publications_atam.bib

@article{tam2022prediction,
  title={Prediction of Cognitive Decline for Enrichment of Alzheimer’s Disease Clinical Trials},
  author={\textbf{Tam, A.} and Laurent, C{\'e}sar and Gauthier, Serge and Dansereau, Christian},
  journal={The Journal of Prevention of Alzheimer's Disease},
  pages={1--10},
  year={2022},
  publisher={Springer},
  doi={10.14283/jpad.2022.49}
}

@article{chen2022shared,
  title={Shared and unique brain network features predict cognition, personality and mental health in childhood in the ABCD study},
  author={*Chen, Jianzhong and *\textbf{Tam, A.} and Kebets, Valeria and Orban, Csaba and Ooi, Leon Qi Rong and Marek, Scott and Dosenbach, Nico and Eickhoff, Simon and Bzdok, Danilo and Holmes, Avram J and others},
  journal={Nature Communications},
  volume={13},
  number={2217},
  year={2022},
  doi={10.1038/s41467-022-29766-8}
}

@article{marek2022reproducible,
  title={Reproducible brain-wide association studies require thousands of individuals},
  author={Marek, Scott and Tervo-Clemmens, Brenden and Calabro, Finnegan J and $35$ others including \textbf{Tam, A.}},
  journal={Nature},
  volume={603},
  number={7902},
  pages={654--660},
  year={2022},
  publisher={Nature Publishing Group},
  doi={10.1038/s41586-022-04492-9}
}

@article{li2022cross,
  title={Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity},
  author={Li, Jingwei and Bzdok, Danilo and Chen, Jianzhong and \textbf{Tam, A.} and Ooi, Leon Qi Rong and Holmes, Avram J and Ge, Tian and Patil, Kaustubh R and Jabbi, Mbemba and Eickhoff, Simon B and others},
  journal={Science Advances},
  volume={8},
  number={11},
  pages={eabj1812},
  year={2022},
  publisher={American Association for the Advancement of Science},
  doi={10.1126/sciadv.abj1812}
}

@article{Tam:2019gz,
    author = {\textbf{Tam, A.} and Dansereau, Christian and Iturria-Medina, Yasser and Urchs, Sebastian and Orban, Pierre and Sharmarke, Hanad and Breitner, John and Bellec, Pierre and Alzheimer's Disease Neuroimaging Initiative},
    title = "{A highly predictive signature of cognition and brain atrophy for progression to Alzheimer's dementia}",
    journal = {GigaScience},
    volume = {8},
    number = {5},
    year = {2019},
    month = {},
    abstract = "{Clinical trials in Alzheimer's disease need to enroll patients whose cognition will decline over time, if left untreated, in order to demonstrate the efficacy of an intervention. Machine learning models used to screen for patients at risk of progression to dementia should therefore favor specificity (detecting only progressors) over sensitivity (detecting all progressors), especially when the prevalence of progressors is low. Here, we explore whether such high-risk patients can be identified using cognitive assessments and structural neuroimaging by training machine learning tools in a high-specificity regime.A multimodal signature of Alzheimer's dementia was first extracted from the ADNI1 dataset. We then validated the predictive value of this signature on ADNI1 patients with mild cognitive impairment (N = 235). The signature was optimized to predict progression to dementia over 3 years with low sensitivity (55.1\%) but high specificity (95.6\%), resulting in only moderate accuracy (69.3\%) but high positive predictive value (80.4\%, adjusted for a “typical” 33\% prevalence rate of true progressors). These results were replicated in ADNI2 (N = 235), with 87.8\% adjusted positive predictive value (96.7\% specificity, 47.3\% sensitivity, 85.1\% accuracy).We found that cognitive measures alone could identify high-risk individuals, with structural measurements providing a slight improvement. The signature had comparable receiver operating characteristics to standard machine learning tools, yet a marked improvement in positive predictive value was achieved over the literature by selecting a high-specificity operating point. The multimodal signature can be readily applied for the enrichment of clinical trials.}",
    issn = {2047-217X},
    doi = {10.1093/gigascience/giz055},
}
@article{Vogel:2018tp,
abstract = {Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T 1 -weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.},
author = {Vogel, Jacob W and Vachon-Presseau, Etienne and Pichet Binette, Alexa and \textbf{Tam, A.} and Orban, Pierre and Joie, Renaud La and Savard, M{\'{e}} Lissa and Picard, Cynthia and Poirier, Judes and Bellec, Pierre and Breitner, John C S and Villeneuve, Sylvia},
doi = {10.1093/brain/awy093},
file = {:home/angela/Documents/Library.papers3/Articles/2018/Vogel/Brain 2018 Vogel.pdf:pdf},
issn = {1460-2156},
journal = {Brain},
keywords = {ADNI = Alzheimer's Disease Neuroimaging Initiative,MCI,biomarkers,functional MRI,machine learning Abbreviations,structural MRI},
number = {July},
pages = {1871--1883},
pmid = {29688388},
title = {{Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease}},
volume = {141},
year = {2018}
}
@article{Badhwar:2017fm,
author = {Badhwar, AmanPreet and \textbf{Tam, A.} and Dansereau, Christian and Orban, Pierre and Hoffstaedter, Felix and Bellec, Pierre},
file = {:home/angela/Documents/Library.papers3/Articles/2017/Badhwar/Alzheimers Dementia Diagnosis Assessment Disease Monitoring 2017 Badhwar.pdf:pdf},
journal = {Alzheimer's \& Dementia: Diagnosis, Assessment \& Disease Monitoring},
pages = {73--85},
title = {{Resting-state network dysfunction in Alzheimer's disease: A systematic review and meta-analysis.}},
volume = {8},
year = {2017},
doi = {10.1016/j.dadm.2017.03.007},
}

