Flex Search Electrode Optimization

References:

• Original Paper: Weise K, Madsen KH, Worbs T, Knösche TR, Korshøj A, Thielscher A. A Leadfield-Free Optimization Framework for Transcranially Applied Electric Currents

• SimNIBS Implementation: Leadfield-free TES Optimization Tutorial

• Haber, I., Jackson, A., Thielscher, A., Hai, A., & Tononi, G. TI-Toolbox: An Open-Source Software for Temporal Interference Stimulation Research. Brain Stimulation

Overview

Flex Search uses differential evolution optimization to determine the best electrode positions for TI stimulation. The public API is run_flex_search(config: FlexConfig) -> FlexResult, with all configuration expressed through type-safe dataclasses and enums.

Core capabilities:

• Optimization Goals (OptGoal enum): mean, max, focality (threshold-based ROC), or focality_tf (threshold-free focality)
• Post-processing Methods (FieldPostproc enum): max_TI, dir_TI_normal, or dir_TI_tangential
• ROI Definition: FlexConfig.SphericalROI, FlexConfig.AtlasROI, or FlexConfig.SubcorticalROI dataclasses – see Defining the ROI below
• Anisotropy Support: Four conductivity models (scalar, vn, dir, mc) with configurable max ratio and conductivity
• Multi-start Optimization: Run multiple optimization iterations and automatically select the best result
• Structured Output: Every run writes a flex_meta.json manifest for downstream consumption

User Interface

The interface provides comprehensive controls for:

• Basic Parameters: Subject selection, optimization goal, and post-processing method
• Electrode Parameters: Radius and current settings
• ROI Definition: A shared ROI picker with three modes – Cortical, Subcortical, and Spherical – used for both the ROI and the optional non-ROI; see Defining the ROI below
• Stability Options: Iteration limits, population size, and CPU utilization
• Mapping Options: EEG net electrode mapping capabilities

Defining the ROI

The ROI picker (ROIPickerWidget, tit/gui/components/roi_picker.py) is shared across flex-search, ex-search, and the analyzer. In flex-search it appears twice – once for the ROI, once for the optional “Specific Region” non-ROI described below – as a radio row with three modes:

• Cortical (default): pick regions from a FreeSurfer .annot atlas. An Atlas combo selects the parcellation, and “List Regions” opens a finder that lists regions from both hemispheres by name. Selected regions become removable chips keyed by hemisphere-prefixed name (e.g. lh.precentral) – there is no separate hemisphere selector, so a single target can span both hemispheres from this one page.
• Subcortical: pick regions from a volumetric atlas. A Subject/MNI “Atlas Space” radio pair (Subject default) selects the coordinate space, a Tissue Type combo (GM / WM / GM+WM) sets the tissue restriction, and a Volume Atlas combo selects the atlas. “List Regions” adds selections as removable chips keyed by integer label id.
• Spherical: a Subject/MNI coordinate-space radio pair (Subject default), a multi-row sphere table (X/Y/Z in -150 to 150 mm, radius 1 to 50 mm, default 10.0 mm), Add Sphere / Duplicate Selected / Remove Selected buttons, and a “View T1 in Freeview” button (relabeled “View MNI Template” in MNI mode) to look up coordinates. A Volumetric checkbox enables a Tissue combo (GM / WM / GM+WM); unchecked, the sphere(s) are evaluated on the cortical surface instead.

Whichever mode is used, the picker serializes to one of three dataclasses nested under FlexConfig. Every field on all three accepts either a single value or a list – a list unions several regions into one combined target: N spheres, cross-hemisphere cortical labels (e.g. lh.insula + rh.insula), or e.g. subcortical labels 17 and 53 for both hippocampi at once.

FlexConfig.SphericalROI

volumetric=False is the default, which evaluates the sphere on the cortical central surface, not a volume. For a deep target – amygdala, hippocampus, thalamus – this is an easy and consequential mistake: the sphere still picks up whatever surface vertices fall inside its radius, which for a subcortical center is the overlying cortex, not the target structure. Set volumetric=True for any target that is not on the cortical ribbon.

