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Towards an unconscious neural reinforcement intervention for common fears

Significance Conventional therapies for the treatment of anxiety disorders are aversive, and as a result, many patients terminate treatment prematurely. We have developed an unconscious method to bypass the unpleasantness in conscious exposure using functional magnetic resonance imaging neural reinforcement. Using this method, participants learn to generate brain patterns similar to the multivariate brain pattern of a feared animal. We demonstrate in a double-blind placebo-controlled experiment that neural reinforcement can lead to reliable reductions in physiological fear responses. Crucially, this intervention can be achieved completely unconsciously and without any aversive reaction. Extending our approach to other forms of psychopathologies, such as posttraumatic stress disorders, might eventually provide another means of intervention for patients currently receiving insufficient exposure treatments.

. Spatial distribution of the sparse-logistic-regression weights.

Supplementary Methods
Patients Three patients (3 females; Mean age = 26.33; SD = 7.77) also took part in a Decoder Construction session. Patients were diagnosed using the Structured Clinical Interview for DSM-IV conducted by three medical doctors trained in psychiatric assessment. Diagnoses were established by inter-rater agreement.

Apparatus for stimuli presentation
Visual stimuli were projected on a translucent screen by an LCD projector (DLA-G150CL, Victor). The projector spanned 20 × 15 deg in visual angle (800 × 600 resolution) and had a refresh rate of 60 Hz. Pre and Post-Reinforcement as well as the Hyperalignment Decoder Construction session, were conducted using the PsychoPy2 software (v1.83)(1).

Recording of electrodermal activity
Electrodermal activity was recorded during all MRI sessions using BrainAmp Ag/AgCl sintered MR electrodes (Brain Products). The electrodes were disposed on the distal phalanges of the index and middle fingers of the left hand.

Stimuli of animal and object categories
During the Hyperalignment Decoder Construction session, participants were presented with 3,600 pictures grouped in chunks of 2, 3, 4 or 6 images of the same category (see Supplementary Methods and Fig. S7). Trials were organized in six runs of 600 trials interleaved with short breaks.To optimize attention to each image's category, participants were asked to perform a 1-back task, i.e. to report when the image category changed with a button press. The sequence of presentation was pseudo-randomized and fixed across participants to facilitate hyperalignment. In order to allow high-pass filtering of the fMRI data, chunks within each category were organized so that their period was smaller than 120 seconds.  Fig. 7).

Within-subject Decoder Construction
For the within-subject decoders, multivoxel patterns in the ventral temporal region were selected in a similar fashion as for the Hyperalignment Decoders. We selected the data of the Target category as well as 90 randomly selected trials of Non-target categories. The multivoxel patterns were averaged within-participants by category (Target vs Non-target) and run (1 to 6).
Here, we used a sparse logistic regression decoder in a within-subject cross-validation approach as a function of the runs (e.g., train the decoder on runs 1-5 and test on run 6), and accuracies were aggregated over all iterations. The results presented in Fig. 2b represent the averaged accuracies of the classifiers over the 30 participants and the 40 categories.

Contrast of the SMLR weights according to the animal and object categories
To determine if the spatial distribution of the sparse-logistic-regression feature (voxel) weights in the native space displayed localization patterns that would be expected according to the categories (human-made objects vs. animals), we conducted a second-level analysis on the weights obtained for each category. For each participant, Hyperalignment Decoders were constructed for each of the 40 categories using 29 'surrogate' participants. The obtained voxel weights were projected in the MNI space of the designated participant and smoothed using Gaussian filtering (FWHM=6 mm). These images were then averaged within categories (human-made objects vs. animals) and contrasted using one-sided t-tests (P < .001; cluster corrected: P = .05). The results are presented in the MNI space in Fig. 2a as well as in Extended Data Table 1. Data in Fig. 2a were presented using PyCortex (3).

