Representation Analysis

library(tidyverse)
library(patchwork)

colors_components <- c("#16607a", "#0b9d86", "#cc734d")
size_label <- 5

index_time <- function(time_id, onset = 51, sampling_rate = 256) {
  (time_id - onset) / sampling_rate * 1000
}

fit_curve <- function(x, y) {
  nls(
    y ~ eta1 * (1 - exp(theta - eta2 * x)),
    start = list(eta1 = 1, eta2 = 0.01, theta = 0)
  )
}

prepare_corr_plotmath <- function(stats,
                                  col_r = "estimate",
                                  col_p = "p.value",
                                  name_r = "italic(r)",
                                  name_p = "italic(p)[Holm]") {
  stats |>
    rstatix::adjust_pvalue(col_p, "p_adj") |>
    rstatix::add_significance(
      "p_adj", "p_adj_sig",
      cutpoints = c(0, 0.001, 0.01, 0.05, 1),
      symbols = c("***", "**", "*", "")
    ) |>
    mutate(
      label = format_r_plotmath(
        .data[[col_r]], p_adj,
        p.sig = p_adj_sig,
        name_r = name_r,
        name_p = name_p
      )
    )
}

format_r_plotmath <- function(r, p,
                              p.sig = "",
                              name_r = "italic(r)",
                              name_p = "italic(p)[Holm]") {
  paste0(
    str_glue("{name_r}*' = '*{round(r, 2)}"),
    if (is.null(name_p)) {
      str_glue("^'{p.sig}'")
    } else {
      paste0(
        "*', '*",
        if_else(
          p < 0.001,
          str_glue("{name_p} < 0.001^'{p.sig}'"),
          str_glue("{name_p}*' = '*{round(p, 3)}^'{p.sig}'")
        )
      )
    }
  )
}

visualize_scatter <- function(data, mem_perf, lab_stat, col_stat,
                              show_legend = FALSE) {
  data_joind <- data |>
    left_join(mem_perf, by = "subj_id") |>
    mutate(cca_id = factor(cca_id))
  stats <- data_joind |>
    reframe(
      cor.test(.data[[col_stat]], .data$dprime) |>
        broom::tidy(),
      .by = cca_id
    ) |>
    prepare_corr_plotmath()
  data_joind |>
    ggplot(aes(.data[[col_stat]], dprime)) +
    geom_point(aes(color = cca_id), show.legend = show_legend) +
    geom_smooth(
      aes(color = cca_id),
      method = "lm",
      formula = y ~ x,
      show.legend = show_legend
    ) +
    geom_text(
      aes(x = min(data_joind[[col_stat]]), y = Inf, label = label),
      stats,
      hjust = 0,
      vjust = 1,
      parse = TRUE
    ) +
    facet_grid(cols = vars(cca_id), scales = "free") +
    scale_x_continuous(name = lab_stat) +
    scale_y_continuous(name = "d'") +
    scale_color_components() +
    theme(axis.line = element_line(linewidth = 1), strip.text = element_blank())
}

visualize_mantel <- function(patterns_x, patterns_y, stats, name_x, name_y,
                             show_legend = FALSE) {
  patterns_flat <- patterns_x |>
    mutate(
      pattern = map(
        pick(last_col())[[1]],
        \(pat) tibble(
          "{name_x}" := unclass(pat),
          "{name_y}" := unclass(patterns_y)
        )
      ),
      .keep = "unused"
    ) |>
    unnest(pattern)
  patterns_flat |>
    ggplot(aes(.data[[name_x]], .data[[name_y]])) +
    geom_hex(
      aes(fill = factor(cca_id), alpha = after_stat(count)),
      show.legend = FALSE
    ) +
    geom_smooth(
      aes(color = factor(cca_id)),
      method = "lm",
      formula = y ~ x,
      show.legend = show_legend
    ) +
    geom_text(
      aes(x = min(patterns_flat[[name_x]]), y = Inf, label = label),
      prepare_corr_plotmath(
        stats, "statistic",
        name_p = "italic(p)[Holm]^{Mantel}"
      ),
      hjust = 0, vjust = 1, parse = TRUE
    ) +
    facet_grid(cols = vars(cca_id), scales = "free") +
    scale_x_continuous(name = name_x) +
    scale_y_continuous(name = name_y) +
    scale_color_components(aesthetics = c("color", "fill")) +
    theme(
      strip.text = element_blank(),
      axis.line = element_line(linewidth = 1)
    )
}

