def fig5():
_, mask, _, networks, regions, brain_axis = get_template(tpt_name, space)
tasks = task_order(True)
df = acw.gen_long_data(tpt_name).groupby(["task", "region"]).mean().reset_index()
df.metric *= 1000
output = np.zeros((len(tasks), mask.size))
for i, task in enumerate(tasks):
values = and_filter(df, task=task).values
for reg, pc in values:
reg_index = np.argmax(regions == reg) + 1
if reg_index == 0:
print("0 reg_index in %s" % reg)
output[i, np.argwhere(mask == reg_index)] = pc
savemap("fig5.acw.map", output, brain_axis, cifti.Series(0, 1, output.shape[0]))
tasks = task_order(True)
df = acz.gen_long_data(tpt_name).groupby(["task", "region"]).mean().reset_index()
df.metric *= 1000
output = np.zeros((len(tasks), mask.size))
for i, task in enumerate(tasks):
values = and_filter(df, task=task).values
for reg, pc in values:
reg_index = np.argmax(regions == reg) + 1
if reg_index == 0:
print("0 reg_index in %s" % reg)
output[i, np.argwhere(mask == reg_index)] = pc
savemap("fig5.acz.map", output, brain_axis, cifti.Series(0, 1, output.shape[0]))
def corr():
df = add_net_meta(normalize(acw.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index()
df1 = add_net_meta(normalize(acz.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index()
correlations = []
for task in task_order():
dft = and_filter(df, task=task)
subjects = dft.subject.unique()
df_corr = np.zeros((len(subjects), len(subjects)))
for i in range(len(subjects)):
df_corr[i, i] = 1
x = and_filter(dft, subject=subjects[i]).metric
for j in range(i + 1, len(subjects)):
y = and_filter(dft, subject=subjects[j]).metric
df_corr[i, j] = df_corr[j, i] = pearsonr(x, y)[0]
correlations.append(df_corr)
correlations1 = []
for task in task_order():
dft = and_filter(df1, task=task)
subjects = dft.subject.unique()
df_corr = np.zeros((len(subjects), len(subjects)))
for i in range(len(subjects)):
df_corr[i, i] = 1
x = and_filter(dft, subject=subjects[i]).metric
for j in range(i + 1, len(subjects)):
y = and_filter(dft, subject=subjects[j]).metric
df_corr[i, j] = df_corr[j, i] = pearsonr(x, y)[0]
correlations1.append(df_corr)
min_val, max_val = 0, 1
ticks = np.arange(min_val, max_val, 0.1)
cmap = cm.get_cmap("jet")
fig, axs = plt.subplots(2, 4, figsize=(20, 10))
for i, task in enumerate(task_order()):
ax = axs[0, i]
isc = correlations[i]
pp = ax.imshow(isc, interpolation="nearest", vmin=min_val, vmax=max_val, cmap=cmap)
ax.xaxis.tick_top()
down, up = sms.DescrStatsW(isc[np.triu_indices(len(isc), 1)]).tconfint_mean()
ax.set_title(f"ACW-50 {task}: {down:.2f}:{up:.2f}")
for i, task in enumerate(task_order()):
ax = axs[1, i]
isc = correlations1[i]
pp = ax.imshow(isc, interpolation="nearest", vmin=min_val, vmax=max_val, cmap=cmap)
ax.xaxis.tick_top()
down, up = sms.DescrStatsW(isc[np.triu_indices(len(isc), 1)]).tconfint_mean()
ax.set_title(f"ACW-0 {task}: {down:.2f}:{up:.2f}")
cbar_ax = fig.add_axes([0.92, 0.125, 0.02, 0.755])
cbar = fig.colorbar(pp, cax=cbar_ax, ticks=ticks, orientation="vertical")
savefig(fig, "isc", low=True)
def fig4():
unique_networks = net_order(tpt_name)
dfs = [[], []]
for i, lib in enumerate([acw, acz]):
for avg in ["net_meta", "network"]:
df = add_net_meta(
and_filter(lib.gen_long_data(tpt_name), subject=lib.find_shared_subjects(tpt_name, task_order())) \
.groupby(["task", "subject", "network", "region"]).mean().reset_index() \
.groupby(["subject", "network", "region"]).apply(calc_pchange).reset_index().drop("level_3", 1),
get_net("pmc", tpt_name)).groupby(["task", "subject", avg]).mean().reset_index()
df.pchange *= -1
dfs[i].append(df)
fig = plt.figure(figsize=(20, 15))
gs = fig.add_gridspec(2, 2, width_ratios=[0.7, 1.9], hspace=0.1, wspace=0.2)
for row, ((df1, df2), label, (min_val, max_val)) in enumerate(
zip(dfs, ["ACW-50", "ACW-0"], [(None, None), (None, None)])):
ax = fig.add_subplot(gs[row, 0])
sns.barplot(data=df1, x="task", y="pchange", hue="net_meta",
order=task_order(False), hue_order=["P", "M", "C"], ax=ax)
ax.set(xlabel="", ylabel=f"Mean \u00B1 %95 CI (% change)", ylim=[min_val, max_val])
if row == 0:
h, l = ax.get_legend_handles_labels()
ax.legend(h, PMC_labels, loc=3, ncol=3, mode="expand", borderaxespad=0, bbox_to_anchor=(0., 1.08, 1, 0.),
handletextpad=0.1)
else:
ax.get_legend().remove()
ax.set_xticklabels(ax.get_xticklabels() if row == 1 else [], rotation=45)
ax = fig.add_subplot(gs[row, 1])
sns.barplot(data=df2, x="network", y="pchange", hue="task", palette=task_colors,
hue_order=task_order(False), order=unique_networks, ax=ax)
ax.set(xlabel="", ylabel="", ylim=[min_val, max_val], yticklabels=[])
if row == 0:
lgn = ax.legend(loc=3, ncol=6, mode="expand", borderaxespad=0, bbox_to_anchor=(0., 1.08, 1, 0.))