FlexConfig.AtlasROI

Always evaluated on the cortical central surface – AtlasROI has no tissues field. Because each label carries its own hemisphere and atlas path, a single AtlasROI can combine lh.insula and rh.insula (or regions from different atlases) into one union.

FlexConfig.SubcorticalROI

atlas_space="mni" targets one of the four bundled MNI atlases (see Atlases) instead of a subject-specific segmentation; SimNIBS transforms the selected label into subject space during ROI setup. A single tissues and atlas_space apply to every label in the union, and every atlas path is verified to exist before the run starts.

Python Example: Volumetric Union

from tit.opt import FlexConfig, run_flex_search

config = FlexConfig(
    subject_id="101",
    goal="mean",
    postproc="max_TI",
    current_mA=2.0,
    electrode=FlexConfig.ElectrodeConfig(),
    roi=FlexConfig.SubcorticalROI(
        atlas_path="/path/to/m2m_101/segmentation/aparc.DKTatlas+aseg.mgz",
        label=[17, 53],       # left + right hippocampus, unioned
        tissues="GM",
        atlas_space="subject",
    ),
)

result = run_flex_search(config)

For a deep spherical target, volumetric=True must be set explicitly, or the sphere silently falls back to the cortical-surface default described above:

roi = FlexConfig.SphericalROI(
    x=-25.0, y=-10.0, z=-20.0,
    radius=8.0,
    use_mni=True,
    volumetric=True,   # required for subcortical targets -- see note above
    tissues="GM",
)

Mean TI Field Optimization Demonstration

We demonstrate the effectiveness of flex-search by optimizing electrode positions for the same target ROI using different post-processing methods. The target was the left insula (lh.insula in the DK40 atlas) with the goal of maximizing the mean TI field.

Optimization Setup

• Subject: 102
• Target ROI: Left insula (lh.insula, DK40 atlas) – picked by name via the cortical ROI picker; the .annot label index is resolved internally, not entered by the user
• Goal: Maximize mean field in ROI
• Electrode: 4mm radius, 8mA current

Results: Maximum TI Field Optimization

Maximum TI field distribution showing optimization results

ROI targeting analysis for maximum TI field optimization

Optimization Summary:

Focality Optimization with Dynamic Thresholding

The focality optimization goal is a multi-objective function balancing ROI targeting with out-of-ROI field minimization:

Non-ROI Definition Methods

• Everything Else: Uses the complement of the ROI (everything outside the target region)
• Specific Region: Define a custom non-ROI using the same ROI picker (spherical, atlas, subcortical). Three constraints apply that are not obvious from the UI: the non-ROI picker is forced onto the same mode as the ROI picker – switching the ROI’s mode switches the non-ROI’s mode with it; the non-ROI’s spherical page has no Subject/MNI toggle, so non-ROI spheres are always subject-space even when the ROI itself is defined in MNI space; and the non-ROI’s own tissue selection is ignored – for volumetric spheres and subcortical atlases the non-ROI always inherits the ROI’s tissue compartment(s), not whatever its own Tissue control shows

Focality Thresholds - Critical for Optimization Success

As described in the original paper, focality optimization is fundamentally a constrained multi-objective problem where:

• Target Region (ROI): Field strength must exceed specified thresholds
• Avoidance Region (Non-ROI): Field strength must remain below specified thresholds
• Optimization Goal: Maximize field intensity in ROI while minimizing field spread outside ROI

Threshold Configuration Options

• Single Threshold: Binary classification where field must be below threshold in non-ROI and above threshold in ROI
• Dual Thresholds: Independent thresholds for each region, allowing asymmetric optimization constraints
• Derived Thresholds: thresholds always takes explicit numeric values – there is no in-loop adaptation. "dynamic"/"auto" are placeholders meaning “do not set a threshold; let SimNIBS use its own static default,” and the ROC-based objective path raises an error if no explicit numeric threshold is available. The workable pattern is an offline two-pass procedure: run a mean TI_max pass first, then derive the ROI/non-ROI thresholds from its field distribution and feed them into a focality run, as described below.

