Residual responses to motion after V1 damage

Blindsight-positive patients demonstrated significant fMRI activity in contralateral hMT+ for moving versus static dots in the blind hemifield (Fig 1Ai and 1Aii). Overall hMT+ blood oxygen level–dependent (BOLD) signal change for all 5 speeds was significantly greater than baseline (Fig 1Aiii, t = 3.0, p = 0.02, df = 7). Activity was somewhat lower in intensity and spatial extent than the sighted field or healthy controls (Fig 1C), but there was no significant difference between blind and sighted responses in blindsight-positive patients (Fig 1Aiii t = 1.3, p = 0.2, df = 39). Whilst the absence of a significant difference may result from a lack of power (n = 8), blindsight-negative patients with a lower ‘n’ (Fig 1B) showed a marked effect of hemifield (paired t = 2.9, p = 0.007, df = 29) and no demonstrable hMT+ activity for moving versus static dots in the blind hemifield (Fig 1Bi and 1Bii) nor averaged across all conditions (Fig 1Biii, t = 0.6, p = 0.6, df = 5).

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Fig 1. fMRI responses to motion for patients with V1 lesions and controls.

Results are shown separately for (A) blindsight-positive patients, (B) blindsight-negative patients, and (C) healthy controls. (i) Significant activity for moving versus static dots in the blind right hemifield of patients with left V1 lesions and (ii) the blind left hemifield of patients with right V1 lesions. Mixed effects analyses, P < 0.001 uncorrected for a priori regions of interest, elsewhere cluster-corrected p < 0.01. Shaded blue areas are binarized Jülich-defined probabilistic maps of hMT+, radiological convention. (iii) Mean contralateral hMT+ signal change averaged across all five stimulus conditions, comparing sighted (blue) and blind (red) hemifields ± SEM. In controls, ‘Left HF’ refers to left hemifield, ‘Right HF’ is right hemifield. * significant activity above baseline p < 0.05, ψ p ≤ 0.001, ns = not significant. P values are from t tests. (iv) Mean signal change in contralateral hMT+ as a function of stimulus speed, shown separately for each hemifield (blue squares are sighted hemifield, red diamonds are blind hemifield). Responses to static stimuli are dotted (left/sighted hemifield) or dashed (right/blind hemifield) lines. Underlying data for iii and iv can be found in S1 Data. fMRI, functional MRI; SEM, standard error of the mean; V1, primary visual cortex.

https://doi.org/10.1371/journal.pbio.2005769.g001

In addition to hMT+ activity, blindsight-positive patients with right V1 damage (Fig 1Aii, n = 2) showed activity in Jülich-defined right V2 and left V4, although there was no such activity in patients with left V1 damage (n = 6). In blindsight-negative patients, visual-evoked responses also appeared within small regions of ipsilesional V4 and V2, as well as the occipital pole and inferior parietal lobule, implying that activity in these regions was insufficient for motion perception.

It was noteworthy that overall hMT+ signal change in the undamaged hemisphere was also slightly reduced in both patient groups compared to age-matched controls. A possible explanation is that unilateral V1 damage can negatively impact sighted processing in the opposite hemisphere, perhaps via a disturbance of interhemispheric interactions [10,11].

Speed of motion (0°–32°/s) had a significant impact on hMT+ activity in the sighted hemifield of patients and equivalent left hemifield of controls (2-way ANOVA: F(4, 14) = 5.1, p < 0.001), with no effect of participant group (Fig 1iv, F(2, 14) = 2.2, p = 0.12). In the blind hemifield of patients (right hemifield of controls) there was a similar effect of speed (F(4, 14) = 3.5, p = 0.01) but also group (F(2, 14) = 7.4, p = 0.001). The pattern of responses in blindsight-positive and -negative patients differed markedly. Blindsight-positive patients showed a positive relationship between hMT+ signal change and speed (r = 0.8, 5 speeds), whilst the correlation coefficient was negative in blindsight-negative patients (r = −0.6). This difference was not simply driven by a difference between motion and static responses, as excluding the static conditions increased the significance even further (r = 0.67 versus r = −0.99, z = −2.44, p = 0.01). Specifically, blindsight-positive patients showed a relatively ‘normal’ hMT+ relationship with speed in the blind hemifield that was similar to the pattern in healthy controls (Pearson r = 0.89, p = 0.04, 5 speeds) and patients’ own sighted hemifield (r = 0.98, p < 0.01). This was not the case for blindsight-negative patients, either when compared to controls (r = 0.21, p = 0.7) or to their own sighted hemifield (r = 0.29, p = 0.6).

Critical functional geniculo-extrastriate connectivity

To determine how activity in hMT+ correlated with subcortical activity, we examined the fMRI time series after stimulus-evoked responses had been regressed out. Specifically, we compared the residual pattern of activity in hMT+ with (i) LGN, (ii) ventral pulvinar, (iii) SC, and (iv) hMT+ in the opposite (undamaged) hemisphere, using subject-specific regions of interest (ROIs) (S3 Fig). We also performed whole-brain analyses to measure the voxels where neural activity most closely matched the time series of hMT+ and these subcortical structures.

