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The left-wing leadership of the Labour Party, headed by Jeremy Corbyn, did not support the mobilisation. Behind the reluctance of Labour leaders to call for a new referendum on Brexit is a simple fact of electoral politics.

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Nearly two-thirds of Labour voters voted in favour of Remain in the referendum, but most constituencies won by Labour in the last election voted Leave. In particular, some economically depressed northern cities where poverty is rife returned huge Leave majorities. Corbyn and his allies do not want to be punished in a future election, or give ammunition to the Tory right who would accuse Labour of betraying the democratic decision of the referendum. Nonetheless a huge majority of young voters, and Corbyn supporters inside and outside Labour, are hostile to Brexit.

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Important left-wing groups outside the Labour Party were scathing in their denunciation of the October 20 march. Whatever the individual motivation of marchers, it is a vehicle to deliver the big business agenda of defending the single market and the neoliberal, racist EU. The problem with this type of analysis is that it is entirely removed from the real meaning of Brexit.

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This was explained by Scottish National Party leader Nicola Sturgeon four days before the referendum, when she denounced the Leave campaign as an attempted putsch by the right wing of the Conservative Party. The hardcore Tory Brexiteers, led by the likes of financier Jacob William Rees-Mogg, want to make a bonfire of EU regulations that set standards for employment conditions and environmental regulations.

They have also used anti-EU rhetoric to attempt to witch hunt and exclude the hundreds of thousands of EU citizens who have come to live and work in Britain. In run-down and poverty-stricken cities, grooming gangs were able to befriend vulnerable young women from poor and often dysfunctional families. They then used them for prostitution or rape by themselves and their friends.

These shocking events have been exploited ruthlessly by the far right to spread Islamophobic propaganda. Several volumetric slices are displayed to show subcortical ROIs.

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For the RS analysis, the mean timeseries were calculated for every ROI, and normalized to percent signal change for each run separately. We did not apply global signal correction as our asymmetry metric is based on the differences in connectivity between the brain and its mirrored counterpart. This means that the global signal cannot contribute to our metric unless it is expressed differently throughout the left and the right hemispheres, and in that case, the global signal would not qualify as confounder. Subsequently, all ROI timeseries-pairs were correlated, and the resulting correlation matrices were Fischer transformed to z values for the second-level analyses.

The AFC matrix was defined as follows:. The elements in the matrix represent differences in z values for Pearson correlations between ROIs A 1, n in the left l and the right r hemispheres. Significance of group wise effects for individual correlations in the correlation matrices was assessed using one-sample and paired samples t tests, which were Bonferroni corrected for the number of values in the original values; as in panel 2A and AFC matrices values; as in panel 2D.

As data were acquired with the phase-encoding directions along the left—right axis. This possibility was extensively tested and refuted see supplement 1. Depiction of the used approach using a simplified and a more concrete example. The simplified example involves connections between four hypothetical areas and is depicted in the first two rows, once in matrix representation row one and once as a brain connectivity diagram row two.

The simplified example includes left hemisphere areas 1 A l 1 and 2 A l 2 and their contralateral homologues A r 1 and A r 2. The concrete example is depicted in matrix representation only row 3 , and includes all the connections between all left and right brain ROIs as represented in Fig.

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The matrices containing the z -transformed RS correlations between the areas is established, with the rows and columns first ordered by hemisphere, then by area a. A mirror matrix is calculated by swapping left and right areas, representing the connectivity of the same brain mirrored across the longitudinal fissure b. Thus, the larger the difference between the original and mirror matrix, the larger the values in the AFC matrix c.

Note that there is a substantial amount of superfluous data in this matrix. To condense the matrix for more compact visualization, two non-redundant portions are extracted. The concrete example shows how these asymmetries of within- and between-hemisphere connections are represented by two triangular portions of the asymmetry matrix, which are combined to form the square condensed asymmetry matrix d.

The AFC-score thus represents the magnitude of the asymmetry of a subject, as defined by the amount of scaling required to optimally fit the mean group AFC matrix to the individual AFC matrix. Thus, a high AFC-score indicates the extent to which a subject expresses the mean AFC of left language lateralized subjects. Further patterns of AFC were explored using principal component analysis PCA , with the entries in the AFC matrix as variables, and the different subjects as the individual observations. Resulting principal component coefficients represent the patterns of asymmetry explaining most variance between subjects, and their corresponding principal component scores the extent to which these patterns are present in individual subjects.

PCA was chosen instead of Independent Component Analysis to allow complete reproduction of the reported results using the same sample of the HCP, and to ascertain that observed components can be fitted to the data independently of an arbitrary number of other components in future studies. Then, the voxel timeseries were analyzed using a general linear model. The first factor in the design matrix represented increased BOLD for story vs. This factor was generated by convolving the boxcar function of story vs. The second factor represented the difference in intercept between the two runs.

Four additional factors were added to the design matrix and consisted of cosine functions with 0. Regional language activity was estimated by taking the mean regressor coefficient for the story vs. Then, these right hemisphere ROI activity estimates were subtracted from their left hemisphere homologues, resulting in a vector with a length of 38 half the number of ROIs that represented language lateralization.

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A high LTL-score thus indicates a strong left language lateralization. We planned to link measures of AFC to the behavioral characteristics that were assessed by the HCP, which included metrics of cognition, emotion, motor and sensory function, personality, psychiatric history, substance abuse, and physical functioning Barch et al.

We used Pearson correlations for estimating relationships between AFC variables and behavioral characteristics at and beyond ordinal scale, and F-statistics for behavioral measures at nominal scale. In addition, when linking AFC-measures to assumed stable behavioral features, it is instructive to know to what extent the acquired AFC metrics are stable features.

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Assessments of test—retest reliability are based on half the amount of data per subject, and thus underestimate the reliability of the full measurements. Reported reliabilities are, however, informative as low-end estimates, and for comparison of reliability between AFC variables. The language processing task showed significant lateralization effects in most ROIs, but the strongest left lateralization was in the known language areas i.

Green bars show the difference between left and right homologue ROIs, which in combination formed the independent variable for assessing the LTL per individual subject. Bars indicate standard errors of the mean. The group-mean z values for the RS correlation matrices were nearly all significantly larger than 0.

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In addition, the reproducibility of the group-mean correlation matrices across the two separate scanning days was nearly perfect ICC 2. This nearly perfect test—retest reliability between scanning days 1 and 2 indicates that there is hardly uncertainty in the established group-mean, meaning that noise can only marginally attenuate the similarity between the original and mirror RS correlation matrices.

When estimating the relationship between z values in the mean original matrix and those of its mirror version, we found an ICC 2. Scatterplot of the original group-mean connectivity matrix vs. Each ROI pair is represented by a single black dot, with its value within the two compared matrices represented by its x and y coordinates.

Errorbars indicate standard errors of the mean. Due to data redundancy in the correlation matrices underlying the scatterplots, the plot contains a kaleidoscopic pattern.

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Homotopic connections are excluded. The strength of the relationship indicates the level of symmetry. Although there was significant AFC regarding a large portion of the correlations Fig. Colors indicate the mean differences in z values between the original and mirror matrix. The lower right portion of the matrix shows asymmetry of within-hemisphere connections, meaning the connectivity between left area x and left area y , minus the connectivity between right area x and right area y. The top left portion of the matrix shows asymmetry of between-hemisphere connections, meaning the connectivity between left area x and right area y , minus the connectivity between left area y and right area x.

The sequence of ROIs along the x and y axes was adjusted, so that ROIs with similar patterns of asymmetry are clustered together.