% article{Reginold:2016qt,
% abstract = {This study used diffusion tensor imaging tractography at 3 T MRI to relate cognitive function to white matter tracts in the brain. Brain T2 fluid attenuated inversion recovery-weighted and diffusion tensor 3 T MRI scans were acquired in thirty-three healthy participants without mild cognitive impairment or dementia. They completed a battery of neuropsychological tests including the Montreal Cognitive Assessment, Stroop test, Trail Making Test B, Wechsler Memory Scale-III Longest span forward, Wechsler Memory Scale-III Longest span backward, Mattis Dementia Rating Scale, California Verbal Learning Test Version II Long Delay Free Recall, and Letter Number Sequencing. Tractography was generated by the Fiber Assignment by Continuous Tracking method. The corpus callosum, cingulum, long association fibers, corticospinal/bulbar tracts, thalamic projection fibers, superior cerebellar peduncle, middle cerebellar peduncle and inferior cerebellar peduncle were manually segmented. The fractional anisotropy (FA) and mean diffusivity (MD) of these tracts were quantified. We studied the association between cognitive test scores and the MD and FA of tracts while controlling for age and total white matter hyperintensities volume. Worse scores on the Stroop test was associated with decreased FA of the corpus callosum, corticospinal/bulbar tract, and thalamic projection tracts. Scores on the other cognitive tests were not associated with either the FA or MD of measured tracts. In healthy persons the Stroop test appears to be a better predictor of the microstructural integrity of white matter tracts measured by DTI tractography than other cognitive tests.},
% author = {Reginold, William and Itorralba, Justine and \textbf{Tam, A.} and Luedke, Angela C. and Fernandez-Ruiz, Juan and Reginold, Jennifer and Islam, Omar and Garcia, Angeles},
% doi = {10.1007/s11682-015-9495-0},
% file = {:home/angela/Documents/Library.papers3/Articles/2016/Reginold/Brain Imag Behav 2016 Reginold.pdf:pdf},
% isbn = {1168201594},
% issn = {19317565},
% journal = {Brain Imaging and Behavior},
% keywords = {Cognitive tests,Diffusion tensor imaging,Tractography},
% number = {4},
% pages = {1223--1230},
% pmid = {26650629},
% publisher = {Brain Imaging and Behavior},
% title = {{Correlating quantitative tractography at 3T MRI and cognitive tests in healthy older adults}},
% url = {http://dx.doi.org/10.1007/s11682-015-9495-0},
% volume = {10},
% year = {2016}
% }

@article{Tam:2016jn,
author = {\textbf{Tam, A.} and Dansereau, Christian and Badhwar, AmanPreet and Orban, Pierre and Belleville, Sylvie and Chertkow, Howard and Dagher, Alain and Hanganu, Alexandru and Monchi, Oury and Rosa-Neto, Pedro and Shmuel, Amir and Breitner, John and Bellec, Pierre and Initiative, for the Alzheimer s Disease Neuroimaging},
file = {:home/angela/Documents/Library.papers3/Articles/2016/Tam/Data in Brief 2016 Tam.pdf:pdf},
journal = {Data in Brief},
month = {dec},
number = {C},
pages = {1122--1129},
title = {{A dataset of multiresolution functional brain parcellations in an elderly population with no or mild cognitive impairment}},
volume = {9},
year = {2016},
doi = {10.1016/j.dib.2016.11.036},
}