LGN–V1 correlation in the nondamaged hemisphere (Fig 2A) was similar across all participant groups (F = 1.0, p = 0.4, df = 2). Bilateral hMT+ correlation was also very similar across all groups, indicative of preserved functional connection between hMT+ in patients irrespective of blindsight status (Fig 2B, F = 1.9, p = 0.2, df = 2).

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Fig 2. Functional connectivity of the visual pathways in patients and controls.

(A) Correlation of LGN and V1 in the same (undamaged) hemisphere over the entire fMRI timeseries, after stimulus-evoked activity has been regressed out. (B) Correlation of hMT+ bilaterally. Box plots show Fischer-corrected mean correlation coefficients comparing participant group ± SEM. Statistical symbols represent significance levels for one sample t-tests against baseline (zero): ε p ≤ 0.0001, * p < 0.001. Scatterplots are individual examples of fMRI signal in ROI1 versus ROI2. Each point represents a single fMRI volume. Plots for patients in panel A are correlations in the contralesional hemisphere. BS+ is blindsight positive, and BS- blindsight negative. Underlying data can be found in S2 Data. fMRI, functional MRI; LGN, lateral geniculate nucleus; ROI, region of interest; SEM, standard error of the mean; V1, primary visual cortex.

https://doi.org/10.1371/journal.pbio.2005769.g002

There was a significant effect of participant group on LGN–hMT+ correlation (Fig 3A, F = 10.1, p = 0.001, df = 2). Pairwise analysis for the damaged hemisphere showed that blindsight-negative patients had a significantly lower mean correlation coefficient compared to blindsight-positive patients (−0.03 ± 0.08 SE versus 0.25 ±0.03 SE, t = 3.6, p = 0.003, df = 12) and remained at zero. In contrast, functional connectivity between hMT+ and ventral pulvinar (Fig 3B) or hMT+ and SC (Fig 3C) showed no effect of group (F = 0.5, p = 0.6, df = 2, for both ROIs) and no difference between blindsight-positive and -negative patients in the damaged hemisphere (pulvinar: t = 0.9, p = 0.4, SC: t = 0.5, p = 0.6, df = 12). Blindsight-negative patients also showed a hemispheric difference for LGN (t = 3.0, p = 0.01, df = 10) but not ventral pulvinar connectivity (t = 2.1, p = 0.06, df = 10).

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Fig 3. Functional connectivity of subcortical hMT+ pathways in patients and controls.

(A) Correlation of LGN and hMT+. (B) Correlation of ventral pulvinar and hMT+. (C) Correlation of SC and hMT+. Box plots show Fischer-corrected mean correlation coefficients comparing each participant group ± SEM. Results are shown separately for the intact (solid boxes) and damaged hemispheres (striped boxes), and for left (striped) and right (solid) hemispheres in controls. Underlying data can be found in S3 Data. LGN, lateral geniculate nucleus; SC, superior colliculus; SEM, standard error of the mean.

https://doi.org/10.1371/journal.pbio.2005769.g003

This suggests that the key difference in functional connectivity between blindsight-positive and -negative patients was the presence of a functional connection between hMT+ and LGN in the damaged hemisphere. We performed an additional analysis without regressing out stimulus-evoked responses and found the same results (one-way ANOVA F = 4.3, p = 0.02, df = 2; paired t = 3.4, p = 0.005, df = 12), implying that in patients with blindsight this functional connection was also present during visual function. These analyses, however, have not evaluated whether differences in connectivity were specific to our predefined regions of interest or if they reflect a global process independent of the ROIs and our hypothesis. To address this, we performed an additional whole-brain mixed effects analysis measuring the voxels where neural activity most closely matched the time series of LGN, ventral pulvinar, and hMT+. This technique was used to generate seed region correlation maps, in which the ‘seed’ ROI should necessarily demonstrate a high correlation coefficient represented by a beta of 1 [12]. Co-active regions would similarly possess a high beta, with maps retaining a high spatial resolution since every voxel is tested [13]. As expected, group correlation maps showed a high beta in the ‘seed’ regions, reflecting consistency between subject-specific ROIs and their precise transformation to standard space (Fig 4). When LGN was the ‘seed’ region, control participants also demonstrated a relatively high beta in a small region of the calcarine cortex corresponding to retinotopically active V1 (Fig 4Aiv and 4Civ), reflecting the functional geniculostriate pathway. A similar region of calcarine cortex was co-activated in the undamaged hemisphere of patients when the seed was LGN in the same hemisphere (Fig 4Cv and 4Cvi). For LGN in the damaged hemisphere of both patient groups, there was no demonstrable V1 co-activation (Fig 4Av and 4Avi), likely reflecting the damage to that region (and/or its input). In contrast, blindsight-positive patients showed a relatively high beta in hMT+ of the damaged hemisphere compared to other participant groups and a small region of calcarine cortex in the undamaged side (Fig 4Aii).

