- Email: [email protected]
ACROSS-SESSION REPRODUCIBILITY OF AUTOMATIC WHITE MATTER HYPERINTENSITIES SEGMENTATION: A EUROPEAN MULTI-SITE 3T STUDY
P126
Poster Presentations: Saturday, July 15, 2017 IC-P-167
the brainstem. The second measurement was the relative tracer influx rate (R1) obtained from kinetic modeling of PiB data, using brainstem as the reference region. Partial volume correction was applied to take into account the atrophic process. Regional crosssectional analyses were performed to evaluate the correlation between images and estimate the relationship of the imaging biomarkers with estimated time to disease progression based on family history. Nine regions were evaluated, including the precuneus and the inferior parietal cortex, known to be greatly affected by hypometabolism. Results: Metabolism and perfusion images were spatially highly correlated, showing decreased signal in similar regions (Figure 1). Across all participants, the R1 values were better correlated to FDG than ePiB was (e.g. r¼0.52, p<0.0001 and r¼0.54, p<0.0001 for R1 vs. FDG in the inferior parietal and the precuneus, respectively, and r¼0.28, p<0.005 and r¼0.10, n.s. for ePiB vs. FDG in the inferior parietal and the precuneus, respectively, Figure 2). Regional R1 values and FDG significantly decreased in the MC vs. NC with estimated-year-to-onset (p<0.05 for the inferior parietal) while ePiB did not decrease but increased instead (p<0.05 for the inferior parietal) (Figure 3). Within the MC, R1 values and FDG significantly decreased with dementia severity (p<0.05 for the inferior parietal) while ePiB had no relationship with dementia for any regions. Conclusions: Neurodegeneration estimated by R1 may provide a new measure of brain function without added radioactivity. EPiB does not provide good neurodegeneration estimates as it may be contaminated with b-Amyloid deposition.
ACROSS-SESSION REPRODUCIBILITY OF AUTOMATIC WHITE MATTER HYPERINTENSITIES SEGMENTATION: A EUROPEAN MULTI-SITE 3T STUDY
Federica Ribaldi1, Moira Marizzoni2, Jorge Jovicich3, Clarissa Ferrari4, Beatriz Bosch5, David Bartres-Faz6, Bernhard W. M€uller7, Jens Wiltfang8, Ute Fiedler9, Luca Roccatagliata10,11, Agnese Picco12, Flavio Nobili13, Olivier Blin14, Stephanie Bombois15, Renaud Lopes16, Regis Bordet17, Julien Sein18, Jean-Philippe Ranjeva19, Mira Didic20,21, Helene GrosDagnac22, Pierre Payoux23,24, Giada Zoccatelli25, Franco Alessandrini25, Alberto Beltramello26, Nuria Bargallo27, Antonio Ferretti28, Massimo Caulo29, Marco Aiello30, Carlo Cavaliere30, Andrea Soricelli30,31, Lucilla Parnetti32, Robertto Tarducci33, Piero Floridi34, Magda Tsolaki35, Manos Constantinides36, Antonios Drevelegas36, Paolo Maria Rossini37, Camillo Marra38, Peter Schonknecht39, Tilman Hensch39, Karl-Titus Hoffmann40, Joost Kuijer41, Pieter Jelle Visser41,42, Frederik Barkhof43, Giovanni B. Frisoni44,45, 1IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; 2IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; 3University of Trento, Trento, Italy; 4 Service of Statistics, IRCCS Fatebenefratelli, Brescia, Italy; 5Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Department, IDIBAPS, Hospital Clınic de Barcelona, Barcelona, Spain; 6University of Barcelona, Barcelona, Spain; 7University of Duisburg-Essen, Essen, Germany; 8Department of Psychiatry and Psychotherapy, University Medical Center G€ottingen, G€ottingen, Germany; 9Institutes and Clinics of the University Duisburg-Essen, Essen, Germany; 10IRCSS San Martino University Hospital and IST, Genoa, Italy; 11Department of Neuroscience, Ophthalmology and Genetics University of Genoa, Genoa, Italy; 12 Department of Neuroscience, Ophthalmology, Genetics and Mother–Child Health (DINOGMI), University of Genoa, Genoa, Italy; 13 University of Genoa, Italy, Genoa, Italy; 14Aix-Marseille University-CNRS, Marseille, France; 15University of Lille, INSERM U1171, Memory Clinic, F-59000 Lille, Lille, France; 16INSERM U1171, Neuroradiology Department, University Hospital, Lille, France; 17Service de Pharmacologie-H^opital Huriez-CHRU, Lille, France; 18 CRMBM-CEMEREM, UMR 7339, Aix Marseille