visualize_mantel_dist <- function(data, stats, label, show_legend = FALSE) {
  data |>
    mutate(
      cca_id = factor(cca_id),
      null = map(mantel, "perm"),
      .keep = "unused"
    ) |>
    unchop(null) |>
    ggplot(aes(null)) +
    geom_histogram(aes(fill = cca_id), show.legend = show_legend) +
    geomtextpath::geom_textvline(
      aes(xintercept = statistic, label = label),
      stats |>
        mutate(cca_id = factor(cca_id)) |>
        prepare_corr_plotmath(
          "statistic",
          name_r = "italic(r)[Obs]",
          name_p = NULL
        ),
      parse = TRUE,
      vjust = -0.1
    ) +
    facet_grid(cols = vars(cca_id)) +
    scale_x_continuous(name = label) +
    scale_y_continuous(name = "Count", expand = expansion(mult = c(0, 0.05))) +
    scale_color_components(aesthetics = "fill") +
    theme(
      axis.line = element_line(linewidth = 1),
      strip.text = element_blank(),
      strip.background = element_blank()
    )
}

visualize_dynamic <- function(stats,
                              clusters_stats = NULL,
                              col_stat = "estimate",
                              lab_stat = "Estimate",
                              col_cis = c("conf.low", "conf.high"),
                              limits = NULL,
                              show_legend = FALSE) {
  if (!is.null(clusters_stats)) {
    clusters_stats <- clusters_stats |>
      mutate(cca_id = factor(cca_id)) |>
      rstatix::adjust_pvalue("p_perm") |>
      rstatix::add_significance(
        "p_perm.adj",
        cutpoints = c(0, 0.001, 0.01, 0.05, 1),
        symbols = c("***", "**", "*", "")
      ) |>
      filter(p_perm < 0.05)
  }
  show_cis <- !is.null(col_cis) && all(has_name(stats, col_cis))
  limits_rect <- if (show_cis) {
    range(c(stats[[col_cis[1]]], stats[[col_cis[2]]]))
  } else {
    range(stats[[col_stat]])
  }
  stats |>
    mutate(
      cca_id = factor(cca_id),
      time = index_time(time_id)
    ) |>
    ggplot(aes(time, .data[[col_stat]])) +
    geom_line(
      aes(color = cca_id),
      linewidth = 1,
      show.legend = show_legend
    ) +
    # TODO: Convert these as functions
    {
      if (show_cis) {
        geom_ribbon(
          aes(
            fill = cca_id,
            ymin = .data[[col_cis[1]]],
            ymax = .data[[col_cis[2]]]
          ),
          alpha = 0.2,
          show.legend = show_legend
        )
      }
    } +
    {
      if (!is.null(clusters_stats)) {
        list(
          geom_rect(
            data = clusters_stats,
            mapping = aes(
              xmin = index_time(start),
              xmax = index_time(end),
              ymin = limits_rect[1],
              ymax = limits_rect[2]
            ),
            inherit.aes = FALSE,
            alpha = 0.1
          ),
          geom_text(
            data = clusters_stats,
            mapping = aes(
              x = index_time((start + end) / 2),
              y = limits_rect[2],
              label = p_perm.adj.signif
            ),
            size = size_label,
            inherit.aes = FALSE
          )
        )
      }
    } +
    facet_grid(cols = vars(cca_id)) +
    geom_hline(yintercept = 0, linetype = "dotted", color = "grey") +
    geom_vline(xintercept = 0, linetype = "dotted", color = "grey") +
    scale_x_continuous(name = "Encoding Time (ms)") +
    scale_y_continuous(name = lab_stat, limits = limits) +
    scale_color_components(aesthetics = c("color", "fill")) +
    theme(
      strip.text = element_blank(),
      strip.background = element_blank(),
      axis.line = element_line(linewidth = 1)
    )
}

scale_color_components <- function(...) {
  scale_color_manual(
    name = "CCA Comp.",
    values = colors_components,
    labels = \(x) paste0("C", x),
    ...
  )
}
theme_set(ggpubr::theme_pubr(base_family = "Gill Sans MT", base_size = 12))