Hyperalignment
To acquire the data necessary for hyperalignment, the fMRI images captured during the Decoder Construction session were realigned to the first fMRI image, coregistered, and motion corrected in SPM 12 (Statistical Parametric Mapping; www.fil.ion.ucl.ac.uk/spm). The anatomical mask of the ventral temporal cortex (fusiform, lingual/parahippocampal and inferior temporal cortex) was hand-drawn using tksurfer, and using the Freesurfer (http://surfer.nmr.mgh.harvard.edu/) segmentation as a guideline. Voxels in the ventral temporal region were detrended, and then deconvolved using the least-square separate approach (4,5).
This method allows us to iteratively construct a general linear model for each trial in the design that includes one parameter modeling the current trial, and two parameters modeling all other trials in the design. Via this method we were able to deconvolve the trials of our rapid-event related design in order to obtain parameter estimates for each individual trial, which were used in the remaining steps of the procedure. Based on previous procedures(6), hyperalignment was conducted in pyMVPA 2.4 (http://www.pymvpa.org ) on a subset of 1,000 ventral temporal voxels that were the most associated with image categories; these relevant voxels were identified using an F-test conducted on all of the categories in the dataset but the Target category. We used the Procrustean transformation (scaling, translation, and rotation) and the common space aggregation by averaging; the hyperalignment was achieved by first iteratively projecting the individual dataset into a common space, and then iteratively projecting the original datasets on the intermediate common space processed in the first level.

Skin conductance response
The skin conductance response was determined during a time window beginning 1 second and ending 5 seconds after stimulus presentation. The amplitude of the response was determined by subtracting the mean baseline activity during the 2 seconds preceding the onset of the image from the maximum value in this time window. Responses smaller than 0.02 microsiemens (µS) were recorded as 0 following previous procedures (7,8). Responses were square root transformed to correct for the skewness of the distribution of skin conductance responses (9, 10).
We carried out a two-level mixed effect model on skin conductance response with trials nested in subjects. The model included 3 fixed effects (Time, Condition, and Time * Condition), and the coefficients of Time and Conditions were allowed to have a random component to correct for potential clustering of errors within subject (11). Simple effect analyses were also carried using two-sided paired-sample t-test, and by splitting the data by Condition. One participant was removed from this analysis because of a problem with the acquisition of the skin conductance data. One block of data was also removed for one participant due to a problem with image presentations.

Amygdala response
The amygdala ROI was determined using the structural mask resulting from Freesurfer segmentation (http://surfer.nmr.mgh.harvard.edu). The data were first realigned and coregistered in SPM. A general linear model was conducted that included, for each block, predictors (and their time derivatives) for the first 2 Target animals, the first 2 Control animals, the baseline animals, the baseline objects, and the motion parameters. The predictors were convolved with the standard canonical hemodynamic response function. Mean ROI parameter estimates were extracted for the Target and Control categories using the MarsBar toolbox (12) implemented in SPM and baseline-corrected by subtracting the baseline condition (i.e., the neutral animal) from the mean ROI parameters.
The data were analyzed with a repeated-measures ANOVA with 2 within-subject factors of Time (Pre-and Post-Reinforcement) and Condition (Target and Control) followed by twosided paired-sample t-tests (see Fig. 3b). One block was removed for one participant due to a problem with image presentations.

Neural-Reinforcement session
Previous studies have shown that participants are able to learn to consistently activate the relevant voxel patterns without ever consciously understanding how to do so (10,(13)(14)(15).
Induction and Feedback periods were separated by a 6s period that allowed us to account for the hemodynamic delay and to perform the online decoding on data from the time window sessions were also conducted on these days.

Information transmission analyses
For the information transmission analysis during Decoder Construction, the preprocessing of the data was the same as for the hyperalignment data, and the data were during Neural-Reinforcement. Here, the sparse linear regression conducted within each sphere was trained on the decoder construction data and tested on the induction data.
To correct for multiple comparisons, a permutation test was conducted on the linearized correlation coefficients projected in the MNI space (10,16). One thousand iterations of randomly permuted data were used to determine the distribution of the maximum t-values. Using this distribution, the critical value for significance was determined as the t-value associated with a 5% false-positive rate.

