was correlated with greater impairment on the Childhood Autism Rating Scale (CARS), including deficits in a wide range of abilities such as body movement. While these anatomical findings appear inconsistent, they nonetheless indicate that autism may be associated with volumetric abnormalities of the caudate nuclei. Furthermore, Singh [20] noted serum antibodies to the caudate in children with autism that were not found in typical children. These antibodies may implicate the caudate nuclei in a type of autoimmune dysfunction associated with autism. Related dysfunction of the caudate nuclei may secondarily affect regions that are anatomically connected to these nuclei.

Retrograde transneural transport of the herpes simplex virus has illuminated the anatomical connectivity between caudate nuclei and other brain regions [21, 22]. These connections are widespread. The caudate nuclei primarily receive input from frontal, temporal, inferior parietal, pre-occipital, and limbic areas including the amygdala, hippocampus and parahippocampal cortex. The various connections between the caudate and other brain regions have been segregated into circuits [21, 22]. Anatomical circuits directly associated with the caudate are the associative, lateral orbitofrontal, and occulomotor circuits. The associative circuit connects the dorsolateral prefrontal cortex with the ventral caudate, and this circuit is believed to regulate executive functions in the brain by unifying cognitive processes such as attention, planning and decision-making [21, 23]. Within this circuit, the caudate nuclei may also aid in the selection of rules by which decisions and plans are made and by enhancing working memory [24–26]. The lateral orbitofrontal circuitry, believed to support set switching and inhibition, extends from Brodmann's areas 10 and 12 to the ventromedial caudate [21, 22, 27, 28]. The caudate nuclei send return projections to areas 10 and 12 via the thalamus. Evidence from human and non-human primate studies show that disruptions to the circuitry at the orbitofrontal level result in deficits in short term memory for objects and the processing of stimulus reinforcement contingencies [29, 30]. The third loop involving the caudate and the frontal eye fields (FEF) is the occulomotor circuit and is thought to be involved in saccadic eye movements [31]. The FEF, located in Brodmann's area 6 [32], project to the body of the caudate which then sends projections to the substantia nigra. Although the caudate is not considered part of the motor circuit, it has been shown to contribute to working memory in the planning and selection of motor sequences [33–36]. A disruption in the anatomical connectivity between the caudate and any region in one of the circuits is likely to be reflected in weaker functional connectivity within that circuit.

A number of fMRI studies of autism have shown atypical levels of activation in the caudate nuclei for a variety of tasks such as spatial processing [37], finger tapping [38] and face perception [39]. In each of the above mentioned studies, autism groups showed reduced caudate activation compared to control subjects. These fMRI findings only provide information on whether the caudate nuclei are involved in a task. As established above, however, participation of the caudate nuclei in sensorimotor, cognitive and executive functions reflect their role in distributed functional networks. The integrity of networks cannot be fully examined by conventional fMRI activation analyses, which are unsuited for analyzing circuitry.

In order to examine network integrity, the present study used functional connectivity magnetic resonance imaging (fcMRI). Functional connectivity measures examine the temporal covariance between spatially remote neurophysiological events [40]. Previous fcMRI studies have shown that regional signal covariance is consistent with anatomical connectivity and functional networks delineated in animal studies [41, 42]. In humans, fcMRI has been used to examine interhemispheric connectivity in the sensory [43, 44] and motor cortex [45]. Belmonte et al. [46], Just and colleagues [47] and Sporns et al. [48] have suggested that local connectivity may be relatively dense whereas long-range connectivity between brain regions may be reduced. However, the functional connectivity between basal ganglia and cerebral cortex has not been previously examined in autism. In the present study, we examine functional connectivity of the caudate nuclei in individuals with autism and healthy controls.


Know Your Brain: Primary Somatosensory Cortex

primary somatosensory cortex (in blue)

The primary somatosensory cortex is located in a ridge of cortex called the postcentral gyrus, which is found in the parietal lobe. It is situated just posterior to the central sulcus, a prominent fissure that runs down the side of the cerebral cortex. The primary somatosensory cortex consists of Brodmann's areas 3a, 3b, 1, and 2.


