WO2020242888A1 - Combined brain stimulation and visual training to potentiate visual learning and speed up recovery after brain damage - Google Patents

Abstract

In period as short at 10 days, visual training coupled with transcranial random noise stimulation (tRNS) over visual areas causes dramatic improvements in visual motion perception. Relative to control conditions and anodal stimulation, tRNS-enhanced learning is at least twice as fast, and, importantly, it persists at least 6 months after the end of training and stimulation. Notably, tRNS also boosted learning in patients with cortical blindness, leading to recovery of motion processing in the blind field after just 10 days of training, a period too short to elicit enhancements with training alone. The results reveal a remarkable enhancement of the capacity for long-lasting plastic and restorative changes when a neuromodulatory intervention is coupled with visual training.

Description

COMBINED BRAIN STIMULATION AND VISUAL TRAINING TO POTENTIATE VISUAL LEARNING AND SPEED UP RECOVERY AFTER BRAIN DAMAGE

GOVERNMENT RIGHTS

[0001] This invention was made with Government support under EY027314 and EY021209 awarded by the National Institutes of Health. The Government has certain rights in the invention.

FIELD

[0002] This patent specification relates to equipment and methods for improving visual function with training, especially after brain damage.

BACKGROUND

[0003] This patent specification cites references identified by author and date in parenthesis throughout the text, which are more fully identified at the end of the Detailed Description. All the cited references and U.S. Patent No. 7,549,743 (which names an inventor also named in the subject patent application) are hereby incorporated by reference in this patent application.

[0004] The human brain changes throughout life (Gilbert & Li, 2012; Liat al. , 2004).

Visual training is a known tool for inducing such changes, improving sensory performance in healthy adults (Dosher & Lu, 2017; Li, 2016; Sagi, 201 1 ; Wang et al., 2016); and in various clinical populations (Deveau et al., 2013; Melnick et al., 2016; Nyquist et al.,

2016), a phenomenon referred to as visual perceptual learning (VPL). The specific role of different cortical visual areas during VPL is debated, with several mechanisms likely contributing to learning. For instance, neurophysiological studies suggest that perceptual learning selectively modifies the signal strength of neurons responding to relevant stimulus features, while concurrently suppressing the activity of task irrelevant information (Yan et al. , 2014). Other studies suggest that learning stems from better read-out mechanisms in higher-level visual areas (Law & Gold, 2009). Psychophysical studies have suggested that boosting sub-threshold, stimulus-related cortical activity can promote perceptual learning (Seitz & Dinse, 2007), with attention and reinforcement (provided by reward) increasing stimulus-related neuronal activity and facilitating learning (Ahissar, 2001 ; Pascucci et al., 2015; Seitz & Watanabe, 2005).

[0005] In parallel, increasing effort is being directed at applying visual perceptual training approaches to rehabilitate patients with various types of vision loss, including cortical blindness (CB), amblyopia (Huang et al., 2008; Levi & Li, 2009; Li et al., 2013; Li et al., 201 1 ; Polat et al., 2004), macular degeneration (Baker et al., 2008; Kwon et al., 2012; Liu et al., 2007), myopia (Camilleri et al., 2014; Tan & Fong, 2008) and even keratoconus (Sabesan et al., 2017). Two factors that limit practical applications of VPL are: 1 ) the long duration of training usually required for adequate performance enhancement (e.g. in chronic CB patients, Huxlin et al., 2009), and 2) persistence of visual learning and/or recovered abilities after training ends. Non-invasive brain stimulation coupled with perceptual training has emerged as a potentially promising solution for both of these limitations in healthy adults (Cappelletti et al., 2013; Chesters et al., 2017; Falcone et al., 2012; Fertonani et al., 201 1 ; Sehm et al., 2013; Snowball et al., 2013; Zoefel & Davis, 2017).

[0006] In CB, a form of vision loss caused by primary visual cortex (V1 ) damage, one approach to help recover vision involves training on motion integration tasks in the blind field (Cavanaugh & Huxlin, 2017; Das et al. , 2014; Huxlin et al. , 2009; Vaina et al. , 2014). However, the training required to restore normal performance on this task in the blind field of CB patients typically involves months of daily practice and is difficult to attain and sustain.

SUMMARY

[0007] A new approach described in this patent specification achieves significant results in weeks and as little in 10 day rather than in several months as in known approaches. Moreover, the benefits of the new approach persist over months, such as 6 months or more. Still further, the new approach has been found to benefit neurotypical subjects and, importantly, cortically blind patients due to stroke and, notably, due to trauma.

[0008] According to one embodiment, the new approach comprises a method of promoting visual recovery of a patient with cortical blindness that includes: displaying to the patient a sequence of visual stimuli with different directionality orientation or other parameters while concurrently applying to the patient transcranial random electrical noise; detecting the patient’s response to said sequence of stimuli; and computer processing results related to the stimuli and the patent’s responses thereto to assess visual recovery.

[0009] The displaying and detecting can repeated multiple times in each of multiple sessions over a period as short as 10 days to achieve a measurable visual recovery or can be extended over a longer time period to achieve additional degrees of visual recovery. The visual recovery can persist over a period of at least 6 months. The cortical blindness can be due to a stroke and, notably, can be due to trauma other than stroke.

[0010] The transcranial electrical noise can be applied through electrodes at positions 01 and 02 of the International 10-20 system of locations of scalp EEG electrodes and can have frequency content in the range of 100-700 Hz and use current less than 2 mA. Coherence noise can be inserted in said stimuli until reaching

directionality discrimination of approximately 50% to thereby establish a starting level for the patient from which to measure visual recovery. The patient can be provided with an indication of correctly recognizing directionality or other aspects of a displayed visual stimulus. The patient can view the stimuli in a circular aperture as small as 10 degrees, or smaller or bigger.

[0011] The patient can be presented with 200-400 of said visual stimuli in a session over a time period as short as 10-30 minutes, or shorter or longer, for example in one session per day over a period as short as two weeks.

