Pith. sign in

REVIEW 3 major objections 5 minor 90 references

Multiplex Nodal Modularity: A novel network metric for the regional analysis of amnestic mild cognitive impairment during a working memory binding task

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A node-level modularity metric isolates the brain regions that mark MCI-to-Alzheimer's conversion.

desk verdict The abstract and conclusion claim nQ clearly differentiated MCI from MCI converters, but the paper's own results show no significant contrast for that comparison; the only significant findings are controls vs. converters. read the letter →

arxiv 2501.09805 v2 pith:ONS36W7H submitted 2025-01-16 q-bio.NC cs.SIphysics.bio-ph

Avalon Campbell-Cousins , Federica Guazzo , Mark Bastin , Mario A. Parra , Javier Escudero This is my paper · ORCID
classification q-bio.NCcs.SIphysics.bio-ph
keywords nodalmodularitymultiplexnetworksAlzheimer'sdiseasemildcognitiveimpairmentvisualshort-termmemorybindingfunctionalMRIdiffusiontensorimagingcommunitydetection
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper introduces nQ, a way to measure how much each individual node contributes to a network's modularity, rather than reporting a single global number. The authors argue that this matters for Alzheimer's disease because the disease disrupts brain community structure at regional scale, and global modularity hides which regions drive the change. Applying nQ to multiplex fMRI and DTI networks from a visual short-term memory binding task, they report that the measure distinguishes MCI patients who will convert to Alzheimer's from stable MCI, with changes concentrated in visual, limbic, and paralimbic regions in the functional data and in right parietal and frontal white-matter regions in the structural data. A sympathetic reader would care because a region-specific, task-sensitive network biomarker could help identify the prodromal turning point of AD and could be transferred to other disciplines where modularity is already used as a global metric.

What carries the argument

The central object is nodal modularity, nQ, defined as the per-node summand of multislice modularity. For node $i$, nQ sums the observed-minus-expected weight of edges from $i$ to nodes in the same community, across layers and including inter-layer couplings, normalized by $2\mu$; because these summands add to the global modularity $Q$, nQ localizes community structure to individual brain regions while preserving the standard multislice null model. The paper computes nQ from a community assignment obtained by an iterated modularity-maximization algorithm, then tests per-region group differences with permutation tests and ROC analysis.

What would settle it

Recompute nQ for every subject across many near-optimal community partitions, for example all partitions within a small modularity gap of the maximum, or across consensus partitions; if the controls-versus-converter regional differences disappear or flip sign, then the reported separation depends on the chosen partition rather than on the brain's regional community structure.

Watch

Extended reading notes

Core claim

The paper claims that extending global modularity to individual nodes yields nQ, a per-node measure that localizes community structure, and that in a visual short-term memory binding task this measure detects regional brain-network reorganization in people with MCI who later convert to Alzheimer's disease. In the multiplex fMRI networks, 25 ROIs, mostly in visual, limbic, and paralimbic systems, showed abnormal nQ in converters versus controls during the binding task, while the shape-only task showed no significant effects; single-layer models found 20 ROIs. In DTI networks, nQ changes appeared in right parietal and frontal regions. The authors interpret these patterns as consistent with known amyloid-β and tau deposition and with white-matter integrity findings, and they conclude from these contrasts that nQ can differentiate MCI from MCI converters, the key prodromal turning point of AD.

Load-bearing premise

The analysis uses one community assignment, the best of 100 modularity-maximization runs, as a fixed backdrop for every node's nQ, and if nodes switch communities between nearly equivalent partitions, the group differences could be a byproduct of that choice.

Editorial extensions

If this is right

  • nQ gives brain-network studies a per-region readout that sums to the global modularity they already report, so existing modularity analyses can be re-examined at node level without changing the null model.
  • In the paper's data, the binding task but not the shape task produced significant nQ differences, reinforcing that short-term memory binding is the AD-sensitive cognitive process and suggesting nQ reflects task-specific reorganization.
  • Multiplex modelling of the encoding/maintenance and probe phases found more ROIs with abnormal nQ than analysing layers separately, implying that temporal-layer coupling carries disease signal that single-layer analyses miss.
  • The regions flagged in fMRI overlap with sites of amyloid-β and tau deposition in the AD literature, so nQ may track molecular pathology at a regional scale.
  • DTI changes in right parietal and frontal ROIs, including the cuneus, link nQ to white-matter integrity and cross-hemispheric communication, suggesting a structural counterpart to the functional reorganization.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, because nQ is defined from any community assignment and not only from modularity maximization, it could serve as a general node-influence diagnostic for the output of any community-detection method, turning a methodological caveat into a feature.
  • Beyond the paper, a natural next experiment is to test nQ on EEG or MEG data, where global modularity has already been linked to AD; if nQ localizes the same effects, it could provide a cheaper and more available biomarker than fMRI.
  • Beyond the paper, the paper compares nQ with degree, clustering coefficient, and PageRank, but not with within-module degree or participation coefficient, the standard nodal community measures; a head-to-head comparison would clarify whether nQ adds information beyond these established metrics.
  • Beyond the paper, the sharp controls-versus-MCI versus controls-versus-converter contrast suggests that nQ may be more sensitive to imminent conversion than to current diagnostic category; validating this would require a prospective study with larger groups and longer follow-up.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces nodal modularity (nQ), defined as the node-level summand of the standard multislice modularity quality function, and applies it to task-fMRI and DTI multiplex networks from a small cohort performing a visual short-term memory binding task (VSTMBT). The authors benchmark nQ against other nodal measures on their own data, the NKI-Rockland cohort, and Zachary's Karate Club, and then compare regional nQ between controls, early MCI, MCI, and MCI converters. They report significant regional nQ differences in the binding task between controls and MCI converters, and claim in the Abstract and Conclusion that nQ 'clearly differentiated MCI from MCI converters', suggesting sensitivity to the MCI-to-AD turning point.

