Advanced Neuroimaging Biomarkers
for Dementia & Alzheimer’s Disease
The most widely used and advanced proprietary imaging biomarkers enable you to measure brain anatomy, disease progression and treatment efficacy, easy to use and all in one place
In 2020 there were more than 55 million people worldwide living with dementia and that number is expected to double every two decades. Still, research has shown that most people living with dementia have not been properly diagnosed. For example, approximately only 35% of dementia cases are diagnosed in primary care in developed countries.
Dementia is a group of symptoms that affects mental cognitive tasks such as reasoning and memory, and it is an umbrella term in which mixed dementia, mild cognitive impairment and Alzheimer’s disease can fall under. The latter one, Alzheimer’s Disease, is a progressive disease of the brain that with age slowly causes impairment in memory and cognitive function. Thus, its early diagnosis is critically important for treatments.
Current diagnosis is mainly based on cognitive assessment, but being able to see and quantify the changes in white matter, connectivity, and volume quantification for different regions of the brain is of utmost importance to guarantee its early detection and for the characterization of its progression.
Changes in Hippocampal volume
Hippocampal atrophy is the most important imaging-derived biomarker in Alzheimer’s disease, commonly used in both research and clinical trials. (14) The QMENTA platform offers tools for volumetric quantification of hippocampal subfields, including hi-resolution hippocampal image quantification.
Longitudinal Volumetric MRI
Whole brain volume is the most commonly used imaging endpoint in clinical trials in Alzheimer’s(16). Additionally, it has been shown in studies that volume/thickness changes or abnormal values for determined areas can be a good biomarker for the detection of the disease and estimation of its progression.(17,18,19) QMENTA’s gray matter volumetry tools allow for quantification of region-specific gray matter volume for each patient timepoint using processing tools such as FreeSurfer, ANTs and SIENAX. The QMENTA Platform’s data management allows longitudinal assessment of gray matter atrophy.
Resting state fMRI seed-based connectivity measures
Resting state fMRI have been shown as a reliable biomarker for characterization of AD and and the AD-related dementia, such as Mild Cognitive Impairment. The default mode network is one of the commonly affected areas of the brain. QMENTA’s seed-based analysis tools allow identification of the changes in the DMN’s resting state connectivity.
Resting state fMRI non-connectivity physiological maps
The non-connectivity physiological maps, including regional homogeneity (ReHO), the Amplitude of the Low Frequency Fluctuations (ALFF), and fractional ALFF, have been shown useful to characterize the physiology of AD, reveal intrinsic network disruption and are sensitive indexes to detect AD-related neurodegeneration. QMENTA’s resting state fMRI physiological maps offer fully automated tools for estimation of the maps and quantifies them for different brain regions.
Change in MRI white matter free water content as a proxy for neuroinflammation
Recent studies have shown that neuroinflammation might play an important role in many neurological diseases. The origin of this phenomenon is still to be understood, although it has been agreed that white matter free water modeling estimated from diffusion MRI could be related to it. In this context, it has been shown how white matter free water levels progressively increase during the different stages throughout the disease progression from healthy, to mild cognitive impairment, to Alzheimer’s disease. This makes free water content a potentially useful biomarker for cognitive decline.
Change in MRI Apparent Fiber Density as a proxy for changes in white matter quality
Apparent Fiber Density is a recently proposed metric that tries to overcome limitations of standard diffusion metrics such as Fractional Anisotropy or Mean Diffusivity since it is sensitive to more specific microstructural features of the neuronal tissue. It can be estimated from the combination of a high number of gradient directions and high b-value diffusion acquisition with advanced multi-compartmental diffusion modelling. In these models, the characteristic water diffusion pattern in each compartment is different (restricted for intra-axonal, hindered for extra-axonal, isotropic for CSF) and by combining all of them one can model the diffusion signal measured in each voxel. Its usage might lead to the computation of advanced Alzheimer’s disease characterization biomarkers since it has been shown how the Apparent Fiber Density significantly decreases in major white matter bundles involved in memory and other cognitive tasks. Standard diffusion metrics have previously shown similar results, however, apparent fiber density overcomes their limitations by providing improved speciﬁcity by identifying which particular white matter tract is affected.
Source: David Raffelt, et al. “Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images.” NeuroImage (2012)
Source: Marcus, Charles et al. “Brain PET in the diagnosis of Alzheimer's disease.” Clinical nuclear medicine (2014)
Fluorodeoxyglucose (FDG) PET assesses the adherence of glucose cells. In the clinical routine FDG PET images are used for diagnosis and assessment of the disease through visual inspection. However, quantitative analysis of the FDG uptake has been shown to aid in diagnosis, treatment monitoring and in tailoring of therapy, since an increased FDG uptake generally corresponds to a dismal course of the disease. Applied to dementia, the patterns of glucose metabolism on brain FDG PET can help in differentiating Alzheimer’s disease from other causes of dementia such as dementia of Lewy body or frontotemporal dementia. FDG PET can also help to predict the evolution of the disease.