@article{Orban:2015tr,
abstract = {We present a test-retest dataset of resting-state fMRI data obtained in 80 cognitively normal elderly volunteers enrolled in the "Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease" (PREVENT-AD) Cohort. Subjects with a family history of Alzheimer's disease in first-degree relatives were recruited as part of an on-going double blind randomized clinical trial of Naproxen or placebo. Two pairs of scans were acquired {\~{}}3 months apart, allowing the assessment of both intra- and inter-session reliability, with the possible caveat of treatment effects as a source of inter-session variation. Using the NeuroImaging Analysis Kit (NIAK), we report on the standard quality of co-registration and motion parameters of the data, and assess their validity based on the spatial distribution of seed-based connectivity maps as well as intra- and inter-session reliability metrics in the default-mode network. This resource, released publicly as sample UM1 of the Consortium for Reliability and Reproducibility (CoRR), will benefit future studies focusing on the preclinical period preceding the appearance of dementia in Alzheimer's disease.},
author = {Orban, Pierre and Madjar, C{\'{e}}cile and Savard, M{\'{e}}lissa and Dansereau, Christian and \textbf{Tam, A.} and Das, Samir and Evans, Alan C. and Rosa-Neto, Pedro and Breitner, John C.S. and Bellec, Pierre and Aisen, Paul and Pascoal, Tharick Ali and Anthal, Elena and Appleby, Melissa and Barkun, Alan and Beaudry, Thomas and Benbouhoud, Fatiha and Bohbot, Veronique and Brandt, Jason and Brunelle, C{\'{e}}line and Carmo, Leopoldina and Cheewakriengkrai, Laksanaun and Collins, Louis and Courcot, Blandine and Couture, Doris and Craft, Suzanne and Cuello, A. Claudio and Dadar, Mahsa and Dauar-Tedeschi, Marina and Dea, Dorothy and Debacker, Cl{\'{e}}ment and Desautels, Ren{\'{e}} and Desrochers, Nicole and Dubuc, Sylvie and Duclair, Guerda and Dufour, Marianne and Eisenberg, Mark and El-Khoury, Rana and Etienne, Pierre and Faubert, Anne Marie and Ferdinand, Fabiola and Fonov, Vladimir S. and Fontaine, David and Francoeur, Renaud and Frappier, Jos{\'{e}}e and Frenette, Joanne and Gauthier, Serge and Gervais, Val{\'{e}}rie and Giles, Renuka and Gordon, Renee and Hoge, Rick and Hyman, Bradley T. and Iturria-Medina, Yasser and Jack, Clifford R. and Kat, Justin and Khachaturian, Zaven S. and Kliegman, Stephanie and Knopman, David S. and Kostopoulos, Penelope and Labont{\'{e}}, Anne and Lafaille-Magnan, Marie Elyse and Lee, Tanya and Lepage, Claude and Leppert, Ilana and Leoutsakos, Jeannie Marie and Mahar, Laura and Maltais, Jean Robert and Mathieu, Axel and Mathotaarachchi, Sulantha and Maultaup, Gerhard and Mayrand, Ginette and Michaud, Diane and Miron, Justin and Montine, Thomas J. and Morris, John C. and M{\"{u}}nter, Lisa Marie and Nair, Vasavan and Near, Jamie and Newbold-Fox, Holly and Pag{\'{e}}, V{\'{e}}ronique and Petkova, Mirela and Picard, Cynthia and Pogossova, Galina and Poirier, Isabelle and Poirier, Judes and Pruessner, Jens and Rajah, Natasha and Rioux, Pierre and Sager, Mark A. and Sperling, Reisa A. and Tariot, Pierre N. and Teigner, Eduard and Th{\'{e}}roux, Louise and Thomas, Ronald G. and Toussaint, Paule Joanne and Tremblay-Mercier, Jennifer and Tuwaig, Miranda and Vall{\'{e}}e, Isabelle and Venogopalan, Vinod and Wan, Karen and Wang, Seqian},
doi = {10.1038/sdata.2015.43},
file = {:home/angela/Documents/Library.papers3/Articles/2015/Orban/Scientific Data 2015 Orban.pdf:pdf},
isbn = {2052-4463 (Electronic) 2052-4463 (Linking)},
issn = {20524463},
journal = {Scientific Data},
pages = {1--11},
pmid = {26504522},
title = {{Test-retest resting-state fMRI in healthy elderly persons with a family history of Alzheimer's disease}},
volume = {2},
year = {2015}
}