Significance. The mathematical definition of nQ is straightforward, the code is publicly provided, and the benchmarking against other nodal measures on multiple datasets is a useful sanity check that partially demonstrates the measure's distinct behavior. If the clinical claim were supported, nQ could offer a regional biomarker for the MCI-to-AD transition and a general tool for local community analysis. However, the central clinical claim is not supported by the reported statistics, which substantially limits the significance of the application while leaving the methodological contribution as the main value.

major comments (3)
  1. [Results, fMRI subsection; S2 Table; Abstract; Conclusion] The Abstract and Conclusion claim that nQ 'clearly differentiated MCI from MCI converters', but the Results state that 'in our comparisons of eMCI vs. MCI and MCI vs. MCI converters for fMRI shape and binding, no p-values survived FDR correction', and S2 Table lists no ROIs for the 'MCI vs. MCI converters' contrast in the DTI networks. The only significant contrast reported is controls vs. MCI converters. Thus the paper's own data provide no evidence for the headline claim of sensitivity to the MCI-to-AD turning point; the claim is contradicted by the reported results.
  2. [Tables 2 and 3] The ROC AUC values in Tables 2 and 3 are computed on the same ROIs that were selected based on permutation-test p-values from the same data. This selection-after-testing procedure makes the AUCs optimistically biased and does not provide an independent measure of discriminative performance. Additionally, with FDR at alpha = 0.2, up to 5 of 25 fMRI ROIs and 1 of 4 DTI ROIs could be false positives, and the paper does not account for the number of group contrasts when interpreting these results.
  3. [Modularity maximization] The paper computes nQ using a single community assignment g obtained as the highest-Q partition from 100 Louvain runs, and reports only that global Q is tightly distributed across runs. It does not assess the stability of node-level community assignments or of per-node nQ across near-optimal partitions. If node assignments vary substantially among equally good partitions, group differences in nQ could reflect partition-choice artifacts rather than biological differences; this premise underlies Eq (1) and is load-bearing for the clinical comparisons.
minor comments (5)
  1. [Abstract and Discussion] The phrase 'compliment studies' (Abstract) and 'complimenting our fMRI results' (Discussion) should be 'complement studies' and 'complementing our fMRI results', respectively.
  2. [Introduction] There is a typo: 'neurospsychological examinations' should be 'neuropsychological examinations'.
  3. [Eq (1) and surrounding text] The notation in Eq (1) is not fully defined: the summation indices 'jsr' should be expanded, and the meaning of the product term 'δ(gis, gjr)' in the multiplex setting should be clarified for readers unfamiliar with Mucha's formulation.
  4. [Modularity maximization] The decision to treat negative correlations as positive for modularity maximization is mentioned in a single sentence; given that this assumption can materially alter the community structure, it should be justified more thoroughly and its potential impact on the results discussed.
  5. [S3 Table] In the S3 Table header, 'E(2,28)' should be 'F(2,28)' for the ANOVA statistics.

Circularity Check

1 steps flagged · score 1.0 of 10

No load-bearing circularity: nQ is the node-level summand of published multislice modularity and is relabeled as novel, but the MCI/DTI/fMRI application and benchmarking are independent of any fitted prediction.

  1. renaming known result [Nodal Modularity section, Eq (1) and following text; Abstract]
    "To tackle this, we extend the standard multislice (multiplex) modularity quality function, Q multislice [25], to individual nodes as in [41]: ... From now on, we refer to nodal modularity as nQ=Q_i, and note that nQ can just as easily be defined for single-layer networks."

    The abstract presents nQ as 'This novel measure of nodal modularity', but Eq (1) is the standard node-level summand of the previously published multislice modularity Q_multislice [25], with node-level decomposition attributed to [41]. Eq (2) states Q_multislice = sum_i Q_i, so nQ is exactly the existing Qi term renamed; the 'novel measure' claim is a relabeling of a known quantity. This is not load-bearing for the application: gamma and C are fixed defaults, no parameter is fitted to the MCI data, and the group comparisons and external benchmarks (Karate Club, NKI) stand independently.

full rationale

The derivation chain for the central empirical claim is not circular. nQ is defined as the node-level contribution to multislice modularity (Eqs 1-3), with the community assignment g obtained from an iterated Louvain maximization using default resolution and coupling parameters (gamma=1, C=1); no parameter was tuned to the MCI/control contrast, and the paper benchmarks nQ against other nodal measures on two external datasets. The only definitional identity is sum_i nQ_i = Q_multislice, which is a property of the measure, not a fitted prediction. The paper does cite prior work by co-author Parra for the VSTMBT's AD sensitivity and for the dataset, but these are empirical foundations, not results derived from nQ. Therefore no circularity of the fitted-input or self-citation-load-bearing kinds is present. One minor issue: the abstract calls nQ a 'novel measure' although Eq (1) explicitly reproduces the known node-level modularity term from [25,41], so the novelty claim is a renaming rather than a new derivation. Separately, the abstract and conclusion state that nQ 'clearly differentiated MCI from MCI converters', but the Results report no FDR-surviving ROIs for that contrast in fMRI and S2 Table leaves the DTI MCI-vs-converter row empty; this is a support/consistency problem, not a circularity, and it is flagged here under the reviewing rule rather than counted as circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new physical or ontological entities are introduced; nQ is a derived quantity and no new particles, forces, or conserved quantities are postulated.