Hyperalignment Decoders used during Neural-Reinforcement
In terms of number of participants, we tried to achieve a level of statistical power similar to that in previous studies. Specifically, we first collected data from 13 participants to determine if accurate Hyperalignment Decoders could be trained using our procedure. We then aimed at conducting Neural-Reinforcement with 17 participants according to the number of participants included in our previous study (17). As a result, the number of 'surrogate' participants included in the Hyperalignment database continuously grew as a function of the number of participants who had completed the experiment. Therefore, the first group of participants to complete Further, hyperalignment of the first six participants was conducted using a subset of the 200 most selective voxels (instead of 1,000), with Hyperalignment Decoders constructed using support vector machine (2 participants) and regularized regression (4 participants) instead of the sparse logistic regression. We later found that the approach including the alignment of 1,000 voxels and the use of sparse logistic regression offered the highest accuracies, so we used this approach for the remaining 11 participants. The association between the accuracy of decoders and changes in amygdala response in the Target condition was also observed only in that subgroup of 11 participants (rho = -.664; P = .026).

Induction accuracy during Neural-Reinforcement
One question regarding the mechanisms of Neural-Reinforcement is whether our procedure involved extinction training per se, or counter-conditioning; that is, whether it was the mere occurrences of the Target representation without threat, or the specific pairing of Target representations with reward, that were critical to fear reduction (18). These two mechanisms are not mutually exclusive. The data show that extinction training could have occurred, to some extent, since the Target category presented a greater likelihood than the Control category during Neural-Reinforcement (Extended Data Fig. 2a)

Amygdala response during Neural-Reinforcement
We also conducted ROI analyses of the activity within the amygdala during the induction period of Neural-Reinforcement using the structural masks described above. The fMRI images

ROI analyses of the transmission of information
Above, we describe the information transmission analyses aimed at determining which brain regions could predict the activation likelihood of the Target category computed in the ventral temporal region. These analyses were implemented using a searchlight approach that provides one value for each individual voxel. However, this approach is not ideally suited to determine the involvement of entire brain regions previously associated with fear learning and Neural-Reinforcement. To address this question, we conducted exploratory region of interest (ROI) analyses.
ROIs were defined using the Freesurfer segmentation of the amygdala, fusiform, lingual, inferior temporal, ventral medial prefrontal cortex, and striatum. The striatum included the caudate, putamen, and nucleus accumbens. Following previous procedures (17), the ventral medial prefrontal cortex ROI was defined as the intersection between the Freesurfer segmentation of the orbitofrontal cortex and a 15 mm radius sphere centered around the peak coordinate [0,40,-12] estimated from previous results (19)(20)(21). For each ROI analysis, the Fisher-transformed correlation coefficients were projected in the MNI space, standardized within participants and averaged within each ROI (Extended Data Fig. 4). This measure reflected the mean transmission of information to this specific region. These exploratory results indicate that during Neural-Reinforcement, the ventral temporal ROIs present a significant, but weaker, association with the activation likelihood of the Target category. The decreased information transmission to the amygdala during Neural-Reinforcement also shows a trend toward significance. This further supports the notion that Neural-Reinforcement might lead to localized activations that might not be broadcasted to other brain regions of the fear system. Also, the result about transmission from visual areas to ventral medial prefrontal cortex was in the same direction as a previous finding using a similar procedure (17), and in line with some other previous studies pointing to the importance and potential benefits of vmPFC disengagement in fear reduction (22,23) (17).

