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The Impact of Denoising Approaches on the Relationship Between Alzheimer's Disease Diagnostic Status and Network Topology Measures.

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Title: The Impact of Denoising Approaches on the Relationship Between Alzheimer's Disease Diagnostic Status and Network Topology Measures.
Authors: Blujus JK; Department of Cognitive and Psychological Sciences, Brown University, Providence, Rhode Island, USA., Oh H; Department of Cognitive and Psychological Sciences, Brown University, Providence, Rhode Island, USA.; Department of Psychiatry and Human Behavior, Brown University, Providence, Rhode Island, USA.; Carney Institute for Brain Science, Brown University, Providence, Rhode Island, USA.
Corporate Authors: Alzheimer's Disease Neuroimaging Initiative
Source: Human brain mapping [Hum Brain Mapp] 2026 Aug; Vol. 47 (11), pp. e70622.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Wiley Country of Publication: United States NLM ID: 9419065 Publication Model: Print Cited Medium: Internet ISSN: 1097-0193 (Electronic) Linking ISSN: 10659471 NLM ISO Abbreviation: Hum Brain Mapp Subsets: MEDLINE
Imprint Name(s): Publication: New York : Wiley
Original Publication: New York : Wiley-Liss, c1993-
MeSH Terms: Alzheimer Disease*/diagnostic imaging , Alzheimer Disease*/physiopathology , Magnetic Resonance Imaging*/methods , Magnetic Resonance Imaging*/standards , Cognitive Dysfunction*/diagnostic imaging , Cognitive Dysfunction*/physiopathology , Brain*/diagnostic imaging , Brain*/physiopathology , Nerve Net*/diagnostic imaging , Nerve Net*/physiopathology , Image Processing, Computer-Assisted*/methods , Image Processing, Computer-Assisted*/standards , Connectome*/methods , Connectome*/standards, Humans ; Female ; Male ; Aged ; Aged, 80 and over ; Artifacts
Abstract: Graph theory provides a promising technique to investigate Alzheimer's disease (AD)-related alterations in brain network properties. However, there are discrepancies in the reported disruptions that occur to network topology across the AD continuum. In this study, we examined whether diagnostic group differences in graph metrics are attributed to differences in denoising approach used in fMRI processing. Resting state data from 60 cognitively normal (CN), 55 Mild Cognitive Impairment (MCI), and 38 AD participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were denoised using 11 pipelines including combinations of confound regression (head motion parameters, white matter [WM], cerebrospinal fluid [CSF], global signal), volume censoring (scrubbing, spike regression), and component-based noise removal (Independent Component Analysis-based Automatic Removal of Motion Artifacts [ICA-AROMA], anatomical and temporal component correction). Graph metrics representing network segregation (clustering coefficient, modularity, local efficiency), network integration (largest connected component, path length, global efficiency), and small-worldness were calculated. The results revealed that diagnostic group differences in modularity and local efficiency were dependent on denoising approach, especially in high-parameter regression models in combination with censoring methods (36 parameters and spike regressor or volume censoring). Independent of denoising approach, CN exhibited more segregated (clustering coefficient) but less integrated (largest component, path length, global efficiency) networks than MCI and AD. Independent of diagnosis, denoising strategy significantly affected the magnitude of all metrics, particularly models including global signal regression. Collectively, these results suggest that the directionality of the diagnostic differences in network topology, particularly in global metrics of network segregation, can vary based upon the denoising approach employed, although the effect size is small. Transparent reporting of preprocessing decisions is critical for the accurate interpretation of graph theoretical findings in the context of AD and a better understanding of the mechanisms underlying pathological aging.
(© 2026 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)
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Grant Information: R01AG068990 United States NH NIH HHS; R01AG069265 United States NH NIH HHS; R01 AG068990 United States AG NIA NIH HHS; U01 AG024904 United States AG NIA NIH HHS; S10 OD025181 United States OD NIH HHS; R01 AG069265 United States AG NIA NIH HHS; S10OD025181 United States NH NIH HHS
Contributed Indexing: Keywords: Alzheimer's disease; denoising; fMRI; graph theory; nuisance regression; resting‐state
Entry Date(s): Date Created: 20260808 Date Completed: 20260808 Latest Revision: 20260826
Update Code: 20260826
PubMed Central ID: PMC13451523
DOI: 10.1002/hbm.70622
PMID: 42568124
Database: MEDLINE
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  Data: The Impact of Denoising Approaches on the Relationship Between Alzheimer's Disease Diagnostic Status and Network Topology Measures.