University, Marseille, France; 19CIC-UPCET, CHU La Timone, AP-HM, UMR CNRS-Universite de la Mediterranee, Marseille, France; 20Aix-Marseille Universite, Marseille, France; 21Service de Neurologie et Neuropsychologie, Marseille, France; 22INSERM, Imagerie Cerebrale Et Handicaps Neurologiquies, UMR825, Toulouse, France; 23INSERM, Imagerie Cerebrale et Handicaps Neurologiques, Toulouse, France; 24Universite de Toulouse, Toulouse, France; 25Department of Neuroradiology, General Hospital, Verona, Italy; 26 Department of Neuroradiology, General Hospital, Verona, Italy; 27 Magnetic Resonance Imaging Core Facility, Institut d’Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; 28 Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio, Chieti, Italy; 29University “G. d’Annunzio” of Chieti, Chieti, Italy; 30IRCCS SDN, Naples, Italy; 31University of Naples Parthenope, Naples, Italy; 32Lab of Clinical Neurochemistry, University of Perugia, Perugia, Italy; 33Perugia General Hospital, Medical Physics Unit, Perugia, Italy; 34Perugia General Hospital, Neuroradiology Unit, Perugia, Italy; 35Aristotle University of Thessaloniki, Thessaloniki, Greece; 36 Interbalkan Medical Centre of Thessaloniki, Thessaloniki, Greece; 37 Catholic University, Policlinic Gemelli, Rome, Italy; 38Catholic University, Rome, Italy; 39Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Germany, Leipzig, Germany; 40Department of Neuroradiology, University Hospital Leipzig, Leipzig, Germany; 41VU University Medical Center, Amsterdam, Netherlands; 42Maastricht University, Maastricht, Netherlands; 43Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, Netherlands; 44Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland; 45Laboratory of Alzheimer’s Neuroimaging and Epidemiology - LANE, IRCCS Institute - The Saint John of God Clinical Research Centre, Brescia, Italy. Contact e-mail: [email protected]
Poster Presentations: Saturday, July 15, 2017 Background: PharmaCOG is an industry-academic European project aimed at identifying reliable biomarkers that are sensitive to disease progression in patients with mild cognitive impairment. Several automated methods for quantitative assessment of white matter hyperintensities (WMH) have been recently developed (for a review see Caligiuri, 2015). However, the longitudinal reproducibility of these approaches has been poorly investigated. Here we present preliminary work aimed at evaluating the across-session test-retest reproducibility of the automated WMH quantification computed with SPM12 (Schmidt, 2012) on a group of healthy elderly subjects. Methods: Eleven 3T MRI sites (Siemens, GE, Philips) participated across Italy, Spain, France, Germany, Greece and The Netherlands. The acquisition protocol includes one 2D FLAIR sequence: acceleration factor in the range of 1.5 to 2 where possible (GRAPPA, SENSE and ASSET in Siemens, Philips and GE systems, respectively), 0.9x0.9x4mm3, with TE/TR/TI as recommended by the ADNI project (Jack, 2008). Five local healthy volunteers (55-90 years) per site were scanned in two sessions a week apart. The segmentation of WMH was performed using lesion segmentation tool (LST) version 2.0.15 (Schmidt, 2012) with optimized parameters and applying the longitudinal correction. The WMH volume and number of lesions reliability for each subject was computed evaluating test-retest absolute differences relative to the mean. Results: Visual assessment indicates good segmentation results. The WMH volume was comparable across sites (Kruskal–Wallis, p¼.143) and was around 1.6 ml (SD¼2.9). The reproducibility error of total WMH volume averaged across sites was 2.3% (SD¼ 7.0) while that reported for the lesion number was 1.9% (SD¼7.0). None of the WMH volume reproducibility metrics showed MRI site effects (Kruskall-Wallis test, p>.185). Conclusions: Despite the differences of MRI scanner configurations across our 11 sites we found consistent WMH volumes and lesion count reliability. Our findings suggest that the WMH volume may be a reliable longitudinal marker in multisite studies. Pharmacog is funded by the EU-FP7 for the Innovative Medicine Initiative (grant n 115009).