free parameters (2)
  • Resolution parameter gamma_s = 1 (default)
    Controls the scale of communities in each layer; set to 1 for all layers. nQ values and group differences depend on this choice, which is standard but not data-driven.
  • Interlayer coupling parameter C_jsr = 1 (default)
    Weight of each node's self-connection between the two layers; set to 1. It modulates how much cross-layer community membership influences nQ.
assumptions (4)
  • domain assumption The configuration-model null model used in multislice modularity is an appropriate baseline for brain connectivity matrices.
    Eq (1) assumes edge probabilities proportional to k_i k_j / 2m; no comparison with alternative nulls for fMRI/DTI networks is provided.
  • domain assumption The iterated general Louvain algorithm returns a community assignment reliable enough for node-level attribution.
    Used to fix g in Eq (1); near-optimal partitions can change nQ and are not audited at node level.
  • ad hoc to paper Negative fMRI correlations can be replaced by their absolute values for modularity maximization.
    Stated in Methods to avoid interpretability issues; this choice affects the partition and therefore nQ.
  • domain assumption Benjamini-Hochberg FDR with alpha=0.2 controls false positives under positive dependence among nodal p-values.
    Authors acknowledge negative dependencies cannot be ruled out; if present, reported discovery sets may overstate significance.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Multiplex Nodal Modularity: A novel network metric for the regional analysis of amnestic mild cognitive impairment during a working memory binding task." pith.science (2026). https://pith.science/paper/ONS36W7H

@misc{pith2026250109805,
  author       = {Pith},
  title        = {Pith review of: Multiplex Nodal Modularity: A novel network metric for the regional analysis of amnestic mild cognitive impairment during a working memory binding task},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ONS36W7H}},
  note         = {Machine review of arXiv:2501.09805}
}
abstract

Modularity is a well-established concept for assessing community structures in various single and multi-layer networks, including those in biological and social domains. Brain networks are known to exhibit community structure at local, meso, and global scale. However, modularity is limited as a metric to a global scale describing the overall strength of community structure, overlooking important variations in community structure at node level. To address this limitation, we extended modularity to individual nodes. This novel measure of nodal modularity (nQ) captures both mesoscale and local-scale changes in modularity. We hypothesized that nQ would illuminate granular changes in the brain due to diseases such as Alzheimer's disease (AD), which are known to disrupt the brain's modular structure. We explored nQ in multiplex networks of a visual short-term memory binding task in fMRI and DTI data in the early stages of AD. While limited by sample size, changes in nQ for individual regions of interest (ROIs) in our fMRI networks were predominantly observed in visual, limbic, and paralimbic systems in the brain, aligning with known AD trajectories and linked to amyloid-$\beta$ and tau deposition. Furthermore, observed changes in white-matter microstructure in our DTI networks in parietal and frontal regions may compliment studies of white-matter integrity in poor memory binders. Additionally, nQ clearly differentiated MCI from MCI converters indicating that nQ may be sensitive to this key turning point of AD. Our findings demonstrate the utility of nQ as a measure of localized group structure, providing novel insights into task and disease-related variability at the node level. Given the widespread application of modularity as a global measure, nQ represents a significant advancement, providing a granular measure of network organization applicable to a wide range of disciplines.

Figures

Figures reproduced from arXiv: 2501.09805 by the authors.

Figure 1
Figure 1. September 10, 2025 4/35 [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 1
Figure 1. Task procedure. Trials were conducted as follows. A warning screen for 2500ms, a fixation period of 3000ms where a white cross turns from white to black, a blank grey screen for 250ms, a reminder of the instructions for 2000ms, the shapes or shapes with colours (depending on shape or binding task) are displayed for 2000ms (encoding phase), a blank grey screen is displayed for a variable time of (2000, 4000, 6000, or… view at source ↗
Figure 2
Figure 2. Multiplex network of the VSTMBT and nQ example. a) Networks for the two task phases of the VSTMBT, encmaint and probe, are constructed from the functional co-activations (correlation in time-series) between all pairs of ROIs. Spatial replicas are connected via an inter-layer edge, as seen in the above figure (light grey edges between the two layers), allowing for continuity of network topology in time. b) Here, mult… view at source ↗
Figures from the paper (6 more)
Figure 3
Figure 3. Figure 3: Random and SBM null models for comparisons of modularity in [PITH_FULL_IMAGE:figures/full_fig_p013_3.png]
Figure 4
Figure 4. Figure 4: nQ vs. other graph measures. Scatter plots of Degree, PageRank (PR), and Clustering Coefficient (CC) vs. nQ. These measures were calculated in our dual-layer binding task-fMRI networks for cognitively normal subjects. The Pearson correlation between these comparisons i…
Figure 5
Figure 5. Figure 5: Behaviour of nQ in ZKC and NKI. Scatter plots of Degree, PageRank (PR), and Clustering Coefficient (CC) vs. nQ for Zachary’s Karate club (a) and NKI-RS (b). r is the Pearson correlation coefficient. September 10, 2025 14/35 [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Changes in nQ for multiplex fMRI binding. Using BrainNet Viewer, we visualize encmaint and probe (left and right brain respectively) layers of our network. Here, nodes in blue represent loss of nQ while those in red represent gains in nQ. The size of the nodes represen…
Figure 7
Figure 7. Figure 7: Changes in nQ for single-layer DTI. As before, blue indicates a loss of nQ while red represents a gain. Here we also see an increase (node size) in nQ for later stage disease comparisons. Furthermore, 1.5% of the network edges are displayed for clarity. Additionally, t…
Figure 8
Figure 8. Figure 8: Comparisons of nQ for early MCI vs. MCI and MCI vs. MCI converters for single-layer DTI. Figure generation and details follow from [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