% article{Reginold:2015wm,
% abstract = {BACKGROUND/AIMS: This study used 3-Tesla magnetic resonance imaging (MRI) tractography to determine if there was an association between tracts crossing white matter hyperintensities (WMH) and cognitive function in elderly persons.$\backslash$n$\backslash$nMETHODS: Brain T2-weighted fluid-attenuated inversion recovery (FLAIR) and diffusion tensor MRI scans were acquired in participants above the age of 60 years. Twenty-six persons had WMH identified on T2 FLAIR scans. They completed a battery of neuropsychological tests and were classified as normal controls (n = 15) or with Alzheimer's dementia (n = 11). Tractography was generated by the Fiber Assignment by Continuous Tracking method. All tracts that crossed WMH were segmented. The average fractional anisotropy and average mean diffusivity of these tracts were quantified. We studied the association between cognitive test scores with the average mean diffusivity and average fractional anisotropy of tracts while controlling for age, total WMH volume and diagnosis.$\backslash$n$\backslash$nRESULTS: An increased mean diffusivity of tracts crossing WMH was associated with worse performance on the Wechsler Memory Scale-III Longest Span Forward (p = 0.02). There was no association between the fractional anisotropy of tracts and performance on cognitive testing.$\backslash$n$\backslash$nCONCLUSION: The mean diffusivity of tracts crossing WMH measured by tractography is a novel correlate of performance on the Wechsler Memory Scale-III Longest Span Forward in elderly persons.},
% author = {Reginold, William and Luedke, Angela C and \textbf{Tam, A.} and Itorralba, Justine and Fernandez-Ruiz, Juan and Reginold, Jennifer and Islam, Omar and Garcia, Angeles},
% doi = {10.1159/000439045},
% file = {:home/angela/Documents/Library.papers3/Articles/2015/Reginold/Dem Ger Cog Dis Ext 2015 Reginold.pdf:pdf},
% isbn = {1664-5464 (Linking)},
% issn = {1664-5464},
% journal = {Dementia and geriatric cognitive disorders extra},
% keywords = {Cognitive function,Diffusion tensor imaging,Tractography,White matter hyperintensities},
% number = {3},
% pages = {387--394},
% pmid = {26628897},
% title = {{Cognitive Function and 3-Tesla Magnetic Resonance Imaging Tractography of White Matter Hyperintensities in Elderly Persons.}},
% url = {http://www.karger.com/Article/FullText/439045},
% volume = {5},
% year = {2015}
% }

@article{Tam:2015ec,
author = {\textbf{Tam, A.} and Dansereau, Christian and Badhwar, AmanPreet and Orban, Pierre and Belleville, Sylvie and Chertkow, Howard and Dagher, Alain and Hanganu, Alexandru and Monchi, Oury and Rosa-Neto, Pedro and Shmuel, Amir and Wang, Seqian and Breitner, John and Bellec, Pierre},
file = {:home/angela/Documents/Library.papers3/Articles/2015/Tam/Front. Aging Neurosci. 2015 Tam.pdf:pdf},
journal = {Frontiers in Aging Neuroscience},
month = {dec},
number = {7},
pages = {2214--2266},
title = {{Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies}},
volume = {7},
year = {2015},
doi = {10.3389/fnagi.2015.00242},
}

@article{Tam:2015rt,
abstract = {Variability in reaction time during task performance may reflect fluctuations in attention and cause reduced performance in goal-directed tasks, yet it is unclear whether the mechanisms behind this phenomenon change with age. Using fMRI, we tested young and cognitively healthy older adults with the Stroop task to determine whether aging affects the neural mechanisms underlying intra-individual reaction time variability. We found significant between-group differences in BOLD activity modulated by reaction time. In older adults, longer reaction times were associated with greater activity in frontoparietal attentional areas, while in younger adults longer reaction times were associated with greater activity in default mode network areas. Our results suggest that the neural correlates of reaction time variability change with healthy aging, reinforcing the concept of functional plasticity to maintain high cognitive function throughout the lifespan.},
author = {\textbf{Tam, A.} and Luedke, Angela C. and Walsh, Jeremy J. and Fernandez-Ruiz, Juan and Garcia, Angeles},
doi = {10.1007/s11682-014-9323-y},
file = {:home/angela/Documents/Library.papers3/Articles/2015/Tam/Brain Imag Behav 2015 Tam.pdf:pdf},
issn = {19317565},
journal = {Brain Imaging and Behavior},
keywords = {Aging,Attention,Performance variability,Reaction time,fMRI},
number = {3},
pages = {609--618},
pmid = {25280971},
title = {{Effects of reaction time variability and age on brain activity during Stroop task performance}},
volume = {9},
year = {2015}
}

