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A detailed investigation of Shared Variance Component Analysis as a tool to characterize neural dimensionality.

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Τίτλος: A detailed investigation of Shared Variance Component Analysis as a tool to characterize neural dimensionality.
Συγγραφείς: Carballosa A; Laboratoire de Physique Théorique et Modélisation, UMR 8089, CY Cergy Paris Université, CNRS, 2 avenue Adolphe Chauvin, 95302, Cergy-Pontoise, France; ICM, Institut du Cerveau - Paris Brain Institute, Sorbonne Université, Inserm, U1127-CNRS UMR 7225, Hôpital Pitié-Salpêtrière, 47 bd de l'Hôpital, Paris, 75013, France. Electronic address: acarball@cyu.fr., Torcini A; Laboratoire de Physique Théorique et Modélisation, UMR 8089, CY Cergy Paris Université, CNRS, 2 avenue Adolphe Chauvin, 95302, Cergy-Pontoise, France; CNR, Consiglio Nazionale delle Ricerche, Istituto dei Sistemi Complessi, via Madonna del Piano 10, Sesto Fiorentino, 59100, Italy; INFN, Sezione di Firenze, via Sansone 1, Sesto Fiorentino, 59100, Italy.
Πηγή: Journal of neuroscience methods [J Neurosci Methods] 2026 Oct; Vol. 434, pp. 110835. Date of Electronic Publication: 2026 Jun 18.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7905558 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-678X (Electronic) Linking ISSN: 01650270 NLM ISO Abbreviation: J Neurosci Methods Subsets: MEDLINE
Imprint Name(s): Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press.
Ιατρικοί όροι (MeSH): Neurons*/physiology , Principal Component Analysis* , Models, Neurological* , Signal Processing, Computer-Assisted*, Calcium/metabolism ; Visual Cortex/physiology ; Animals ; Mice
Περίληψη: Background: The relevance of spontaneous activity has been unlocked thanks to recent large scale recordings that revealed, via Shared Variance Component Analysis (SVCA), the high-dimensional nature of the ongoing activity. A fundamental problem is how the dimension is modified when more neurons are included in the analysis. Contradictory results have been reported on this subject based on SVCA and Principal Component Analysis (PCA).
New Method: We investigate pro et contra of SVCA and PCA for the identification of reliable responses encoding underlying state variables. We focus on common features of the spectra of the reliable variances (RVs) and on their dimensionality. The analysis is demonstrated on previously published Ca2+ data from the visual and the dorsal cortex in head fixed mice during spontaneous behavior.
Results: RVs grow proportionally to the number N of neurons and show a power-law decay k-α with the kth SVC dimension over a range bounded by a maximal dimension kc, initially diverging as N1/α and then saturating at sufficiently large N. The reliable dimensionality, estimated with different methodologies, also shows a clear saturation to an asymptotic value for large N. Furthermore, its value decreases when α becomes larger, as demonstrated by employing experimental data as well as theoretical predictions.
Conclusion: We have shown that SVCA is an extremely effective tool to extract reliable features from the neural signals, and that the exponent α represents a biomarker able to reveal the level of correlation of the neurons as well as the dimensionality of the reliable space.
(Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Neural data; Neural dimensionality; Principal Component Analysis; Reliable and unreliable neural responses; Shared Variance Component Analysis; Spontaneous activity
Substance Nomenclature: SY7Q814VUP (Calcium)
Entry Date(s): Date Created: 20260618 Date Completed: 20260708 Latest Revision: 20260708
Update Code: 20260709
DOI: 10.1016/j.jneumeth.2026.110835
PMID: 42314994
Βάση Δεδομένων: MEDLINE
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  Data: <searchLink fieldCode="AU" term="%22Carballosa+A%22">Carballosa A</searchLink>; Laboratoire de Physique Théorique et Modélisation, UMR 8089, CY Cergy Paris Université, CNRS, 2 avenue Adolphe Chauvin, 95302, Cergy-Pontoise, France; ICM, Institut du Cerveau - Paris Brain Institute, Sorbonne Université, Inserm, U1127-CNRS UMR 7225, Hôpital Pitié-Salpêtrière, 47 bd de l'Hôpital, Paris, 75013, France. Electronic address: acarball@cyu.fr.<br /><searchLink fieldCode="AU" term="%22Torcini+A%22">Torcini A</searchLink>; Laboratoire de Physique Théorique et Modélisation, UMR 8089, CY Cergy Paris Université, CNRS, 2 avenue Adolphe Chauvin, 95302, Cergy-Pontoise, France; CNR, Consiglio Nazionale delle Ricerche, Istituto dei Sistemi Complessi, via Madonna del Piano 10, Sesto Fiorentino, 59100, Italy; INFN, Sezione di Firenze, via Sansone 1, Sesto Fiorentino, 59100, Italy.
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  Data: Background: The relevance of spontaneous activity has been unlocked thanks to recent large scale recordings that revealed, via Shared Variance Component Analysis (SVCA), the high-dimensional nature of the ongoing activity. A fundamental problem is how the dimension is modified when more neurons are included in the analysis. Contradictory results have been reported on this subject based on SVCA and Principal Component Analysis (PCA).<br />New Method: We investigate pro et contra of SVCA and PCA for the identification of reliable responses encoding underlying state variables. We focus on common features of the spectra of the reliable variances (RVs) and on their dimensionality. The analysis is demonstrated on previously published Ca2<superscript>+</superscript> data from the visual and the dorsal cortex in head fixed mice during spontaneous behavior.<br />Results: RVs grow proportionally to the number N of neurons and show a power-law decay k<superscript>-α</superscript> with the kth SVC dimension over a range bounded by a maximal dimension k<subscript>c</subscript>, initially diverging as N<superscript>1/α</superscript> and then saturating at sufficiently large N. The reliable dimensionality, estimated with different methodologies, also shows a clear saturation to an asymptotic value for large N. Furthermore, its value decreases when α becomes larger, as demonstrated by employing experimental data as well as theoretical predictions.<br />Conclusion: We have shown that SVCA is an extremely effective tool to extract reliable features from the neural signals, and that the exponent α represents a biomarker able to reveal the level of correlation of the neurons as well as the dimensionality of the reliable space.<br /> (Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved.)
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  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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  Data: <i>Keywords: </i>Neural data; Neural dimensionality; Principal Component Analysis; Reliable and unreliable neural responses; Shared Variance Component Analysis; Spontaneous activity
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