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Fig 4. Seed region correlation maps for LGN and ventral pulvinar, in patients and controls.

‘Seed regions’ are (A) LGN in the damaged hemisphere (left in controls), (B) ventral pulvinar in the damaged hemisphere (left in controls), (C) LGN in the undamaged hemisphere (right in controls), (D) ventral pulvinar in the undamaged hemisphere (right in controls). Results are shown separately for controls (left column), blindsight-positive patients (middle column), and blindsight-negative patients (right column). Mixed effects analyses, displayed on average high-resolution structural scans transformed to MNI space (radiological convention). Shaded blue areas are binarized Jülich-defined probabilistic maps of hMT+. LGN, lateral geniculate nucleus; MNI, Montreal Neurological Institute.

https://doi.org/10.1371/journal.pbio.2005769.g004

In contrast to the LGN, when ventral pulvinar was used as the ‘seed’, all groups showed robust co-activity in the SC, an area known to share an important connection with the pulvinar [14,15]. There was also notable connectivity with V1 in the undamaged hemisphere (Fig 4D, [16]) but no major connectivity with hMT+ (Fig 4B).

When hMT+ was used as the ‘seed region’ (Fig 5), controls demonstrated marked functional connectivity throughout the visual cortex, including hMT+ in the opposite hemisphere and V1 in both hemispheres, consistent with previous reports [17]. In subcortical regions, connectivity was also demonstrable in the LGN bilaterally, albeit to a lesser extent (Fig 5A and 5B, left column). No equivalent connectivity was seen in the ventral pulvinar or SC, although these regions were co-activated in both hemispheres of all three participant groups if a slightly lower beta threshold of 0.32 was used (rather than 0.35). This pattern of connectivity was likely to reflect the major visual pathway and its rich network of intra- and interhemispheric connections. A very similar pattern was demonstrated in blindsight-positive patients, except for a relatively low beta in calcarine cortex of the affected hemisphere, reflecting the region of tissue damage. This was less apparent in blindsight-negative patients. Blindsight-negative patients also showed relatively poor connectivity with LGN, particularly in the damaged hemisphere, where ipsilateral geniculate co-activity was not demonstrable even when a low threshold was applied (beta > 0.2).

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Fig 5. Seed region correlation maps for human motion area (hMT+), in patients and controls.

‘Seed region’ is (A) hMT+ in the damaged hemisphere (left in controls) and (B) hMT+ in the undamaged hemisphere (right in controls). Results shown separately for controls (left column), blindsight-positive patients (middle column), and blindsight-negative patients (right column). Upper rows show axial slices through early visual cortex and hMT+; lower rows show coronal slices through LGN. Mixed effects analyses, displayed on average high-resolution structural scans, transformed to MNI space (radiological convention). Shaded green areas are binarized Jülich-defined probabilistic maps of LGN. LGN, lateral geniculate nucleus; MNI, Montreal Neurological Institute.

https://doi.org/10.1371/journal.pbio.2005769.g005

Behavioural–neuroimaging comparisons

Of the eight blindsight-positive patients, only half could discriminate direction of motion above chance. We were interested in whether this subgroup showed greater BOLD activity and/or connectivity compared to blindsight-positive patients who were unable to discriminate motion direction. Indeed, these four patients did show slightly stronger motion responses in hMT+ (0.3 ± SE 0.13 versus 0.1 ± 0.07, t = 1.6, p = 0.2, df = 6), as well as slightly greater LGN/hMT+ functional connectivity (0.27 ± 0.05 versus 0.22 ± SE 0.06, t = 0.6, p = 0.5, df = 6), although the differences were relatively small and nonsignificant.

To investigate the behavioural–neuroimaging association further, we performed a correlation analysis between mean behavioural performance in both experiments and fMRI activity and connectivity across all patients (n = 14). hMT+ motion responses showed a weak but positive correlation with behavioural performance (r = 0.38, p = 0.18), as did LGN–hMT+ connectivity (r = 0.38, p = 0.18). There was also a positive but weaker correlation between behavioural performance and pulvinar–hMT+ (r = 0.29, p = 0.31) or SC–hMT+ connectivity (r = 0.21, p = 0.47). More notable was a significant correlation between behavioural performance and the ratio of blind to sighted fMRI responses in hMT+ (r = 0.62, p = 0.018). In other words, patients with the strongest hMT+ activity for motion in the blind field relative to their sighted field performed best at behavioural assessments of blindsight using the same visual stimuli.