IC-P-168
LONGITUDINAL NEURITE ORIENTATION DISPERSION AND DENSITY IMAGING IN YOUNG-ONSET ALZHEIMER’S DISEASE
Catherine F. Slattery1, Jiaying Zhang2, Ross W. Paterson1, Alexander JM. Foulkes1, Laura Mancini3, David L. Thomas3, Marc Modat4, Nicolas Toussaint4, David M. Cash4, John S. Thornton3, Daniel C. Alexander2, Sebastien Ourselin4, Nick C. Fox1, Hui Zhang2, Jonathan M. Schott1, 1Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom; 2Department of Computer Science and Centre for Medical Image Computing, UCL, London, United Kingdom; 3Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, United Kingdom; 4Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, United Kingdom. Contact e-mail: [email protected] Background: Longitudinal white matter damage in Alzheimer’s dis-
ease (AD) has been identified using diffusion tensor imaging (DTI). However, DTI indices are not tissue-specific and cannot differentiate between axonal loss and changes in axonal morphology. We used neurite orientation dispersion and density imaging (NODDI) to investigate longitudinal changes in white matter neurite density index (NDI), thought to be a specific measure of axonal loss, in a cohort of patients with young-onset Alzheimer’s disease
P127
(YOAD). Methods: Twenty-four patients with YOAD (age: 61.465.7 yrs, 38% male) and 19 controls (age 60.766.3yrs, 53% male) were imaged on the same 3T Siemens Trio scanner at baseline and after one year using a sequence optimised for NODDI (64, 32, and 8 diffusion-weighted directions at b¼2000, 700 and 300s/ mm2; 14 b¼0 images; 55 slices; voxel size 2.5x2.5x2.5mm3; TR/ TE¼7000/92ms; TA¼15mins). After motion and eddy-current distortion correction using FSL, neurite density index (NDI) was estimated using the NODDI toolbox. An optimised longitudinal framework implemented in DTI-TK was used for spatial normalization. Cross-sectional between-group comparisons of NDI were performed at each time point using Tract-Based Spatial Statistics (TBSS), co-varying for age and gender (5000 permutations corrected for multiple comparisons with Threshold-Free Cluster Enhancement p<0.05). Annualised rate of change in NDI indices within groups was computed as follow-up NDI minus baseline, divided by the between-scan interval, and then rates of change between groups compared using TBSS. Results: At baseline, individuals with YOAD had extensive NDI reduction relative to controls in the cingulum, corpus callosum, superior and inferior longitudinal fasciculi, fronto-occipital fasciculus and fornix (A). After one year, this NDI reduction was more extensive and additionally involved the internal capsule and brainstem white matter tracts (B). The annualized rate of NDI reduction in YOAD was significantly greater in patients in the cingulum, splenium of corpus callosum, inferior and superior longitudinal fasciculi, fornix and fronto-occipital fasciculus (C). Conclusions: We show that regions of NDI reduction seen in YOAD are similar to the areas of decreased FA reported in longitudinal DTI studies. Longitudinal changes in neurite density measured using NODDI may be a sensitive outcome measure for clinical trials aiming to prevent neural loss.