90 extracted references · 52 canonical work pages

  1. [1]

    Mild Cognitive Impairment

    Sanford AM. Mild Cognitive Impairment. Clinics in geriatric medicine. 2017;33(3):325–337. doi:10.1016/J.CGER.2017.02.005

    work page doi:10.1016/j.cger.2017.02.005 2017

  2. [2]

    Mild cognitive impairment

    Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K, Broich K, et al. Mild cognitive impairment. Lancet. 2006;367(9518):1262–1270. doi:10.1016/S0140-6736(06)68542-5

    doi:10.1016/s0140-6736(06)68542-5 2006

  3. [3]

    Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis

    Rossini PM, Di Iorio R, Vecchio F, Anfossi M, Babiloni C, Bozzali M, et al. Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts. Clinical Neurophysiology. 2020;131(6):1287–1310. doi:10.1016/j.clinph.2020.03.003

    work page doi:10.1016/j.clinph.2020.03.003 2020

  4. [4]

    Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI)

    Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, Jagust W, et al. Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s and Dementia. 2005;1(1):55–66. doi:10.1016/j.jalz.2005.06.003

    doi:10.1016/j.jalz.2005.06.003 2005

  5. [5]

    Revising the definition of Alzheimer’s disease: a new lexicon

    Dubois B, Feldman HH, Jacova C, Cummings JL, DeKosky ST, Barberger-Gateau P, et al. Revising the definition of Alzheimer’s disease: a new lexicon. The Lancet Neurology. 2010;9(11):1118–1127. doi:10.1016/S1474-4422(10)70223-4

    work page doi:10.1016/s1474-4422(10)70223-4 2010

  6. [6]

    Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria

    Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. The Lancet Neurology. 2014;13(6):614–629. doi:10.1016/S1474-4422(14)70090-0

    work page doi:10.1016/s1474-4422(14)70090-0 2014

  7. [7]

    Visual short-term memory binding deficits in familial Alzheimer’s disease

    Parra MA, Abrahams S, Logie RH, M´ endez LG, Lopera F, Della Sala S. Visual short-term memory binding deficits in familial Alzheimer’s disease. Brain. 2010;133(9):2702–2713. doi:10.1093/brain/awq148

    work page doi:10.1093/brain/awq148 2010

  8. [8]

    Short-term memory binding is impaired in AD but not in non-AD dementias

    Della Sala S, Parra MA, Fabi K, Luzzi S, Abrahams S. Short-term memory binding is impaired in AD but not in non-AD dementias. Neuropsychologia. 2012;50(5):833–840. doi:10.1016/J.NEUROPSYCHOLOGIA.2012.01.018

    work page doi:10.1016/j.neuropsychologia.2012.01.018 2012

Show all 90 references
  1. [9]

    Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer’s Disease

    Supekar K, Menon V, Rubin D, Musen M, Greicius MD. Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer’s Disease. PLOS Computational Biology. 2008;4(6):e1000100. doi:10.1371/JOURNAL.PCBI.1000100

    2008 doi

  2. [10]

    Memory binding and white matter integrity in familial Alzheimer’s disease

    Parra MA, Saarim¨ aki H, Bastin ME, Londo˜ no AC, Pettit L, Lopera F, et al. Memory binding and white matter integrity in familial Alzheimer’s disease. Brain. 2015;138(5):1355–1369. doi:10.1093/BRAIN/A WV048

    2015 doi

  3. [11]

    Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease

    Kabbara A, Eid H, El Falou W, Khalil M, Wendling F, Hassan M. Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease. Journal of neural engineering. 2018;15(2). doi:10.1088/1741-2552/AAAA76. September 10, 2025 29/35

    2018 doi

  4. [12]

    The Role of Diffusion Tensor Imaging in Detecting Microstructural Changes in Prodromal Alzheimer’s Disease

    Zhang B, Xu Y, Zhu B, Kantarci K. The Role of Diffusion Tensor Imaging in Detecting Microstructural Changes in Prodromal Alzheimer’s Disease. CNS Neuroscience & Therapeutics. 2014;20(1):3–9. doi:10.1111/CNS.12166

    2014 doi

  5. [13]

    Network neuroscience

    Bassett DS, Sporns O. Network neuroscience. Nature Neuroscience. 2017;20(3):353–364. doi:10.1038/nn.4502

    2017 doi

  6. [14]

    Exploring the brain network: A review on resting-state fMRI functional connectivity

    van den Heuvel MP, Hulshoff Pol HE. Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology. 2010;20(8):519–534. doi:10.1016/j.euroneuro.2010.03.008

    2010 doi

  7. [15]

    Complex brain networks: graph theoretical analysis of structural and functional systems

    Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience. 2009;10(3):186–198. doi:10.1038/nrn2575

    2009 doi

  8. [16]

    Structural measures for multiplex networks

    Battiston F, Nicosia V, Latora V. Structural measures for multiplex networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics. 2014;89(3):032804. doi:10.1103/PhysRevE.89.032804

    2014 doi

  9. [17]

    Multilayer modeling and analysis of human brain networks

    De Domenico M. Multilayer modeling and analysis of human brain networks. GigaScience. 2017;6(5):1–8. doi:10.1093/gigascience/gix004