Relating lesion size to behavioural performance and functional connectivity

In order to determine the extent to which blindsight performance and the underlying neural mechanisms relate to the size of the lesions, we quantified the damage in each patient. Lesion size did not differ significantly between blindsight-positive and -negative groups (t = 1.5; df = 12; p = 0.16), although blindsight patients on average had smaller lesions (13,693 mm³ ± 1,825 mm³ SEM) than those without blindsight (21,212 mm³ ± 5355 mm³ SEM). This is illustrated by the summed lesion masks for each patient group on a standard space template (S2D and S2E Fig) and the individual lesion maps in structural space (S4 Fig). Reflecting the small difference in lesion size between the two patient groups, there was a moderate inverse relationship between lesion size and behavioural performance across both tasks (r = −0.48; p = 0.08). However, there was no relationship between lesion size and functional LGN–hMT+ connectivity (r = 0.21, p = 0.5), or hMT+ signal change (r = −0.24, p = 0.4). Thus, lesion size did not appear to be a critical factor in determining neural response.

Aside from lesion size, the degree to which lesions involve hMT+ and its innervating connections would also be critically important. Lesion maps (S4 Fig) suggest that the lesion of one blindsight-negative patient (P12) did encroach upon hMT+, while it appeared intact in the other five blindsight-negative patients, including its afferent white matter. P1 also had a large lesion extending to the posteromedial border of hMT+; however, this should not have impacted upon the subcortical or interhemispheric white matter connections (see S4 Fig for map). Accordingly, we quantified lesion involvement of hMT+ and its surrounding white matter in each patient (S5 Fig). We found no difference when comparing blindsight-positive and -negative patients (t = 1.7, p = 0.11, df = 12), and hMT+ lesion size showed no association with LGN–hMT+ functional connectivity (t = 1.6, p = 0.13, df = 13), hMT+ activity (t = 1.4, p = 0.19, df = 13), or behavioural performance (t = 1.6, p = 0.13, df = 13) in paired t tests using lesion size as the dependent variable.

When recruiting a relatively large group of patients, it is challenging to ensure a completely homogenous lesion pattern. However, any specific differences in lesion size or extent can be informative. P12, whose lesion may have encroached on hMT+, demonstrated weak functional connectivity (outside one SD of the mean) in all three subcortical pathways (pulvinar–hMT+: r = 0.004, SC–hMT+: r = −0.13) and between hMT+ bilaterally (r = 0.33), and this contributed to slightly lower averages for pulvinar, collicular, and interhemispheric (but not LGN) connectivity in blindsight-negative patients (see Fig 2B and Fig 3B and 3C). The variability amongst naturally occurring human V1 lesions has been highlighted as a limitation of human research compared to nonhuman primates [18]. It certainly emphasizes the limitations in carrying out individual case studies, which have predominated in the blindsight literature over the last several decades. However, the heterogeneity in the precise location of structural damage can also be extremely useful and has permitted patients to be classified according to their distinct residual visual performance. By determining which connections and characteristic fMRI responses are consistent amongst blindsight-positive and -negative patients, it may be possible to identify which underlying structures and pathways are involved.

To summarise our results, blindsight-positive patients showed (i) significant neural activity in hMT+ to motion stimuli in the ‘blind’ visual field, (ii) a relatively preserved response to speed in hMT+, and (iii) a correlation between resting BOLD signal in LGN and hMT+ in the damaged hemisphere. None of these findings were demonstrable in patients without blindsight. However, blindsight-negative patients did show functional connectivity between hMT+ and (i) SC and (ii) ventral pulvinar that was no different to healthy controls or patients with blindsight, suggesting that the LGN has a specific functional role in blindsight.

Conclusions

In summary, we identified a functional connection between LGN and hMT+ in patients with blindsight that was absent in patients without blindsight, despite a retained functional connection with ventral pulvinar and SC. This supports a critical functional role for the LGN in human blindsight, and in particular its connection with hMT+, reinforced by recent evidence for an intact anatomical connection between these structures [3]. Our results also revealed that hMT+ does not require intact V1 for a normal speed response, although it does require a functional connection with the LGN. This suggests that the LGN may support motion-selective input to hMT+ in the absence of V1. These results focus on behavioural and neural responses to visual motion, which is a critical component of blindsight (see [66] for recent review). In future work, it will be necessary to explore how such pathways interact with other aspects of blindsight function and whether distinct tasks or stimuli might engage separate mechanisms in the absence of V1.

Participant details

Fourteen patients with adult-onset unilateral V1 damage took part in this study (see S1 Table for details). The location of any additional non-V1 damage is shown in S2D and S2E Fig, and S4 Fig. No patients sustained damage to subcortical structures, including the LGN and pulvinar. Average age at the time of participation was 55.6 years ± 15.2 SD; average time after pathology onset was 49 months (6–252 months). Eight age-matched, healthy participants (50.1 ± 14.6 SD years) served as controls. Written informed consent was obtained from all participants, and ethical approval was provided by the Oxford Research Ethics Committee (Ref B08/H0605/156). All experiments adhered to the Declaration of Helsinki.