[0012] In another embodiment, the new approach uses a system for promoting visual recovery of a patient with cortical blindness that includes: a display configured to present to the patient a sequence of visual stimuli with different directionality or other parameters; a source of transcranial electrical noise configured to apply said noise to the patient concurrently with presenting said visual stimuli to the patient; an interface device configured to detect the patient’s response to said of stimuli; and a computer processor configured to process results related to the stimuli and the patent’s responses thereto to assess visual recovery. The source can apply transcranial electrical noise with frequency content in the range of 100-700 Hz. The noise can be essentially random and can be applied at current less than 2 mA. The display can be configured to present 200-400 of said visual stimuli to the patient in a session over a time period of 10-30 minutes.

[0013] Transcranial random noise stimulation (tRNS) has been shown to enhance cortical excitability in the motor cortex (Terney et al. , 2008) and subsequent studies have reported that it can improve perceptual functions when delivered over the visual cortex (Campana et al., 2014; Pirulli et al., 2013; Tyler et al., 2018; van der Groen et al., 2018), while the effect of a-tDCS is less clear (Miniussi & Ruzzoli, 2013; Ding et al., 2013).

[0014]

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To further clarify the above and other advantages and features of the subject matter of this patent specification, specific examples of embodiments thereof are illustrated in the appended drawings. It should be appreciated that elements or components illustrated in one figure can be used in place of comparable or similar elements or components illustrated in another, and that these drawings depict only illustrative embodiments and are therefore not to be considered limiting of the scope of this patent specification or the appended claims. The subject matter hereof will be described and explained with additional specificity and detail through the use of the accompanying drawings in which: [0016] Fig. 1 illustrates an experimental procedure and behavior task relevant to training with and without brain stimulation.

[0017] Fig. 2 illustrates examples of psychometric data fits for trials of visual stimulation coupled with transcranial random noise.

[0018] Fig. 3 illustrates neuroradiolocal images and visual perimetric performance illustrating the visual deficit of cortically blind (CB) patients tested.

[0019] Fig. 4 illustrates effects of brain stimulation on perceptual learning in visually intact subjects.

[0020] Fig. 5 illustrates effects of brain stimulation on perceptual learning in cortically blind (CB) patients.

[0021] Fig. 6 schematically illustrates an example of equipment suitable for brain stimulation training according to some embodiments.

DETAILED DESCRIPTION

[0022] A detailed description of examples of preferred embodiments is provided below. While several embodiments are described, the new subject matter described in this patent specification is not limited to any one embodiment or combination of embodiments described herein, but instead encompasses numerous alternatives, modifications, and equivalents. In addition, while numerous specific details are set forth in the following description in order to provide a thorough understanding, some embodiments can be practiced without some or all these details. Moreover, for the purpose of clarity, certain technical material that is known in the related art has not been described in detail in order to avoid unnecessarily obscuring the new subject matter described herein. It should be clear that individual features of one or several of the specific embodiments described herein can be used in combination with features of other described embodiments or with other features. Further, like reference numbers and designations in the various drawings indicate like elements.

[0023] The experiments described below address whether brain stimulation could improve visual learning when administered during training in visually intact humans and whether these improvements persist. They further address the translational potential of this approach to promote visual recovery in chronic cortically blind (CB) patients, whether from stroke or trauma. Early visual areas of the brain were targeted for stimulation because of their apparent role in mediating training-induced visual plasticity in physiological, imaging and brain stimulation studies (Barbot et al., 2018; Camilleri et al. , 2016; Gratton et al., 2017; Kang et al., 2014; Rokem & Silver, 2010; Schwartz et al., 2002; Yang & Maunsell, 2003). Results from the present study show that targeting other brain areas with the same stimulation (e.g. parietal cortex) does not result in enhancements of visual learning. However, it is likely that targeting other brain areas can enhance learning of other (non-visual) functions.

[0024] This patent specification first describes an illustrative experiment of tRNS- mediated learning in healthy participants and then describes an illustrative experiment of tRNS-mediated visual recovery in patients with cortical blindness and discusses results. Of course, these experiments are only examples of the new approach, which is not limited to the specific experimental parameters. In fact, additional other work has confirmed that tRNS enhances visual discrimination of static, oriented discrimination tasks. Gabors (Melnick et al., in preparation; Melnick M., Park W.J., Kroom S., Chen S., Busza A., Battelli L, Huxlin K.R. and Tadin D. (2018). Transcranial random noise stimulation over early visual cortex improves processing of noisy visual stimuli. (VSS Meeting (poster), Journal of Vision 18 (10), 766-766.) These results show that tRNS is effective not only for visual motion discrimination, but also for static, visual form discrimination tasks (relevant for object and face recognition, as well as for reading).

[0025] Experiment 1: tRNS-mediated learning in healthy participants.

[0026] Subjects. This experiment involved a total of 45 subjects (mean age: 19.9 years old; range: 19-36; 32 females and 13 males). All subjects were right-handed, neurologically normal, with normal or corrected-to-normal vision.

[0027] Study design. The subjects were randomly assigned to one of five groups. This included two experimental groups. In the first group, tRNS was delivered over early visual areas (electrodes positioned bilaterally, centered over 01 and 02 of the EEG system coordinates, for the left and right hemisphere respectively). In the second group, anodal transcranial direct current stimulation (a-tDCS) was delivered over the occipital cortex (the anode and the cathode were positioned over Oz and Cz, respectively). Bilateral occipital montage for the tRNS condition could match the positioning of other successful studies that found improved performance with tRNS and likely increased excitability in the visual cortex (Romanska et al. , 2015; Herpich et al. , 2018) particularly with motion discrimination tasks (van der Groen et al., 2018 ). However, Experiment 1 used unilateral montage for the a-tDCS condition, the optimal montage to increase cortical excitability with anodal stimulation of the visual cortex (Antal et al., 2004). Stimulation was concurrent with the training task. There were also three control groups: a sham control, a no stimulation control, and an active control where bilateral tRNS was applied over parietal cortex (over P3 and P4, regions likely involved in but not critical for global motion discrimination (Battelli et al. , 2001 ; Greenlee & Smith, 1997)). Over 10 days, all subjects were trained to discriminate the left or right, global direction of random dot motion stimuli (350 trials/session/day). Day 1 was considered the pre-training session, while Day 10 was used as the post-training session, as illustrated in Fig. 1. Finally, a long-term follow-up was performed six months after the post-training session. During this follow-up, participants repeated the behavioral baseline tests. Importantly, no stimulation was delivered at this time.