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  Data: <searchLink fieldCode="AU" term="%22Blujus+JK%22">Blujus JK</searchLink>; Department of Cognitive and Psychological Sciences, Brown University, Providence, Rhode Island, USA.<br /><searchLink fieldCode="AU" term="%22Oh+H%22">Oh H</searchLink>; Department of Cognitive and Psychological Sciences, Brown University, Providence, Rhode Island, USA.; Department of Psychiatry and Human Behavior, Brown University, Providence, Rhode Island, USA.; Carney Institute for Brain Science, Brown University, Providence, Rhode Island, USA.
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  Data: <searchLink fieldCode="CA" term="%22Alzheimer's+Disease+Neuroimaging+Initiative%22">Alzheimer's Disease Neuroimaging Initiative</searchLink>
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  Data: <i>Publication</i>: New York : Wiley<br /><i>Original Publication</i>: New York : Wiley-Liss, c1993-
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  Data: <searchLink fieldCode="MM" term="%22Alzheimer+Disease%22">Alzheimer Disease*</searchLink>/<searchLink fieldCode="MM" term="%22Alzheimer+Disease+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Alzheimer+Disease%22">Alzheimer Disease*</searchLink>/<searchLink fieldCode="MM" term="%22Alzheimer+Disease+physiopathology%22">physiopathology</searchLink> <br /><searchLink fieldCode="MM" term="%22Magnetic+Resonance+Imaging%22">Magnetic Resonance Imaging*</searchLink>/<searchLink fieldCode="MM" term="%22Magnetic+Resonance+Imaging+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Magnetic+Resonance+Imaging%22">Magnetic Resonance Imaging*</searchLink>/<searchLink fieldCode="MM" term="%22Magnetic+Resonance+Imaging+standards%22">standards</searchLink> <br /><searchLink fieldCode="MM" term="%22Cognitive+Dysfunction%22">Cognitive Dysfunction*</searchLink>/<searchLink fieldCode="MM" term="%22Cognitive+Dysfunction+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Cognitive+Dysfunction%22">Cognitive Dysfunction*</searchLink>/<searchLink fieldCode="MM" term="%22Cognitive+Dysfunction+physiopathology%22">physiopathology</searchLink> <br /><searchLink fieldCode="MM" term="%22Brain%22">Brain*</searchLink>/<searchLink fieldCode="MM" term="%22Brain+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Brain%22">Brain*</searchLink>/<searchLink fieldCode="MM" term="%22Brain+physiopathology%22">physiopathology</searchLink> <br /><searchLink fieldCode="MM" term="%22Nerve+Net%22">Nerve Net*</searchLink>/<searchLink fieldCode="MM" term="%22Nerve+Net+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Nerve+Net%22">Nerve Net*</searchLink>/<searchLink fieldCode="MM" term="%22Nerve+Net+physiopathology%22">physiopathology</searchLink> <br /><searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted%22">Image Processing, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted%22">Image Processing, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted+standards%22">standards</searchLink> <br /><searchLink fieldCode="MM" term="%22Connectome%22">Connectome*</searchLink>/<searchLink fieldCode="MM" term="%22Connectome+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Connectome%22">Connectome*</searchLink>/<searchLink fieldCode="MM" term="%22Connectome+standards%22">standards</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink> ; <searchLink fieldCode="MH" term="%22Aged%22">Aged</searchLink> ; <searchLink fieldCode="MH" term="%22Aged%2C+80+and+over%22">Aged, 80 and over</searchLink> ; <searchLink fieldCode="MH" term="%22Artifacts%22">Artifacts</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Graph theory provides a promising technique to investigate Alzheimer's disease (AD)-related alterations in brain network properties. However, there are discrepancies in the reported disruptions that occur to network topology across the AD continuum. In this study, we examined whether diagnostic group differences in graph metrics are attributed to differences in denoising approach used in fMRI processing. Resting state data from 60 cognitively normal (CN), 55 Mild Cognitive Impairment (MCI), and 38 AD participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were denoised using 11 pipelines including combinations of confound regression (head motion parameters, white matter [WM], cerebrospinal fluid [CSF], global signal), volume censoring (scrubbing, spike regression), and component-based noise removal (Independent Component Analysis-based Automatic Removal of Motion Artifacts [ICA-AROMA], anatomical and temporal component correction). Graph metrics