Stimuli and experimental design

Visual stimuli were generated using MATLAB (Mathworks) and the Psychophysics Toolbox [67,68]. Each dot was 0.075° in diameter and had an infinite lifetime, with an average dot density of 8 dots/°². Visual stimuli consisted of an aperture of 5° or 8° diameter containing static or coherently moving black dots (luminance 0.5 cd/m^-2) at 4, 8, 20, or 32°/s on a uniform grey background of luminance 50 cd/m^-2. Stimuli were positioned inside a region of dense visual field loss in patients a minimum of 2.5° from fixation (S6 Fig). The extent to which stimuli covered the scotoma (as a percentage) was estimated for each patient from the Perimetry Visual Field Index (VFI), provided in S6 Fig. Stimulus size and position was matched as closely as possible in eight age-matched controls (S2A Fig), with no significant difference in distance between fixation and stimulus edge (x or y coordinates) when comparing patients to controls (mean x = 3.6 ± 0.30 SE patients versus 3.8 ± 0.4 SE controls, t = 0.4, p = 0.7, df = 20; mean y = 0.65 ± 0.66 SE patients versus 0.49 ± 0.68 SE controls, t = 0.2, p = 0.9, df = 20). Stimuli in blindsight-negative patients were also no deeper into the visual field than blindsight-positive patients (stimulus edge 4.0° ± SD 0.9 in blindsight-positive versus 3.5° ± SD 1.2 in blindsight-negative patients, t = 0.9, p = 0.4, df = 12).

To select the stimulus location in patients, we required a perimetry threshold p < 0.005 or < −20dB (which ever was more stringent) for pattern deviation compared to age-matched controls at the stimulus location. This meant that the patients in our study were unable to see even the brightest unattenuated stimuli at that location in the visual field. To verify that we had not inadvertently chosen locations in blindsight-positive patients that were more sensitive than those in blindsight-negative patients, we calculated the average pattern deviation by taking the value closest to the stimulated location using Humphrey Perimetry. The residual visual sensitivity was no different in the two groups (−32.8dB ± SE 0.8 blindsight positive versus −33.2dB ± SE 0.9 blindsight negative, t = 0.3, p = 0.8, df = 12).

Outside the scanner, two behavioural experiments were performed: (1) 2AFC temporal detection and (2) 2AFC direction discrimination (S1 Fig). The experiments were conducted on the same day as scanning, using a 60-Hz CRT monitor at a viewing distance of 68 cm. Throughout behavioural experiments, participants were asked to maintain fixation, with the investigator observing this in real time using an Eyelink 1000 Eye Tracker (SR Research Limited, Ontario, Canada). Anyone making even a small eye movement into their damaged hemifield was given specific instruction not to do so, and it was explained that these data would have to be discarded.

At the start of the experiment, an identical, static test stimulus was used to confirm that patients were unable to see the stimulus at its selected size and location in the visual field. This was done using a predicted aperture size and locus based upon prior perimetry results. Stimulus location had to be restricted to the boundary of the fMRI display, which subtended 23° horizontally and 13° vertically. This influenced whether a 5° or 8° diameter stimulus was chosen, as the stimulus had to stay inside the ‘blind’ field while remaining on screen. The stimulus of choice was an 8° diameter aperture, but if this was not possible, the stimulus was reduced to 5° diameter. If the criteria were unachievable using either stimulus size, the patient was excluded from the study. If the patient was able to see any part of the test stimulus whilst fixating on the central cross, the aperture was repositioned 0.5° deeper into the scotoma (according to the Perimetry report) until the patient could no longer see any part of the stimulus at all. Any trials with eye position more than 1° from fixation were excluded from analysis.

Blindsight definition

The presence or absence of residual visual function (blindsight) was determined according to patients’ ability to detect stimuli above chance, i.e., Experiment 1. Specifically, this was defined as achieving either an average score or a score for individual conditions that was significantly above chance, using a statistical threshold of p < 0.05 and a cumulative binomial distribution. This was an identical method to our previous work, except that the stimulus was moving dots rather than a drifting Gabor [33].

We selected stimulus location based upon perimetry results, as detailed above. Necessarily, this meant that all patients showed the same abnormal visual performance for their test locations. Aside from demonstrating the same visual sensitivity on perimetry, stimuli in blindsight-negative patients were no deeper into the visual field than they were for blindsight-positive patients, suggesting this was not a critical factor for the difference in behavioural performance.

Previous blindsight studies have employed a variety of visual stimuli (moving dots, gratings, moving bars, high luminance targets [31,37,63,69]) and a number of different techniques for assessment, including 2AFC, indirect behavioural performance, saccadic eye movements, and navigational performance (e.g., [5,70–72]). It is also common to target only one retinal location in blindsight testing [31,37,63,69,70]. The critical point for the definition of blindsight is that patients show significant performance despite absent visual capacity in the targeted region of the visual field. We ensured that a conservative threshold was used to target truly ‘blind’ regions of scotoma, which we demonstrated to be no different in patients with or without blindsight. Since we were particularly interested in the role of hMT+, we assessed whether a moving stimulus could be detected without awareness for our definition of blindsight.