[0028] As illustrated in part A of Fig. 1 , all participants were tested on a motion integration task to determine baseline performance in the first session (Day 1 ). They then underwent 9 days of training with or without online brain stimulation (Days 2 -10). Behavioral testing was performed again 6 months after the end of training/stimulation (Post 6 Months Follow Up). As illustrated in part B of Fig. 1 , an example of stimuli with different direction ranges (0 degrees, 90 degrees, and 360 degrees) was used for the motion integration task. These stimuli had target dots embedded in noise dots that are not shown in Fig. 1 for clarity purposes (see Methods section for details). Normalized direction range (NDR) 0 indicates fully random motion directions (360 degrees range), while NDR = 100 indicates all signal dots moving in one direction (0 degrees range). As illustrated in part C of Fig. 1 , a two-alternative forced choice, adaptive staircase procedure was used to estimate the largest range of dot directions that subjects could correctly integrate to discriminate the global motion direction (leftward vs. rightward). A trial sequence was used for training and to measure left-right motion discrimination thresholds, in which the subjects were first asked to fixate the central cross for 1000ms, immediately followed by a tone signaling the appearance of the stimulus, which was presented for 500ms. Subjects had to indicate the perceived global motion direction by pressing the left or right arrow key on the keyboard.

[0029] Apparatus and procedures. For subjects who underwent brain stimulation, all experiments took place in the same room, under the same light and noise conditions, and with the same apparatus. During each session, participants were positioned on a chinrest- forehead bar combination to stabilize their heads, and to place their eyes 57 cm from the stimulus-presenting computer monitor. Visual stimuli were generated on a MacBook Pro running software based on the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997) in Matlab ( MathWorks ). Stimuli were presented on a linearized SensEye 3 LED 24 Inch ( BenQ ) monitor with a refresh rate of 120 Hz, and it was luminance-calibrated with gamma=1 using a professional monitor calibrator (Datacolor Spyder 5). Eye fixation for all subjects was controlled in real time using an EyeLink 1000 Plus Eye Tracking System {SR Research Ltd., Canada) whose infrared camera monitored the pupil center and corneal reflection of the left eye. Limits were set so that if the participant’s eye moved more than 1 .5 degrees in any direction away from the fixation spot during stimulus presentation, loud tones sounded and the currently displayed trial was aborted and excluded from the final analysis.

[0030] Global direction discrimination testing and training. The first measured parameter was direction range (DR) thresholds for left-right motion discrimination of circular stimuli that contained a limited percentage of signal dots (Newsome & Pare, 1988, Huxlin & Pasternak, 2004; Levi et al. , 2015) that were centered at [-5, 5] degrees in the visual periphery. To match initial task difficulty across observers, motion coherence (Newsome & Pare, 1988) was calibrated for each subject individually, as previously reported (Levi et al. , 2015). The motion coherence of the stimulus was chosen based on preliminary testing aimed to identify a motion signal level that allowed participants to perform the discrimination task just above chance (50% correct). For all but 3 subjects, random dot stimuli contained 40% motion signal. Three subjects were trained with a stimulus containing 30% coherent motion. Adding coherence noise to the stimuli ensured at all subjects started at about the same difficulty level, and, more importantly, allowed plenty of room for improvement for the healthy participants. Once a motion signal level was selected for each participant, the task used a QUEST adaptive staircase (Watson & Pelli, 1983) to estimate the broadest distribution of dot directions that subjects could correctly integrate to discriminate the global direction of motion as leftward or rightward. During training, task difficulty was adaptively modulated by adjusting direction range of signal dots (Huxlin & Pasternak, 2004) using twelve randomly interleaved 25-trial Quest staircases in each daily session. These adaptive Quest staircases were used to ensure that stimuli presented to participants are around their threshold performance. Because 300 trials per session were collected, it was practical to fit full psychometric functions to the data. Here, single trial data were fit with the Weibull function (where percent correct = 1 -(1 -chance) ^* exp(-(k^*x/threshold)^slope), and k = (-log((1 -0.82)/(1 -chance)))^(1/slope)). As customary, the 82% threshold criterion was used as that is close to the steepest point of the Weibull function.

[0031] Fig. 2 illustrates the quality of data fits. This Fig. shows data for an example subject. Thresholds corresponding to 82% correct were taken from these estimated Weibull functions and are reported as normalized direction range (NDR) thresholds, such that an NDR of 0% equals fully random motion (360 degrees range of dot directions) and NDR of 100% indicates all signal dots moving in one direction (0 degrees range). The random dot stimuli were presented within a circular aperture 5 degrees in diameter at a density of 2.6 dots/degree². Each dot had a diameter of 0.06 degrees and moved at a speed of 10 degrees/s with a lifetime of 250ms. Stimulus duration was 500ms. Each participant started training with direction range in the random dot stimulus set to 0°.

[0032] The information Fig. 2 shows results from the tRNS subject whose data was closest to the average of all 9 tRNS subjects (going from 94% NDR (poor performance) to 30% NDR (good performance) over 10 sessions). For each session (1 -10), the symbols that are along the top and bottom horizontal lines show all individual trial data (correct trials at the top and incorrect trials are the bottom of each panel). Psychometric function fits are shown by the curved lines. For illustration purposes, individual trials are binned into ten 30-trial bins (red circles). The trial sequence was as follows: participants were asked to fixate on a central cross for 1000ms, immediately followed by a tone signaling the appearance of the stimulus, which was presented for 500ms. Once the stimulus disappeared, participants had to indicate the perceived global direction of motion by pressing the left or right arrow keys on the keyboard. The two motion directions (leftward and rightward) were randomized across trials. Auditory feedback was provided indicating the correctness of the response on each trial.

[0033] During training, stimuli were presented monocularly to the left eye for 10 days (one session/day from Monday to Friday, for 2 consecutive weeks, as seen in Fig. 1 ) while subjects received either active, sham or no stimulation. Monocular presentation was chosen to closely match the procedure used by Huxlin and colleagues (Huxlin et al. , 2009) on patients and by Levi et al. (2015) on healthy participants using the same visual stimuli. Subjects performed 350 trials/day for a total of 3500 trials by the end of the 2 weeks of training. The total duration of the daily training session for each group was set to last about 20 min.