Focality optimization analysis: Comparative evaluation of threshold strategies reveals critical insights: threshold selection profoundly impacts results, with relative thresholds (50% of peak) yielding 75% higher focality than fixed thresholds, while 80% thresholds reduce focality by 37%, compared to fixed thresholds (0.1V/m and 0.3V/m) highlighting the importance of threshold optimization for precise neuromodulation. Dynamic % based thresholds were derived automatically from an intial pass of mean TImax search and applied to the upper bound only. The lower bound was kept at 20% from that value. Data regarding focality thresholds and optimization performance comes from the supplementary information of the TI-Toolbox reference.

Threshold-Free Focality Optimization

The focality_tf goal targets the same objective as the ROC goal above – concentrate the field in the ROI and keep it out of the non-ROI – without asking the user to commit to a field threshold in advance. Instead of counting elements above and below a cutoff, it scores the ratio of the ROI field to the upper tail of the non-ROI field:

focality_tf = mean(E_ROI)^1+w / p95(E_non-ROI)

Larger is better; the optimizer minimizes its negative. The denominator uses the 95th percentile rather than the mean or the maximum, so the score is driven by the hot spots that actually matter for off-target stimulation while staying insensitive to a handful of extreme elements.

The Intensity Weight

The exponent w is the intensity_weight parameter (range [0, 1], default 0.0) and controls the trade-off between focality and on-target strength:

• intensity_weight = 0.0 – balanced “tail” form. The ROI field enters linearly, so a montage that halves the off-target field is worth as much as one that doubles the on-target field. This is the default.
• intensity_weight = 1.0 – intensity-first. The ROI field is squared, so the objective strongly prefers solutions that deliver field to the target even at the cost of some spread.
• Intermediate values interpolate between the two.

Why Threshold-Free Matters

The ROC goal’s result depends entirely on a threshold that the user has to choose before the optimization runs, and the threshold analysis above shows how much that choice moves the outcome. The deeper problem is that a threshold pair which is well-calibrated for one target can be jointly infeasible for another: if no montage on the scalp can simultaneously push the ROI above the lower bound and hold the non-ROI below the upper bound, every candidate scores identically, the objective landscape goes flat, and differential evolution has no gradient to follow. This is most likely exactly where optimization is needed most – deep targets.

focality_tf has no such failure mode. It is a continuous ratio, so it always ranks one candidate above another and always hands the optimizer a usable landscape, whatever the depth of the target.

That does not make it failure-proof, though – a scale-free ratio objective trades one failure mode for a different one. Because it scores separation, not dose, it can rate a candidate highly for being cleanly separated from the non-ROI even when almost no field reached the target at all. In the mini-study below, a threshold-free direct-AUC run scored AUC 0.931 on just 0.047 V/m of on-target field, and focality_tf itself bottomed out at 0.084 V/m in its worst cell. Always read a focality or AUC score next to the ROI-mean field it was computed on.

Summary across all 75 flex-search runs (5 subjects x 3 deep atlas targets x 5 optimization goals): mean AUC per goal, the focality-vs-on-target-strength tradeoff for every run, and each goal’s worst-case AUC and worst-case ROI field strength. On average across the 15 subject-target cells, the threshold-free objectives outperformed both ROC threshold settings on focality (AUC); focality_tf (the shipped goal, w = 0) beat both ROC arms in 13 of 15 cells (86.7%), losing only at sub-101 and sub-103 L-hippocampus – the one target where the aggressive threshold pair (0.4/0.2 V/m) turned out to be feasible. That same aggressive ROC goal failed outright where its threshold pair was jointly infeasible at depth: it scored below chance (AUC < 0.5) in 5 of its 15 cells, and collapsed to near-zero on-target field – a separate, dose-based criterion – in 6. focality_tf had no failures under either criterion. This does not make focality obsolete – where a defensible and attainable threshold exists, it optimizes precisely the criterion the user cares about – but it does mean the threshold has to be validated for the target at hand. See What the Objectives Do in Practice below for the full mini-study behind this figure.

When to Use Which

* From the mini-study below; the focality column here is specifically the aggressive thresholds=0.4,0.2 V/m arm – a different threshold choice will perform differently.

