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arxiv: 2605.18118 · v1 · pith:UMZUBGK4new · submitted 2026-05-18 · 🧬 q-bio.NC

Functional Whole-Brain Models: A New Framework for Unifying Brain Structure and Cognitive Function

Mario Senden , Leonardo Dalla Porta , Jan Fousek , Jorge F. Mejias , Gorka Zamora-L\'opez This is my paper

Pith reviewed 2026-05-20 00:26 UTC · model grok-4.3

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keywords functional whole-brain modelswhole-brain modelingbrain connectivitycognitive functioncomputational neurosciencedynamical systemsneuroimaging
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The pith

Functional whole-brain models integrate empirical brain wiring, realistic dynamics, and task performance through four minimal criteria.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Current brain modeling splits into detailed bottom-up simulations that capture anatomy and physics but lack cognitive ability, and top-down networks that perform tasks well but ignore most biological constraints. The paper proposes functional whole-brain models to close this gap by enforcing four requirements at once: grounding in measured connectomes and regional properties, continuous-time dynamics that respect biophysical rules, competence at a range of cognitive functions, and outputs that can be directly compared with imaging, electrophysiological, and behavioral recordings. Meeting these criteria together would supply a shared framework for asking how structure gives rise to function and for generating predictions that span scales. The authors sketch a three-pillar roadmap with near-, medium-, and long-term milestones to guide construction of such models and note the scientific and clinical uses that would follow.

Core claim

Functional whole-brain models are defined by four minimal criteria that together integrate structural and dynamical realism with task-performing capacity: structural grounding in empirical connectomes and regional biology, continuous-time dynamical realism, functional competence across cognitive domains, and mappable observables to neuroimaging, electrophysiological and behavioral data.

What carries the argument

Functional whole-brain models (fWBMs) defined by the four minimal criteria that force a single model class to satisfy biological, dynamical, and functional demands simultaneously.

If this is right

  • Supplies a common language and set of tools for linking structure to function across scales.
  • Enables generation of cross-scale hypotheses that can be tested against multiple data modalities.
  • Creates integrated models usable for both basic science questions and clinical applications.
  • Guides coordinated progress through a three-pillar roadmap spanning short-, mid-, and long-term horizons.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If realized, these models could support individualized simulations that predict how a person's connectome relates to their specific cognitive profile.
  • The framework might encourage development of hybrid architectures that inherit constraints from both anatomy and task demands.
  • Progress on the roadmap would likely highlight which minimal biological features are sufficient for particular cognitive capacities.

Load-bearing premise

It is possible to satisfy all four criteria simultaneously in a single model class without prohibitive trade-offs between biological detail and functional performance.

What would settle it

Successful construction of even one model that meets all four criteria, or clear demonstration that any attempt to meet one criterion blocks another, would settle whether the proposed unification is feasible.

Figures

Figures reproduced from arXiv: 2605.18118 by Gorka Zamora-L\'opez, Jan Fousek, Jorge F. Mejias, Leonardo Dalla Porta, Mario Senden.

Figure 1
Figure 1. Figure 1: The functional whole-brain modeling paradigm. fWBMs are defined by four minimal criteria, illustrated here as an integrated system. Structural grounding (top center): model architecture is derived from empirical meso- and macro-scale structural connectivity to￾gether with region-specific biological properties. Continuous-time dynamical realism (top left): local dynamics are governed by differential equatio… view at source ↗
read the original abstract

Contemporary computational neuroscience features two prominent modeling traditions. Bottom-up whole-brain modeling (WBM) builds biophysically detailed simulations of brain structure and dynamics, whereas top-down neuroconnectionism optimizes deep neural networks for functional performance. Each has achieved remarkable success yet remains incomplete with WBMs lacking functional competence and neuroconnectionist models showing limited biological grounding. Here we propose functional whole-brain models (fWBMs) as a unified modeling paradigm that integrates structural and dynamical realism with task-performing capacity. fWBMs are defined by four minimal criteria: structural grounding in empirical connectomes and regional biology, continuous-time dynamical realism, functional competence across cognitive domains, and mappable observables to neuroimaging, electrophysiologcal and behavioral data. To formalize this integration, we establish a three-pillar roadmap across short-, mid-, and long-term horizons, and outline the scientific and clinical opportunities this paradigm enables. We argue that the disciplined pursuit of this integrative vision will generate the tools, common language, and cross-scale hypotheses needed to advance our understanding of the brain.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes functional whole-brain models (fWBMs) as a unifying paradigm that combines the structural and dynamical realism of bottom-up whole-brain modeling with the functional competence of top-down neuroconnectionism. fWBMs are defined by four minimal criteria—structural grounding in empirical connectomes and regional biology, continuous-time dynamical realism, functional competence across cognitive domains, and mappable observables to neuroimaging, electrophysiological, and behavioral data—and the paper sketches a three-pillar roadmap across short-, mid-, and long-term horizons together with potential scientific and clinical opportunities.