[0034] Stimulation Protocols. Transcranial direct current stimulation (tDCS) and transcranial random noise stimulation (tRNS) were delivered using a battery-driven stimulator (DC-Stimulator-Plus, NeuroConn GmbH, llmenau, Germany) through a pair of saline-soaked conductive rubber electrodes (35cm²). Each subject was randomly assigned to one of the 5 stimulation groups as described earlier ( Study Design). The electrodes were bilaterally placed over the target areas identified following the 10-20 EEG reference system. The subjects wore a Lycra swimmers’ cap to keep electrode in place, and the skin and hair between the electrodes were completely dry, otherwise preventing the current from reaching other parts of the brain. The intensity of stimulation was set to 1 .0mA and was delivered for 20 minutes with a fade in/out period of 20 seconds. For the a-tDCS group, the polarity of the active electrode was anodal. For the tRNS condition, the random noise stimulation was applied with a 0mA offset at frequencies of alternating current ranging from 101 to 640 Hz (high frequency tRNS). For the Sham stimulation group, the stimulation (using the same electrode montage as in the tRNS condition) was shut down after 20 seconds. At the end of each session, all subjects were asked to fill out a questionnaire about potential discomfort or any unusual sensation they experienced during the stimulation. Only minor side-effects were reported by the tDCS group (2 subjects reported slight itching under the electrode, 1 subject reported a slight subjective temperature increase under the electrode), whereas none of the tRNS group participants reported any sensation of being stimulated.

[0035] Data Analysis. The Shapiro-Wilk test was used to control for the normality of data distribution. Data Sphericity was addressed using Maulchy’s test, and Greenhouse-Geisser correction was used in case of non-sphericity of the data. Levene’s test was used to address the assumption of equality of variances. P-values were considered significant for values of < 0.05. To correct for multiple comparisons in post- hoc testing, Tukey HSD correction was used. The effect sizes are reported as the partial Eta-squared (rp²) values.

[0036] Experiment 2: tRNS-mediated visual recovery in patients with cortical blindness.

[0037] Cortically-blind patients. Eleven patients participated in the study. The patients were recruited 2.5-108 months after damage to their early visual areas (see Table 1 below, median post-stroke time: 15 months). Both the location of damage and the homonymous visual defects were confirmed by neurological reports, neuro-radiological exams and automated visual perimetry (Optopol PTS 1000 Visual Field, Canon or Humphrey Field Analyzer HFA II 750, Carl Zeiss Meditec). Ten of the patients (RNS1 -2, Sham 1 -2, U1 -6) suffered from stroke involving the territory of the posterior cerebral artery, as confirmed by radiological examinations and reports (see Tablel and Figure 3). One patient (RNS3) suffered from traumatic brain injury. Although from the neuro-radiological report, V1 was not directly affected by trauma, there were indications of visual fields defects and his visual perimetry showed a clear, homonymous, bilateral upper quadrantanopia; hence, he was enrolled in the training procedure (note that data for each patient were computed and shown individually). None of the patients had history or evidence of degenerative or psychiatric disorders. All participants were right-handed, with normal or corrected-to-normal visual acuity and none exhibited visual or other forms of neglect, as determined by neurological examination.

[0038] Table 1 below shows demographic data for the CB patients. Visual fields defects were assessed with automated perimetry. The last column indicates the time between stroke and in-lab testing. Patient RNS3 had a traumatic brain injury, while all others suffered strokes.

[0039] Fig. 3 illustrates neuroradiological images and visual perimetries of CB patients. All patients sustained damage of early visual areas or the optic radiations resulting in homonymous visual field defects as shown by the visual field perimetries, next to each brain image. Within the perimetry images (patients in top two rows: Shaml , Sham2, RNS1 , RNS2 and RNS3): the darker (originally red) marks and shading areas indicate the patients’ blind field. Bottom two rows: Humphrey visual field maps for each of the unstimulated patient (U1 -6), with superimposed shading indicating the blind field and numbers indicating the luminance detection sensitivity in the given position expressed in decibels. For all patients, the small circles (originally blue) indicate the training location and size (see“Global direction discrimination testing and training in patients” in the Methods section for details). Radiological images were not available for patients RNS3 and U6.

[0040] Study design. All cortically blind patients underwent 10 days of training, following the same Day 1 to Day 10 procedures as neurotypical subjects (Fig. 1 ). Five patients (in one country) were randomly assigned to one of two experimental groups: three patients received tRNS over early visual areas during training, whereas two patients received sham stimulation during training, with electrodes placed in the same locations as for tRNS. The remaining patients (in another country) underwent global direction discrimination training without brain stimulation.

[0041] Apparatus and procedures. The same apparatus as in Experiment 1 was used, except that stimuli were presented on a CRT monitor (HP 7217 A, 48.5 ^c 31 .5 cm, 1024x640 pixels resolution, 120 Hz frame rate) calibrated with a ColorCal II automatic calibration system (Cambridge Research Systems), for the patients who underwent global direction discrimination training without brain stimulation. Eye fixation for all patients was controlled in real time using an EyeLink 1000 Plus Eye Tracking System (SR Research Ltd., Canada).

[0042] Global direction discrimination testing and training in patients. First, motion discrimination performance in each patient was spatially mapped to identify a blind field location where training should be performed. The same task described in Experiment 1 for neurotypical subjects was used, with two adjustments intended to make the task easier for the patients: coherence and NDR were set to 100% (the easiest possible settings) and the number of trials per training day was lowered to 250. Fixation was enforced, as in visually intact subjects, and each trial was initiated by fixation of a small circle in the center of the screen. During mapping, stimuli were first presented in the intact field, at locations close to the border with the blind visual field, and patients performed 100 trials of the global direction discrimination task per location. This allowed ensuring that each patient understood task demands and to assess normal baseline performance on an individual basis. Stimulus location was then moved progressively into the blind field, with 100 trials of the global direction discrimination task performed at each location, until global motion direction discrimination dropped below chance (Huxlin et al., 2009); this was selected as the training location, and care was taken to ensure that it was situated fully inside the perimetrically-defined blind field (small (originally blue) circles in Figure 3).