Both goals use the same ROI and non-ROI setup, so switching between them requires no change to the region definitions. One restriction applies only to focality_tf: it cannot be run with detailed_results=True (see Detailed Results).

What the Objectives Do in Practice

The figure and claims above come from a mini-study run to check the goal comparison beyond a single demonstration: 5 subjects (101, 102, 103, 106, 107) x 3 deep FreeSurfer atlas targets (L-hippocampus, label 17; R-thalamus, label 49; L-insula, label 1035, all from aparc.DKTatlas+aseg; ROI = the gray-matter elements inside the label) x 5 optimization goals = 75 flex-search runs. Every run used SimNIBS’s default differential-evolution budget (popsize=13, maxiter=1000, tol=0.1), 2 mA per channel, two electrode pairs, 8 mm circular electrodes with 4 mm gel, free-direction max_TI post-processing, and a single DE seed per cell (no multi-start).

Caveats

Read every number in this section with these in mind:

• n = 5 subjects, descriptive only. No inferential statistics were run – no p-values, no confidence intervals, no multiple-comparison correction.
• One DE seed per cell. Run-to-run optimizer variability was not characterized, so differences of roughly 0.02-0.05 AUC are within plausible seed noise. The 0.899 vs. 0.889 AUC gap between direct AUC and focality_tf in the table below is not a demonstrated real difference on that basis alone.
• Direct-AUC’s lead is partly definitional. AUC is both its optimization objective and the evaluation metric used to score all five goals here, so treat its AUC column as a ceiling reference, not an independent win.
• The non-ROI is all other gray matter, not the whole brain. The study used SubcorticalROI(tissues="GM"), and SimNIBS’s everything_else non-ROI inherits that tissue restriction – white matter and CSF were never part of the non-ROI in any run.
• Statistics are volume-weighted, computed on the final-electrode FEM meshes rather than the optimizer’s mid-search arrays. This changes how the summary statistics are computed, not which tissue is included, and the ranking of the five goals is unchanged versus unweighted means.
• Three deep targets only – hippocampus, thalamus, insula. Nothing here speaks to superficial cortical targets, where the ROC thresholds are far more likely to be jointly feasible.
• AUC is scale-free. It measures separation, not dose. Never read an AUC value without the ROI-mean field next to it.

Pooled results (n = 15 subject-target cells per goal)

Correction: the “intensity-weighted” row above is not intensity_weight = 1.0. That study arm maximized mean(E_ROI)^2 / mean(E_non-ROI) – a mean in the denominator. The shipped focality_tf at w = 1.0 computes mean(E_ROI)^(1+w) / p95(E_non-ROI) – a 95th-percentile denominator (see The Intensity Weight above and threshold_free_focality in tit/opt/flex/objectives.py). Do not read the row above as “what intensity_weight = 1.0 gives” in the shipped code; no w > 0 run of the actual shipped objective was included in this study.

Reliability

focality (ROC 0.4/0.2 V/m) has a pooled AUC standard deviation of ±0.239 – four to five times either threshold-free ranking goal’s (±0.046 for direct AUC, ±0.056 for focality_tf). The aggressive ROC goal is not uniformly bad, it is unreliable: its outcome depends heavily on whether its fixed thresholds happen to be feasible for the subject and target at hand.

Failures

Five of the 75 runs (6.7%) scored AUC below 0.5 – worse than chance separation – and all five are the aggressive focality (ROC 0.4/0.2 V/m) goal: 3 at R-thalamus, 2 at L-insula, 0 at L-hippocampus. Neither threshold-free goal (direct AUC, focality_tf) had a single run below chance.

A separate, dose-based flag counts runs whose ROI mean fell below one third of that target’s median ROI mean across all goals and subjects – catching cases where the AUC looks acceptable but almost no field reached the target. By goal: focality (ROC 0.4/0.2 V/m) = 6, threshold-free direct AUC = 1, focality (ROC 0.2/0.1 V/m) = 0, focality_tf (w = 0) = 0, threshold-free intensity-weighted = 0. Neither criterion alone catches everything: sub-106 R-thalamus under ROC 0.4/0.2 scored AUC 0.760 (above chance) while delivering only 0.048 V/m on target – caught by the dose flag but not by the below-chance criterion.