Behavioral Results

p_perf <- targets::tar_read(mem_perf) |>
  ggplot(aes(dprime)) +
  geom_histogram(fill = "grey") +
  scale_x_continuous(name = "d' (Overall)") +
  scale_y_continuous(name = "Count") +
  theme(axis.line = element_line(linewidth = 1))

p_smc <- targets::tar_read(smc) |>
  enframe() |>
  ggplot(aes(value)) +
  geom_histogram(fill = "grey") +
  scale_x_continuous(name = "SMC") +
  scale_y_continuous(name = "Count") +
  theme(axis.line = element_line(linewidth = 1))

p_memorability <- targets::tar_read(memorability) |>
  ggplot(aes(pc)) +
  geom_histogram(fill = "grey") +
  scale_x_continuous(name = "Memorability") +
  scale_y_continuous(name = "Count") +
  theme(axis.line = element_line(linewidth = 1))

p_memorability_content <- targets::tar_read(memorability_content) |>
  ggplot(aes(r)) +
  geom_histogram(fill = "grey") +
  geom_vline(
    xintercept = mean(targets::tar_read(memorability_content)$r),
    linetype = "dotted"
  ) +
  geom_vline(xintercept = sqrt(0.5), linetype = "dotted", color = "red") +
  scale_x_continuous(
    name = "Individual-to-Group\nMemory Content Similarity"
  ) +
  scale_y_continuous(name = "Count", expand = expansion(c(0, 0))) +
  theme(axis.line = element_line(linewidth = 1))

p_perf + p_smc + p_memorability + p_memorability_content

ggsave("figures/behavioral.png", width = 6, height = 5, dpi = 600)

targets::tar_read(mem_perf) |>
  left_join(
    targets::tar_read(mem_perf_precise),
    by = "subj_id"
  ) |>
  select(!subj_id) |>
  rename(
    Overall = dprime,
    Recollection = dprime_rem,
    Familiarity = dprime_know
  ) |>
  GGally::ggpairs(
    diag = list(continuous = "barDiag"),
    lower = list(
      continuous = function(data, mapping, ...) {
        ggplot(data, mapping) +
          geom_point(color = "grey") +
          geom_smooth(method = "lm", color = "black") +
          ggpmisc::stat_correlation(
            ggpmisc::use_label(c("r", "p.value")),
            small.r = TRUE,
            small.p = TRUE
          )
      }
    ),
    upper = "blank",
    switch = "both"
  ) +
  theme(
    axis.line = element_line(linewidth = 1),
    strip.text = element_text(size = 16),
    strip.background = element_blank(),
    strip.placement = "outside"
  )

ggsave("figures/behavioral_correlation.png", width = 6, height = 6, dpi = 600)

Group averaged representation

This is supplementary figure showing the stability.

targets::tar_load(patterns_group_stability)
size_subjs <- seq(20, 200, by = 20)
predictions <- patterns_group_stability |>
  reframe(
    fit_curve(size, r) |>
      predict(newdata = data.frame(x = size_subjs)) |>
      as_tibble_col("r") |>
      add_column(size = size_subjs, .before = 1L),
    .by = cca_id
  )
p_stability <- patterns_group_stability |>
  ggplot(aes(size, r, color = factor(cca_id))) +
  ggdist::stat_dotsinterval() +
  geom_point(aes(size, r), predictions, size = 1) +
  geom_line(aes(size, r), predictions, linetype = "longdash") +
  scale_x_continuous(name = "# Participants", breaks = scales::breaks_width(40)) +
  scale_y_continuous(name = "Stability") +
  scale_color_components() +
  facet_grid(cols = vars(cca_id), scales = "free") +
  theme(axis.line = element_line(linewidth = 1), strip.text = element_blank())

p_trend_gss <- targets::tar_read(data_gss_whole_resampled) |>
  ggplot(aes(size, gss, color = factor(cca_id))) +
  ggdist::stat_dotsinterval() +
  scale_x_continuous(name = "# Participants", breaks = scales::breaks_width(40)) +
  scale_y_continuous(name = "GSS") +
  scale_color_components(guide = "none") +
  facet_grid(cols = vars(cca_id), scales = "free") +
  theme(
    axis.line = element_line(linewidth = 1),
    strip.text = element_blank()
  )

p_stability / p_trend_gss +
  plot_layout(guides = "collect", axes = "collect") &
  theme(legend.position = "bottom")

ggsave("figures/group_representation_stability.png", width = 6, height = 4, dpi = 600)

This is Figure 2 now.

# IGS predicts memory ----
p_igs_mem <- visualize_scatter(
  targets::tar_read(data_igs_whole),
  targets::tar_read(mem_perf),
  lab_stat = "IGS",
  col_stat = "igs",
  show_legend = TRUE
)

p_igs_mem_dynamic <- visualize_dynamic(
  targets::tar_read(stats_igs_mem_dynamic),
  targets::tar_read(clusters_stats_igs_mem_dynamic),
  lab_stat = expression(italic(r)[IGS - "d'"])
)