@article{Ruthirakuhan:2012np,
abstract = {Lifestyle nonpharmacological interventions can have a deep effect on cognitive aging. We have reviewed the available literature on the effectiveness of physical activity, intellectual stimulation, and socialization on the incidence of dementia and on the course of dementia itself. Even though physical activity appears to be beneficial in both delaying dementia onset and in the course of the disease, more research is needed before intellectual stimulation and socialization can be considered as treatments and prevention of the disease. Through our paper, we found that all three nonpharmacological treatments provide benefits to cognition and overall well-being in patients with age-related cognitive impairments. These interventions may be beneficial in the management of dementia.},
author = {*Ruthirakuhan, Myuri and *Luedke, Angela C. and *\textbf{Tam, A.} and Goel, Ankita and Kurji, Ayaz and Garcia, Angeles},
doi = {10.1155/2012/384875},
file = {:home/angela/Documents/Library.papers3/Articles/2012/Ruthirakuhan/Journal Aging Research 2012 Ruthirakuhan.pdf:pdf},
isbn = {2090-2212 (Electronic) 2090-2204 (Linking)},
issn = {20902204},
journal = {Journal of Aging Research},
pmid = {23365752},
title = {{Use of physical and intellectual activities and socialization in the management of cognitive decline of aging and in dementia: A review}},
volume = {2012},
year = {2012}
}

%%%%%%%%%%%%%%%%%%%%%
% Preprints
%%%%%%%%%%%%%%%%%%%%%

% article{Dansereau:2017up,
% author = {Dansereau, Christian and \textbf{Tam, A.} and Badhwar, AmanPreet and Urchs, Sebastian and Orban, Pierre and Rosa-Neto, Pedro and Bellec, Pierre},
% file = {:home/angela/Documents/Library.papers3/Articles/2017/Dansereau/arxiv 2017 Dansereau.pdf:pdf},
% journal = {arXiv preprint arXiv:1712.08058},
% title = {{A brain signature highly predictive of future progression to Alzheimer's dementia}},
% year = {2017},
% url = {https://arxiv.org/abs/1712.08058}
% }

% article{Orban:2017ep,
% author = {Orban, Pierre and \textbf{Tam, A.} and Urchs, Sebastian and Savard, Melissa and Madjar, Cecile and Badhwar, AmanPreet and Dansereau, Christian and Vogel, Jacob and Shmuel, Amir and Dagher, Alain and Villeneuve, Sylvia and Poirier, Judes and Rosa-Neto, Pedro and Breitner, John and Bellec, Pierre and {for the Alzheimer's Disease Neuroimaging Initiative} and PreventAD},
% file = {:home/angela/Documents/Library.papers3/Articles/2017/Orban/biorxiv 2017 Orban.pdf:pdf},
% journal = {bioRxiv},
% month = {jan},
% pages = {195164},
% title = {{Subtypes of functional brain connectivity as early markers of neurodegeneration in Alzheimer's disease}},
% year = {2017},
% doi = {10.1101/195164},
% }

% article{urchs2020reproducible,
%   title={Reproducible functional connectivity endophenotype confers high risk of ASD diagnosis in a subset of individuals},
%   author={Urchs, Sebastian GW and Nguyen, Hien Duy and Moreau, Clara and Dansereau, Christian and \textbf{Tam, A.} and Evans, Alan C and Bellec, Pierre},
%   journal={BioRxiv},
%   year={2020},
%   publisher={Cold Spring Harbor Laboratory},
%   doi={10.1101/2020.06.01.127688}
% }

% article{urchs2020subtypes,
%   title={Subtypes of functional connectivity associate robustly with ASD diagnosis},
%   author={Urchs, Sebastian GW and \textbf{Tam, A.} and Orban, Pierre and Moreau, Clara and Benhajali, Yassine and Nguyen, Hien Duy and Evans, Alan C and Bellec, Pierre},
%   journal={BioRxiv},
%   year={2020},
%   publisher={Cold Spring Harbor Laboratory},
%   doi={10.1101/2020.04.14.040576}
% }