IC-P-169
GRADIENT ECHO PLURAL CONTRAST MRI PROVIDES NEW SURROGATE MARKERS OF BRAIN PATHOLOGY IN ALZHEIMER’S DISEASE
Dmitriy A. Yablonskiy1, Yue Zhao1, Nigel J. Cairns2, Jason Hassenstab1,2, Tammie LS. Benzinger1,2, Serguei V. Astafiev1, Jie Wen1, Marcus E. Raichle1,2, John C. Morris1,2, 1Washington University in St. Louis, St. Louis, MO, USA; 2Knight Alzheimer’s Disease Research Center, St. Louis, MO, USA. Contact e-mail: [email protected] Background: One of the important directions in AD research is devel-
oping widely accessible surrogate markers that could detect AD brain pathology at the clinically silent preclinical stages. Our approach relies on the MRI-based Gradient Echo Plural Contrast Imaging (GEPCI) technique that provides in vivo quantitative high resolution 3D
Poster Presentations: Saturday, July 15, 2017 IC-P-167
the brainstem. The second measurement was the relative tracer influx rate (R1) obtained from kinetic modeling of PiB data, using brainstem as the reference region. Partial volume correction was applied to take into account the atrophic process. Regional crosssectional analyses were performed to evaluate the correlation between images and estimate the relationship of the imaging biomarkers with estimated time to disease progression based on family history. Nine regions were evaluated, including the precuneus and the inferior parietal cortex, known to be greatly affected by hypometabolism. Results: Metabolism and perfusion images were spatially highly correlated, showing decreased signal in similar regions (Figure 1). Across all participants, the R1 values were better correlated to FDG than ePiB was (e.g. r¼0.52, p<0.0001 and r¼0.54, p<0.0001 for R1 vs. FDG in the inferior parietal and the precuneus, respectively, and r¼0.28, p<0.005 and r¼0.10, n.s. for ePiB vs. FDG in the inferior parietal and the precuneus, respectively, Figure 2). Regional R1 values and FDG significantly decreased in the MC vs. NC with estimated-year-to-onset (p<0.05 for the inferior parietal) while ePiB did not decrease but increased instead (p<0.05 for the inferior parietal) (Figure 3). Within the MC, R1 values and FDG significantly decreased with dementia severity (p<0.05 for the inferior parietal) while ePiB had no relationship with dementia for any regions. Conclusions: Neurodegeneration estimated by R1 may provide a new measure of brain function without added radioactivity. EPiB does not provide good neurodegeneration estimates as it may be contaminated with b-Amyloid deposition.
ACROSS-SESSION REPRODUCIBILITY OF AUTOMATIC WHITE MATTER HYPERINTENSITIES SEGMENTATION: A EUROPEAN MULTI-SITE 3T STUDY
Federica Ribaldi1, Moira Marizzoni2, Jorge Jovicich3, Clarissa Ferrari4, Beatriz Bosch5, David Bartres-Faz6, Bernhard W. M€uller7, Jens Wiltfang8, Ute Fiedler9, Luca Roccatagliata10,11, Agnese Picco12, Flavio Nobili13, Olivier Blin14, Stephanie Bombois15, Renaud Lopes16, Regis Bordet17, Julien Sein18, Jean-Philippe Ranjeva19, Mira Didic20,21, Helene GrosDagnac22, Pierre Payoux23,24, Giada Zoccatelli25, Franco Alessandrini25, Alberto Beltramello26, Nuria Bargallo27, Antonio Ferretti28, Massimo Caulo29, Marco Aiello30, Carlo Cavaliere30, Andrea Soricelli30,31, Lucilla Parnetti32, Robertto Tarducci33, Piero Floridi34, Magda Tsolaki35, Manos Constantinides36, Antonios Drevelegas36, Paolo Maria Rossini37, Camillo Marra38, Peter Schonknecht39, Tilman Hensch39, Karl-Titus Hoffmann40, Joost Kuijer41, Pieter Jelle Visser41,42, Frederik Barkhof43, Giovanni B. Frisoni44,45, 1IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; 2IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; 3University of Trento, Trento, Italy; 4 Service of Statistics, IRCCS Fatebenefratelli, Brescia, Italy; 5Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Department, IDIBAPS, Hospital Clınic de Barcelona, Barcelona, Spain; 6University of Barcelona, Barcelona, Spain; 7University of Duisburg-Essen, Essen, Germany; 8Department of Psychiatry and Psychotherapy, University Medical Center G€ottingen, G€ottingen, Germany; 9Institutes and Clinics of the University Duisburg-Essen, Essen, Germany; 10IRCSS San Martino University Hospital and IST, Genoa, Italy; 11Department of Neuroscience, Ophthalmology and Genetics University of Genoa, Genoa, Italy; 12 Department of Neuroscience, Ophthalmology, Genetics and Mother–Child Health (DINOGMI), University of Genoa, Genoa, Italy; 13 University of Genoa, Italy, Genoa, Italy; 14Aix-Marseille