# semantics related to group pattern (not shape) ----
p_gss_whole <- visualize_mantel(
  targets::tar_read(patterns_group_whole),
  targets::tar_read(pattern_semantics),
  targets::tar_read(stats_gss_whole),
  "Group Averaged Neural Representation",
  "Semantic Resprentation"
)

p_gss_dynamic <- visualize_dynamic(
  targets::tar_read(stats_gss_dynamic),
  targets::tar_read(clusters_stats_gss_dynamic),
  col_stat = "statistic",
  lab_stat = "GSS"
)

(p_igs_mem | p_igs_mem_dynamic) /
  (p_gss_whole | p_gss_dynamic) +
  plot_layout(guides = "collect") &
  # plot_annotation(tag_levels = "A") &
  theme(legend.position = "bottom")

ggsave("figures/igs_memory_gss_semantics.png", width = 13, height = 5, dpi = 600)

Semantic representation

Semantic information is important but non-semantic information is also important.

This will be Figure 3.

stats_iss_whole <- targets::tar_read(stats_iss_whole) |>
  rstatix::adjust_pvalue() |>
  rstatix::add_significance(
    cutpoints = c(0, 0.001, 0.01, 0.05, 1),
    symbols = c("***", "**", "*", "")
  ) |>
  add_column(y = 0.13, .after = 1L)
iss_comparison <- targets::tar_read(iss_comparison) |>
  filter(adj.p.value < 0.05) |>
  mutate(
    across(c(start, end), \(x) factor(x, levels = 1:3)),
    y_position = 0.14 * (1 + 0.12 * seq_len(n()))
  ) |>
  rstatix::add_significance(
    "adj.p.value",
    cutpoints = c(0, 0.001, 0.01, 0.05, 1),
    symbols = c("***", "**", "*", "")
  )

p_iss_dist <- targets::tar_read(data_iss_whole) |>
  mutate(cca_id = factor(cca_id)) |>
  ggplot(aes(cca_id, iss)) +
  ggdist::stat_dotsinterval(
    aes(slab_color = cca_id, slab_fill = cca_id),
    slab_alpha = 0.4,
    side = "both"
  ) +
  geom_text(
    aes(cca_id, y, label = p.value.adj.signif),
    stats_iss_whole,
    size = size_label,
    vjust = 0,
    inherit.aes = FALSE
  ) +
  ggsignif::geom_signif(
    aes(
      xmin = start, xmax = end,
      annotations = adj.p.value.signif,
      y_position = y_position
    ),
    iss_comparison,
    size = 0.8,
    textsize = size_label,
    vjust = 0.5,
    inherit.aes = FALSE,
    manual = TRUE
  ) +
  scale_x_discrete(name = NULL, labels = \(x) paste0("C", x)) +
  scale_y_continuous(name = "ISS") +
  scale_color_components(
    aesthetics = c("slab_color", "slab_fill"),
    guide = "none"
  ) +
  theme(axis.line = element_line(linewidth = 1))

p_iss_dynamic <- visualize_dynamic(
  targets::tar_read(stats_iss_dynamic),
  targets::tar_read(clusters_stats_iss_dynamic),
  col_stat = "estimate",
  lab_stat = "ISS",
  show_legend = TRUE
)

p_iss_mem_scatter <- visualize_scatter(
  targets::tar_read(data_iss_whole),
  targets::tar_read(mem_perf),
  lab_stat = "ISS",
  col_stat = "iss"
)

p_iss_mem_dynamic <- visualize_dynamic(
  targets::tar_read(stats_iss_mem_dynamic),
  targets::tar_read(clusters_stats_iss_mem_dynamic),
  col_stat = "estimate",
  lab_stat = expression(italic(r)[ISS - "d'"])
)

targets::tar_load(igs_comp_partial)
mult <- 1
stats <- igs_comp_partial$stats |>
  mutate(
    ymax = estimate + mult * std.error,
    ymin = estimate - mult * std.error
  )
htests <- igs_comp_partial$htest |>
  separate_wider_delim(
    contrast,
    " - ",
    names = c("start", "end")
  ) |>
  rstatix::add_significance(
    "p.value",
    cutpoints = c(0, 0.001, 0.01, 0.05, 1),
    symbols = c("***", "**", "*", "")
  ) |>
  mutate(y_position = max(stats$ymax)) |>
  filter(p.value < 0.05)
p_compare_igs_partial <- stats |>
  ggplot(aes(type, estimate, ymax = ymax, ymin = ymin, color = cca_id)) +
  geom_point(size = size_label) +
  geom_errorbar(width = 0.1, linewidth = 1) +
  geom_line(aes(group = cca_id), linewidth = 1) +
  ggsignif::geom_signif(
    aes(
      xmin = start, xmax = end,
      annotations = p.value.signif,
      y_position = y_position
    ),
    htests,
    size = 0.8,
    textsize = size_label,
    vjust = 0.5,
    inherit.aes = FALSE,
    manual = TRUE
  ) +
  facet_grid(cols = vars(cca_id), scales = "free") +
  scale_x_discrete(name = NULL, labels = c("IGS", "IGS|S")) +
  scale_y_continuous(name = expression(italic(r))) +
  scale_color_components(guide = "none") +
  theme(
    strip.text = element_blank(),
    strip.background = element_blank(),
    axis.line = element_line(linewidth = 1)
  )