The association between fear and the accuracy of Hyperalignment Decoders
One specific concern is whether the accuracies of the Hyperalignment To explicitly determine whether Hyperalignment Decoders built from 'normal surrogate' participants could generalize to patients diagnosed with specific phobias, we recruited patients to take part in our Hyperalignment Decoder Construction session. We recruited eight participants, three of whom met the DSM-IV criteria for specific phobia for at least one animal in our database. The results indicate that Hyperalignment Decoders constructed with our group of 'normal surrogate' participants presented decoding accuracy levels just as high as the accuracies established within the group of 'normal surrogates'. This was specifically true for the phobic category (all within +/-1 STD; Extended Data Fig. 6).
To further determine if the fear of patients can affect the hyperalignment procedure, we compared hyperalignment metrics between patients and 'normal surrogates' when the feared categories were included in the construction of the common space. This was achieved using two different hyperalignment procedure. The first procedure, All-Categories hyperalignment, was achieved using the trials of the 40 categories in the dataset, which, for each patient, included the trials of the feared category. The second procedure, Feared-Category hyperalignment, involved using only the trials of the feared category of a specific patient to perform the hyperalignment. Therefore, for the Feared-Category hyperalignment, we conducted three different hyperalignment procedures, each including only the trials of the feared category of a specific patient (i.e., cockroach, snake and mouse). Taken together, these results suggest that fear do not change significantly the multivoxel representations in the ventral temporal cortex, which indicates that representations from 'normal surrogates' could be used to build Hyperalignment Decoders for patients presenting specific phobias. Fig. S1. The Training and Test datasets used for Hyperalignment Decoders construction.

Supplementary Figures
After the hyperalignment procedure, all data for the designated participant and 'surrogate' participants were brought in the common space (left). By inverting the transformation matrix for the designated participant, the data of the 'surrogate' participants were brought into the native space of the designated participant. This approach allows us to develop a dataset for training the Hyperalignment Decoders that includes the data of Target (blue) and Non-target (red) categories from 29 surrogates. The Hyperalignment Decoder was tested on the 90 individual trials of the Target category of the designated participant as well as 90 other Non-target trials randomly selected from the remaining categories (right). The test dataset was not included in the hyperalignment or in the training of the Hyperalignment Decoder.       To determine if these participants (see Table S2) influenced the overall effects, we computed, independently for the amygdala and skin conductance response, an index of the Neural-  Imagining singing a song in his head, driving a car, counting money, playing a sport, eating food thinking about a previous job. 6 Doing mental calculation and linking words together according to their first and last phonemes. Imagining scenes from the wilderness, shapes and colors.

7
Imagining his part-time job, singing a song in his head, playing in the park, riding a rollercoaster ride, playing with a dog and what he ate the day before.

8
Imagining cooking different dishes, being in her class, her birthday dinner, Christmas dinner and cartoons. Tried to figure out philosophical questions.

9
Recalled memories from elementary school and high school. Mental calculations involving additions and subtractions. Imagining painting everyday objects and geometric forms. Imagining the taste of certain food. Linking words together according to their first and last phonemes. Imagining different emotions didn't work very well.

10
Imagining different kind of food, snowboarding, scuba diving, fishing, attending the summer festival with close friends, fireworks, buildings and sceneries from the wilderness.

11
On Day 1, the participant imagined cockroaches and mouses. The Target of Neural-Reinforcement was 'snake' and 'cockroach' was the Control category. The participant then moved on to imagine musical instruments, working, situations where he was nervous or situations where he was not nervous. He then tried to concentrate on the green circle or on the scanner noise.
traveled, geometric forms, buildings, giraffes, airplanes, reading manga and cockroaches. The Target of Neural-Reinforcement was indeed cockroaches. The participant changed his strategy after trying to think of cockroaches for a few trials on Day 3.

13
Imagining playing soccer, playing baseball, working out at a gym. Mental calculation.
14 Imagining balancing a budget, studying bookkeeping, driving a car and her future job. Mental calculation.

15
Imagining emotional scenes, yesterday's events, happy memory, job hunting, memories from recent travels, what she ate for breakfast, movies she watched, being lost in the woods, images of insects and animals amongst which she imagined on Day 4, bees which was the Target of Neural-Reinforcement. On Day 5, she imagined images of dogs, cats, cute animals and bees. She mentioned that imagining bees was still a good strategy on Day 5.

16
Imagining her favorite singer, songs, yesterday's events, swimming, running and playing music. Linking words together according to their first and last phonemes.

17
Imagining the circle bigger, being at the beach, playing with friends, cartoons and having a girlfriend. Staring at the green circle.