Depth dependence

The aggressive focality (ROC 0.4/0.2 V/m) goal performs best, among its own three targets, at L-hippocampus (AUC 0.908 ± 0.013, ROI mean 0.447 ± 0.037 V/m – the second-highest ROI mean of any goal at that target) and worst at R-thalamus (AUC 0.541 ± 0.177) and L-insula (AUC 0.576 ± 0.258). The same 0.4/0.2 V/m threshold pair happens to be feasible at hippocampus depth and infeasible at the other two – and the goal gives the user no warning about which case they are in before the run finishes.

Group ROC curves of the TI envelope (target gray matter vs. the other gray matter) for each of the three deep targets, comparing all five optimization goals by mean AUC over the 5 subjects; the shaded band shows ±1 SD across subjects for the shipped focality_tf (w = 0) arm only. AUC drops sharply for the ROC threshold goals at R-thalamus and L-insula, while the threshold-free goals (direct AUC, focality_tf, intensity-weighted) stay high across all three targets.

Worst case

The shipped goal’s worst run across all 15 cells, AUC 0.783, essentially matches the milder ROC goal’s average (0.787 ± 0.084). The aggressive ROC goal’s worst run drops to AUC 0.282 (sub-103, L-insula); the lowest on-target field reached by that same goal anywhere in the study is 0.015 V/m (sub-107, R-thalamus) – two separate worst-case runs, not necessarily the same one.

Pooled, volume-weighted TI envelope field-strength distributions (target gray matter vs. the other gray matter), element samples pooled over the 5 subjects, shown as a 3-target x 5-goal grid with the group ROI/non-ROI mean ratio annotated in each panel. At R-thalamus, the ROC 0.4/0.2 goal’s ROI/non-ROI ratio inverts – the pooled group ratio annotated in the figure is 0.97, and the per-subject mean for that cell is 0.975 +/- 0.267 – meaning the off-target gray matter received, on average, slightly more field than the target. This is consistent with the near-zero on-target fields shown in the summary figure above.

Current-Ratio Optimization

By default the two TI channels carry equal current. The optimize_current_ratio flag opts into searching that split as well: the total injected current is held constant and divided between the two channels, with ratio_levels (default 21) setting the density of the grid, which spans 1:3 to 3:1. The total defaults to 2 x current_mA and can be set explicitly with ratio_total_mA.

What Each Channel Carries

Holding the total fixed means the per-channel current necessarily moves. Over the 1:3 to 3:1 grid each channel spans a quarter to three quarters of the total – that is, 0.5x to 1.5x the configured current_mA (the GUI’s Electrode Current). With the default total of 2 x current_mA and current_mA = 2.0, a channel is driven anywhere between 1.0 mA and 3.0 mA, with the pair always summing to 4.0 mA. current_mA is therefore the center of the searched range, not a per-channel ceiling: choose it (or set ratio_total_mA directly) so that the 1.5x end is still within the dose you intend to deliver.

The grid always contains the balanced 1:1 split – ratio_levels is rounded up to the next odd number so the midpoint of the sweep is the exact even split. Enabling the ratio search therefore cannot return a worse solution than the equal-current montage it would otherwise have used.

That guarantee is about the objective value only. It is not a guarantee about the delivered field: under a focality-only objective with a tight off-target bound, the optimizer can pick an extreme split simply to turn the whole field down, since a weaker envelope is an easy way to satisfy a hard cap on off-target field. Measured on sub-101 L-hippocampus at a fixed 4 mA total, holding electrode positions fixed and varying only the split: 1:3 gives 0.185 V/m on target (focality ratio 1.64, attenuated); 2:2 gives 0.361 V/m (focality ratio 1.92, best on both); 3:1 gives 0.196 V/m (focality ratio 1.16, attenuated). Focality peaks near 1:1 in this example and drops off toward both extremes – the extreme splits attenuate the whole field rather than sharpening it.