[0043] For comparison purposes, data were included from six patients trained in the Huxlin lab at the Flaum Eye Institute of the University of Rochester (USA) with the same behavioral protocol, but without any brain stimulation. Five out of six unstimulated patients, trained with 300 trials per training day, while one trained with 225 trials/day. Thus, on average, unstimulated patients completed 15% more trials than tRNS/sham- stimulated patients, which makes them a conservative comparison group.

[0044] On Day 1 , all patients performed considerably worse on global motion integration in their blind field compared to neurotypical subjects, even at the easiest stimulus level (100% coherence, NDR = 100%). On Day 10, no patient performed better than 85% correct at NDR of 100% (i.e. , with all dots going in the same direction). Specifically, at 100% coherence, patients’ global motion direction range (NDR) threshold averaged 232 ±24.3 degrees in their intact hemifield and 9.25 ±15.91 degrees in their blind field (paired Student’s t test, p=8.07 x105), indicating that when the global motion was higher than 9 degrees around the right or leftward vector none of the subjects were able to discriminate the global direction of motion of the dot stimuli. Thus, given the range of performance by patients, this specification reports percent correct at 100% NDR as the measure of performance. This allows avoiding issues with noisy thresholds estimates for sub-threshold performance, while still retaining a sufficient dynamic range to capture training-induced improvements in performance.

[0045] Stimulation Protocols. The same stimulation protocols (sham and tRNS) were used as with the visually intact participants in Experiment 1 .

[0046] Data Analysis. To analyze data from individual patients, the following bootstrap analysis was performed. First, for each subject and each training day, 10,000 bootstrap samples were generated by selecting, with replacement, from the set of available individual trials. Then Weibull functions were fitted to all 10,000 samples, and from resultant fits, percent correct performance at 100% NDR (the easiest difficulty level) was estimated. Because most of the individual trials for patients were collected near 100% NDR, these percent correct estimates were more robust than threshold estimates, which in many cases were estimated to be higher than 100% NDR. Thus, for each training day, 10,000 estimates were available for each patient’s percent correct performance, allowing the estimation of 95% confidence intervals (see Fig. 5). From this set of estimates, created 100,000 full data sets were generated for each patient (random sampling with replacement). This allowed estimating p-values for learning slope and amount of learning analyses reported in the Results portion of this specification. For the slope analysis, the computation involved the proportion of data sets that had negative learning slopes, multiplying results by 2 to get two-tailed p-values. For the amount of learning analysis, the process was to compute the proportion of data sets where Day 1 -2 performance was better than Day 9-10 performance, multiplying results by 2 to get two-tailed p-values.

[0047] Results. Impact of pairing brain stimulation with training in visually intact subjects.

[0048] Learning of motion integration in control groups. The subjects recruited for the experiments described above were divided into five training groups. Two experimental groups (bilateral tRNS and anodal stimulation) received stimulation over early visual areas. Their results were compared to three control groups: bilateral tRNS over parietal cortex, a sham control, and a no-stimulation control. Prior to the onset of training, there were no significant differences in normalized direction range (NDR) thresholds, a measure of direction integration performance, between the five training groups (F4, 40=1.15, p=0.35). This confirmed that all five groups started with relatively similar levels of performance. As expected, all five groups benefited from perceptual training— for each group, performance at Day 10 was better than at Day 1 (all t8 > 2.7, all p < 0.027). This result is consistent with well-established effects of training on visual perception (Levi & Shaked, 2016; Watanabe, & Sasaki 2015). However, no significant differences in learning were observed between three of the groups— no-stimulation (training only), sham-stimulation + training and tRNS over parietal cortex + training. The lack of difference was observed irrespective of whether learning was expressed as a raw change in thresholds (NDRDayl - NDRDayl O; F2, 24 = 1 .58, p = 0.23), a percent change in thresholds ((NDRDayl - NDRDayl 0)/NDRDay1 ; F2, 24 = 2.93, p = 0.072) or learning speed (linear regression slope; F2, 24 = 2.36, p = 0.12). An additional control analysis on the amount of learning between the first and the last session (Day 1 - Day 10) was performed. A repeated measure t-test showed that the tRNS group (Mean±SD=56.7±16.2 NDR) differed significantly from each of the control conditions: Sham (34.9±19.6; t8=2.57, p=0.01 ), Behavioral (25.8±23.6; t8=-2, 19, p=0.02) and Parietal tRNS (21 3±15.8; t8=4.68, p<0.001 ). To minimize the number of multiple comparisons between experimental and control groups, data from these three control groups were thus combined into a single, control data set for all subsequent analyses.

[0049] Figure 4 illustrates effects of brain stimulation on perceptual learning in visually intact subjects. Part A of Fig. 4 illustrates normalized direction range (NDR) thresholds for the control groups, tRNS and a-tDCS. Dashed lines are linear fits, indicating the learning slope. Part B of Fig. 4 illustrates the same data as in part A but expressed as percent improvement relative to Day 1 thresholds. Part C of Fig. 4 illustrates a learning index computed in three different ways. tRNS group exhibited a significantly stronger amount of learning (Day 1 - Day 10; F2, 42 = 9.39, p = 0.0004; all Tukey HSD p < 0.002), percent improvement (100^*(Day 1 - Day 10)/Day 1 ; F2, 42 = 10.8, p = 0.00016; all Tukey HSD p < 0.001 ) and learning slope (F2, 42 = 7.8, p = 0.001 ; all Tukey FISD p < 0.008) than both the control and a-tDCS groups. Part D of Fig. 4 shows amount of learning, defined as the difference from Day 1 thresholds, at the end of the training (left) and 6 months after (right). Error bars are ±1 SEM.