p_igs_partial_scatter <- visualize_scatter(
  targets::tar_read(data_igs_partial_whole),
  targets::tar_read(mem_perf),
  lab_stat = "IGS|S",
  col_stat = "igs"
)

targets::tar_load(c(lm_mem_igs_partial, lm_mem_iss_igs_partial))
preds <- bind_rows(
  `IGS|S` = lm_mem_igs_partial$pred,
  `ISS + IGS|S` = lm_mem_iss_igs_partial$pred,
  .id = "model"
) |>
  mutate(model = factor(model, c("ISS + IGS|S", "IGS|S")))
model_eval <- tibble(
  x = min(preds$obs),
  y = max(preds$pred) * (1 + c(0.1, 0.02)),
  model = factor(c("ISS + IGS|S", "IGS|S")),
  r_squared = c(
    caret::getTrainPerf(lm_mem_iss_igs_partial)$TrainRsquared,
    caret::getTrainPerf(lm_mem_igs_partial)$TrainRsquared
  ) |>
    signif(2)
)
p_compare_predictions <- preds |>
  ggplot(aes(obs, pred, color = model)) +
  geom_point(shape = 16) +
  geom_smooth(method = "lm") +
  ggtext::geom_richtext(
    aes(
      x, y,
      color = model,
      label = paste0(
        "*R*<sup>2</sup><sub>",
        model, "</sub> = ",
        r_squared
      )
    ),
    model_eval,
    size = 3,
    fill = NA, label.color = NA, # remove background and outline
    label.padding = grid::unit(rep(0, 4), "pt"), # remove padding
    hjust = 0, vjust = 0.5, # bottom-left corner
    inherit.aes = FALSE,
    show.legend = FALSE
  ) +
  # facet_grid(cols = vars(model)) +
  scale_x_continuous(name = "Observed") +
  scale_y_continuous(name = "Predicted") +
  scale_color_grey(start = 0.1, end = 0.6, name = "Model", guide = "none") +
  theme(strip.background = element_blank(), axis.line = element_line(linewidth = 1))

layout <- "
AAABBB
CCCDDD
EEFFFG
"

p_iss_dist + p_iss_dynamic + p_iss_mem_scatter + p_iss_mem_dynamic +
  p_compare_igs_partial + p_igs_partial_scatter + p_compare_predictions +
  plot_layout(design = layout, guides = "collect") +
  plot_annotation(tag_levels = "A") &
  theme(legend.position = "bottom")

ggsave("figures/semantics_memory.png", width = 13, height = 8, dpi = 600)

Control analysis: word shape similarity pattern

p_gfs_whole <- visualize_mantel(
  targets::tar_read(patterns_group_whole),
  targets::tar_read(pattern_shapes),
  targets::tar_read(stats_gfs_whole),
  "Group Averaged Neural Representation",
  "Word Shape Similarity"
)

p_gfs_dynamic <- visualize_dynamic(
  targets::tar_read(stats_gfs_dynamic),
  targets::tar_read(clusters_stats_gfs_dynamic),
  col_stat = "statistic",
  lab_stat = "GWS",
  show_legend = TRUE
)

p_ifs_mem_whole <- visualize_scatter(
  targets::tar_read(data_ifs_whole),
  targets::tar_read(mem_perf),
  lab_stat = "IWS",
  col_stat = "iss"
)

p_ifs_mem_dynamic <- visualize_dynamic(
  targets::tar_read(stats_ifs_mem_dynamic),
  targets::tar_read(clusters_stats_less_ifs_mem_dynamic),
  col_stat = "estimate",
  lab_stat = expression(italic(r)[IWS - "d'"])
)

(p_gfs_whole | p_gfs_dynamic) /
  (p_ifs_mem_whole | p_ifs_mem_dynamic) +
  plot_layout(guides = "collect") &
  plot_annotation(tag_levels = "A") &
  theme(legend.position = "bottom")

ggsave("figures/word_shape_pattern.png", width = 12, height = 6, dpi = 600)

Predicting Shared Memory Content (SMC)