else:
ax.get_legend().remove()
ax.set_xticklabels(ax.get_xticklabels() if row == 1 else [], rotation=45)
ax.set_title(label, ha="center", loc="left", x=0.3)
savefig(fig, "fig4.bar", extra_artists=(lgn,))
def isc():
df = and_filter(add_net_meta(normalize(acw.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index(), NOTnet_meta="M")
df1 = and_filter(add_net_meta(normalize(acz.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index(), NOTnet_meta="M")
correlations = []
for task in task_order():
temp = []
for meta in ["C", "P"]:
dft = and_filter(df, task=task, net_meta=meta)
subjects = dft.subject.unique()
df_corr = np.zeros((len(subjects), len(subjects)))
for i in range(len(subjects)):
df_corr[i, i] = 1
x = and_filter(dft, subject=subjects[i]).metric
for j in range(i + 1, len(subjects)):
y = and_filter(dft, subject=subjects[j]).metric
df_corr[i, j] = df_corr[j, i] = pearsonr(x, y)[0]
temp.append(df_corr)
correlations.append(temp)
correlations1 = []
for task in task_order():
temp = []
for meta in ["C", "P"]:
dft = and_filter(df1, task=task, net_meta=meta)
subjects = dft.subject.unique()
df_corr = np.zeros((len(subjects), len(subjects)))
for i in range(len(subjects)):
df_corr[i, i] = 1
x = and_filter(dft, subject=subjects[i]).metric
for j in range(i + 1, len(subjects)):
y = and_filter(dft, subject=subjects[j]).metric
df_corr[i, j] = df_corr[j, i] = pearsonr(x, y)[0]
temp.append(df_corr)
correlations1.append(temp)
min_val, max_val = 0, 1
ticks = np.arange(min_val, max_val + 0.01, 0.1)
cmap = cm.get_cmap("jet")
fig, axs = plt.subplots(4, 4, figsize=(20, 20))
cbar_ax = fig.add_axes([0.92, 0.125, 0.02, 0.755])
for i, task in enumerate(task_order()):
for j, meta in enumerate(["Core", "Periphery"]):
ax = axs[j, i]
isc = correlations[i][j]
xy_ticks = np.linspace(1, len(isc), 10, dtype=np.int)
pp = ax.imshow(isc, interpolation="nearest", vmin=min_val, vmax=max_val, cmap=cmap)
ax.set(xticks=xy_ticks, yticks=xy_ticks)
ax.xaxis.tick_top()
down, up = sms.DescrStatsW(isc[np.triu_indices(len(isc), 1)]).tconfint_mean()
if j == 0:
ax.set_title(task, fontsize=18)
else:
ax.set_xticks([])
if i == 0:
ax.set_ylabel(meta, fontsize=18)
else:
ax.set_yticks([])
# ax.set_title(f"ACW-50: {down:.2f}:{up:.2f}")
for i, task in enumerate(task_order()):
for j, meta in enumerate(["Core", "Periphery"]):
ax = axs[j + 2, i]
isc = correlations1[i][j]
xy_ticks = np.linspace(1, len(isc), 10, dtype=np.int)
pp = ax.imshow(isc, interpolation="nearest", vmin=min_val, vmax=max_val, cmap=cmap)
ax.set(xticks=[], yticks=xy_ticks)
ax.xaxis.tick_top()
down, up = sms.DescrStatsW(isc[np.triu_indices(len(isc), 1)]).tconfint_mean()
if i == 0:
ax.set_ylabel(meta, fontsize=18)
else:
ax.set_yticks([])
# ax.set_title(f"ACW-0: {down:.2f}:{up:.2f}")
cbar = fig.colorbar(pp, cax=cbar_ax, ticks=ticks, orientation="vertical")
txt1 = fig.text(0.06, 0.67, "ACW-50", rotation=90, fontsize=18)
txt2 = fig.text(0.06, 0.27, "ACW-0", rotation=90, fontsize=18)
savefig(fig, "isc1", extra_artists=(txt1, txt2))
def fig6():
tasks = task_order(with_rest=False)
unique_networks = net_order(tpt_name)
palette = make_net_palette(unique_networks)
_, mask, _, _, regions, brain_axis = get_template(tpt_name, space)
df = acw.gen_long_data(tpt_name).groupby(["task", "subject", "network", "region"]).mean().reset_index() \
.groupby(["subject", "network", "region"]).apply(split, "task", "metric").reset_index().drop("level_3", 1) \
.sort_values("subject")