    2017 doi

  10. [18]

    Dynamic reconfiguration of human brain networks during learning

    Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST. Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences of the United States of America. 2011;108(18):7641–7646. doi:10.1073/pnas.1018985108

    2011 doi

  11. [19]

    Frequency-based brain networks: From a multiplex framework to a full multilayer description

    Buld´ u JM, Porter MA. Frequency-based brain networks: From a multiplex framework to a full multilayer description. Network Neuroscience. 2018;2(4):418–441. doi:10.1162/netn a 00033

    2018 doi

  12. [20]

    Multilayer motif analysis of brain networks

    Battiston F, Nicosia V, Chavez M, Latora V. Multilayer motif analysis of brain networks. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2017;27(4):047404. doi:10.1063/1.4979282

    2017 doi

  13. [21]

    The economy of brain network organization

    Bullmore E, Sporns O. The economy of brain network organization. Nature Reviews Neuroscience. 2012;13(5):336–349. doi:10.1038/nrn3214

    2012 doi

  14. [22]

    Small-world human brain networks: Perspectives and challenges

    Liao X, Vasilakos A V, He Y. Small-world human brain networks: Perspectives and challenges. Neuroscience & Biobehavioral Reviews. 2017;77:286–300. doi:10.1016/J.NEUBIOREV.2017.03.018

    2017 doi

  15. [23]

    Multi-scale brain networks

    Betzel RF, Bassett DS. Multi-scale brain networks. NeuroImage. 2017;160:73–83. doi:10.1016/j.neuroimage.2016.11.006

    2017 doi

  16. [24]

    Modular brain networks

    Sporns O, Betzel RF. Modular brain networks. Annual Review of Psychology. 2016;67:613–640. doi:10.1146/annurev-psych-122414-033634

    2016 doi

  17. [25]

    Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

    Mucha PJ, Richardson T, Macon K, Porter MA, Onnela JP. Community Structure in Time-Dependent, Multiscale, and Multiplex Networks. Science. 2010;328(5980):876–878. doi:10.1126/SCIENCE.1184819

    2010 doi

  18. [26]

    Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer’s Disease

    Pereira JB, Mijalkov M, Kakaei E, Mecocci P, Vellas B, Tsolaki M, et al. Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer’s Disease. Cerebral Cortex. 2016;26(8):3476–3493. doi:10.1093/CERCOR/BHW128. September 10, 2025 30/35

    2016 doi

  19. [27]

    Neural correlates of shape-color binding in visual working memory

    Parra MA, Della Sala S, Logie RH, Morcom AM. Neural correlates of shape-color binding in visual working memory. Neuropsychologia. 2014;52(1):27–36. doi:10.1016/J.NEUROPSYCHOLOGIA.2013.09.036

    2014 doi

  20. [28]

    Memory markers in the continuum of the Alzheimer’s clinical syndrome

    Parra MA, Calia C, Pattan V, Della Sala S. Memory markers in the continuum of the Alzheimer’s clinical syndrome. Alzheimer’s Research and Therapy. 2022;14(1):1–16. doi:10.1186/s13195-022-01082-9

    2022 doi

  21. [29]

    Test–retest reliability of structural brain networks from diffusion MRI

    Buchanan CR, Pernet CR, Gorgolewski KJ, Storkey AJ, Bastin ME. Test–retest reliability of structural brain networks from diffusion MRI. NeuroImage. 2014;86:231–243. doi:10.1016/J.NEUROIMAGE.2013.09.054

    2014 doi

  22. [30]

    Memory Encoding and Dopamine in the Aging Brain: A Psychopharmacological Neuroimaging Study

    Morcom AM, Bullmore ET, Huppert F A, Lennox B, Praseedom A, Linnington H, et al. Memory Encoding and Dopamine in the Aging Brain: A Psychopharmacological Neuroimaging Study. Cerebral Cortex. 2010;20(3):743–757. doi:10.1093/CERCOR/BHP139

    2010 doi

  23. [31]

    A fast diffeomorphic image registration algorithm

    Ashburner J. A fast diffeomorphic image registration algorithm. NeuroImage. 2007;38(1):95–113. doi:10.1016/J.NEUROIMAGE.2007.07.007

    2007 doi

  24. [32]

    Computing average shaped tissue probability templates

    Ashburner J, Friston KJ. Computing average shaped tissue probability templates. NeuroImage. 2009;45(2):333–341. doi:10.1016/J.NEUROIMAGE.2008.12.008

    2009 doi

  25. [33]

    Investigation of low frequency drift in fMRI signal

    Smith AM, Lewis BK, Ruttimann UE, Ye FQ, Sinnwell TM, Yang Y, et al. Investigation of low frequency drift in fMRI signal. NeuroImage. 1999;9(5):526–533. doi:10.1006/NIMG.1999.0435

    1999

  26. [34]

    Comparing the pearson and spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data

    de Winter JCF, Gosling SD, Potter J. Comparing the pearson and spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological Methods. 2016;21(3):273–290. doi:10.1037/MET0000079

    2016 doi

  27. [35]

    Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches

    Bishara AJ, Hittner JB. Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches. Psychological Methods. 2012;17(3):399–417. doi:10.1037/A0028087

    2012 doi

  28. [36]

    Brain-wide functional connectivity artifactually inflates throughout functional magnetic resonance imaging scans

