IC-P-049: Exploring the biomarker cascade model: Relating cortical volume loss to [11C]PiB, [18F]FDG and MMSE over time in Alzheimer's disease patients and normal controls

Background and Hypothesis: Magnetic resonance imaging (MRI) has become a useful tool in monitoring the progression of Alzheimer's disease. Previous surface-based analysis has focused on changes in cortical thickness associated with the disease1. The objective of this study is to analyze MRI-derived cortical reconstructions for patterns of atrophy in terms of both cortical thickness and cortical volume. We hypothesize that Alzheimer’s Disease progression will be associated with a more significant change in volume than thickness. Experimental Design or Project Methods: MRI data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). All subjects with baseline and two-year 3T MRI scans were included. Segmentation of MRIs into gray and white matter was performed with FreeSurfer2,3,4,5. Subjects whose scans did not segment accurately were excluded. Surfaces were then registered to a common atlas with Ciftify6, and anatomically-constrained Multimodal Surface Matching (aMSM) was used to analyze longitudinal changes in each subject7. This produced continuous surface maps showing changes in cortical surface area and thickness. These maps were multiplied to create cortical volume maps8. Permutation Analysis of Linear Models (PALM) was used to perform two-sample t-tests comparing the maps of the Alzheimer’s and control groups9. Results: Preliminary analysis of nine Alzheimer’s subjects and nine control subjects produced surface maps displaying patterns that were expected given previous research findings10,11. There was increased volume and thickness loss in Alzheimer’s subjects relative to controls, with relatively high loss in structures of the medial temporal lobe. Future analysis of a larger sample will determine whether statistically significant differences exist between the Alzheimer’s and control groups in terms of thickness loss and volume loss. Conclusion and Potential Impact: If significant results are found, surface-based analysis of cortical volume may allow for detection of atrophy at an earlier stage in disease progression than would be possible based on cortical thickness. References 1. Clarkson MJ, Cardoso MJ, Ridgway GR, Modat M, Leung KK, Rohrer JD, Fox NC, Ourselin S. A comparison of voxel and surface based cortical thickness estimation methods. NeuroImage. 2011 Aug 1; 57(3):856-65. 2. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179194. 3. Fischl B, Sereno M, Dale A. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195–207. 4. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33:341-355. 5. Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, Dale AM. Sequence-independent segmentation of magnetic resonance images. Neuroimage 2004;23 Suppl 1:S69-84. 6. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, Webster M, Polimeni JR, Van Essen DC, Jenkinson M, WU-Minn HCP Consortium. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013 Oct 15;80:105-24. 7. Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, Bozek J, Wright R, Schuh A, Webster M, Hutter J, Price A, Cordero Grande L, Hughes E, Tusor N, Bayly PV, Van Essen DC, Smith SM, Edwards AD, Hajnal J, Jenkinson M, Glocker B, Rueckert D. Multimodal surface matching with higher-order smoothness constraints. Neuroimage. 2018;167:453-65. 8. Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, Van Essen DC. Informatics and data mining tools and strategies for the human connectome project. Front Neuroinform 2011;5:4. 9. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage, 2014;92:381-397 10. Matsuda, H. MRI morphometry in Alzheimer’s disease. Ageing Research Reviews. 2016 Sep;30:17-24. 11. Risacher SL, Shen L, West JD, Kim S, McDonald BC, Beckett LA, Harvey DJ, Jack CR Jr, Weiner MW, Saykin AJ. Alzheimer's Disease Neuroimaging Initiative (ADNI). Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiol Aging. 2010 Aug;31(8):1401-18.

Download Full-text

Crossed Cerebellar Diaschisis in Alzheimer’s Disease

Current Alzheimer Research ◽

10.2174/1567205015666180913102615 ◽

2018 ◽

Vol 15 (13) ◽

pp. 1267-1275 ◽

Cited By ~ 1

Author(s):

F.E. Reesink ◽

D. Vállez García ◽

C.A. Sánchez-Catasús ◽

D.E. Peretti ◽

A.T. Willemsen ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Left Hemisphere ◽