RESULTS

Behavioral Results

As seen from Table ​ TableI, I , there were no differences in accuracy between all sensorimotor tasks. With RTs, there was no overall difference between manual (AMViM) and vocal (AVoViVo) responses, F(1,19) = 0.90. However, responses to auditory (AM and AVo 849 ms) stimuli were overall slower than responses to visual (ViM and ViVo 675 ms) stimuli, F(1,19) = 16.06, P < 0.001. AM responses were marginally slower than ViVo responses [t(11) = 2.19, P = 0.051], and AVo responses were slower than responses in the three other tasks (see Table ​ TableI I ).

Table I

Vocal responseManual response
Visual stimulus647 msec (96.3%)712 msec (98.2%)ns (ns)
Auditory stimulus935 msec (97.9%)785 msec (96.0%)* (ns)
* (ns)ns (ns)

The diagonal comparison, using paired t‐tests, between ViVo and AM revealed no significant differences in reaction time and accuracy measures. Although the accuracy difference between ViM and AVo was not significant, responses were significantly (P < 0.05) faster in the ViM condition than they were in the AVo condition. A * denotes a significant difference, using unpaired t‐tests (P < 0.05).

Reaction time (and percentage accuracy) for sensorimotor tasks.

The slower responses to auditory tasks might be due to the presence of background noise from the scanner and/or the discriminative difficulty of the auditory stimuli. Whatever the cause for the slower RTs in auditory tasks, it should have little bearing on isolating brain regions coactivated by all tasks.

We also considered whether overall performance differed between the AVo/ViM and AM/ViVo groups. The RT difference was significant [F(1,19) = 4.64, P < 0.05] owing to faster average responses for the AM/ViVo group (716 ms) than the AVo/ViM group (824 ms). However, this difference may have been the result of a speed�uracy tradeoff as the slower AVo/ViM group (98%) responded marginally [t(19) = 2.05, P = 0.055] more accurately than the ViVo/AM group (96%).

FMRI Results

Activated regions for each sensorimotor task

SPMs for the AVo𠄏ixation, AM𠄏ixation, ViVo𠄏ixation, and ViM𠄏ixation contrasts from the blocked fMRI runs are illustrated in Figure ​ Figure1. 1 . Extensive activation foci were not only observed in primary sensory and motor cortices, but also in association areas of all cortical lobes. Although all but the ViVo tasks recruited large expanses of cortex, auditory cortex activation was more prominent in the auditory tasks, whereas visual cortex activation was more readily observed in the visual tasks. Similarly, manual responses recruited more dorsal and slightly more posterior regions of the frontal lobe than vocal responses. In addition, subcortical (thalamus and superior colliculus) activations (not shown) were also common across tasks.

Although this analysis reveals that each sensorimotor task recruits a relatively large extent of cortical and subcortical tissue, it does not identify brain regions that are either commonly activated across all four tasks or that are specific to each sensory and motor modalities. The following analyses serve these purposes.

Identification of sensory and motor areas

We sought to identify brain regions that were more activated for a particular sensory (e.g. auditory) or motor (e.g. manual) modality than for the other (i.e., visual and vocal, respectively). Regions that exhibited modality‐preferred activation in both blocked and ER runs are illustrated in Figure ​ Figure3 3 .

Not surprisingly, visual stimulation preferentially recruited Brodmann areas (BAs) 18 and 19 of the occipital lobe, whereas auditory stimuli preferentially recruited the superior temporal cortex and adjacent areas. In addition, the vocal responses preferentially recruited lateral regions of the precentral gyrus, whereas manual responses engaged more dorsal regions of the pre‐ and postcentral gyrus. Examination of the time courses from the ER runs in these areas confirms that they demonstrate preferential, but not specific responses to a given sensory or motor modality (see Fig. ​ Fig.3). 3 ). That is to say, foci showed significant activation (all Ps < 0.01) for the nonpreferred modality (e.g., auditory activation in visual cortex or manual response activation in vocal ROI). However, these nonpreferred activations were significantly weaker than the preferred modality activations (Fig. ​ (Fig.3 3 and Table ​ TableII). II ). Because these group�ined ROIs were relatively large, it is not possible to ascertain whether the present results were obtained, because these sensory or motor ROIs do not exhibit strict modality specificity or because they are composed of a mixture of multimodal and modality‐specific areas.