Using these criteria, eight patients were categorized as ‘blindsight positive’, as they could detect the stimulus inside their blind hemifield significantly above chance (P2, P3, P5, P8, P10, P11, P13, P14). Of these individuals, four could also discriminate motion direction above chance (Experiment 2; P5, P8, P10, P14). With regard to subjective awareness, only two patients reported any awareness of the stimuli during the experiment (P3 and P10). Both had been completely unaware of static dots in the pre-experiment assessment, in which static dots were positioned at the same coordinates, without fast onset/offset. For moving stimuli, P10 reported knowing that something was there, but was unable to distinguish what it was. P3 also could not describe what she saw, suggesting she had been looking at ‘streaks or shadows’. Both patients were at ceiling on the detection task, but P3 remained at chance on the direction discrimination task.

Of note, five of the patients (P3, P8, P10, P11, P13) took part in a previous fMRI study [33], in which they also demonstrated significant blindsight performance for detection of a drifting Gabor.

Behavioural eye-tracker results

Eye movements were defined as a change in fixation towards the scotoma of 1 degree or more. This would capture all eye movements irrespective of their type, i.e., saccadic, slow drift, nystagmus. The threshold of 1 degree ensured that stimuli could never be directly fixated but would always remain inside the scotoma. Although microsaccades were possible, these would not bring the visual stimulus into the seeing portion of the visual field. This methodology was the same as previous work (Fig 2B in [33]), in which we also provided examples of successfully identified saccades. Seven trials were removed from analysis in Experiment 1 and 2 trials from Experiment 2 due to eye movements of more than 1 degree towards the stimulus calculated from retrospective eye tracker data analysis. At the time of the experiment, a further 6 trials were flagged for exclusion in Experiment 1 and 4 in Experiment 2 due to real-time observation of the experimenter or feedback from the patient. In total, this accounted for 0.93% of trials in Experiment 1 and 0.54% of trials in Experiment 2 that were excluded from analysis due to inappropriate eye position.

fMRI procedure

The same stimuli were viewed during fMRI, presented separately to each hemifield. Stimuli during scanning were presented on a 1,280 × 1,040 resolution monitor at the back of the MRI scanner bore. Participants viewed stimuli via a double mirror mounted on the head coil. When in position, the screen subtended a visual angle of 23° × 13°. The same 5 speed levels were presented separately to each hemifield, representing a 10-condition block design (S2B Fig). For each block, the aperture of moving or stationary black dots appeared for 16 s. Direction coherence was 100%, and dots moved at a constant speed. Angle of drift changed at random every two seconds from a choice of 8 directions. A 10-s rest period followed each block. Throughout all experiments, participants performed a task to maintain fixation by pressing a button every time a central fixation cross changed colour from black to red (S2B Fig). Colour changes occurred at random, lasting 300 ms in duration, and participants were instructed at the start to try not to miss any red crosses. It was emphasised that they must try to maintain fixation throughout and avoid moving their eyes around the screen. An EyeLink 1000 eye tracker (SR Research Limited, Ontario, Canada) was again used to confirm central fixation by recording eye position (see section fMRI eye tracking).

MRI acquisition and preprocessing

Scanning took place using a 3T Siemens Verio MRI scanner at the Functional Magnetic Resonance Imaging Centre of the Brain (FMRIB, University of Oxford). At the start of each sequence, magnetisation was allowed to reach a steady state by discarding the first five volumes, an automated feature of the scanner. T2*-weighted EPI volumes covered 34 sequential 3-mm slices (repetition time, TR 2000 ms; echo time, TE 30 ms) with three runs, each lasting 260 s. In a single session lasting 13.2 min, 396 functional volumes were acquired. For one participant (P4) we collected one additional session of fMRI data. For three patients, we collected four runs of data (i.e., 526 volumes, P1, P2, P3). For one patient (P7) and one control, we only collected two runs of data, i.e., 266 volumes (8.9 min).

We also acquired a high-resolution (1 mm³) whole-head T1-weighted MPRAGE anatomical image (TE 4.68 ms; TR 2040 ms; flip angle, 8°) and a field map (TE1, 5.19 ms; TE2, 7.65 ms; 2 mm3) for each participant.