[0050] tRNS, but not a-tDCS, enhances learning. Comparison of the control data set with tRNS + training and a-tDCS + training reveales large differences in learning (Figure 4, parts A-B). In addition to the expected main effect of training day (F3.4, 144.1 = 34.7, p = 10-18), there is a main effect of group (F2, 42 = 3.35, p = 0.045) and, notably, a significant group by day interaction (F6.9, 144.1 = 4.01 , p = 0.01 ). As suggested by this significant interaction, the amount of learning differed among the three groups (Figure 4A; F2, 42 = 9.39, p = 0.0004). Specifically, tRNS + training induced stronger learning than both the combined control group (p = 0.002) and a-tDCS + training (p = 0.001 ; all post- hoc tests are Tukey FISD), whereas a-tDCS outcomes did not differ significantly from those attained by the combined control group (p = 0.53). The same pattern of results was observed when considering group differences in terms of percent improvement from pre- to post-test (Figure 4B, F2, 42=10.8, p=0.00016). Again, tRNS + training resulted in larger percent improvement than attained by controls (p=0.001 ) and a-tDCS + training (p=0.0002). Once again, performance following a-tDCS + training did not differ significantly from that in the combined control group (p = 0.28). In all groups, learning was well described by a linear trend (Figure 4A). Slopes, however, differed among groups (Figure 4C; F2,42=7.8, p=0.001 ), with tRNS + training generating faster learning than in the combined control group (p=0.008) or a-tDCS + training group (p=0.001 ). In contrast, a-tDCS did not induce significantly faster learning than attained by the combined control group (p=0.30).

[0051] As tRNS administered during training appeared to cause faster learning, a further analysis examined at what time point the tRNS group began to diverge from the other two groups. This occurred on Day 6 (F2, 42=5.03, p=0.01 ), at which point, tRNS showed stronger learning than both the combined control group (p=0.03; Tukey HSD) and a-tDCS + training (p=0.01 ; Tukey HSD).

[0052] In sum, the experiments described above showed strong evidence for enhanced learning in the tRNS + training group, with faster learning than both the combined control group and a-tDCS + training. As detailed above, this finding was supported irrespective of how learning was defined. It may seem that a-tDCS, as administered in these experiments might actually hinder learning (Figure 4A-B) but this effect was not statistically significant.

[0053] Persistence of stimulation-enhanced perceptual learning. To test whether the observed enhancement of perceptual learning by tRNS remained stable over an extended period of time, the participants were re-tested 6 months after completing the 10-days of training with and without the different forms of stimulation. The post-test, however, was performed without brain stimulation. The subjects re-tested at 6 months included 37 of 45 original participants (n=8 for tRNS + training group, n=22 for the combined control group, and n=7 for a-tDCS + training group).

[0054] Figure 4, in part D, contrasts the amount of learning at Day 10 (NDRDay 1 - NDRDay 10) with that exhibited 6 months after the end of training (NDRDay 1 - NDR6- months). There was a small, non-significant loss in performance for the three groups (no main effect of testing day; F1 , 34 = 3.32, p = 0.8) and no interaction (F2, 34 = 0.88, p = 0.43). The group differences at the end of 10 days of training remained unaltered 6 months after training (F2, 34 = 4.68, p = 0.02). These results show that tRNS enhanced perceptual learning persists significantly over the long-term - at least 6 months after the end of both training and brain stimulation. Moreover, this persistent enhancement was observed without brain stimulation at the 6 months follow-up. This suggested that the enhancement of perceptual learning by tRNS was not due to online or short-term optimization of visual processing, but instead, resulted in consolidated sensory learning.

[0055] tRNS boosts training-induced visual recovery in cortically-blind patients. tRNS has not been attempted in brain-damaged patients to the knowledge of the inventors named in this patent specification. Moreover, whether tRNS over early visual areas could enhance learning in cortically blind patients is a valid question, as learning in this patient population can exhibit properties not found in neurotypical subjects (e.g. Cavanaugh & Huxlin, 2017; Das et al., 2014; Vaina et al. , 2014), and since by definition, part of early visual cortex that would normally be stimulated is damaged. An initial study is reported below involving five patients with occipital damage resulting in homonymous visual field defects measured with visual perimetry (see Table 1 and Fig. 3). Visual perimetry was used to identify the blind field borders and select training locations in the blind field (Fig. 3). Five patients were randomly assigned to either tRNS + training (n=3, RNS1 -3) or sham stimulation + training (n=2, Sham1 -2). Data from an additional six patients who trained identically, but without brain stimulation (unstimulated, U1 -6), were also analyzed for comparison. All patients trained for 10 days using random dot stimuli, as in neurotypical subjects (Fig. 1 ). Because patients have difficulty seeing motion, their stimuli, unlike those for neurotypical subjects, did not include noise dots.

[0056] As per (Huxlin et al. , 2009; Das et al. , 2014; Cavanaugh et al. , 2015; Cavanaugh and Huxlin 2017), all patients performed considerably worse on global motion integration in their blind field compared to neurotypical subjects. This was the case even at the easiest stimulus level (NDR = 100, with all dots moving in the same direction), where none of the patients exhibited ceiling level performance in their blind field. As such, percent correct at 100 NDR was used as the measure of performance (see Materials and Methods for details). Sham-stimulated patients exhibited no significant change in performance across the 10 days of training (Fig. 5, part A) as evidenced by learning slopes that were not significantly different from 0 (all p>0.63; see Methods for bootstrap procedure used to analyze data from individual patients). This was comparable to the lack of learning observed in the six unstimulated patients, who also did not exhibit significant learning slope over their first 10 days of training (Figure 5C; all p > 0.13). In contrast, tRNS coupled with training enhanced the rate of global motion discrimination learning in all 3 tested patients (Figure 5, part B), who exhibited significantly positive learning slopes (all p<0.0048). The change in performance from Days 1 -2 to Days 9-10 was examined, averaging results over two consecutive days to minimize the effects of day-by-day fluctuations. The analysis showed significant change only for patients trained with tRNS rather than sham stimulation (Figure 5D; tRNS: all p < 0.0002; Sham: all p > 0.24; Unstimulated: all p > 0.080).

[0057] Fig. 5 illustrates the effects of brain stimulation on perceptual learning in CB patients and shows task performance over 10 training days for: part A -- patients who underwent sham stimulation; part B -- patients who received tRNS; and part C -- six unstimulated patients. Raw percent correct performance was normalized by subtracting the average percent correct for the first two training days. D. Comparison of raw percent correct averaged over the first two days against raw percent correct for the final two training days. Significant learning was observed only for patients who trained with tRNS. All error bars= 95% confidence intervals. For panels A-C, all lines are linear fits, indicating learning slope.

