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. Author manuscript; available in PMC: 2011 Sep 2.
Published in final edited form as: J Neurosci. 2008 May 14;28(20):5141–5148. doi: 10.1523/JNEUROSCI.0803-08.2008

Visual motion area MT+/V5 responds to auditory motion in human sight-recovery subjects

Melissa Saenz 1, Lindsay B Lewis 2, Alexander G Huth 1, Ione Fine 3, Christof Koch 1
PMCID: PMC3165167  NIHMSID: NIHMS88046  PMID: 18480270

Abstract

Using functional magnetic resonance imaging (fMRI), we found that cortical visual motion area MT+/V5 responded to auditory motion in two rare subjects who had been blind since early-childhood and whose vision was partially recovered in adulthood. Visually normal control subjects did not show similar auditory responses. These auditory responses in MT+ were specific to motion as compared to other complex auditory stimuli including frequency sweeps and speech. Thus, MT+ developed motion-specific responses to non-visual input, suggesting that cross-modal plasticity can be influenced by the normal functional specialization of a cortical region. Regarding sight-recovery after early blindness, our results further demonstrate that cross-modal responses co-exist with regained visual responses within the visual cortex.

Keywords: multisensory, plasticity, blindness, motion, human, MT, V5

INTRODUCTION

Multiple brain imaging studies have shown that the visual cortex of people who become blind at an early age responds to a variety of auditory (Weeks et al., 2000; Gougoux et al., 2005), tactile (Sadato et al., 1996; Buchel et al., 1998), and verbal tasks (Burton et al., 2002b; Amedi et al., 2003). This cross-modal plasticity in blind people has been associated with superior performance in non-visual tasks (Cohen et al., 1997; Amedi et al., 2003; Gougoux et al., 2005). However, it remains unclear how these non-visual responses map onto specialized sub-regions of the visual cortex. This is because human visual cortical sub-regions cannot be reliably mapped based on anatomy, and are therefore typically identified based on functional visual responses (either retinotopic or other stimulus preferences) that are not measurable in blind subjects.

Here using functional magnetic resonance imaging (fMRI), we tested whether human cortical area MT+ /V5, an area strongly implicated in visual motion processing (Watson et al., 1993; Tootell et al., 1995), was recruited for sound motion processing in two formerly early-blind persons with partial sight recovery in adulthood. Sight-recovery subjects afforded a unique opportunity to localize MT+ functionally based on responses to a moving vs. stationary visual stimulus (as is the standard method with sighted subjects), while observing cross-modal responses (since these subjects had been blind most of their lives) - all within the same individual's visual cortex. If MT+ in these subjects responded selectively to auditory motion, this would suggest that cross-modal reorganization can be guided by the normal functional specialization of a cortical region.

Sight-recovery subject MM, age 53, was blinded in a chemical accident at age 3, and had vision partially restored (post-operative acuity 20:1000) after a corneal stem cell replacement in the right eye seven years ago at age 46. Postoperatively, MM showed successful performance on many visual motion tasks, and exhibited normal MT+ responses to visual motion as measured using fMRI (Fine et al., 2003). Subject MS, also age 53, whose blindness was congenital due to retinopathy of prematurity and cataracts, had vision partially restored (post-operative acuity 20:400) after cataract removal in the right eye ten years ago at age 43.

Our first goal was to determine whether cross-modal responses due to blindness would remain after the restoration of vision: would cross-modal responses within the visual cortex co-exist with regained visual responses? Understanding the implications of cross-modal plasticity for visual restoration, and vice versa, is of increasing importance given current developments in technologies for restoring vision to the blind such as corneal stem cell transplants, retinal prosthetics and gene therapy (Merabet et al., 2005; Aguirre et al., 2007). If we found cross-modal responses, our second goal was to determine whether responses in MT+ would be functionally selective: would the auditory responses within MT+ be motion-specific? We tested these two hypotheses by functionally identifying MT+ in two sight-recovery subjects using moving visual stimuli, and then measuring responses within this region for a wide range of auditory stimuli that included auditory motion.

METHODS

Subjects

12 subjects (2 partial sight recovery: both age 53, 1 man; 10 normally sighted controls: ages 21–53, 6 men) without neurological or psychiatric problems participated having given written, informed consent. Data from 1 additional control subject was excluded from analysis due to significant head motion artefacts. Experimental procedures were approved by the Caltech Institutional Review Board.

MRI scanning

Blood oxygenation-level dependent (BOLD) functional imaging was performed with a 3 Tesla Siemens TRIO scanner at Caltech (3×3×3 mm voxels, TR=12 s, TE=30, flip angle=90, FOV=192, 30 slices). Slices were obliquely oriented for optimal coverage of visual and auditory cortices. 3D anatomical images were acquired using a T1-weighted MPRAGE sequence.

