% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Hasoon:272081,
      author       = {Hasoon, Jahfer and Hamilton, Calum A and Schumacher, Julia
                      and Colloby, Sean and Donaghy, Paul C and Thomas, Alan J and
                      Taylor, John-Paul},
      title        = {{EEG} {F}unctional {C}onnectivity {D}ifferences {P}redict
                      {F}uture {C}onversion to {D}ementia in {M}ild {C}ognitive
                      {I}mpairment {W}ith {L}ewy {B}ody or {A}lzheimer {D}isease.},
      journal      = {International journal of geriatric psychiatry},
      volume       = {39},
      number       = {9},
      issn         = {0885-6230},
      address      = {Chichester [u.a.]},
      publisher    = {Wiley},
      reportid     = {DZNE-2024-01124},
      pages        = {e6138},
      year         = {2024},
      abstract     = {Predicting which individuals may convert to dementia from
                      mild cognitive impairment (MCI) remains difficult in
                      clinical practice. Electroencephalography (EEG) is a widely
                      available investigation but there is limited research
                      exploring EEG connectivity differences in patients with MCI
                      who convert to dementia.Participants with a diagnosis of MCI
                      due to Alzheimer's disease (MCI-AD) or Lewy body disease
                      (MCI-LB) underwent resting state EEG recording. They were
                      followed up annually with a review of the clinical diagnosis
                      (n = 66). Participants with a diagnosis of dementia at year
                      1 or year 2 follow up were classed as converters (n = 23)
                      and those with a diagnosis of MCI at year 2 were classed as
                      stable (n = 43). We used phase lag index (PLI) to estimate
                      functional connectivity as well as analysing dominant
                      frequency (DF) and relative band power. The Network-based
                      statistic (NBS) toolbox was used to assess differences in
                      network topology.The converting group had reduced DF (U =
                      285.5, p = 0.005) and increased relative pre-alpha power (U
                      = 702, p = 0.005) consistent with previous findings. PLI
                      showed reduced average beta band synchrony in the converting
                      group (U = 311, p = 0.014) as well as significant
                      differences in alpha and beta network topology. Logistic
                      regression models using regional beta PLI values revealed
                      that right central to right lateral (Sens = $56.5\%,$ Spec =
                      $86.0\%,$ -2LL = 72.48, p = 0.017) and left central to right
                      lateral (Sens = $47.8\%,$ Spec = $81.4\%,$ -2LL = 71.37, p =
                      0.012) had the best classification accuracy and fit when
                      adjusted for age and MMSE score.Patients with MCI who
                      convert to dementia have significant differences in EEG
                      frequency, average connectivity and network topology prior
                      to the onset of dementia. The MCI group is clinically
                      heterogeneous and have underlying physiological differences
                      that may be driving the progression of cognitive symptoms.
                      EEG connectivity could be useful to predict which patients
                      with MCI-AD and MCI-LB convert to dementia, regardless of
                      the neurodegenerative aetiology.},
      keywords     = {Humans / Cognitive Dysfunction: physiopathology / Cognitive
                      Dysfunction: etiology / Lewy Body Disease: physiopathology /
                      Female / Alzheimer Disease: physiopathology /
                      Electroencephalography: methods / Male / Aged / Disease
                      Progression / Aged, 80 and over / Lewy body disease (Other)
                      / cognitive dysfunction (Other) / dementia (Other) / disease
                      progression (Other) / electroencephalography (Other) /
                      functional connectivity (Other) / prediction (Other)},
      cin          = {AG Storch},
      ddc          = {610},
      cid          = {I:(DE-2719)5000014},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39261275},
      doi          = {10.1002/gps.6138},
      url          = {https://pub.dzne.de/record/272081},
}