    Korponay C, Janes AC, Frederick BB. Brain-wide functional connectivity artifactually inflates throughout functional magnetic resonance imaging scans. Nature Human Behaviour. 2024;8(8):1568–1580. doi:10.1038/s41562-024-01908-6

    2024 doi

  29. [37]

    Unconsciousness reconfigures modular brain network dynamics

    del Pozo SM, Laufs H, Bonhomme V, Laureys S, Balenzuela P, Tagliazucchi E. Unconsciousness reconfigures modular brain network dynamics. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2021;31(9):093117. doi:10.1063/5.0046047

    2021 doi

  30. [38]

    Complex network measures of brain connectivity: Uses and interpretations

    Rubinov M, Sporns O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage. 2010;52(3):1059–1069. doi:10.1016/J.NEUROIMAGE.2009.10.003

    2010 doi

  31. [39]

    Modern graph theory

    Bollob´ as B. Modern graph theory. Springer; 1998

    1998

  32. [40]

    The structure and dynamics of multilayer networks

    Boccaletti S, Bianconi G, Criado R, del Genio CI, G´ omez-Garde˜ nes J, Romance M, et al. The structure and dynamics of multilayer networks. Physics Reports. 2014;544(1):1–122. doi:10.1016/j.physrep.2014.07.001

    2014 doi

  33. [41]

    Size reduction of complex networks preserving modularity

    Arenas A, Duch J, Fern´ andez A, G´ omez S. Size reduction of complex networks preserving modularity. New Journal of Physics. 2007;9(6):176. doi:10.1088/1367-2630/9/6/176. September 10, 2025 31/35

    2007 doi

  34. [42]

    A generalized Louvain method for community detection implemented in MATLAB; 2011

    Jeub LGS, Bazzi M, Jutla SI, Mucha PJ. A generalized Louvain method for community detection implemented in MATLAB; 2011. Available from: https://github.com/GenLouvain/GenLouvain

    2011

  35. [43]

    Community Detection in Temporal Multilayer Networks, with an Application to Correlation Networks

    Bazzi M, Porter MA, Williams S, McDonald M, Fenn DJ, Howison SD. Community Detection in Temporal Multilayer Networks, with an Application to Correlation Networks. Multiscale Modeling & Simulation. 2016;14(1):1–41. doi:10.1137/15M1009615

    2016 doi

  36. [44]

    Performance of modularity maximization in practical contexts

    Good BH, De Montjoye YA, Clauset A. Performance of modularity maximization in practical contexts. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics. 2010;81(4):046106. doi:10.1103/PhysRevE.81.046106

    2010 doi

  37. [45]

    Consensus clustering in complex networks

    Lancichinetti A, Fortunato S. Consensus clustering in complex networks. Scientific Reports. 2012;2(1):1–7. doi:10.1038/srep00336

    2012 doi

  38. [46]

    Robust detection of dynamic community structure in networks

    Bassett DS, Porter MA, Wymbs NF, Grafton ST, Carlson JM, Mucha PJ. Robust detection of dynamic community structure in networks. Chaos. 2013;23(1). doi:10.1063/1.4790830/153214

    2013 doi

  39. [47]

    Multi-scale detection of hierarchical community architecture in structural and functional brain networks

    Ashourvan A, Telesford QK, Verstynen T, Vettel JM, Bassett DS. Multi-scale detection of hierarchical community architecture in structural and functional brain networks. PLOS ONE. 2019;14(5):e0215520. doi:10.1371/JOURNAL.PONE.0215520

    2019 doi

  40. [48]

    Unspoken Assumptions in Multi-layer Modularity maximization

    Hanteer O, Magnani M. Unspoken Assumptions in Multi-layer Modularity maximization. Scientific Reports. 2020;10(1):1–15. doi:10.1038/s41598-020-66956-0

    2020 doi

  41. [49]

    Community Detection and Stochastic Block Models: Recent Developments

    Abbe E. Community Detection and Stochastic Block Models: Recent Developments. Journal of Machine Learning Research. 2018;18(177):1–86

    2018

  42. [50]

    MATLAB toolbox for fitting discrete-time dynamic stochastic block models; 2018

    Xu KS. MATLAB toolbox for fitting discrete-time dynamic stochastic block models; 2018. Available from: https://github.com/IdeasLabUT/Dynamic-Stochastic-Block-Model

    2018

  43. [51]

    Multiplex PageRank

    Halu A, Mondrag´ on RJ, Panzarasa P, Bianconi G. Multiplex PageRank. PLOS ONE. 2013;8(10):e78293. doi:10.1371/JOURNAL.PONE.0078293

    2013 doi

  44. [52]

    Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses

    Good P. Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. Springer Science and Business Media; 2013

    2013

  45. [53]

    Permutation Test; 2024

    Krol LR. Permutation Test; 2024. Available from: https://github.com/lrkrol/permutationTest

    2024

  46. [54]

    Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine

    Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry. 1993;39(4):561–577. doi:10.1093/CLINCHEM/39.4.561

    1993 doi

  47. [55]

    Multiple Testing Toolbox, MATLAB Central File Exchange

    Mart ´ ınez-Cagigal V. Multiple Testing Toolbox, MATLAB Central File Exchange

  48. [56]

    BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics

    Xia M, Wang J, He Y. BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics. PLoS ONE. 2013;8(7):68910. doi:10.1371/JOURNAL.PONE.0068910. September 10, 2025 32/35

    2013 doi

  49. [57]

    Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

    Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289–300. doi:10.1111/J.2517-6161.1995.TB02031.X

    1995

  50. [58]