Inter-Subject Pattern Similarity (ISPS)

p_isps_dist <- targets::tar_read(data_isps_whole) |>
  mutate(cca_id = factor(cca_id)) |>
  unnest(isps) |>
  ggplot(aes(cca_id, isps)) +
  ggdist::stat_slabinterval(
    aes(slab_color = cca_id, slab_fill = cca_id),
    slab_alpha = 0.4,
    side = "both"
  ) +
  geom_hline(
    aes(yintercept = isps_baseline),
    targets::tar_read(summary_isps_whole_permuted) |>
      summarise(isps_baseline = mean(isps_mean), .by = cca_id) |>
      mutate(cca_id = factor(cca_id)),
    linetype = "dotted"
  ) +
  scale_x_discrete(name = NULL, labels = \(x) paste0("C", x)) +
  scale_y_continuous(name = "ISPS") +
  scale_color_components(
    aesthetics = c("slab_color", "slab_fill"),
    guide = "none"
  ) +
  theme(axis.line = element_line(linewidth = 1))

# p_isps_dynamic <- visualize_dynamic(
#   targets::tar_read(stats_isps_dynamic) |>
#     mutate(
#       ymax = isps_mean + isps_se,
#       ymin = isps_mean - isps_se
#     ),
#   col_stat = "isps_mean",
#   lab_stat = "ISPS",
#   col_cis = c("ymin", "ymax")
# )

# the cluster based permutation test is not useful (maybe we need TFCE)
p_isps_clusters <- visualize_dynamic(
  targets::tar_read(stats_isps_dynamic) |>
    mutate(
      ymax = isps_mean + isps_se,
      ymin = isps_mean - isps_se
    ),
  targets::tar_read(clusters_stats_isps_dynamic),
  col_stat = "isps_mean",
  lab_stat = "ISPS",
  col_cis = c("ymin", "ymax")
)

p_isps_smc <- visualize_mantel_dist(
  targets::tar_read(data_isps_smc_whole),
  targets::tar_read(stats_isps_smc_whole),
  expression(italic(r)[ISPS - SMC]),
  show_legend = TRUE
)

p_isps_smc_dynamic <- visualize_dynamic(
  targets::tar_read(stats_isps_smc_dynamic),
  targets::tar_read(clusters_stats_isps_smc_dynamic),
  col_stat = "statistic",
  lab_stat = expression(italic(r)[ISPS - SMC])
)

p_isps_dist + p_isps_clusters + p_isps_smc + p_isps_smc_dynamic +
  plot_layout(guides = "collect") &
  theme(legend.position = "bottom")

ggsave("figures/isps_smc.png", width = 13, height = 5, dpi = 600)

p_isps_smc_partial_memory <- visualize_mantel_dist(
  targets::tar_read(data_isps_smc_partial_ability_whole),
  targets::tar_read(stats_isps_smc_partial_ability_whole),
  expression(italic(r)["ISPS|Mem" - SMC]),
  show_legend = TRUE
)

p_isps_smc_partial_memory_dynamic <- visualize_dynamic(
  targets::tar_read(stats_isps_smc_partial_ability_dynamic),
  targets::tar_read(clusters_stats_isps_smc_partial_ability_dynamic),
  col_stat = "statistic",
  lab_stat = expression(italic(r)["ISPS|Mem" - SMC])
)

p_isps_smc_partial_group_memory <- visualize_mantel_dist(
  targets::tar_read(data_isps_smc_partial_group_ability_whole),
  targets::tar_read(stats_isps_smc_partial_group_ability_whole),
  expression(italic(r)["ISPS|(GRSM, Mem)" - SMC])
)

p_isps_smc_partial_group_memory_dynamic <- visualize_dynamic(
  targets::tar_read(stats_isps_smc_partial_group_ability_dynamic),
  targets::tar_read(clusters_stats_isps_smc_partial_group_ability_dynamic),
  col_stat = "statistic",
  lab_stat = expression(italic(r)["ISPS|(GRSM, Mem)" - SMC])
)

p_isps_smc_partial_semantic_memory <- visualize_mantel_dist(
  targets::tar_read(data_isps_smc_partial_semantic_ability_whole),
  targets::tar_read(stats_isps_smc_partial_semantic_ability_whole),
  expression(italic(r)["ISPS|(Sem, Mem)" - SMC])
)

p_isps_smc_partial_semantic_memory_dynamic <- visualize_dynamic(
  targets::tar_read(stats_isps_smc_partial_semantic_ability_dynamic),
  targets::tar_read(clusters_stats_isps_smc_partial_semantic_ability_dynamic),
  col_stat = "statistic",
  lab_stat = expression(italic(r)["ISPS|(Sem, Mem)" - SMC])
)