[0058] Discussion. Results from Experiment 1 show that tRNS applied bilaterally over healthy, early visual cortex speeds-up and boosts performance during visual perceptual learning. Over 10 days, neurotypical subjects in the tRNS + training group exhibited about a 60% improvement in motion integration thresholds (Figure 4B), which was two and three times as strong as learning attained by the control and a-tDCS groups, respectively. This finding was supported irrespective of whether learning was defined as a raw change in NDR threshold, a percent improvement or the slope of a linear fit to the data. The observed benefit of tRNS over training alone or sham stimulation + training, or tDCS over parietal cortex is consistent with evidence that tRNS is especially effective at promoting plasticity when coupled with a relevant stimulus, and when it is applied over relevant [in this case, occipital] brain areas (Cappelletti et al. , 2013). In contrast, no such benefit of a-tDCS over occipital cortex was found. Finally, the experimental data reported in this specification provides the first known evidence that tRNS enhances vision recovery in patients with V1 damage. Moreover, the experiments reported above demonstrate seemingly safe usage of this technique in a class of stroke patients, with no side-effects reported. Notably, with respect to training-induced recovery, tRNS enabled improvements in visual task performance of chronic patients in their blind field, over a tiny fraction of the training days typically required to induce such improvements in the absence of brain stimulation (on average, 72 to 80 training days are required to recover global direction discrimination performance at a given blind field location - see Cavanaugh & Huxlin, 2017; Das et al. , 2014; Das & Huxlin, 2010; Huxlin et al., 2009; Melnick et al. , 2016). Indeed, a steady and significant increase in performance was observed for three patients trained with tRNS over 10 days, with no such effects in patients that trained with sham stimulation. Chronic cortically blind patients are a population that would especially benefit from enhanced perceptual learning because vision recovery using conventional training methods usually takes many months of daily training (Cavanaugh & Huxlin, 2017 ; Das et al., 2014; Das & Huxlin, 2010; Huxlin et al., 2009; Melnick et al., 2015).

[0059] While the position of the stimulating electrodes corresponded to early visual areas that included V1 , improvement occurred despite V1 being damaged in the CB patients. This might suggest that tRNS’ neuro-modulatory benefit likely impacts any residual V1 , but also adjacent visual areas such as V2 and V3, which might also support visual learning as indicated in imaging studies in cortically blind patients patients (Henriksson et al., 2007; Barbot et al., 2018; Ajina et al., 2015; Martin et al., 2012; Raemaekers et al., 201 1 ). V2 and V3 are usually spared in these patients and they might have played a pivotal role in supporting recovery during training. It is not known whether some form of cortical re-organization took place in two weeks of training in our patients, as no imaging data was collected in the reported experiment. However, it can be speculated that since bilateral stimulation was delivered, this either boosted cortical functioning within the affected hemisphere, promoting activity in the damaged, early visual cortex in response to concurrent visual/brain stimulation or, alternatively, visual areas in the healthy hemisphere, homologues to the damaged ones, might have promoted recovery (Henriksson et al. , 2007). Physiological studies have shown that V2/V3 inactivation may degrade cortical motion sensitive areas’ ability to work efficiently (Ponce et al., 201 1 ), while they might support learning when they function normally (Law & Gold, 2008). In summary, while plasticity of spared visual circuits is generally believed to underlie visual recovery, the specific neural mechanisms involved remain unclear at this time.

[0060] An important finding in the study described in this patent specification was that enhancement of perceptual learning induced by tRNS can persist over an extended period of time (at least 6 months study) beyond the end of stimulation and training. This is important since stimulation-enhanced perceptual learning would have limited practical use if its beneficial effects diminished over time. Additionally, because all subjects performed the behavioral task with no stimulation at the follow-up, 6 months timepoint, it can be inferred that the benefits of tRNS go beyond an online enhancement of visual processing and likely involve plastic changes that persist within the visual system, allowing it to more effectively process global motion stimuli. As such, the conclusion is that consolidation of learning occurred in the subjects. The persistence effects observed here are particularly notable, as many VPL studies failed to see long lasting effects and/or transfer of learning to other tasks (Dosher & Lu, 2017).

[0061] Some important questions may be: 1 ) how does brain stimulation enhance the effects of perceptual learning, and 2) why do some forms of stimulation prove effective and others not? Anodal tDCS did not exert a beneficial effect in the above experiments - a surprising result given results from previous studies stimulating the early visual cortex (Antal et al. , 2004). One possible explanation is that the strongest effects of tDCS were reported offline, where a-tDCS was delivered prior to the measured behavior (Pirulli et al., 2013). Another possibility is that a-tDCS is not the ideal neuro-modulatory technique for repeated sessions. While it alters membrane potentials and hence exerts increased excitability, it may also engage inhibitory homeostatic mechanisms during repeated sessions (Fertonani et al., 201 1 ; Peters et al., 2013).

[0062] The finding that visual enhancements persist long after training and tRNS ended may constrain possible mechanistic explanations. Multiple types of transcranial electrical stimulation have been shown to alter excitability in cortex, and the longer time course of direct current stimulation effects has been suggested to relate to homeostatic changes in membrane potential (Ardolino et al., 2005; Liebetanz et al., 2002; Nitsche et al., 2003; Terney et al., 2008) or gate threshold (Bikson & Rahman, 2013). However, direct evidence that transcranial stimulation alters the dynamics of networks known to be related to perceptual learning, such as dopaminergic reward networks (Pascucci et al., 2015; Seitz & Watanabe, 2005), has not yet been provided to the knowledge of the inventors named in this patent application. It can be said, however, that tRNS does not appear to globally affect reward networks as there was no boost in visual performance or learning seen from stimulation over parietal cortex.