University-CNRS, Marseille, France; 15University of Lille, INSERM U1171, Memory Clinic, F-59000 Lille, Lille, France; 16INSERM U1171, Neuroradiology Department, University Hospital, Lille, France; 17Service de Pharmacologie-H^opital Huriez-CHRU, Lille, France; 18 CRMBM-CEMEREM, UMR 7339, Aix Marseille University, Marseille, France; 19CIC-UPCET, CHU La Timone, AP-HM, UMR CNRS-Universite de la Mediterranee, Marseille, France; 20Aix-Marseille Universite, Marseille, France; 21Service de Neurologie et Neuropsychologie, Marseille, France; 22INSERM, Imagerie Cerebrale Et Handicaps Neurologiquies, UMR825, Toulouse, France; 23INSERM, Imagerie Cerebrale et Handicaps Neurologiques, Toulouse, France; 24Universite de Toulouse, Toulouse, France; 25Department of Neuroradiology, General Hospital, Verona, Italy; 26 Department of Neuroradiology, General Hospital, Verona, Italy; 27 Magnetic Resonance Imaging Core Facility, Institut d’Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; 28 Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio, Chieti, Italy; 29University “G. d’Annunzio” of Chieti, Chieti, Italy; 30IRCCS SDN, Naples, Italy; 31University of Naples Parthenope, Naples, Italy; 32Lab of Clinical Neurochemistry, University of Perugia, Perugia, Italy; 33Perugia General Hospital, Medical Physics Unit, Perugia, Italy; 34Perugia General Hospital, Neuroradiology Unit, Perugia, Italy; 35Aristotle University of Thessaloniki, Thessaloniki, Greece; 36 Interbalkan Medical Centre of Thessaloniki, Thessaloniki, Greece; 37 Catholic University, Policlinic Gemelli, Rome, Italy; 38Catholic University, Rome, Italy; 39Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Germany, Leipzig, Germany; 40Department of Neuroradiology, University Hospital Leipzig, Leipzig, Germany; 41VU University Medical Center, Amsterdam, Netherlands; 42Maastricht University, Maastricht, Netherlands; 43Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, Netherlands; 44Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland; 45Laboratory of Alzheimer’s Neuroimaging and Epidemiology - LANE, IRCCS Institute - The Saint John of God Clinical Research Centre, Brescia, Italy. Contact e-mail: [email protected]
Poster Presentations: Saturday, July 15, 2017 Background: PharmaCOG is an industry-academic European project aimed at identifying reliable biomarkers that are sensitive to disease progression in patients with mild cognitive impairment. Several automated methods for quantitative assessment of white matter hyperintensities (WMH) have been recently developed (for a review see Caligiuri, 2015). However, the longitudinal reproducibility of these approaches has been poorly investigated. Here we present preliminary work aimed at evaluating the across-session test-retest reproducibility of the automated WMH quantification computed with SPM12 (Schmidt, 2012) on a group of healthy elderly subjects. Methods: Eleven 3T MRI sites (Siemens, GE, Philips) participated across Italy, Spain, France, Germany, Greece and The Netherlands. The acquisition protocol includes one 2D FLAIR sequence: acceleration factor in the range of 1.5 to 2 where possible (GRAPPA, SENSE and ASSET in Siemens, Philips and GE systems, respectively), 0.9x0.9x4mm3, with TE/TR/TI as recommended by the ADNI project (Jack, 2008). Five local healthy volunteers (55-90 years) per site were scanned in two sessions a week apart. The segmentation of WMH was performed using lesion segmentation tool (LST) version 2.0.15 (Schmidt, 2012) with optimized parameters and applying the longitudinal correction. The WMH volume and number of lesions reliability for each subject was computed evaluating test-retest absolute differences relative to the mean. Results: Visual assessment indicates good segmentation results. The WMH volume was comparable across sites (Kruskal–Wallis, p¼.143) and was around 1.6 ml (SD¼2.9). The reproducibility error of total WMH volume averaged across sites was 2.3% (SD¼ 7.0) while that reported for the lesion number was 1.9% (SD¼7.0). None of the WMH volume reproducibility metrics showed MRI site effects (Kruskall-Wallis test, p>.185). Conclusions: Despite the differences of MRI scanner configurations across our 11 sites we found consistent WMH volumes and lesion count reliability. Our findings suggest that the WMH volume may be a reliable longitudinal marker in multisite studies. Pharmacog is funded by the EU-FP7 for the Innovative Medicine Initiative (grant n 115009).