Table II

Average Talaraich coordinates and size of regions showing sensory‐ (auditory vs. visual) or motor‐ (manual vs. vocal) preferred activation

SideBrodmann areas x, y, zVoxels (mm 3 )Sensory/motor preference t‐value
VisualLeft18,19�, �, 317063.71**
(ViM𠄊Vo) − (AM ‐ViVo)Right18,1929, �, 414723.32**
AuditoryLeft13, 22, 40, 41, 42�, �, 12116675.67**
(AVo‐ViM) − (ViVo𠄊M)Right13, 22, 40, 41, 4252, �, 10179434.73**
ManualLeft2, 3, 4, 5, 6, 40�, �, 5555316.31**
(ViM𠄊Vo) − (ViVo𠄊M)Right441, �, 4342.90*
VocalLeft4, 6�, 𢄩, 3522026.91**
(AVo‐ViM) − (AM‐ViVo)Right4, 646, 𢄩, 3826075.63**
Right6, 4357, 𢄧, 13893.62**

The equations below the modality names describe the contrasts used to isolate the modality‐preferred activations. A * denotes a significant t‐value uncorrected (P < 0.05) and a ** denotes a significant t‐value using the Bonferroni adjustments for multiple comparisons (P < 0.006).

Identification of brain regions commonly activated across sensorimotor tasks

Group‐level conjunction analysis

We first sought to identify candidate convergence zones for all sensorimotor tasks in group𠄊veraged SPMs. To achieve this, we identified, for the block and fast𠄎R runs, voxels that were conjointly activated by all four tasks (AVo, ViM, ViVo, and AM) in a random�ts analysis. Thus, only regions that were significantly activated for all four tasks and in both designs (block and fast ER) were accepted. This analysis revealed the following regions as commonly activated across the four tasks (Fig. ​ (Fig.4 4 Table III ): a region of the medial wall comprising the bilateral SMA, pre‐SMA, and ACC the right anterior insula the posterior lateral frontal/prefrontal cortex (pLPFC) in BA 6 and 9 intersecting the inferior precentral sulcus and inferior frontal sulcus [Brass et al., 2005] a more dorsal bilateral premotor (PM) cortex region in BA6 left thalamus and a segment of the brainstem near the superior colliculus. All these brain regions showed significant peak volume activity relative to baseline in all four sensorimotor tasks (AVo, ViM, ViVo, and AM). Except for the left pLPFC, right PM, and brainstem, which responded more to the vocal than the manual tasks, these brain regions did not demonstrate sensory or motor preference (ANOVAs on the percent signal change at peak amplitude) (Table III ). Thus, these brain regions are distinct from the sensory and motor regions in that they generally respond strongly and similarly to all sensory and motor modalities tested.

Brain regions exhibiting coactivation across all four sensorimotor tasks (AVo, AM, ViVo, and ViM) in both the block and fast𠄎vent runs [q(FDR) < 0.05] in group𠄊veraged SPMs. The timecourses are from the fast𠄎vent‐related runs. (a) Medial regions (b) Lateral ROIs (c) Time courses in subcortical structures (cerebellum, brainstem, and thalamus).

Table III

Average Talaraich coordinates and size of brain regions showing conjunction of task activation

x,y,zVoxels (mm 3 )Sensory preference t‐valueMotor preference t‐value
Left pLPFC (BAs 6,9)�, 0, 368241.644.54**
Right pLPFC (BAs 6,9)48,2,231721.201.61
Left PM (BA 6)�, 𢄥, 48451.460.24
Right PM (BA 6)41, 𢄣, 45750.672.94*
Right anterior insula37,8,81051.151.50
Medial wall (BAs 6, 24, 32)𢄢, 4, 5112571.641.33
Brainstem𢄢, �, 03651.112.57*
Left thalamus�, �, 108301.881.33

Group�ined ROIs with random effects analysis. A * denotes a significant t‐value (P < 0.05), and ** denotes a significant t‐value using a Bonferroni adjustment for 12 comparisons (P < 0.004). The medial wall ROI consists of pre‐SMA, SMA, and anterior cingulate. A right precentral gyrus ROI (BAs 3,4) was removed from further analysis due to its close proximity between auditory and vocal ROIs. pLPFC, posterior lateral prefrontal cortex PM, premotor.