A sparse EPI pulse sequence was used in all experiments so that the presentation of stimuli (both auditory and visual) was uninterrupted by MRI scanner noise (Hall et al., 1999). 2 s volume acquisitions were preceded by 8–10 s quiet delay periods (10 s in Experiment 1, 8 s in Experiments 2 and 3) during which visual or auditory stimuli were presented. Due to the hemodynamic delay (approximately five seconds to peak response (Boynton et al., 1996)), each volume acquisition measured the BOLD response to stimulation during the middle of the stimulus presentation period, with relatively little contribution from the auditory noise of the previous acquisition. Note that the shorter delay period of Experiments 2 and 3 may have resulted in lower measured response amplitudes in those experiments.

Auditory Stimuli

Stimuli were generated using MATLAB and the psychophysics toolbox (www.psychtoolbox.org). See http://www.klab.caltech.edu/~saenz/soundstimuli.html for samples of the auditory stimuli. Auditory stimuli were delivered via MRI compatible stereo headphones (MRCONFON) and all subjects were instructed to keep their eyes closed during all auditory scans.

Inter-aural level difference (ILD) motion noise bursts (Experiments 1–3) were created by linearly ramping (between 0 and max-intensity) the volume of a white noise stimulus in opposite directions between the left and right speakers, creating the vivid percept of a sound source moving horizontally from one side of the head to the other. Maximum auditory intensity was approximately 50 dB and was adjusted to a comfortable level for individual subjects. All subjects reported a strong motion percept. ILD motion responses were contrasted with responses to stationary auditory white noise bursts that had equal intensity (0.5 of max) in the two speakers, creating the percept of a centrally located stationary sound source. All noise bursts had a duration of 1 s +/− 200 ms.

Inter-aural time difference (ITD) motion noise bursts (Experiment 2) consisted of low-pass filtered (at 2KHz) auditory white noise presented to the two ears. The time lag between the two ears was stepped from +1 ms to −1 ms in 16 evenly spaced increments. In Experiment 3, noise bursts were re-sampled so that inter-aural time lags could be smoothly and linearly ramped. The corresponding stationary stimulus had an inter-aural time lag of 0.

Volume changing stimuli (Experiment 2) were stationary auditory white noise bursts presented binaurally. These stimuli (identical in both speakers) alternated between half and maximum volume. Volume levels were chosen to match the maximum monaural sound difference present in the ILD moving vs. stationary stimuli.

Frequency modulated (FM) sweeps (Experiment 2) were generated by linearly ramping the auditory frequency from 75 to 800 Hz over time. FM sweeps were normalized using ISO226 equal-loudness curves to minimize perceived loudness changes that could be associated with spatial motion in depth. FM sweeps were contrasted with an unchanging, mid-range monotone (438 Hz).

Speech stimuli (Experiment 2) consisted of nouns (recorded voice) spoken by a male native English speaker. Common concrete nouns (e.g. “chair”, “fork”) were chosen from the MRC psycholinguistic database (www.psy.uwa.edu.au/MRCDataBase/uwa_mrc.htm). Speech stimuli were contrasted with the same speech recordings played in reverse.

Stimulus Procedures

Sight-recovery subjects MM and MS participated in Experiments 1–3. 6 control subjects (C1–C6) participated in experiment 1 (ILD motion responses). An additional 4 control subjects (C7–C10) were tested for auditory ITD motion responses in Experiment 2. Two control subjects (C3, C7) participated in Experiment 3.

Experiment 1: ILD Auditory motion vs. stationary white noise and rest

Auditory scans (4 per subject) consisted of thirty 12 s blocks of moving white noise (ILD motion), stationary white noise, and silent rest (10 alternated blocks of each condition, for a total of 372 s total including an initial dropped acquisition). Block order was counterbalanced across subjects. Each 12 s block consisted of a 10 s stimulation period followed by a 2 s data acquisition period.

Each stimulation period contained four 2-IFC trials (2500 ms each). During each trial, two noise bursts were presented, separated by a 100 ms blank interval, followed by a 400 ms response interval. During motion blocks, the sound moved in opposite directions during the two intervals. Each trial contained one shorter noise burst (800 ms) and one longer noise burst (1200 ms), the order of which was randomized across trials. Subjects pressed one of two keys to indicate which interval contained the longer duration noise burst. The same task was used on both moving and stationary trials, so task demands did not vary across the compared conditions. Task performance was not significantly different across moving vs. stationary conditions (93.1% vs. 92.1% correct, p=0.56) nor across control vs. sight-recovery subjects (91.7% vs. 93.7% correct, p=0.22).