Quantification and statistical analysis

fMRI preprocessing and statistical analyses were carried out using tools from FMRIB's Software Library (FSL, www.fmrib.ox.ac.uk/fsl). Non-brain tissue was excluded from analysis using the Brain Extraction Tool (BET) [73], motion correction was carried out using MCFLIRT [74], and images were corrected for distortion using field maps. For cortical ROIs and group contrast maps, spatial smoothing used a Gaussian kernel of FWHM 5 mm, and high-pass temporal filtering (Gaussian-weighted least-squares straight line fitting, with sigma = 13.0 s) was applied. For all subcortical ROI analyses, no spatial smoothing was applied to ensure that signals were not contaminated with adjacent structures. Functional images were registered to high-resolution structural scans using FLIRT [75] and to a standard Montreal Neurological Institute (MNI) brain template using FLIRT and FNIRT [76]. This enabled us to transform anatomical and probabilistic regions of interest into functional space for analysis (see S3 Fig for an illustration of subcortical ROIs in functional, structural, and standard space for each participant).

fMRI eye tracking

Eye movements during fMRI can be a legitimate concern when considering results for visual stimulation inside a scotoma. In this study, three main lines of evidence suggest that this was not a problem and could not have accounted for the results. First, concurrent eye movement data was collected on most patients using an eye tracker positioned at the base of the MRI bore (n = 10). All of these patients underwent successful eye-tracker calibration, with accurate data throughout fMRI runs. For this group of patients, the mean number of eye movements was 5.8 ± 3.5 SEM, defined as a movement of 1.5 degrees or more towards the scotoma. This accounted for <0.3% of the scan duration, suggesting that any effects on the results are likely to be negligible. To confirm this, when scanner volumes corresponding to eye movements were regressed out of analyses, the results remained unchanged (r > 0.99). For the patients without eye movement data, there had been difficulty either with calibration due to their dense field loss or with visualisation due to the presence of corrective acuity lenses. In those situations, direct visualisation was used via video recording of the pupil to observe any overt eye movements during the experiment.

Second, participants performed over 90% on a concurrent behavioural task that required fixation throughout the experiment (S2C Fig). Brief colour changes of the fixation cross (300-ms duration) occurred at frequent and random intervals, and participants were given a window of 1 s to press a button connected to the stimulus computer via a parallel port, being specifically instructed not to miss any red crosses or move their eyes around the screen. In addition, before the fMRI scan, all participants took part in behavioural testing lasting at least 60 min, focussed on their damaged region of vision. Participants became very experienced at maintaining fixation during this assessment.

Regions of interest

hMT+ masks were derived from probabilistic maps (Jülich atlas implemented in FSL) [77,78]. These were transformed into functional space for patients and controls to ensure consistency between participant groups. V1 masks in controls and in the undamaged hemisphere of patients were functionally defined so that they corresponded to stimulated regions of calcarine cortex. In native space, average hMT+ ROI volume was 94.8 ± 35.2 SD voxels in patients and 100.9 ± 42.0 SD voxels in controls (t = 0.5, p = 0.6, df = 42). Average V1 ROI volume was 16.2 ± 7.5 SD voxels in patients (undamaged hemisphere) and 24.4 ± 7.1 SD voxels in controls (averaged across hemispheres), the small volume reflective of the small 5°- or 8°-diameter stimulus used.

For the LGN and SC, binary masks were created by manual inspection and drawing over the anatomical T1-weighted images [79], using a radiological brain atlas to aid identification of landmarks (See S3 Fig for masks in all patients). The average LGN volume in patients measured 248 mm³ in the right and 246 mm³ in the left. In controls, average LGN volume was 240 mm³ in the right and 239 mm³ in the left. The average SC volume in patients was 195mm³ in the left and 177 mm³ in the right. For the ventral pulvinar, binary masks were created in MNI152 standard space according to the description of Arcaro and colleagues [50]. It was possible to visualise the nucleus as a region of low T1 intensity relative to surrounding tissue in the posterior most part of the thalamus. Masks were transformed to anatomical and functional space for each participant and were manually inspected to ensure accuracy (S3 Fig). Average ventral pulvinar volume was 385 mm³ in the left and 365 mm³ in the right. There were no significant differences in ROI volume between blindsight-positive and -negative patients.

Lesion masks

Lesion masks were drawn manually in structural space (S4 Fig). For S2D and S2E Fig, these were nonlinearly transformed to standard space and binarised before being summed. To assess whether lesions encroached upon hMT+ and/or its surrounding white matter, we created subject-specific cuboidal ROIs that were centred on the ‘centre of gravity’ of the ipsilesional hMT+ ROI in structural space. The isotropic cubes measured 40 × 40 × 40 mm³, thus containing 64,000 1-mm³ voxels (see S5A and S5B Fig for examples in P6 and P12). We superimposed the binarized lesion mask over the cuboidal mask and counted the number of voxels that overlapped (red voxels in S5B Fig). The voxel counts are shown in the table in S5 Fig.

fMRI analysis

All graphs, signal change calculations, and correlation statistics were calculated using data from participants’ native space.

For region of interest analysis, each experimental condition (e.g., left hemifield, 8°/s speed) was entered into the general linear model as a separate explanatory variable and was contrasted against the baseline fixation task to generate contrast of parameter estimates (COPEs) for each condition in every voxel. Signal change was then extracted from regions of interest within functional-space for each individual. The percentage of signal change was calculated by scaling the COPE by the peak-peak height of the regressor and dividing by the mean over time. These measures were averaged across participants to generate group plots for signal change as a function of the condition under investigation and were used in all correlation and regression analyses.