representing network segregation (clustering coefficient, modularity, local efficiency), network integration (largest connected component, path length, global efficiency), and small-worldness were calculated. The results revealed that diagnostic group differences in modularity and local efficiency were dependent on denoising approach, especially in high-parameter regression models in combination with censoring methods (36 parameters and spike regressor or volume censoring). Independent of denoising approach, CN exhibited more segregated (clustering coefficient) but less integrated (largest component, path length, global efficiency) networks than MCI and AD. Independent of diagnosis, denoising strategy significantly affected the magnitude of all metrics, particularly models including global signal regression. Collectively, these results suggest that the directionality of the diagnostic differences in network topology, particularly in global metrics of network segregation, can vary based upon the denoising approach employed, although the effect size is small. Transparent reporting of preprocessing decisions is critical for the accurate interpretation of graph theoretical findings in the context of AD and a better understanding of the mechanisms underlying pathological aging.<br /> (© 2026 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)
– Name: Ref
  Label: References
  Group: RefInfo
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– Name: GrantInfo
  Label: Grant Information
  Group: Grant
  Data: R01AG068990 United States NH NIH HHS; R01AG069265 United States NH NIH HHS; R01 AG068990 United States AG NIA NIH HHS; U01 AG024904 United States AG NIA NIH HHS; S10 OD025181 United States OD NIH HHS; R01 AG069265 United States AG NIA NIH HHS; S10OD025181 United States NH NIH HHS
– Name: SubjectMinor
  Label: Contributed Indexing
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  Data: <i>Keywords: </i>Alzheimer's disease; denoising; fMRI; graph theory; nuisance regression; resting‐state
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  Data: <i>Date Created: </i>20260808 <i>Date Completed: </i>20260808 <i>Latest Revision: </i>20260826
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  Data: 10.1002/hbm.70622
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  Data: 42568124
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=42568124
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      – Type: doi
        Value: 10.1002/hbm.70622
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: e70622
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      – SubjectFull: Humans
        Type: general
      – SubjectFull: Female
        Type: general
      – SubjectFull: Male
        Type: general
      – SubjectFull: Aged
        Type: general
      – SubjectFull: Aged, 80 and over
        Type: general
      – SubjectFull: Artifacts
        Type: general
      – SubjectFull: Alzheimer Disease diagnostic imaging
        Type: general
      – SubjectFull: Alzheimer Disease physiopathology
        Type: general
      – SubjectFull: Magnetic Resonance Imaging methods
        Type: general
      – SubjectFull: Magnetic Resonance Imaging standards
        Type: general
      – SubjectFull: Cognitive Dysfunction diagnostic imaging
        Type: general
      – SubjectFull: Cognitive Dysfunction physiopathology
        Type: general
      – SubjectFull: Brain diagnostic imaging
        Type: general
      – SubjectFull: Brain physiopathology
        Type: general
      – SubjectFull: Nerve Net diagnostic imaging
        Type: general
      – SubjectFull: Nerve Net physiopathology
        Type: general
      – SubjectFull: Image Processing, Computer-Assisted methods
        Type: general
      – SubjectFull: Image Processing, Computer-Assisted standards
        Type: general
      – SubjectFull: Connectome methods
        Type: general
      – SubjectFull: Connectome standards
        Type: general
    Titles:
      – TitleFull: The Impact of Denoising Approaches on the Relationship Between Alzheimer's Disease Diagnostic Status and Network Topology Measures.
        Type: main
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            – D: 01
              M: 08
              Text: 2026 Aug
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 1097-0193
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            – Type: volume
              Value: 47
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Human brain mapping
              Type: main
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