Why the Split Matters

The TI envelope amplitude is capped by the weaker of the two channels – for co-linear fields the modulation depth is 2 * min(|E1|, |E2|). The split therefore does two things at once: it scales the envelope, because the ceiling moves with the weaker channel, and it steers it, because the locus where the two channels balance shifts toward the weaker one. Because the FEM is linear, a per-channel rescale is enough to evaluate any split without a new solve (Lee et al. 2022). Published TI optima are rarely the 1:1 split that flex-search fixes by default (Lee et al. 2020; Inoue et al. 2025, n = 60), so leaving the split fixed gives up a free axis of the search space.

How the two-channel current split scales and steers the TI envelope. Because the envelope is bounded by the weaker channel, an unequal split trades peak amplitude against the position of the interference locus. This particular montage was itself optimized at 1:1, so 1:1 is near-optimal for it by construction – post-hoc ratio tuning on top of an already-1:1-optimized placement gains only about +0.8% here. The payoff from a free ratio comes from searching it jointly with electrode placement (below), which can reach placements a 1:1-constrained search would never consider in the first place.

Calibrating the Off-Target Bound

The trap described above only bites when a focality objective’s off-target bound is tight enough to be binding well away from the 1:1 split. The fix is to calibrate the bound against what the target can actually achieve rather than picking a number in isolation: sweep the bound across the field’s achievable range and check whether the optimizer’s chosen split still moves as a result. On a single subject (101), the achievable off-target RMS field ranged:

These ranges are from a single subject and from a reduced-budget sweep (maxiter=4, popsize=6, well under the SimNIBS default of maxiter=1000, popsize=13), so the front they trace is under-converged. Treat them as a starting point for calibrating a bound on a new subject, not as a target’s true achievable range.

Searched Jointly, Not Afterwards

The ratio is optimized jointly with electrode placement, not applied as a post-hoc refinement. Every candidate placement is scored at its own best split, so the optimizer sees the landscape it will actually be evaluated on. Running a ratio sweep after the fact would only refine a montage that had already been selected under a 1:1 assumption – a different, and worse, optimum.

The joint search costs almost nothing. The FEM is linear, so for a fixed electrode placement the per-channel fields simply scale with the injected current: evaluating an additional split is a rescale of the already-computed fields and a re-combination of the envelope, with no new FEM solve. Candidate splits are ranked on a deterministic subsample of the non-ROI, and only the winning split is re-scored on the full non-ROI, so the several-million-element non-ROI is traversed once per evaluation rather than once per split.

Current-ratio optimization works with any goal (mean, max, focality, focality_tf). The winning split is applied to the final electrode simulation, so the fields written to disk are the ones the objective actually scored, and it is recorded in the run manifest (flex_meta.json).

Python Usage

from tit.opt import FlexConfig, run_flex_search

config = FlexConfig(
    subject_id="101",
    goal="focality_tf",           # threshold-free focality
    postproc="max_TI",
    current_mA=2.0,
    intensity_weight=0.0,         # 0 = balanced, 1 = intensity-first
    electrode=FlexConfig.ElectrodeConfig(
        shape="ellipse",
        dimensions=[8.0, 8.0],
        gel_thickness=4.0,
    ),
    roi=FlexConfig.SphericalROI(
        x=-35.0, y=5.0, z=5.0,
        radius=10.0,
        use_mni=True,
    ),
    non_roi_method="everything_else",
    optimize_current_ratio=True,  # search the channel split jointly
    ratio_total_mA=4.0,           # defaults to 2 * current_mA
    ratio_levels=21,              # grid density, spanning 1:3 .. 3:1
)

result = run_flex_search(config)
print(f"Best value: {result.best_value:.4f}")

Note that thresholds is not used by focality_tf and can be omitted; it remains required for the ROC-based focality goal.

Detailed Results

detailed_results=True cannot be combined with the current-ratio search or with the focality_tf goal. Both install a Python callable as the optimization goal, and SimNIBS cannot serialize a callable into its detailed-results HDF5 file, so requesting the combination raises an error before the run starts rather than failing partway through. Leave detailed_results at its default of False – the GUI does not expose it – and read run metadata from flex_meta.json instead.

