Quantitative analysis of grey matter degeneration in FTD patients using fractal dimension analysis.

Fractal dimension (FD) is a quantitative parameter that can characterizes the complexity of human brain tissue. Extensive grey matter (GM) pathology has been previously identified in Frontotemporal dementia (FTD) and its variants. The aim of the present study was to investigate the GM morphometric a...

Descripción completa

Detalles Bibliográficos
Publicado en:Brain Imaging & Behavior Vol. 12; no. 5; pp. 1221 - 1229
Autores principales: Sheelakumari, Raghavan, Venkateswaran Rajagopalan, Chandran, Anuvitha, Varghese, Tinu, Zhang, Luduan, Yue, Guang H., Mathuranath, Pavagadha S., Kesavadas, Chandrasekharan
Formato: Journal Article
Publicado: Springer Nature Oct2018
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133509390&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 133509390
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        19317557
        3GSC
      jtl: Brain Imaging & Behavior
      issn: 19317557
      maglogo: N
    pubinfo:
      dt: Oct2018
      vid: 12
      iid: 5
      pid: 237
      pub: Springer Nature
      place: New York, New York
    artinfo:
      ui:
        133509390
        133509390
        NLM29086152
        10.1007/s11682-017-9784-x
        NLM29086152
        133509390
      ppf: 1221
      ppct: 8
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
      tig:
        atl: Quantitative analysis of grey matter degeneration in FTD patients using fractal dimension analysis.
      aug:
        au:
          Sheelakumari, Raghavan
          Venkateswaran Rajagopalan
          Chandran, Anuvitha
          Varghese, Tinu
          Zhang, Luduan
          Yue, Guang H.
          Mathuranath, Pavagadha S.
          Kesavadas, Chandrasekharan
        affil: Cognition and Behavioural Neurology Section, Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, 695011, Trivandrum, India
      sug:
        subj:
          Aphasia
          Frontotemporal Dementia
          Image Interpretation, Computer Assisted Methods
          Magnetic Resonance Imaging Methods
          Gray Matter
          Brain
          Middle Age
          Male
          Algorithms
          Nerve Degeneration
          Mathematics
          Female
          Information Science Methods
          Imaging, Three-Dimensional
          Questionnaires
          Middle Aged: 45-64 years
          Male
          Female
      ab: Fractal dimension (FD) is a quantitative parameter that can characterizes the complexity of human brain tissue. Extensive grey matter (GM) pathology has been previously identified in Frontotemporal dementia (FTD) and its variants. The aim of the present study was to investigate the GM morphometric abnormalities in the behavioral variant FTD (bvFTD) and primary progressive aphasia (PPA) using FD analysis. Twenty-seven bvFTD, 12 PPA and 20 controls were studied. SPM8 was used to segment the brain into GM tissue. Then the FD values were estimated for the GM skeleton, surface and general structure in patients and controls using our previously published algorithm. We found that patients with bvFTD had significant reduction in FD values of skeleton and general structure when compared to controls. In PPA, more significant decrease in FD was noted in the whole brain and left hemisphere skeleton along with left hemisphere general structure. Only the right hemisphere skeleton had a significant correlation with total score of Frontal Systems Behavior Scale (FrSBe). The results showed that the variants of FTD are associated with disease specific morphometric complexity patterns. These results indicate that FD can be used as a biomarker for the structural changes associated with neurodegenerative diseases.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N