Experiment 2: Measuring responses to auditory ITD motion, ILD motion, volume changes, frequency sweeps and speech

Auditory scans (2 per subject per condition) consisted of forty 10 s blocks that alternately presented the given test stimulus and its respective contrast stimulus. We measure responses to: (1) ITD motion vs. stationary noise bursts, (2) ILD motion vs. stationary noise bursts, (3) stationary volume changes (white noise bursts at maximum vs. half-maximum volume, (4) frequency sweeps vs. mid-range monotone (438 Hz), and (5) forward spoken words vs. unintelligible reverse spoken words. Two additional conditions were run with subject MS only (with whom we had more time): (6) peripheral vs. central stationary white noise bursts, and (7) ILD moving vs. stationary noise bursts played monaurally (thus removing the motion information).

The same 2-IFC duration judgement task from Experiment 1 was used in all conditions of Experiment 2 (except the speech condition) in order to keep task demands as similar as possible across all experiments. Each 10 s block contained three 2-AFC trials (2500ms) that fit within the 8 s silent delay period followed by a 2 s scanning period. During FM sweep trials, the two intervals swept in opposite directions (from low to high and high to low), in order to parallel the motion conditions. In the speech condition, a single noun was presented every 2 s during the 8 s stimulation periods (4 words/stimulation period). Subjects were asked to make a covert word association with the heard noun during forward speech blocks and to listen passively during the reverse speech blocks. The stimulus presentation and task for speech blocks were modelled after those used in previous studies of cross-modal verbal responses in blind subjects (Burton et al., 2002a; Amedi et al., 2003).

Experiment 3: Measuring responses as a function of ITD and ILD motion strength

ITD and ILD motion strength were parametrically varied by changing the slopes of the inter-aural temporal and volume ramps, respectively, as shown in Figure 4. For the ITD data session, each scan (5 per subject) each consisted of forty 10 s blocks of ITD motion presented at five different motion strengths (8 blocks per motion strength). The same procedure was repeated for the ILD data session on a separate day. During both sessions, single 1 s noise bursts were presented every 2 s during each 8 s stimulation period (4 noise bursts/stimulation period) followed by 2 s of scanning. Each single noise burst swept from left to right or right to left in randomized order, and subjects indicated the perceived direction by pressing one of two keys (2-AFC direction discrimination task). Trials were blocked by motion strength and were presented either in order of increasing or decreasing motion strength (counterbalanced across scans). Task performance for the sight recovery subjects at the 0, 25, 50, 75, and 100% levels of motion strength was 51% (chance level since there was no motion), 88%, 90%, 92%, and 93% correct for ITD stimuli and 56%, 93%, 95%, 93%, and 91% correct for ILD stimuli.

Figure 4. Experiment 3. MT+ responds to a range of auditory motion strengths.

Figure 4

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(A) For ITD motion, inter-aural temporal differences were linearly ramped from positive to negative (e.g. leading in left ear to leading in right ear, or vice versa). The slope of the ITD ramp was parametrically varied using even increments. The 0% condition (stationary percept, no temporal lags) and the 100% condition (temporal lag ramped from +1ms to −1ms during the stimulus duration of 1s) were equivalent to the stationary and moving conditions of Exp. 2. (B) MT+ responses (% fMRI signal change) to each motion condition are relative to the response to the 0% (stationary) condition for sight-recovery subjects (n=2) and controls (n=2). MT+ responded to all ITD motion strengths within sight-recovery subjects, but not controls. (C) For ILD motion, inter-aural level (volume) differences were linearly ramped from positive to negative (e.g. louder in left ear to louder in right ear, or vice versa), with the slope again varying between 0% (stationary) and 100% (volume difference ramped from +max to −max value). (D) MT+ responses to each motion condition are plotted relative to the 0% (stationary) condition. MT+ responses increased linearly (R2=0.97) with ILD motion strength within sight-recovery subjects, but not controls. Error bars denote s.e.m. Stars denote significant differences from zero (*p<0.05, ** p<0.01).

Data Analysis

Brain Voyager QX (Brain Innovation) and MATLAB were used for data analysis. FMR data pre-processing included linear trend removal, temporal high-pass filtering, and motion correction.

For general linear model (GLM) analyses (Experiment 1 only), individual 3-D anatomical images were transformed into Talairach space and were segmented at the gray/white matter boundary allowing for cortical surface reconstruction of each individual subject's brain hemispheres. Cortex-based alignment was applied to further improve inter-subject alignment beyond Talairach correspondence. The reconstructed cortical surfaces were each transformed into a spherical representation that was subjected to non-rigid alignment to a selected target brain sphere based on the gyral/sulcal folding pattern (Fischl et al., 1999). FMR data were aligned to same-session anatomical volumes and transformed into the cortex-based aligned coordinate space. Fixed-effects GLM analyses were corrected for serial correlations and for multiple comparisons using the false discovery rate (FDR) method (Genovese et al., 2002).