Although these results are consistent with the notion that these regions may constitute neural convergence zones for all sensorimotor tasks, they may also result from the blurring of adjacent foci displaying sensory‐ and/or motor‐specific activation in this standardized group𠄊veraged data. To address this issue, we carried out an analysis on individual subject's nonstandardized data.

Individual 2D analysis

Within each convergence ROI, using in‐plane 2D images, we assessed whether the majority of subjects exhibited at least one voxel of conjunction and/or task‐specific activity (see Methods section and Table ​ TableIV). IV ). Given that this brain region is thought to be functionally heterogeneous (Picard and Strick, 2001), the frontal medial wall ROI was divided into a ventral ACC ROI and a dorsal pre‐SMA/SMA ROI. It was also divided according to the left and right hemisphere. Regions that demonstrated conjunction‐related activation for all conditions in most subjects consisted of the right anterior insula, bilateral pLPFC, bilateral pre‐SMA/SMA, bilateral ACC, and the left thalamus. The PM cortex demonstrated conjunction‐related activation in only the AVo/ViM tasks, whereas the brainstem did not exhibit robust conjunction activity. In addition, the pLPFC and the pre‐SMA/SMA showed task‐specific activation in at least one of the four sensorimotor tasks (AVo, ViM, AM, and ViVo).

Table IV

Number of participants demonstrating conjunction and/or task‐specific activation

SideROIAM/ViVoAVo/ViM
ConjunctionAMViVoConjunctionAVoViM
LeftThalamus6*4210*11
Brainstem201510
pLPFC7*438*6*3
PM5357*32
Pre‐SMA/SMA9*47*9*7*3
Anterior Cingulate8*5311*35
RightBrainstem301320
Anterior Insula6*437*22
pLPFC6*247*7*3
PM5348*55
Pre‐SMA/SMA10*6*510*33
Anterior Cingulate7*559*03

The total number of participants per cell is 12 (i.e., only subjects with sagittal prescriptions were analyzed in 2D). Cells with asterisks (*) have six or more participants, indicating that there is 99.72% confidence, using the Bonferroni corrected Clopper–Pearson test (see Methods section), that there is at least one participant with conjunction or task‐specific activation. SP, superior–posterior pLPFC, posterior lateral prefrontal cortex PM, premotor SMA, supplementary motor area.

Thus, several of the conjunction ROIs defined at the group level also demonstrated robust conjunction activity in 2D analysis of single subjects. However, some of these ROIs, namely the pLPFC and pre‐SMA/SMA, also showed task‐specific activation. The latter finding can be interpreted in at least two ways. First, the conjunction activity observed both at the group and individual levels could be an artifact of the hemodynamic blurring of distinct single‐task activation foci. Alternatively, the conjunction ROIs may include distinct subregions demonstrating true task‐specific and conjunction‐related activity. Importantly, although the pLPFC showed both conjunction activity and task‐specific activity, the latter was limited to a vocal task (AVo see Table ​ TableIV). IV ). These vocal‐specific voxels were not only posterior to the conjunction voxels in the individual subject 2D analysis, they were also immediately anterior to the vocal motor ROI (cf. Figs. ​ Figs.3 3 and ​ and4), 4 ), suggesting that they belong to the vocal motor/premotor cortex. Thus, the posterior extent of the pLPFC ROI may have been tainted by vocal‐specific activity from the adjacent vocal motor cortex. This finding, together with the fact that no significant manual‐specific activation was observed in pLPFC, suggests that this brain region contains genuine conjunction‐related activity. By contrast, the pre‐SMA/SMA showed significant task‐specific activity in both vocal and manual tasks, casting doubt as to whether this brain region truly contains conjunction foci.

Replication of individual subject analysis

For three of the ROIs that exhibited conjunction activity at the group level, the individual‐level analysis revealed a more complex pattern of activation. Specifically, examination of individual subjects' activity profile in pLPFC and pre‐SMA/SMA showed both conjunction‐related and task‐specific activations, whereas the PM ROI showed conjunction‐related activity in the AVo/ViM, but not in the AM/ViVo, pairing. To determine whether the complex activation patterns in these brain regions are robust, we rescanned four of the participants who showed conjunction and task‐specific activations in these three ROIs in a fast ER experiment that was identical to the first scanning session. The conjunction and task‐specific voxels were separately identified in each session of each subject and assessed for overlap of activation across sessions using a conjunction analysis that required voxels to be significantly activated in each of the two sessions (see Methods section).