output = np.zeros((len(tasks), mask.size))
for ti, task in enumerate(tasks):
shared_subj = acw.find_shared_subjects(tpt_name, ["Rest", task])
for ri, region in enumerate(regions):
mask_reg_ind = np.argwhere(mask == ri + 1)
df_rgn = and_filter(df, region=region, subject=shared_subj)
output[ti, mask_reg_ind] = stats.pearsonr(df_rgn.task_Rest, df_rgn[f"task_{task}"])[0]
savemap("fig6.acw", output, brain_axis, cifti.Series(0, 1, output.shape[0]))
df_fig = df.groupby(["network", "region"]).mean().reset_index()
for task in task_order(True):
df_fig[f"task_{task}"] *= 1000
fig, axs = plt.subplots(1, 3, figsize=(16, 5))
for ti, task in enumerate(tasks):
ax = axs[ti]
sns.scatterplot(data=df_fig, x="task_Rest", y=f"task_{task}", hue="network", hue_order=unique_networks, ax=ax,
palette=palette)
slope, intercept, r_value, _, _ = stats.linregress(df_fig.task_Rest, df_fig[f"task_{task}"])
sns.lineplot(df_fig.task_Rest, slope * df_fig.task_Rest + intercept, ax=ax, color='black')
ax.text(30, 80, f"$r^2$={r_value ** 2:.2f}", ha='center', va='center')
ax.set(xlabel=f"Rest ACW-50", ylabel=f"{task} ACW-50", xlim=[25, 60], ylim=[25, 90])
ax.get_legend().remove()
# fig.subplots_adjust(wspace=0.22)
legend_handles = []
for net, color in zip(unique_networks, palette):
legend_handles.append(Line2D([], [], color=color, marker='o', linestyle='None', markersize=5, label=net))
fig.legend(handles=legend_handles, loc=2, ncol=6, handletextpad=0.1, mode="expand",
bbox_to_anchor=(0.037, 0.05, 0.785, 1))
txt = fig.text(0.1, 1, "test", color="white")
savefig(fig, "fig6.acw.scatter", extra_artists=(txt,))
df = acz.gen_long_data(tpt_name).groupby(["task", "subject", "network", "region"]).mean().reset_index() \
.groupby(["subject", "network", "region"]).apply(split, "task", "metric").reset_index().drop("level_3", 1) \
.sort_values("subject")
output = np.zeros((len(tasks), mask.size))
for ti, task in enumerate(tasks):
shared_subj = acz.find_shared_subjects(tpt_name, ["Rest", task])
for ri, region in enumerate(regions):
mask_reg_ind = np.argwhere(mask == ri + 1)
df_rgn = and_filter(df, region=region, subject=shared_subj)
output[ti, mask_reg_ind] = stats.pearsonr(df_rgn.task_Rest, df_rgn[f"task_{task}"])[0]
savemap("fig6.acz", output, brain_axis, cifti.Series(0, 1, output.shape[0]))
df_fig = df.groupby(["network", "region"]).mean().reset_index()
for task in task_order(True):
df_fig[f"task_{task}"] *= 1000
fig, axs = plt.subplots(1, 3, figsize=(16, 5))
for ti, task in enumerate(tasks):
ax = axs[ti]
sns.scatterplot(data=df_fig, x="task_Rest", y=f"task_{task}", hue="network", hue_order=unique_networks, ax=ax,
palette=palette)
slope, intercept, r_value, _, _ = stats.linregress(df_fig.task_Rest, df_fig[f"task_{task}"])
sns.lineplot(df_fig.task_Rest, slope * df_fig.task_Rest + intercept, ax=ax, color='black')
ax.text(200, 500, f"$r^2$={r_value ** 2:.2f}", ha='center', va='center')
ax.set(xlabel=f"Rest ACW-0", ylabel=f"{task} ACW-0", xlim=[130, 510], ylim=[40, 550])
ax.get_legend().remove()
# fig.subplots_adjust(wspace=0.22)
legend_handles = []
for net, color in zip(unique_networks, palette):
legend_handles.append(Line2D([], [], color=color, marker='o', linestyle='None', markersize=5, label=net))
fig.legend(handles=legend_handles, loc=2, ncol=6, handletextpad=0.1, mode="expand",
bbox_to_anchor=(0.045, 0.05, 0.785, 1))
txt = fig.text(0.1, 1, "test", color="white")
savefig(fig, "fig6.acz.scatter", extra_artists=(txt,))