Region-of-Interest (ROI) analyses (as used in Experiments 1–3) have the benefit of being based on individual subject data, and are more sensitive than whole-brain analyses. This allows us to localize regions with more precision and to quantify responses to multiple auditory stimulus conditions. ROIs were defined within each subject's 3-dimensional Talairached anatomical coordinate space (not on 2-D surfaces).

Defining MT+ ROIs

MT+, the probable human homologue of visual motion-responsive macaque areas MT and MST, is typically located posterior to the intersection of the lateral occipital sulcus (LOS) and the inferior temporal sulcus (ITS). However, identifying human MT+ by anatomical landmarks and/or stereotaxic coordinates alone is problematic because of significant anatomical variability across individuals (Watson et al., 1993; Dumoulin et al., 2000), and because of its proximity to polysensory temporal lobe regions (Beauchamp et al., 2004). As a result, the location of MT+ is normally defined functionally, by its response to moving visual stimuli (Watson et al., 1993; Tootell et al., 1995).

The visual MT+ localizer stimulus was projected onto a rear-projection screen visible from within the MRI scanner via an angled mirror. There were 2–4 visual scans per subject, each consisting of 30 alternating blocks of moving (8 deg/s radially inward and outward) vs. stationary white dots on a black background. Random dot arrays subtended +/− 10 deg from a central fixation point. Individual dots (50/field) subtended 1 deg. This large dot size was used with all subjects to compensate for MM's and MS's limited acuity. Both MM and MS, despite very low spatial acuity, reliably reported when the visual stimulus was moving vs. stationary. MT+ ROIs for sight recovery subjects and controls were individually selected as contiguous 3-dimensional regions near the LOS/ITS that responded more to moving than to stationary conditions, q(FDR)<0.05. To show that results did not dependent on the particular threshold used for defining the ROI, MT+ ROI thresholds were also defined at q(FDR)<0.01 and q(FDR)<0.1 (Supplementary Data Figs. 2 and 3).

Region-of-Interest (ROI) analyses (as used in Experiments 1–3) have the benefit of being based on each individual subject's data, thereby allowing us to localize (and measure responses to multiple auditory stimulus conditions within) regions sensitive to visual motion with precision for each individual subject. It was important to precisely define MT+ because of its proximity to multimodal areas. ROIs were defined within each subject's 3-dimensional Talairached anatomical coordinate space (not on 2-D surfaces).

Defining Other ROIs

Auditory cortex ROIs were defined as contiguous regions on the lateral sulcus that responded more to stationary white noise than to silent conditions, q(FDR)<0.05. This ROI likely included primary and secondary regions of auditory cortex. Ventral occipito-temporal voxels (inferior to MT+) that responded to ILD motion in Experiment 1 were chosen as a third ROI (from subject MM only, as no such activation was evident in subject MS).

ROI Statistics

In Figures 2–4, error bars represent s.e.m. In all cases, t-tests are performed over repeated scan runs per hemisphere per subject. The following are examples: Auditory ILD motion responses of each subject (Fig. 1): 4 repeated runs × 2 hemispheres yields n=8. ITD motion responses in MT+ of sight-recovery subjects (Fig. 3a): 2 repeated runs × 2 hemispheres × 2 subjects yields n=8.

Figure 2. Experiment 1: ROI Analysis. MT+ responds to both visual and auditory motion stimuli in sight-recovery subjects.

Figure 2

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Responses (% fMRI signal change) to moving vs. stationary visual stimuli and to moving (ILD) vs. stationary auditory stimuli within visually-defined MT+ ROIs. MT+ responded to visual motion in all individual subjects. MT+ responded to auditory ILD motion in sight-recovery subjects MM and MS, but not in normally-sighted controls. Error bars denote s.e.m. Stars denote significant differences from zero (** p<0.01).

Figure 1. Experiment 1: Surface Maps of Auditory ILD motion responses and MT+.

Figure 1

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Yellow regions responded more to moving (ILD) vs. stationary auditory white noise in the (A) control group, (B) subject MM, and (C) subject MS. Statistical activation maps are the result of a fixed-effects general linear model analysis (GLM), q(FDR)<0.01. Green and blue regions show MT+ location as determined by visual MT+ localizer scans run in the same subjects (green = MT+ overlapped by auditory ILD motion responses, blue = MT+ not overlapped by auditory ILD motion responses). Note the near-complete overlap (very little blue) in subjects MM and MS indicating co-localization of auditory ILD responses with their visually-defined MT+. (D) A subtraction analysis shows regions more activated by auditory ILD motion (vs. stationary) in sight recovery subjects compared to controls, q(FDR)<0.05. Data from all subjects are projected onto a single anatomical image (inflated cortical surface) using cortex-based alignment. LOS, lateral occipital sulcus; ITS, inferior temporal sulcus; STS, superior temporal sulcus; LatS, lateral sulcus.