(p_isps_smc_partial_memory | p_isps_smc_partial_memory_dynamic) /
  (p_isps_smc_partial_group_memory | p_isps_smc_partial_group_memory_dynamic) /
  (p_isps_smc_partial_semantic_memory | p_isps_smc_partial_semantic_memory_dynamic) +
  plot_layout(guides = "collect") +
  plot_annotation(tag_levels = "A") &
  theme(legend.position = "bottom")

ggsave("figures/isps_smc_control.png", width = 13, height = 7, dpi = 600)

Neural Synchronization

targets::tar_load(sync_inter_intra)
summary_sync <- sync_inter_intra |>
  summarise(
    broom::tidy(t.test(sync)),
    .by = c(cca_id, type)
  ) |>
  rstatix::adjust_pvalue() |>
  rstatix::add_significance(
    cutpoints = c(0, 0.001, 0.01, 0.05, 1),
    symbols = c("***", "**", "*", "")
  )
compare_sync_p <- sync_inter_intra |>
  pivot_wider(
    id_cols = c(subj_id, cca_id),
    names_from = type,
    values_from = sync
  ) |>
  summarise(
    broom::tidy(t.test(intra, inter_ahead, paired = TRUE)),
    .by = cca_id
  ) |>
  rstatix::adjust_pvalue() |>
  rstatix::add_significance(
    cutpoints = c(0, 0.001, 0.01, 0.05, 1),
    symbols = c("***", "**", "*", "")
  ) |>
  mutate(
    start = "intra", end = "inter_ahead",
    y_position = max(summary_sync$conf.high) * 1.1
  )
p_sync_compare <- summary_sync |>
  ggplot(aes(type, estimate)) +
  geom_col(
    aes(color = cca_id, fill = cca_id, alpha = type),
    width = 0.75
  ) +
  geom_errorbar(aes(ymin = conf.low, ymax = conf.high), width = 0.1) +
  geom_text(
    aes(y = conf.high, label = p.value.adj.signif),
    vjust = 0,
    size = size_label
  ) +
  ggsignif::geom_signif(
    data = compare_sync_p,
    aes(
      xmin = start,
      xmax = end,
      annotations = p.value.adj.signif,
      y_position = y_position
    ),
    textsize = size_label,
    inherit.aes = FALSE,
    manual = TRUE
  ) +
  facet_grid(cols = vars(cca_id)) +
  scale_x_discrete(name = NULL, labels = c("Intra", "Inter")) +
  scale_y_continuous(
    name = "Neural Sync",
    expand = expansion(c(0, 0.1))
  ) +
  scale_color_components(aesthetics = c("fill", "color")) +
  scale_alpha_manual(
    name = NULL,
    values = c(1, 0),
    guide = "none"
  ) +
  theme(
    strip.text = element_blank(),
    axis.line = element_line(linewidth = 1)
  )

sync_mem <- sync_inter_intra |>
  left_join(targets::tar_read(mem_perf), by = "subj_id") |>
  mutate(cca_id = factor(cca_id))
stats_sync_mem <- sync_mem |>
  summarise(
    broom::tidy(cor.test(sync, dprime)),
    .by = c(cca_id, type)
  ) |>
  rstatix::adjust_pvalue() |>
  rstatix::add_significance(
    cutpoints = c(0, 0.001, 0.01, 0.05, 1),
    symbols = c("***", "**", "*", "")
  ) |>
  prepare_corr_plotmath() |>
  mutate(
    x = min(sync_mem$sync),
    y = max(sync_mem$dprime) * 1.2 * (1 - 0.1 * as.integer(type))
  )

p_pred_mem <- sync_mem |>
  ggplot(aes(x = sync, y = dprime, alpha = type)) +
  geom_point(aes(color = cca_id, shape = type)) +
  geom_line(
    aes(color = cca_id),
    stat = "smooth",
    method = "lm",
    formula = y ~ x
    # linewidth = 2,
    # fullrange = TRUE
  ) +
  geom_text(
    aes(x, y, label = label),
    stats_sync_mem,
    hjust = 0, vjust = 1, parse = TRUE,
  ) +
  facet_grid(cols = vars(cca_id)) +
  scale_x_continuous(name = "Neural Sync") +
  scale_y_continuous(name = "Memory (d')") +
  scale_alpha_manual(
    name = NULL,
    values = c(1, 0.5),
    guide = "none"
  ) +
  scale_shape_manual(
    name = NULL,
    values = c(16, 1),
    labels = c("Intra", "Inter")
    # guide = "none"
  ) +
  scale_color_manual(values = colors_components, guide = "none") +
  theme(
    strip.text = element_blank(),
    axis.line = element_line(linewidth = 1)
  )