[0063] One hypothesis is that tRNS-related visual performance improvements might derive from the state of the neurons at the time of stimulation (Silvanto et al., 2008), and that adding noise to the cortex might enhance sensory detection, in particular when stimuli are presented at threshold and embedded in noise (Abrahamyan et al., 2015). Several short-term mechanisms have also been proposed to explain the effects of tRNS, and a favored hypothesis involves stochastic resonance, whereby random frequency stimulation in tRNS appears to boost responses of neural populations to weak inputs, thus the stochastic effect is expected to be highest for stimuli presented below or just above threshold, like in the experiment described above (Moss et al., 2004; Pirulli et al. , 2013; Miniussi & Ruzzoli, 2013; Schwarzkopf et al., 201 1 ; van der Groen & Wenderoth, 2016; van der Groen et al., 2018; Herpich et al., 2018). An alternative hypothesis proposes temporal summation of excitatory signals between visual stimulation and electrical stimulation (Fertonani et al., 201 1 ; Pirulli et al., 2013; Terney et al., 2008), and selective enhancement of active neural networks (Bikson et al., 2013; Fertonani & Miniussi, 2016; Luft, 2014; Miniussi et al., 2013). It is important to note that one or more of these short-term mechanisms may be the first step in a longer-term cascade that results in persistence of learning. For instance, stronger activation of task-relevant neurons due to temporal summation or stochastic resonance may encourage a shift towards greater plasticity in sensory processing and/or readout. Flowever, the time course of effects observed in the present study, and especially their persistence, suggests that online phenomena (i.e. during stimulation or shortly thereafter) are not the only ones at play with respect to learning enhancements induced by tRNS. Studies on perceptual learning in animal models have shown that learning might boost the modulation in neuronal tuning to stimulus components relevant to the task (Liu & Pack, 2017). If learning was associated with changes in the tuning characteristics of neurons (for a review see (Gilbert & Li, 2012), it could be speculated that tRNS coupled with behavioral training might facilitate and consolidate this plastic change, which could then persist across months (Snowball et al. , 2013).

[0064] Regardless of its precise mechanism of action, the experiments described above provide empirical evidence for the potential usefulness of tRNS coupled with visual training on a patient population that requires perceptual learning in order to attain visual recovery. V1 -damaged patients with chronic cortically blind can recover some visual abilities within their scotoma, but only after intensive and repetitive training over many months of daily practice (Das et al., 2014; Huxlin et al., 2009). The application of safe, painless neurostimulation in situations like this, where perceptual learning is directly proportional to the quantity of vision recovered (Cavanaugh & Huxlin, 2017), has the potential to dramatically improve quality of life and treatment outcomes (Cavanaugh et al., 2016). Therefore, results from the experiment reported above with cortically blind patients suggest that tRNS can be be a viable adjunct procedure to speed up the recovery process. Remarkably, even though the physiological effects of tRNS upon the damaged early visual cortex are currently unknown, the data reported above show that tRNS can help overcome reduced and/or partially absent functionality and boost learning in the blind field.

[0065] Fig. 6 schematically illustrates an example of equipment that can be used for the experiments described above. A subject 600 is fitted with electrodes 602 communicating with the appropriate cranial areas, and rests chin and forehead on support 604. A display screen 606 shows visual stimuli provided by a source 608 and may include a speaker (not separately shown) for audio messages to the patient that can be from a microphone 610. The subject operates a manual switch 612 to indicate response to stimuli. A computer processor coupled to switch 612 and stimuli source performs the calculations described above to generate the results discussed that also are discussed above.

[0066] Although the foregoing has been described in some detail for purposes of clarity, it will be apparent that certain changes and modifications may be made without departing from the principles thereof. It should be noted that there are many alternative ways of implementing both the processes and apparatuses described herein.

Accordingly, the present embodiments are to be considered as illustrative and not restrictive, and the body of work described herein is not to be limited to the details given herein, which may be modified within the scope and equivalents of the appended claims.

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[00156]

Claims

CLAIMS What it claimed is:

1. A method of promoting visual recovery of a patient with cortical blindness,

comprising:

displaying to the patient a sequence of visual stimuli with different directionality orientation or other parameters while concurrently applying to the patient transcranial random electrical noise;

detecting the patient’s response to said sequence of stimuli; and

computer-processing results related to the stimuli and the patent’s responses thereto to assess visual recovery.

2. The method of claim 1 , in which said displaying and detecting is repeated

multiple times in each of multiple sessions over a period as short as 10 days to achieve a measurable visual recovery.

3. The method of claim 2, in which said sessions are repeated over a longer

period to thereby achieve additional visual recovery.

4. The method of claim 2, in which said measurable visual recovery persists over a period of at least 6 months.

5. The method of claim 1 , in which the patient has cortical blindness due to a stroke.

6. The system of claim 1 , in which the patient has cortical blindness due to trauma other than stroke.

7. The method of claim 1 , in which said transcranial electrical noise is applied through electrodes at positions 01 and 02 of the International 10-20 system of locations of scalp EEG electrodes.

8. The method of claim 1 , in which the transcranial electrical noise has frequency content in the range of 100-700 Hz.

9. The method of claim 1 , in which said transcranial electrical noise is applied to the patient through scalp electrodes driven with current less than 2 mA.

10. The method of claim 1 , further comprising adding coherence noise in said stimuli until reaching directionality discrimination of approximately 50% to thereby establish a starting level for the patient from which to measure visual recovery.

11. The method of claim 1 , in which the patient is provided with an indication of correctly recognizing directionality or other aspects of a displayed visual stimulus.

12. The method of claim 1 , in which the transcranial electrical noise has

frequency content in the range of 100-700 Hz.

13. The method of claim 1 , in which said transcranial electrical noise is applied to the patient through scalp electrodes driven with current less than 2 mA.

14. The method of claim 1 , in which the patient views the stimuli in a circular

aperture as small as 10 degrees.

15. The method of claim 1 , in which 200-400 of said visual stimuli are presented to the patient in a session over a time period of 10-30 minutes.

16. The method of claim 15 in which the patient is presented with said visual

stimuli in one session per day over a period as short as two weeks.

17. A system for promoting visual recovery of a patient with cortical blindness, comprising:

a display (606) configured to present to the patient a sequence of visual stimuli with different directionality orientation or other parameters;

a source of transcranial electrical noise (602) configured to apply said noise to the patient concurrently with presenting said visual stimuli to the patient; an interface device (612) configured to detect the patient’s response to said of stimuli; and a computer processor (614) configured to process results related to the stimuli and the patent’s responses thereto to assess visual recovery.

18. The system of claim 17, in which said source of transcranial electrical noise is configured to apply noise with frequency content in the range of 100-700 Hz.

19. The system of claim 17, in which said source of transcranial electrical noise is configured to apply said noise at current less than 2 mA.

20. The system of claim 17, in which said display is configured to present 200-400 of said visual stimuli to the patient in a session over a time period as short as 10-30 minutes.

21. The system of claim 17, in which said source of noise is configured to apply transcranial electrical noise that is essentially random.