Figure 3. Experiment 2. MT+ auditory responses are motion-specific.

Figure 3

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Responses (% fMRI signal change) from MM and MS to ITD motion, ILD motion, stationary volume changes, FM sweeps, and human speech within (A) MT+, (B) auditory cortex, and (C) ventral occipitotemporal cortex ROIs. Responses to all stimuli are relative to their respective baseline control stimuli. Only area MT+ had motion-specific responses. To the left, sample coronal slice views from subject MM illustrate ROI locations (Y- Talairach coordinates given). Note that ventral occipitotemporal ROIs were identified in MM only and left/right hemispheric responses within that ROI are shown separately for the speech condition because it evoked a highly lateralized response. All other responses are averaged across left and right hemispheres. Error bars denote s.e.m. Stars denote significant differences from zero (*p<0.05, ** p<0.01).

RESULTS

We first present statistical activation maps that give an initial broad overview of cortical responses to auditory ILD motion in sight-recovery subjects and controls. Then we use specific ROI (region-of-interest) analyses and a range of auditory stimuli to specifically test for the motion specificity of auditory responses within MT+ on an individual-subject basis. Samples of auditory stimuli can be found at http://www.klab.caltech.edu/~saenz/soundstimuli.html. A sparse MRI pulse sequence was used in all experiments so that stimulus presentation was uninterrupted by scanner noise.

Experiment 1: Cross-modal auditory motion responses co-exist with regained visual responses in MT+

In Experiment 1, we measured responses to visual motion (standard MT+ localizer stimulus) and to auditory ILD motion in the 2 sight-recovery subjects (MM and MS) and 6 normally-sighted controls.

GLM analyses

In Fig. 1, yellow regions show cortical brain areas that responded more to moving (ILD) than to stationary auditory white noise stimuli. Statistical activation maps are the result of fixed-effects general linear model (GLM) analyses, corrected for multiple comparisons [q(FDR)<0.01]. In both the control group and in subjects MM and MS, auditory ILD motion activated the auditory cortex with a right hemispheric dominance along the planum temporale (Fig. 1A–C) [Talairach coordinates: control group, RH: (50,−31,19), LH: (−48,−33,17)], consistent with previous reports of auditory motion responses in sighted subjects (Baumgart et al., 1999; Warren et al., 2002).

In the control group (Fig. 1A), auditory ILD motion also activated the bilateral temporal lobes, beginning on the inferior temporal sulci (ITS) and extending across the middle temporal gyri (MTG) towards the superior temporal sulci (STS) [RH: (52,−57,6.4), LH: (−51,−62,3.1)]. The auditory response partially overlapped with the anterior end of MT+ in the control group analysis (Fig. 1A, green), although blurring due to inter-subject averaging contributes to this overlap in the group-averaged analysis. (See Supplementary Fig. 1 for individual control subject activation maps.) This auditory response is consistent with previously reported responses to complex auditory stimuli (not limited to motion) on the posterior MTG, which may partially overlap with the anterior end of MT+ (Lewis et al., 2004). The existence of auditory responses adjacent to MT+ in control subjects further emphasizes the importance of functionally verifying MT+ location on an individual-subject basis.

Auditory ILD motion responses from subjects MM and MS are shown individually (Fig. 1B–C). MM and MS had auditory ILD motion responses that extended posteriorly into the visual occipital lobe (q(FDR)<0.01). Unlike control subjects, their auditory responses co-localized very well with their own visually-defined MT+.

A subtraction analysis (Fig. 1D) shows regions that were more strongly activated during auditory ILD motion (vs. auditory stationary) in sight-recovery subjects compared to controls (q(FDR)<0.01). Auditory ILD motion more strongly activated a bilateral occipital region in the sight-recovery subjects [LH:(−40,−78,−2.6), RH:(42,−70,−0.7)], consistent with MT+ location in those subjects. Thus, these initial results are consistent with auditory ILD motion responses co-localizing with MT+ in sight-recovery subjects but not controls.

ROI analyses

Next, we sought to further verify this result by performing ROI analyses to measure the amplitude of the BOLD response to auditory ILD motion within the MT+ region-of-interest of all subjects. These ROI analyses offers several important advantages. First, these ROI analyses are carried out separately on each individual subject, and are performed in 3-dimensional anatomical space (not surface projections). This avoids potential distortions resulting from group-averaging and transformation onto surface representations. These concerns are of particular importance in a region of the cortex that shows high anatomical inter-subject variability. Second, the ROI analyses report actual BOLD response amplitudes (not statistical values) and are therefore capable of revealing any auditory responses within MT+ that might be sub-threshold in a given GLM analysis.

