Premium
[P4–260]: INVESTIGATION OF A TASK FOR EARLY DETECTION OF COGNITIVE DECLINE IN ELDERLY PEOPLE USING FUNCTIONAL NEAR‐INFRARED SPECTROSCOPY (FNIRS) FOR DIAGNOSIS OF ALZHEIMER'S DISEASE
Author(s) -
Yomota Satoshi,
Hiroko Nakata,
Suzuki Hideaki,
Uchida Kazuhiko,
Ando Eiji,
Nakamura Shin,
Asada Takashi
Publication year - 2017
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2017.06.2129
Subject(s) - functional near infrared spectroscopy , neuroimaging , task (project management) , cognition , audiology , modality (human–computer interaction) , psychology , brain activity and meditation , elementary cognitive task , physical medicine and rehabilitation , neuroscience , electroencephalography , medicine , computer science , artificial intelligence , prefrontal cortex , management , economics
require manual intervention. As such, these methods are susceptible to intra/inter-segmentor variability and require trained resource for quality control (QC). Herewe present a fully automated pipeline for estimation of whole-brain atrophy with subsequent automatic QC testing, minimising sources of potential variability and need for visual inspection. Methods: We employed a subset (n1⁄4372) of the ADNI1 cohort with 1.5T T1 images and KMN-BSI values available (Fox Lab, ADNI) at Baseline, 12 month and a 24 month follow-up as validation data set. Baseline images were brain extracted with PinCram (Heckemann;PLOS,2015) and N4 Bias field corrected (Tustison;IEEE,2010). Longitudinal images were registered to the baseline image, bias corrected and intensity normalised to the baseline intensity profile. Segmentation was performed on baseline images using a whole-brain multi-atlas segmentation approach (WB LEAP; Wolz, NeuroImage, 2010; Ledig,Proc ISBI,2012). From this, we obtain subject probabilistic maps of cerebral tissue types used to initialise a probabilistic temporospatial segmentation using expectation maximisation based optimisation (Ledig,IEEE,2014). To ensure data quality is maintained, a visual inspection of the input baseline tissue segmentations is required only. QC of image registrations and longitudinal segmentations is performed automatically through tolerance testing on the spatial cross-correlation between registered images, annualised atrophy and normalised volumetric change. Resultant atrophy estimations were compared to KMNBSI values for validation. Results: The proposed automatic QC reported a 90% pass rate for longitudinal segmentations; upon inspection, all automatic QC pass grades were found to be correct. Segmentations failing automatic QC were visually inspected, 8 subjects failed due to poor registrations. Good agreement was observed between atrophy estimated with KNM-BSI and the proposed method; significant correlation was observed in atrophy estimations at month 12 (r1⁄40.62,p<<0.01) and month 24 (r1⁄40.73,p<<0.01). Reported effect and sample sizes were not significantly different between methods for any clinical group or follow-up. Conclusions: We have proposed a fully automatic method for longitudinal assessment of whole-brain atrophy with semi-automatic QC, reducing potential segmentor/rater variance and reporting atrophy consistent with the BSI method.