For whole-brain group analyses (Figs 4 and 5), it was necessary to align patient brains to a uniform pathological template with lesions located in the same ‘left’ hemisphere corresponding to a ‘right-sided’ visual field deficit. This required that the structural and functional images of three patients (P3, P5, P12) be flipped in the horizontal plane. All activation coordinates and images were in MNI space, with beta values displayed on mean structural images for the group transformed to standard space.

For the whole time series analyses, a value for residual BOLD signal in the ROI was obtained for each volume, and this was plotted against time. This was done separately for each participant and performed in functional space.

Brain imaging maps

As control participants demonstrated slightly different hMT+ localization in left and right hemispheres, we decided to show hMT+ group maps for left and right V1 lesions separately. This generated a group size of n = 6 for blindsight-positive patients with left V1 lesions (Fig 1Ai), n = 5 for blindsight-negative patients with left V1 lesions (Fig 1Bi), n = 2 for blindsight positive-patients with right V1 lesions (Fig 1Aii), and n = 1 for blindsight-negative patients with a right V1 lesion (Fig 1Bii). Mixed effects analyses were used for all group analyses where n > 3. A statistical threshold of p < 0.001 uncorrected was used to test for significance within V1 and extrastriate cortex, for which there were a priori hypotheses. Elsewhere, correction for multiple comparisons was made using a cluster threshold of p < 0.05 unless otherwise stated.

Quantitative statistics

Statistical tests to quantify differences in functional activity and co-activation between ROIs or participant groups were implemented in Excel or MATLAB. For overall hMT+ percent BOLD responses (Fig 1, bar charts), activity was averaged across all five motion speeds (0–32°/s). Two statistical analyses were then performed: (i) mean activity comparing sighted and blind hemifield (and hemisphere) used a paired t test and (ii) mean activity compared to baseline using a one-sample t test versus zero. A two-way ANOVA was also used to assess the effect of participant group and speed on blind hemifield responses (right hemifield in controls) and separately on sighted hemifield responses (left hemifield in controls).

In fMRI time series correlations, it is known that task conditions can influence intrinsic temporal correlations (e.g., see [12,17]). To ensure that correlations only reflected resting block activity, we used the residuals timeseries for ROIs once stimulus responses had been regressed out. This allowed us to determine resting ROI1 versus ROI2 correlation analyses for each participant.

For correlation analyses, two main statistical methods were used. For correlations between participant groups, a Pearson correlation coefficient was derived from mean activity in ROI1 versus ROI2 at each level of speed, i.e., n = 5. For correlations between ROIs within participant groups, a Pearson correlation coefficient was determined separately for each participant (activity in ROI1 versus ROI2, at each level of speed). We then calculated weighted averages of r coefficients for the group, using a Fischer transformation to approximate correlations to a normally distributed measure (Figs 2 and 3). A significant effect of group was determined by performing a one-way ANOVA. Pairwise comparisons between participant groups or ROIs were then calculated using post-hoc t tests. One-sample t tests were used to compare r coefficients to zero. Whenever activity was compared in the same participant, a paired correlation analysis was performed.

Seed region correlation maps

For each participant, the raw signal time series for the seed ROI was entered into the model as an explanatory variable. This was applied to the filtered and motion corrected whole brain timecourse. Stimulus conditions were also entered as regressors so that the model would better describe the data. Since the model was identical to the ROI time series, the parameter estimate was always 1 in the seed region. Any participant with voxels outside the seed region with a parameter estimate >1.5 were excluded from analysis, as these results were likely to be driven by noise. This was only a problem when using subcortical structures as seeds, as these are small regions with relatively weak signal that are more susceptible to artefact. This led to the exclusion of three participants from subcortical seed analyses (both hemispheres); two were blindsight positive (P5, P10), and one was blindsight negative (P7). Corroborating this, the raw LGN signal range in those three participants was significantly greater than the other 11 patients (66.6 ± SD 10.5 versus 39.6 ± SD 6.07, t = 2.2, p = 0.04) and controls (30.6 ± 1.9, t = 5.2, p < 0.001, df = 12).

To generate seed region correlation maps (Figs 4 and 5), the COPEs for each included participant were entered into a higher-level mixed effects analysis, and output parameter estimate (beta) maps were used to represent seed region correlation maps. This was performed separately for each participant group and for each seed region. The resulting maps were not intended to determine statistical significance but to allow visual inspection of the results from the separate groups. Visualisation thresholds were based upon control participants, and an optimal cutoff was used to display correlations in either V1, visual subcortex, and/or hMT+ without excess background noise. The same thresholds were applied to all participant groups. For LGN-oriented maps (Fig 5Aiv–5Avi and 5Biv–5Bvi), although not shown, no other subcortical regions showed equivalent beta levels, although ventral pulvinar was co-activated in both hemispheres of all participant groups when using a reduced threshold of 0.32.