Bilateral MT+ ROIs were individually defined in each subject (in 3-D coordinate space) based on that individual subject's response to the visual MT+ localizer stimulus (visual moving vs. stationary). The stereotaxic locations (Table 1) and volumes (Supplementary Table 1) of the MT+ ROIs in subjects MM and MS were consistent with previous studies (Watson et al., 1993; Tootell et al., 1995; Dumoulin et al., 2000) and were within the ranges found in our own control subjects.

Table 1.

Center-of-gravity Talairach Coordinates for MT+ ROIs

Left hemisphere
 Right hemisphere
Subject X Y Z X Y Z
MM −44 −77 4 40 −73 4
MS −40 −67 9 39 −65 1
C1 −42 −67 0 45 −67 −1
C2 −49 −70 14 45 −67 2
C3 −46 −60 −11 42 −63 −9
C4 −41 −66 −9 49 −64 −9
C5 −51 −67 1 43 −67 −1
C6 −45 −69 −4 49 −62 −5
C7 −47 −64 −6 44 −59 −5
C8 −47 −61 2 47 −60 −4
C9 −48 −67 −5 39 −61 −2
C10 −49 −69 −5 43 −68 −7
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In Fig. 2, fMRI response magnitudes (% BOLD signal change) to visual motion and to auditory ILD motion are plotted within each subject's MT+ ROI. In each control subject, MT+ responded positively to visual motion (t test, p<.001 each subject, as expected since the MT+ ROI was defined using this condition), but not to auditory ILD motion (p>0.07 minimum each subject). For each control subject, there was a significant difference between their own visual and auditory motion responses (p<.001 each subject). In MM and MS, MT+ responded to both visual motion (p<.001 each subject) and auditory ILD motion (p<.001 each subject; with no significant difference between visual and auditory responses: MM p=0.6, MS =0.9).

In additional control analyses, we verified that these results were highly consistent over a range of thresholds used to define the MT+ ROI, which included equating MT+ ROI size across subjects (Supplementary Fig. 2).

Experiment 2: MT+ auditory response is specific to motion

In Experiment 2, we sought to verify whether the sight-recovery subjects' auditory responses within MT+ were specific to motion. If motion-specific, MT+ should also respond to auditory motion defined by inter-aural temporal differences (ITD), but should not respond to other complex or temporally changing auditory stimuli that do not induce the percept of motion.

We measured responses within the MT+ ROIs of MM and MS to: (1) moving vs. stationary noise defined by inter-aural time differences (ITD); (2) moving vs. stationary white noise defined by ILD (replication of Experiment 1); (3) stationary volume changes (white noise at maximum vs. half-maximum volume); (4) frequency-modulated (FM) tonal sweeps vs. monotone (FM sweeps - a rising and falling of pitch); and (5) human speech vs. unintelligible reverse speech. Additionally, in subject MS only, we measured responses to two other control conditions: (6) peripheral vs. central stationary white noise (to test for peripheral bias); and (7) the ILD moving vs. stationary stimulus played monaurally (thus removing the motion information).

Consistent with the hypothesis of motion-specificity in sight-recovery subjects, MT+ (Fig. 3A) responded both to ITD motion (p<.001) and to ILD motion (p<.003), consistent with Experiment 1) with no difference between responses to the two types of motion (p=.5). MT+ did not respond to stationary volume changes (p=.7), frequency sweeps (p=.22), or speech (p=.21). Nor did MT+ (measured in MS only, not shown in Figure) respond to peripheral stationary stimuli (mean=.06 % signal change +/− .1, p=.6) or to ILD stimuli played monaurally (mean=0.01 % signal change +/− .03, p=.9). The same pattern of results was observed for MM and MS individually (ITD p<.01 each; ILD p<.01 each; volume change p>.4 each; frequency sweeps p>.1 each; speech p>.2 each). We also verified that these results were consistent over a range of thresholds used to define the MT+ ROI (Supplementary Fig. 3)

We also measured auditory ITD motion responses within the MT+ ROI of four normally-sighted control subjects (not shown in Figure). In contrast to the sight-recovery subjects, MT+ in controls was weakly inhibited by ITD motion (mean=−0.11 % signal change +/− .05, p<0.05). This is consistent with a previous study that reported slight deactivation of MT+ by auditory ITD motion stimuli (Lewis et al., 2000).

Next, we defined bilateral auditory cortex ROIs in MM and MS based on responses to stationary auditory white noise vs. silence (Fig. 3B). Responses within this ROI were significant for ITD motion (p<.05), ILD motion (p<.01), FM sweeps (p<.001), and for volume changes (p=.05). Unlike MT+, this region responded to auditory stimuli that were not limited to motion.