Significance. If realized, the framework could supply a shared language and set of tools for cross-scale brain modeling, enabling tighter integration of connectome-based structure, biophysical dynamics, and task-driven function. The proposal itself, however, remains prospective: it offers no explicit construction, loss function, or even toy-scale demonstration that the four criteria can be satisfied simultaneously, so its significance hinges on future work that the manuscript only outlines.

major comments (2)
  1. [three-pillar roadmap] The central claim that the four criteria can be jointly satisfied without prohibitive trade-offs between biological detail and functional performance is load-bearing for the entire proposal yet receives no supporting construction. The three-pillar roadmap section defines the horizons but supplies neither an explicit training objective nor a worked example on even a small connectome that would show how structural grounding, continuous-time dynamics, and task competence are enforced together.
  2. [definition of fWBM criteria] The criterion of 'functional competence across cognitive domains' is stated at a high level without operationalization. No specific tasks, performance metrics, or comparison baselines are given, leaving open whether this requirement adds anything beyond existing neuroconnectionist objectives or simply restates them.
minor comments (2)
  1. [abstract] The abstract contains a typographical error in 'electrophysiolgcal' (should be 'electrophysiological').
  2. [criteria section] Several terms (e.g., 'mappable observables') are introduced without a precise definition or reference to existing mapping techniques in the literature, which would aid clarity for readers outside the immediate subfield.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive review and for recognizing the potential of the fWBM framework to bridge bottom-up and top-down modeling traditions. We appreciate the identification of areas where greater concreteness would strengthen the proposal. Below we respond point by point to the major comments. We will revise the manuscript to incorporate additional illustrative material while preserving the conceptual scope of the original submission.

read point-by-point responses

  1. Referee: [three-pillar roadmap] The central claim that the four criteria can be jointly satisfied without prohibitive trade-offs between biological detail and functional performance is load-bearing for the entire proposal yet receives no supporting construction. The three-pillar roadmap section defines the horizons but supplies neither an explicit training objective nor a worked example on even a small connectome that would show how structural grounding, continuous-time dynamics, and task competence are enforced together.

    Authors: We agree that the manuscript presents a high-level conceptual framework rather than a concrete implementation or toy demonstration. The three-pillar roadmap is deliberately prospective, outlining research horizons rather than delivering a solved optimization problem. To address this, the revised manuscript will include a new subsection with a schematic multi-objective formulation (combining a structural regularization term on the empirical connectome, a continuous-time dynamical consistency loss, and a task-performance term) together with a worked pseudocode example on a small synthetic connectome. This addition will illustrate joint enforcement of the criteria without claiming a full empirical solution. revision: yes

  2. Referee: [definition of fWBM criteria] The criterion of 'functional competence across cognitive domains' is stated at a high level without operationalization. No specific tasks, performance metrics, or comparison baselines are given, leaving open whether this requirement adds anything beyond existing neuroconnectionist objectives or simply restates them.

    Authors: We accept that the functional-competence criterion is currently described at a conceptual level. In revision we will expand the criteria section to list concrete task families (e.g., visual categorization from the CIFAR-10 or ImageNet benchmarks, n-back working-memory tasks, and simple decision-making paradigms) together with quantitative metrics (accuracy, reaction-time correlation with human data, and generalization across domains). We will also explicitly state how simultaneous satisfaction of all four criteria distinguishes fWBMs from standard neuroconnectionist models that optimize only the functional term. revision: yes

Circularity Check

0 steps flagged

No circularity: definitional proposal with no equations or reductions

full rationale

The paper proposes fWBMs as a new paradigm by enumerating four minimal criteria (structural grounding, continuous-time dynamics, functional competence, mappable observables) and sketching a three-pillar roadmap. No equations, fitted parameters, or derivations appear; the text defines the framework from prior literature on WBMs and neuroconnectionism without any step that reduces a prediction or result back to its own inputs by construction. The central claim is therefore a self-contained definitional integration rather than a mathematical or empirical derivation that could exhibit circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The proposal rests on the domain assumption that the two existing traditions are each incomplete in complementary ways and that the four listed criteria are jointly sufficient to create a useful unified class; the central new entity is the fWBM concept itself.

axioms (1)
  • domain assumption Bottom-up whole-brain models lack functional competence while top-down neuroconnectionist models lack biological grounding.
    Explicitly stated in the opening paragraph of the abstract as the motivation for the new framework.
invented entities (1)
  • functional whole-brain models (fWBMs) no independent evidence
    purpose: A modeling paradigm that simultaneously satisfies structural, dynamical, and functional requirements.
    Introduced as a new named class defined by the four criteria; no independent empirical demonstration is provided.

pith-pipeline@v0.9.0 · 5735 in / 1254 out tokens · 59021 ms · 2026-05-20T00:26:59.198155+00:00 · methodology

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