The analysis revealed that both conjunction and task‐specific activations replicated in three or all the subjects for the pre‐SMA/SMA and pLPFC, whereas the replication for task‐specific activation held for about half the subjects in PM cortex (Table ​ (TableV). V ). Figure ​ Figure5 5 illustrates the replication of activation across sessions for single‐task activity in the pre‐SMA/SMA and for conjunction‐related activity in the pLPFC. Thus, the replication analysis provides additional support for the presence of genuine conjunction‐related activity in pLPFC and of task‐specific activity in pre‐SMA/SMA. By contrast, the pattern of activation in PM proved to be less consistent.

Replication of activation patterns across fMRI sessions in a representative subject. Left, 2D SPMs and right, time courses. (a) Replication of task‐specific (AVo/ViM) activation across sessions in pre‐SMA/SMA. (b) Replication of conjunction activation across fMRI sessions in pLPFC. White voxels were those that demonstrated replicable coactivation for the AVo/ViM tasks across sessions. Maps were thresholded at q(FDR) < 0.22 for an omnibus q(FDR) < 0.05 across sessions.

Table V

Number of participants exhibiting replicable conjunction and task‐specific activation

SideROIConjunction replicationsSingle‐task replications
LeftpLPFC4/44/4
RightpLPFC4/43/4
LeftPM3/31/3
RightPM3/42/4
LeftPre‐SMA/SMA4/43/4
RightPre‐SMA/SMA4/43/3

Three participants performed the AVo/ViM tasks and the other participant performed the AM/ViVo tasks. The numerator indicates the number of subjects who demonstrated intersession replicability of activation (i.e., significant conjunction or task‐specific activation in Sessions 1 and 2), whereas the denominator indicates the number of subjects who showed conjunction or task‐specific activation in Session 1 (as activation in session 1 is a prerequisite for assessing reliability). Note that one participant did not demonstrate any activity in left PM in Session 1, and another did not demonstrate single‐task activity in right pre‐SMA/SMA in Session 1, so the denominator in those cases is three, rather than four. pLPFC, posterior lateral prefrontal cortex PM, premotor SMA, supplementary motor area.

Summary

The results of the group𠄊verage and individual‐level analyses specifically highlight the anterior insula, pLPFC, ACC, and thalamus as candidate neural substrates subserving response selection irrespective of the sensory and motor modalities. The pre‐SMA/SMA is another candidate brain region, but the presence of task‐related activity neighboring the conjunction activity does not allow us to rule out the possibility that the conjunction activation is a result of overlap of the hemodynamic spread of task‐specific activity. A similar issue limits the interpretability of the PM activation, especially considering that the conjunction activation for one set of tasks (i.e. AM/ViVo) failed to demonstrate a sufficient number of participants with conjunctive activation.

Comparison of Sensorimotor Pairings

In a final analysis, we considered whether the AVo/ViM and ViVo/AM pairings differentially affected brain activations. It has been argued that some sensorimotor mappings are more naturally compatible than others [Hazeltine and Ruthruff, 2006 McCleod and Posner, 1984 Stelzel et al., 2006 Wickens, 1984] and that this compatibility effect may differentially recruit the (left) prefrontal cortex [Stelzel et al., 2006]. Specifically, the AVo/ViM mapping may be less demanding on limited resources than the ViVo/AM pairing given that in the former pairing the input and outputs may be preferentially linked: we tend to respond verbally to auditory input and often make manual movements toward visual objects. However, our behavioral results are inconsistent with this hypothesis, as subjects were generally faster in the AM/ViVo pairing than in the AVo/ViM pairing [t(19) = 2.15, P < 0.05]. Furthermore, a between‐group comparison of these two sensorimotor mappings ([ViVo + AM] − [AVo + ViM]) revealed no activation differences at q(FDR) < 0.05 or even with an uncorrected threshold of P < 0.001. Thus, even if sensorimotor pairings differ in their compatibilities, it does not appear that these compatibility differences are expressed by differential activation of brain tissue. These results apply, at least, under conditions in which the sensorimotor tasks are performed separately. However, it is possible that different results could be obtained under dual‐task situations [Stelzel et al., 2006].


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