We also defined bilateral ventral occipitotemporal ROIs (Fig. 3C) in subject MM because this region responded to ILD motion in Experiment 1 (see starred region inferior to MT+ in Fig. 1; no such activation was evident for subject MS). We chose to investigate this region further, since it is more typically associated with object than with motion processing (Grill-Spector et al., 2001). In MM, responses were marginally significant to ILD motion (p=.05), but not to ITD motion (p=.4), volume changes (p=.9), or to FM sweeps (p=.5). The left hemisphere of this region responded strongly t speech (LH p<.01, RH p=.6), consistent with previous reports of left-hemispheric verbal responses within visual cortex of early-blind subjects (Amedi et al., 2003). Thus, visual regions adjacent to MT+ that are not normally implicated in visual motion processing did not respond specifically to auditory motion.

The results of Experiment 2 demonstrated that in sight-recovery subjects volume changes were neither necessary nor sufficient to evoke an MT+ response, nor did MT+ respond more generally to complex, continuously changing, peripheral, and/or meaningful stimuli including frequency sweeps or speech. Other regions of the brain that were not expected to show motion-specificity responded more generally to this battery of auditory stimuli.

Experiment 3: MT+ is sensitive to weak auditory motion signals

In normally-sighted subjects, MT+ is highly motion-sensitive, responding even to weak motion signals (Britten et al., 1992). In Experiment 3, we tested whether MT+ in the sight-recovery subjects was sensitive to a range of auditory motion signal strengths. For the ITD stimulus (low-pass filtered auditory white noise), motion was generated by linearly ramping inter-aural temporal differences. We parametrically varied ITD motion strength by varying the slope of that linear ramp (Fig. 4A). For the ILD stimulus (auditory white noise), motion was generated by linearly ramping inter-aural volume differences. We parametrically varied ILD motion strength by varying the slope of that volume ramp (Fig. 4C). In both cases, increasing the slope of the ramp effectively increased both the apparent speed and path-length of the motion stimuli.

Within sight-recovery subjects MM and MS, MT+ was highly sensitive to ITD motion, responding to motion stimuli with small ITD ramps (Fig. 4B, p<.005 at all motion strengths), and rapidly saturating. In contrast, MT+ of controls (n=2) was not activated by ITD motion of any strength, and was in some cases weakly inhibited (not different from zero at 50% and 75% levels, p>.3; below zero at 25% and 100% levels, p<.05). For ILD motion, MT+ responses within sight-recovery subjects increased monotonically with motion level and were well fit by a linear model (Fig. 4D, R2=0.97, slope=0.34/100 and intercept=0.01 of linear fit, difference from zero at 50%, 75%, and 100% levels, p<.001; individual subject data also had a good linear dependence: MM, R2=0.97 and MS, R2=0.95). In controls (n=2), MT+ did not respond to ILD motion at any level (p>.1, R2=0.31). These results were consistent over a range of thresholds used to define the MT+ ROI. Additionally, the response within the ventral occipitotemporal ROI of subject MM did not show a linear dependence on ILD motion strength (R2=0.01).

DISCUSSION

To summarize, we have shown that: (1) MT+ responded to two types of auditory motion as a result of cross-modal plasticity in two sight-recovery subjects and did not respond to either type of auditory motion in visually normal controls; (2) this auditory response in MT+ was motion-specific and could not be attributed to volume changes, a peripheral bias, or a responsiveness to complex or changing auditory stimuli in general; and (3) MT+ responded to a range of auditory motion strengths, consistent with high sensitivity to visual motion in MT+ of normally-sighted subjects. Furthermore, these results demonstrate for the first time that robust and specific auditory responses co-exist with regained visual responses following sight-recovery after long-term blindness.

Experiment 3 demonstrated that MT+ in the sight-recovery subjects was sensitive to a range of auditory motion strengths. One possible explanation for the observed linear dependence with ILD motion stimuli (but not ITD) is that with the ILD stimulus the motion information, like the noise, is carried by the volume. Previous results in normally sighted subjects have shown that MT+ population responses increase linearly when the visual motion signal is increased relative to noise (i.e. visual motion coherence) (Rees et al., 2000), whereas MT+ responses as a function of speed are relatively invariant.

Previous studies of cross-modal responses in MT+

In subjects MM and MS, we do not know to what extent cross-modal plasticity occurred during their years of blindness or in the time following sight recovery (or both). MM's and MS's sight had been restored for over seven and ten years, respectively. Thus, cross-modal responses co-exist with restored visual responses in MT+, even many years after sight-recovery in adulthood.

A previous study of early-blind subjects reported auditory ILD motion responses in a region consistent with MT+ location and we have replicated this result in five blind subjects (data not shown), suggesting that similar cross-modal responses may exist in individuals who are still blind (Poirier et al., 2005; Poirier et al., 2006). However, as described above, it not possible to functionally verify MT+ location in subjects who are blind. Nor did Poirier et al. test whether the responses that they found near the presumed location of MT+ were selective for motion stimuli. Interestingly, another study in early-blind subjects reports acquired tactile motion responses in a region consistent with MT+ (Ricciardi et al., 2007). Independent of when plasticity occurred in our sight-recovery subjects, what is remarkable is the specificity of the acquired MT+ response to auditory motion and the consistency of these findings across two rare individuals.