    Use and misuse of corrections for multiple testing

    Garc ´ ıa-P´ erez MA. Use and misuse of corrections for multiple testing. Methods in Psychology. 2023;8:100120. doi:10.1016/J.METIP.2023.100120

    2023

  51. [59]

    The Control of the False Discovery Rate in Multiple Testing under Dependency

    Benjamini Y, Yekutieli D. The Control of the False Discovery Rate in Multiple Testing under Dependency. The Annals of Statistics. 2001;29(4):1165–1188

    2001

  52. [60]

    The importance of discriminative power rather than significance when evaluating potential clinical biomarkers in epilepsy research

    Slinger G, Stevelink R, van Diessen E, Braun KPJ, Otte WM. The importance of discriminative power rather than significance when evaluating potential clinical biomarkers in epilepsy research. Epileptic Disorders. 2023;25(3):285–296. doi:10.1002/epd2.20010

    2023 doi

  53. [61]

    Violin Plots for Matlab, Github Project; 2016

    Bechtold B. Violin Plots for Matlab, Github Project; 2016. Available from: https://github.com/bastibe/Violinplot-Matlab

    2016

  54. [62]

    Medial temporal lobe function during emotional memory in early Alzheimer’s disease, mild cognitive impairment and healthy ageing: an fMRI study

    Parra MA, Pattan V, Wong D, Beaglehole A, Lonie J, Wan HI, et al. Medial temporal lobe function during emotional memory in early Alzheimer’s disease, mild cognitive impairment and healthy ageing: an fMRI study. BMC Psychiatry. 2013;13:76. doi:10.1186/1471-244X-13-76

    2013 doi

  55. [63]

    Brain Information Sharing During Visual Short-Term Memory Binding Yields a Memory Biomarker for Familial Alzheimer’s Disease

    Parra MA, Mikulan E, Trujillo N, Sala SD, Lopera F, Manes F, et al. Brain Information Sharing During Visual Short-Term Memory Binding Yields a Memory Biomarker for Familial Alzheimer’s Disease. Current Alzheimer research. 2017;14(12). doi:10.2174/1567205014666170614163316

    2017 doi

  56. [64]

    The human brain is intrinsically organized into dynamic, anticorrelated functional networks

    Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America. 2005;102(27):9673–9678. doi:10.107...

    2005 doi

  57. [65]

    Evolution of neuronal changes in the course of Alzheimer’s disease

    Braak H, Braak E. Evolution of neuronal changes in the course of Alzheimer’s disease. Journal of Neural Transmission, Supplementa. 1998;53:127–140. doi:10.1007/978-3-7091-6467-9 11

    1998 doi

  58. [66]

    Exploring the Association between Amyloid-βand Memory Markers for Alzheimer’s Disease in Cognitively Unimpaired Older Adults

    Parra MA, Gazes Y, Habeck C, Stern Y. Exploring the Association between Amyloid-βand Memory Markers for Alzheimer’s Disease in Cognitively Unimpaired Older Adults. Journal of Prevention of Alzheimer’s Disease. 2024;11(2):339–347. doi:10.14283/jpad.2024.11

    2024 doi

  59. [67]

    Four distinct trajectories of tau deposition identified in Alzheimer’s disease

    Vogel JW, Young AL, Oxtoby NP, Smith R, Ossenkoppele R, Strandberg OT, et al. Four distinct trajectories of tau deposition identified in Alzheimer’s disease. Nature Medicine. 2021;27(5):871–881. doi:10.1038/s41591-021-01309-6

    2021 doi

  60. [68]

    Tau accumulation and its spatial progression across the Alzheimer’s disease spectrum

    St-Onge F, Chapleau M, Breitner JCS, Villeneuve S, Binette AP. Tau accumulation and its spatial progression across the Alzheimer’s disease spectrum. medRxiv. 2024;6(1). doi:10.1101/2023.06.02.23290880

    2024 doi

  61. [69]

    Altered Functional Connectivity of Basal Ganglia in Mild Cognitive Impairment and Alzheimer’s Disease

    Xiong Y, Ye C, Chen Y, Zhong X, Chen H, Sun R, et al. Altered Functional Connectivity of Basal Ganglia in Mild Cognitive Impairment and Alzheimer’s Disease. Brain Sciences. 2022;12(11):1555. doi:10.3390/brainsci12111555. September 10, 2025 33/35

    2022 doi

  62. [70]

    Clinical features of early-onset Alzheimer disease in a large kindred with an E280A presenilin-1 mutation

    Lopera F, Ardilla A, Mart ´ ınez A, Madrigal L, Arango-Viana JC, Lemere CA, et al. Clinical features of early-onset Alzheimer disease in a large kindred with an E280A presenilin-1 mutation. JAMA. 1997;277(10):793–799. doi:10.1001/JAMA.277.10.793

    1997 doi

  63. [71]

    Graph theoretical analysis of Alzheimer’s disease: Discrimination of AD patients from healthy subjects

    Jalili M. Graph theoretical analysis of Alzheimer’s disease: Discrimination of AD patients from healthy subjects. Information Sciences. 2017;384:145–156. doi:10.1016/J.INS.2016.08.047

    2017 doi

  64. [72]

    Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer’s disease

    De Haan W, Van der Flier WM, Koene T, Smits LL, Scheltens P, Stam CJ. Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer’s disease. NeuroImage. 2012;59(4):3085–3093. doi:10.1016/J.NEUROIMAGE.2011.11.055

    2012 doi

  65. [73]