Pet Imaging ◽

Fdg Pet ◽

Pathophysiological Mechanism ◽

Crossed Cerebellar Diaschisis ◽

Amyloid Accumulation ◽

Cerebellar Diaschisis ◽

18F Fdg

Background: We describe the phenomenon of crossed cerebellar diaschisis (CCD) in four subjects diagnosed with Alzheimer’s disease (AD) according to the National Institute on Aging - Alzheimer Association (NIA-AA) criteria, in combination with 18F-FDG PET and 11C-PiB PET imaging. Methods: 18F-FDG PET showed a pattern of cerebral metabolism with relative decrease most prominent in the frontal-parietal cortex of the left hemisphere and crossed hypometabolism of the right cerebellum. 11C-PiB PET showed symmetrical amyloid accumulation, but a lower relative tracer delivery (a surrogate of relative cerebral blood flow) in the left hemisphere. CCD is the phenomenon of unilateral cerebellar hypometabolism as a remote effect of supratentorial dysfunction of the brain in the contralateral hemisphere. The mechanism implies the involvement of the cortico-ponto-cerebellar fibers. The pathophysiology is thought to have a functional or reversible basis but can also reflect in secondary morphologic change. CCD is a well-recognized phenomenon, since the development of new imaging techniques, although scarcely described in neurodegenerative dementias. Results: To our knowledge this is the first report describing CCD in AD subjects with documentation of both 18F-FDG PET and 11C-PiB PET imaging. CCD in our subjects was explained on a functional basis due to neurodegenerative pathology in the left hemisphere. There was no structural lesion and the symmetric amyloid accumulation did not correspond with the unilateral metabolic impairment. Conclusion: This suggests that CCD might be caused by non-amyloid neurodegeneration. The pathophysiological mechanism, clinical relevance and therapeutic implications of CCD and the role of the cerebellum in AD need further investigation.

Download Full-text

Subjective Spatial Navigation Complaints - A Frequent Symptom Reported by Patients with Subjective Cognitive Decline, Mild Cognitive Impairment and Alzheimer's Disease

Current Alzheimer Research ◽

10.2174/1567205014666171120145349 ◽

2018 ◽

Vol 15 (3) ◽

pp. 219-228 ◽

Cited By ~ 6

Author(s):

Jiri Cerman ◽

Ross Andel ◽

Jan Laczo ◽

Martin Vyhnalek ◽

Zuzana Nedelska ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Cognitive Decline ◽

Spatial Navigation ◽

Subjective Cognitive Decline ◽

Great Effort ◽

Frequent Symptom ◽

Normal Controls

Background: Great effort has been put into developing simple and feasible tools capable to detect Alzheimer's disease (AD) in its early clinical stage. Spatial navigation impairment occurs very early in AD and is detectable even in the stage of mild cognitive impairment (MCI). Objective: The aim was to describe the frequency of self-reported spatial navigation complaints in patients with subjective cognitive decline (SCD), amnestic and non-amnestic MCI (aMCI, naMCI) and AD dementia and to assess whether a simple questionnaire based on these complaints may be used to detect early AD. Method: In total 184 subjects: patients with aMCI (n=61), naMCI (n=27), SCD (n=63), dementia due to AD (n=20) and normal controls (n=13) were recruited. The subjects underwent neuropsychological examination and were administered a questionnaire addressing spatial navigation complaints. Responses to the 15 items questionnaire were scaled into four categories (no, minor, moderate and major complaints). Results: 55% of patients with aMCI, 64% with naMCI, 68% with SCD and 72% with AD complained about their spatial navigation. 38-61% of these complaints were moderate or major. Only 33% normal controls expressed complaints and none was ranked as moderate or major. The SCD, aMCI and AD dementia patients were more likely to express complaints than normal controls (p's<0.050) after adjusting for age, education, sex, depressive symptoms (OR for SCD=4.00, aMCI=3.90, AD dementia=7.02) or anxiety (OR for SCD=3.59, aMCI=3.64, AD dementia=6.41). Conclusion: Spatial navigation complaints are a frequent symptom not only in AD, but also in SCD and aMCI and can potentially be detected by a simple and inexpensive questionnaire.

Download Full-text

Allocentric to Egocentric Spatial Switching: Impairment in aMCI and Alzheimer's Disease Patients?

Current Alzheimer Research ◽

10.2174/1567205014666171030114821 ◽

2018 ◽

Vol 15 (3) ◽

pp. 229-236 ◽

Cited By ~ 8

Author(s):