Some previous studies have measured both auditory and visual motion responses in normally-sighted subjects. In these studies, auditory motion stimuli were found to have a suppressive effect (Lewis et al., 2000) or no effect (Baumann and Greenlee, 2007) on MT+ responses. More recently, Alink et al. found that MT+ responses to combined audiovisual motion stimuli were modulated by whether or not the auditory and visual stimuli moved in congruent directions (Alink et al., 2008). Overall, these studies suggest a modulatory, but not driving, effect of auditory motion on MT+ responses in normally-sighted subjects.

Functional Specificity of Cross-modal Plasticity

Some previous studies have suggested that during cross-modal reorganization, cortical regions may retain their normal functional specialization, regardless of the input modality. For example the LOtv, a subregion of the lateral occipital complex (LOC) that is normally responsive to object-related tactile and visual information, became responsive to object-related auditory information in blind and sighted users trained on a visual-to-auditory sensory substitution device (Amedi et al., 2007). Our results provide further evidence that during cross-modal reorganization, the colonization of a cortical region by a novel modality can be influenced by the normal functional specialization of the region.

Retaining functional specificity may make efficient use of existing neural circuitry both within and between cortical areas that are already optimized for a particular function (in this case motion processing). One possibility is that MT+ is susceptible to “colonization” by auditory motion processing because the computational principles underlying the representation of auditory motion may have similarities to those underlying the representation of visual motion. A second possibility, not mutually exclusive with the first, is that retaining functional specificity may help a colonized area to continue to play its functional role within a pathway of multiple cortical areas.

However, it may not be the case that all instances of cross-modal plasticity retain functional specificity. Some studies report that in blind subjects, early visual areas are recruited to serve verbal and memory functions that do not clearly map onto to the known function of the visual cortex (Burton et al., 2002a; Amedi et al., 2003; Raz et al., 2005; Ofan and Zohary, 2007). In these cases cross-modal plasticity could potentially lead to competition between the acquired function and any later restored visual function. In the case of MT+, we speculate that the preservation of motion responses by cross-modal plasticity may even contribute to the relatively good restoration of visual motion perception (as compared to acuity and form perception) that has been consistently reported within the few documented accounts of sight-recovery after long-term, early blindness (Gregory and Wallace 1963; Sacks, 1995; Fine et al., 2003).

Conclusions

Much remains to be learned about the nature of these cross-modal responses in MT+. One question is where these cross-modal signals originate. Multiple mechanisms for cross-modal plasticity have been proposed including the growth of new axons, altered synaptic pruning during development, and the unmasking of cross-modal connections that are weak, modulatory, or silent in the mature brain (Bavelier and Neville, 2002; Pascual-Leone et al., 2005). Projections from auditory cortex to primary visual cortex have been reported in the adult primate (Falchier et al., 2002; Clavagnier et al., 2004), but there have been no reports yet of direct projections between auditory cortex and MT. Nearby multisensory regions of the temporal lobe are a potential source of cross-modal connections to MT+ during cross-modal reorganization (Beauchamp, 2005).

In our sight-recovery subjects, it remains an open question to what extent single neurons in MT+ respond to both visual and auditory motion, and whether these neurons show directional tuning. Neither subject MM nor MS reported obvious difficulty in distinguishing visual from auditory events or synesthetic motion percepts. However cross-modal interactions, in which auditory stimulation influences visual motion perception, are measurable even in visually normal subjects (Sekuler et al., 1997; Seitz et al., 2006; Brooks et al., 2007). Further testing will be needed to determine whether audiovisual interactions are enhanced in sight-recovery patients. Improved knowledge of how visual and auditory responses interact in sight-recovery patients may be important in aiding patients to achieve optimal use of their restored vision.

Supplementary Material

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Acknowledgements

We thank our study participants in particular MM and MS, and also thank Ulrik Beierholm for help with the sparse MRI pulse sequence design. This research was supported by the Gordon and Betty Moore Foundation, the Mind Science Foundation, the Caltech SURF program, the NIH, and the NSF.

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Associated Data

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Supplementary Materials

SFig1
NIHMS88046-supplement-SFig1.gif (148KB, gif)
SFig2
NIHMS88046-supplement-SFig2.gif (46.5KB, gif)
SFig3
NIHMS88046-supplement-SFig3.gif (34.8KB, gif)
legends
NIHMS88046-supplement-legends.pdf (66.7KB, pdf)
table
NIHMS88046-supplement-table.pdf (45.9KB, pdf)

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