p_sync_smc <- visualize_mantel_dist(
  targets::tar_read(sync_smc_whole),
  targets::tar_read(stats_sync_smc_whole),
  expression(italic(r)[Sync - SMC])
)

p_sync_smc_dynamic <- visualize_dynamic(
  targets::tar_read(stats_sync_smc_dynamic),
  targets::tar_read(clusters_stats_sync_smc_dynamic),
  col_stat = "statistic",
  lab_stat = expression(italic(r)[Sync - SMC])
)

p_sync_compare + p_pred_mem + p_sync_smc + p_sync_smc_dynamic +
  plot_layout(guides = "collect") +
  plot_annotation(tag_levels = "A") &
  theme(legend.position = "bottom")

ggsave("figures/sync_results.png", width = 13, height = 6.5, dpi = 300)

set.seed(1)

# representational patterns ----
vis_pat <- function(pat) {
  corrplot::corrplot(
    pat,
    method = "color",
    outline = TRUE,
    col = corrplot::COL1("Greys"),
    tl.pos = "n",
    cl.pos = "n"
  )
}
x_indiv <- pracma::squareform(rnorm(15, 0.5, 0.25))
x_group <- pracma::squareform(rnorm(15, 0.5, 0.25))
x_sem <- pracma::squareform(rnorm(15, 0.5, 0.25))
diag(x_indiv) <- diag(x_group) <- diag(x_sem) <- 1
png("figures/diagrams/indiv_matrix.png", width = 400, height = 400)
vis_pat(x_indiv)
dev.off()
png("figures/diagrams/group_matrix.png", width = 400, height = 400)
vis_pat(x_group)
dev.off()
png("figures/diagrams/sem_matrix.png", width = 400, height = 400)
vis_pat(x_sem)
dev.off()

# word2vec embeddings ----
embedding <- as_tibble(split(rnorm(14, 0.5, 0.2), rep(1:2, each = 7))) |>
  mutate(id = seq_len(n()), label = "")
embedding[6, 1:2] <- NA
embedding$label[6] <- "…"

embedding |>
  ggplot(aes(id, 1, fill = `1`)) +
  geom_tile() +
  geom_text(aes(label = label), size = 12, vjust = 0.2) +
  scale_fill_gradient(low = "grey80", high = "grey20", na.value = "white", guide = "none") +
  coord_fixed() +
  theme_void()
ggplot2::ggsave("figures/diagrams/embedding_1.png", width = 8, height = 2, dpi = 300)

embedding |>
  ggplot(aes(id, 1, fill = `2`)) +
  geom_tile() +
  geom_text(aes(label = label), size = 12, vjust = 0.2) +
  scale_fill_gradient(low = "grey80", high = "grey20", na.value = "white", guide = "none") +
  coord_fixed() +
  theme_void()
ggplot2::ggsave("figures/diagrams/embedding_2.png", width = 8, height = 2, dpi = 300)

# neural activity ----
targets::tar_load(file_cca_y)
data_trials <- arrow::open_dataset(file_cca_y) |>
  filter(subj_id == 1, cca_id == 1, trial_id %in% 1:6) |>
  collect()
data_trials |>
  nest(.by = trial_id) |>
  mutate(
    walk2(
      data, trial_id,
      ~ {
        ggplot(.x, aes(time_id, y)) +
          geom_line(linewidth = 3, color = "grey") +
          scale_x_continuous(name = "Time (ms)") +
          scale_y_continuous(name = "Neural Activity") +
          theme_void()
        ggsave(
          paste0("figures/diagrams/trial_", .y, ".png"),
          width = 8, height = 2, dpi = 300
        )
      }
    )
  )

data_group <- arrow::open_dataset(file_cca_y) |>
  filter(trial_id %in% 1:2, cca_id == 1) |>
  collect() |>
  summarise(y = mean(y, na.rm = TRUE), .by = c(trial_id, time_id))
data_group |>
  nest(.by = trial_id) |>
  mutate(
    walk2(
      data, trial_id,
      ~ {
        ggplot(.x, aes(time_id, y)) +
          geom_line(linewidth = 3, color = "black") +
          scale_x_continuous(name = "Time (ms)") +
          scale_y_continuous(name = "Neural Activity") +
          theme_void()
        ggsave(
          paste0("figures/diagrams/group_avg_trial_", .y, ".png"),
          width = 8, height = 2, dpi = 300
        )
      }
    )
  )