IC-P-168
LONGITUDINAL NEURITE ORIENTATION DISPERSION AND DENSITY IMAGING IN YOUNG-ONSET ALZHEIMER’S DISEASE
Catherine F. Slattery1, Jiaying Zhang2, Ross W. Paterson1, Alexander JM. Foulkes1, Laura Mancini3, David L. Thomas3, Marc Modat4, Nicolas Toussaint4, David M. Cash4, John S. Thornton3, Daniel C. Alexander2, Sebastien Ourselin4, Nick C. Fox1, Hui Zhang2, Jonathan M. Schott1, 1Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom; 2Department of Computer Science and Centre for Medical Image Computing, UCL, London, United Kingdom; 3Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, United Kingdom; 4Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, United Kingdom. Contact e-mail: [email protected] Background: Longitudinal white matter damage in Alzheimer’s dis-
ease (AD) has been identified using diffusion tensor imaging (DTI). However, DTI indices are not tissue-specific and cannot differentiate between axonal loss and changes in axonal morphology. We used neurite orientation dispersion and density imaging (NODDI) to investigate longitudinal changes in white matter neurite density index (NDI), thought to be a specific measure of axonal loss, in a cohort of patients with young-onset Alzheimer’s disease
P127
(YOAD). Methods: Twenty-four patients with YOAD (age: 61.465.7 yrs, 38% male) and 19 controls (age 60.766.3yrs, 53% male) were imaged on the same 3T Siemens Trio scanner at baseline and after one year using a sequence optimised for NODDI (64, 32, and 8 diffusion-weighted directions at b¼2000, 700 and 300s/ mm2; 14 b¼0 images; 55 slices; voxel size 2.5x2.5x2.5mm3; TR/ TE¼7000/92ms; TA¼15mins). After motion and eddy-current distortion correction using FSL, neurite density index (NDI) was estimated using the NODDI toolbox. An optimised longitudinal framework implemented in DTI-TK was used for spatial normalization. Cross-sectional between-group comparisons of NDI were performed at each time point using Tract-Based Spatial Statistics (TBSS), co-varying for age and gender (5000 permutations corrected for multiple comparisons with Threshold-Free Cluster Enhancement p<0.05). Annualised rate of change in NDI indices within groups was computed as follow-up NDI minus baseline, divided by the between-scan interval, and then rates of change between groups compared using TBSS. Results: At baseline, individuals with YOAD had extensive NDI reduction relative to controls in the cingulum, corpus callosum, superior and inferior longitudinal fasciculi, fronto-occipital fasciculus and fornix (A). After one year, this NDI reduction was more extensive and additionally involved the internal capsule and brainstem white matter tracts (B). The annualized rate of NDI reduction in YOAD was significantly greater in patients in the cingulum, splenium of corpus callosum, inferior and superior longitudinal fasciculi, fornix and fronto-occipital fasciculus (C). Conclusions: We show that regions of NDI reduction seen in YOAD are similar to the areas of decreased FA reported in longitudinal DTI studies. Longitudinal changes in neurite density measured using NODDI may be a sensitive outcome measure for clinical trials aiming to prevent neural loss.
IC-P-169
GRADIENT ECHO PLURAL CONTRAST MRI PROVIDES NEW SURROGATE MARKERS OF BRAIN PATHOLOGY IN ALZHEIMER’S DISEASE
Dmitriy A. Yablonskiy1, Yue Zhao1, Nigel J. Cairns2, Jason Hassenstab1,2, Tammie LS. Benzinger1,2, Serguei V. Astafiev1, Jie Wen1, Marcus E. Raichle1,2, John C. Morris1,2, 1Washington University in St. Louis, St. Louis, MO, USA; 2Knight Alzheimer’s Disease Research Center, St. Louis, MO, USA. Contact e-mail: [email protected] Background: One of the important directions in AD research is devel-
oping widely accessible surrogate markers that could detect AD brain pathology at the clinically silent preclinical stages. Our approach relies on the MRI-based Gradient Echo Plural Contrast Imaging (GEPCI) technique that provides in vivo quantitative high resolution 3D