Multi-Start Optimization

Flex Search supports multi-start optimization to ensure robust and reliable results by running multiple optimization iterations and selecting the best solution:

• Multiple Runs: Configure the number of optimization runs (default: 1, recommended: 3-5 for critical applications)
• Best Solution Selection: Automatically selects the optimization run with the lowest function value
• Comprehensive Reporting: Generates multi-start summary files with run-by-run analysis

Multi-start optimization validation: Analysis demonstrates that running multiple independent optimizations with different random seeds yields superior solutions compared to single runs; 4.18% improvement in mean TImax. While statistically significant, the modest gains should be weighed against the increased computational cost. Data regarding multi-start optimization performance comes from the supplementary information of the TI-Toolbox reference.

Electrode Mapping and Target Accessibility

The transition from unconstrained optimization solutions to practical electrode montages represents a critical step in clinical translation. While genetic algorithms can identify theoretically optimal electrode positions anywhere on the scalp, its transition to clinical application may be difficult. Our electrode mapping algorithm bridges this gap by finding the best approximation of optimized positions using available electrode sites. For this study, we utilized the inner 185 electrodes of the GSN-HydroCel-256 system (EGI/Philips), which provides high-density coverage. A combinatorial optimization method that solves the assignment problem in polynomial time. By minimizing the total Euclidean distance between optimized and standard positions, this approach ensures good representation of the intended field distribution while maintaining practical feasibility.

Electrode mapping challenges: Analysis of optimized electrode positions reveals depth-dependent mapping distances across anatomical targets, with subcortical structures like the hippocampus requiring significantly larger electrode separations (11.74 ± 5.33 mm) compared to cortical regions like the insula (7.30 ± 1.38 mm) or spherical ROIs (8.01 ± 1.43 mm). This pattern reflects the fundamental challenge of targeting deep brain structures with scalp electrodes, where optimal montages often requires large distances between electrodes which may be positioned on the lower scalp that does not have dense electrode coverage. Data regarding electrode mapping distances comes from the supplementary information of the TI-Toolbox reference.

Reports and mapped electrode labels

Flex-search reports include the selected optimized configuration, subject-space optimized coordinates, and mapped EEG-net labels/positions when electrode_mapping.json is produced. Ranked rows show mapped labels for the selected mapped solution; rows without mapping data are intentionally left unlabeled rather than guessing electrode names. If optional montage or field-map images are unavailable, the report includes an explicit note and the tables remain the source of truth.

For template/MNI workflows, confirm that the selected EEG net is available and that final mapped-electrode simulation is enabled if you need report imagery from the practical mapped montage.

Output Manifest (flex_meta.json)

Every flex-search run writes a flex_meta.json file to the output folder. This manifest is the single source of truth for run metadata – downstream consumers (simulator tab, GUI) read this instead of parsing folder names.

The manifest contains:

• Run configuration (goal, postproc, electrode, ROI, anisotropy)
• Result summary (success, best value, all function values)
• Timestamps and labels for display

Advanced Features

Anisotropy Support

Flex-search passes anisotropy parameters directly to SimNIBS, enabling optimization with direction-dependent tissue conductivity. The anisotropy_type parameter accepts four models:

Additional parameters aniso_maxratio (default: 10.0) and aniso_maxcond (default: 2.0) control the anisotropy bounds.

Valid Skin Region Validation

Flex-search optimization is constrained to valid skin regions where electrodes can be safely placed. The green region represents the valid skin area for electrode placement during optimization, while red “x” marks indicate HD-EEG electrodes that fall outside this valid region.

If electrode positions fall outside the valid skin region, the valid skin region can be manipulated through preprocessing, or ex-search can be used as an alternative since it is not constrained by skin region limitations.

Valid Skin Region Margin

The flex-search skin constraint can be adjusted directly from scripts or from the GUI. skin_region_margin_mm applies a signed millimeter margin to the default SimNIBS valid skin region: negative values constrict the region, positive values expand it. The default is 0.0, which preserves the standard SimNIBS mask.

For positive margins, avoid_landmark_regions=True keeps fiducial-derived ear and orbital exclusion zones invalid. This guard uses scalp landmarks only (Nz, LPA, and RPA) and does not depend on eye tissue labels.