    The emergence of modularity in biological systems

    Lorenz DM, Jeng A, Deem MW. The emergence of modularity in biological systems. Physics of Life Reviews. 2011;8(2):129–160. doi:10.1016/J.PLREV.2011.02.003

    2011 doi

  66. [74]

    Epidemic spreading in modular time-varying networks

    Nadini M, Sun K, Ubaldi E, Starnini M, Rizzo A, Perra N. Epidemic spreading in modular time-varying networks. Scientific Reports. 2018;8(1):1–11. doi:10.1038/s41598-018-20908-x

    2018 doi

  67. [75]

    Models for the modern power grid

    Nardelli PHJ, Rubido N, Wang C, Baptista MS, Pomalaza-Raez C, Cardieri P, et al. Models for the modern power grid. The European Physical Journal Special Topics 2014 223:12. 2014;223(12):2423–2437. doi:10.1140/EPJST/E2014-02219-6

    2014 doi

  68. [76]

    The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis

    Brown JA, Rudie JD, Bandrowski A, Van Horn JD, Bookheimer SY. The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis. Frontiers in Neuroinformatics. 2012;0(NOV):28. doi:10.3389/FNINF.2012.00028

    2012 arXiv

  69. [77]

    An Information Flow Model for Conflict and Fission in Small Groups

    Zachary WW. An Information Flow Model for Conflict and Fission in Small Groups. Journal of Anthropological Research. 1977;33(4):452–473. doi:10.1086/JAR.33.4.3629752

    1977 doi

  70. [78]

    Community structure in social and biological networks

    Girvan M, Newman MEJ. Community structure in social and biological networks. Proceedings of the National Academy of Sciences. 2002;99(12):7821–7826. doi:10.1073/PNAS.122653799

    2002 doi

  71. [79]

    The NKI-Rockland sample: A model for accelerating the pace of discovery science in psychiatry

    Nooner KB, Colcombe SJ, Tobe RH, Mennes M, Benedict MM, Moreno AL, et al. The NKI-Rockland sample: A model for accelerating the pace of discovery science in psychiatry. Frontiers in Neuroscience. 2012;0(OCT):152. doi:10.3389/fnins.2012.00152

    2012 arXiv

  72. [80]

    The Addenbrooke’s Cognitive Examination Revised (ACE-R): a brief cognitive test battery for dementia screening

    Mioshi E, Dawson K, Mitchell J, Arnold R, Hodges JR. The Addenbrooke’s Cognitive Examination Revised (ACE-R): a brief cognitive test battery for dementia screening. International Journal of Geriatric Psychiatry. 2006;21(11):1078–1085. doi:10.1002/GPS.1610

    2006 doi

  73. [81]

    Hopkins Verbal Learning Test – Revised: Normative Data and Analysis of Inter-Form and Test-Retest Reliability

    Benedict RHB, Schretlen D, Groninger L, Brandt J. Hopkins Verbal Learning Test – Revised: Normative Data and Analysis of Inter-Form and Test-Retest Reliability. The Clinical Neuropsychologist. 1998;12(1):43–55. doi:10.1076/CLIN.12.1.43.1726

    1998 doi

  74. [82]

    Word fluency and brain damage

    Borkowski JG, Benton AL, Spreen O. Word fluency and brain damage. Neuropsychologia. 1967;5(2):135–140. doi:10.1016/0028-3932(67)90015-2. September 10, 2025 34/35

    1967 doi

  75. [83]

    The Wechsler Digit Symbol Substitution Test as the Best Indicator of the Risk of Impaired Driving in Alzheimer Disease and Normal Aging

    Lafont S, Marin-Lamellet C, Paire-Ficout L, Thomas-Anterion C, Laurent B, Fabrigoule C. The Wechsler Digit Symbol Substitution Test as the Best Indicator of the Risk of Impaired Driving in Alzheimer Disease and Normal Aging. Dementia and Geriatric Cognitive Disorders. 2010;29(...

    2010 doi

  76. [84]

    Rey-Osterrieth Complex Figure Copying Test

    Rey A, Osterrieth PA. Rey-Osterrieth Complex Figure Copying Test. Psychological Assessment. 2011;doi:10.1037/T07717-000

    2011 doi

  77. [85]

    Graded naming test: Manual

    Mckenna P, Warrington EK. Graded naming test: Manual. NFER-Nelson; 1983

    1983

  78. [86]

    Genuine memory deficits in dementia

    Grober E, Buschke H, Korey SR. Genuine memory deficits in dementia. Developmental Neuropsychology. 1987;3(1):13–36. doi:10.1080/87565648709540361

    1987 doi

  79. [87]

    Advanced clinical solutions for W AIS-IV and WMS-IV: Administration and scoring manual

    Pearson N. Advanced clinical solutions for W AIS-IV and WMS-IV: Administration and scoring manual. San Antonio: The Psychological Corporation. 2009;7

    2009

  80. [88]

    Trail Making Test as a screening device for the detection of brain damage

    Brown EC, Casey A, Fisch RI, Neuringer C. Trail Making Test as a screening device for the detection of brain damage. Journal of Consulting Psychology. 1958;22(6):469–474. doi:10.1037/H0039980

    1958 doi

  81. [89]

    9/Geriatric depression scale (GDS) recent evidence and development of a shorter version

    Yesavage JA, Sheikh JI. 9/Geriatric depression scale (GDS) recent evidence and development of a shorter version. Clinical gerontologist. 1986;5:165–173. September 10, 2025 35/35

    1986

  82. [2025]

    Available from:https://uk.mathworks.com/matlabcentral/ fileexchange/70604-multiple-testing-toolbox

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.