Gennaro Ruggiero ◽

Alessandro Iavarone ◽

Tina Iachini

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Reference Frame ◽

Reference Frames ◽

Memory Task ◽

Spatial Representations ◽

The Novel ◽

Spatial Memory Task ◽

Switching Condition ◽

Normal Controls

Objective: Deficits in egocentric (subject-to-object) and allocentric (object-to-object) spatial representations, with a mainly allocentric impairment, characterize the first stages of the Alzheimer's disease (AD). Methods: To identify early cognitive signs of AD conversion, some studies focused on amnestic-Mild Cognitive Impairment (aMCI) by reporting alterations in both reference frames, especially the allocentric ones. However, spatial environments in which we move need the cooperation of both reference frames. Such cooperating processes imply that we constantly switch from allocentric to egocentric frames and vice versa. This raises the question of whether alterations of switching abilities might also characterize an early cognitive marker of AD, potentially suitable to detect the conversion from aMCI to dementia. Here, we compared AD and aMCI patients with Normal Controls (NC) on the Ego-Allo- Switching spatial memory task. The task assessed the capacity to use switching (Ego-Allo, Allo-Ego) and non-switching (Ego-Ego, Allo-Allo) verbal judgments about relative distances between memorized stimuli. Results: The novel finding of this study is the neat impairment shown by aMCI and AD in switching from allocentric to egocentric reference frames. Interestingly, in aMCI when the first reference frame was egocentric, the allocentric deficit appeared attenuated. Conclusion: This led us to conclude that allocentric deficits are not always clinically detectable in aMCI since the impairments could be masked when the first reference frame was body-centred. Alongside, AD and aMCI also revealed allocentric deficits in the non-switching condition. These findings suggest that switching alterations would emerge from impairments in hippocampal and posteromedial areas and from concurrent dysregulations in the locus coeruleus-noradrenaline system or pre-frontal cortex.

Download Full-text

Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

International Journal of Molecular Sciences ◽

10.3390/ijms22157911 ◽

2021 ◽

Vol 22 (15) ◽

pp. 7911

Author(s):

Eugene Lin ◽

Chieh-Hsin Lin ◽

Hsien-Yuan Lane

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Future Research ◽

Learning Approaches ◽

Learning Models ◽

Learning Techniques ◽

Neuroimaging Data ◽

Similarities And Differences ◽

Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

Download Full-text

The risk prediction of Alzheimer’s disease based on the deep learning model of brain 18F-FDG positron emission tomography

Saudi Journal of Biological Sciences ◽

10.1016/j.sjbs.2019.12.004 ◽

2020 ◽

Vol 27 (2) ◽

pp. 659-665

Author(s):

Zhiguang Yang ◽

Zhaoyu Liu

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Positron Emission Tomography ◽

Deep Learning ◽

Risk Prediction ◽

Learning Model ◽

Emission Tomography ◽

Positron Emission ◽

18F Fdg ◽

Deep Learning Model

Download Full-text

18F-FDG PET hypometabolism patterns reflect clinical heterogeneity in sporadic forms of early-onset Alzheimer's disease

Neurobiology of Aging ◽

10.1016/j.neurobiolaging.2017.08.009 ◽

2017 ◽

Vol 59 ◽

pp. 184-196 ◽

Cited By ~ 13

Author(s):

Matthieu Vanhoutte ◽

Franck Semah ◽

Adeline Rollin Sillaire ◽

Alice Jaillard ◽

Grégory Petyt ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Fdg Pet ◽

Early Onset ◽

Clinical Heterogeneity ◽

Early Onset Alzheimer’S Disease ◽

18F Fdg

Download Full-text

Moderating Effect of Insulin Resistance on the Relationship between Gray Matter Volumes and Cognitive Function

Journal of Clinical Medicine ◽

10.3390/jcm7110413 ◽

2018 ◽

Vol 7 (11) ◽

pp. 413 ◽

Cited By ~ 2

Author(s):

Jiyeon Lee ◽

Jihyeon Kim ◽

Seong Shin ◽

Soowon Park ◽

Dong Yoon ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Insulin Resistance ◽

Alzheimer's Disease ◽

Cognitive Function ◽

Cognitive Decline ◽

Gray Matter ◽

Structural Changes ◽

Gray Matter Volume ◽

Volume Loss ◽

Matter Volume

Background: It is controversial whether exposure to insulin resistance accelerates cognitive deterioration. The present study aimed to investigate the association between insulin resistance and gray matter volume loss to predict the cognitive decline. Methods: We recruited 160 participants (78 with Alzheimer’s disease and 82 without Alzheimer’s disease). Insulin resistance, regional gray matter volume, and cognitive function were assessed. A hierarchical moderated multiple regression (MMR) model was used to determine any associations among insulin resistance, structural changes in the brain, and cognitive decline. Results: The volumes of 7 regions in the gray matter were negatively related to insulin resistance in Alzheimer’s disease (p =0.032). Hierarchical MMR analysis indicated that insulin resistance did not directly affect the cognitive decline but moderated the cognitive decline through the decrease in gray matter volume in the key brain regions, i.e., inferior orbitofrontal gyrus (left), middle cingulate gyrus (right), hippocampus (right), and precuneus (right) (p < 0.05 in each case). Conclusion: Insulin resistance appears to exacerbate the cognitive decline associated with several gray matter volume loss.

Download Full-text