Amyloid PET, which assesses brain amyloid deposition, provides valuable information for the characterization of Alzheimer’s disease. It has been proposed that amyloid deposition precedes neurodegeneration for several years before cognitive symptoms start appearing. Thus, quantitative computation of the uptake value ratios of amyloid deposition on Alzheimer’s disease relevant regions through the combination MRI and PET imaging pipelines might provide an excellent biomarker for the early detection of Alzheimer’s disease.
Source: “Amyloid PET Scan for Alzheimer's Disease Assessment". UCSF, radiology.ucsf.edu
Source: Fichou, Yann, et al. “The elusive tau molecular structures: can we translate the recent breakthroughs into new targets for intervention?.” Acta Neuropathologica Communications (2019)
Tau PET evaluates the deposition of tau neurofibrillary tangles in the brain. Many studies have shown noticeable uptake of tau in patients with AD. Additionally, the tau deposition (uptake) has been demonstrated that highly correlates with the neurological deficits as well as there has been noticeable relationship between tau uptake and gray matter volume in typical and atypical clinical variants of AD, suggesting that tau uptake is related with preceding rate of volume loss.
Arterial Spin Labeling MRI (ASL)
Arterial spin-labeled (ASL) perfusion MRI permits noninvasive quantification of blood flow which can be used to diagnose perfusion disorders appearing in neurodegenerative disease such as mild cognitive impairment, Alzheimer’s disease, and other types of dementia. Cerebral Blood Flow metrics have been found to decline in patients with Alzheimer’s disease making them useful to distinguish between Alzheimer’s disease patients and healthy controls or to predict cognitive decline on longitudinal studies. QMENTA’s Cerebral Blood Flow computation tool allows quantification of perfusion on ROIs such as putamen, cerebellar gray matter and white matter by using BASIL FSL processing tools.
White matter integrity by diffusion tensor imaging (DTI)
Diffusion-weighted imaging quantification has been identified as a promising biomarker in Alzheimer’s disease, revealing white matter alteration and enabling neurobiologically meaningful prognosis and outcomes.(20,21) QMENTA offers a suite of automated tools for analysis of diffusion-weighted imaging analysis including evaluation of diffusion-based measures (among others, fractional anisotropy [FA], radial diffusivity [RD], axial diffusivity [AD], mean diffusivity [MD]) over regions of interest (e.g. peridentate region of the superior cerebellar peduncle, dentate nucleus, …) or over white-matter tract based statistics.
Patterns of Whole Brain Structural Connectivity
Connectomics allows the study of structural brain connectivity, both at the level of individual connections between relevant areas and through graph-measures to characterize whole-brain connectivity. It can be used to research the possible impact of these diseases on brain connectivity. (22) The QMENTA platform provides fully automatic connectome analysis tools.
Source: Qunxi Dong, et al. “Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline.” NeuroImage: Clinical (2020)
Ventricular volume is a commonly used imaging endpoint in clinical trials for Alzheimer’s therapies.(16) QMENTA’s volumetric tools provide ventricular volume quantification.
Basal Ganglia Volumetry
Aside from the whole brain and the hippocampus, regions of the basal ganglia are also found to exhibit atrophy in the progression of Alzheimer’s.(15) QMENTA offers tools for volumetric quantification of basal ganglia structures.
Source: Tang, Xiaoying, et al. “Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: Testing using data with a broad range of anatomical and photometric profiles”. Frontiers in neuroscience (2015)
14. Henneman, W. J. P., Sluimer, J. D., Barnes, J., Van Der Flier, W. M., Sluimer, I. C., Fox, N. C., ... & Barkhof, F. (2009). Hippocampal atrophy rates in Alzheimer disease added value over whole brain volume measures. Neurology, 72(11), 999-1007.
15. De Jong, L. W., Van der Hiele, K., Veer, I. M., Houwing, J. J., Westendorp, R. G. J., Bollen, E. L. E. M., ... & Van Der Grond, J. (2008). Strongly reduced volumes of putamen and thalamus in Alzheimer's disease: an MRI study. Brain, 131(12), 3277-3285.
16. Cash, David M., et al. "Imaging endpoints for clinical trials in Alzheimer’s disease." Alzheimer's research & therapy 6.9 (2014): 87.
17. Querbes, Olivier, et al. "Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve." Brain 132.8 (2009): 2036-2047.
18. Racine, Annie M., et al. "The personalized Alzheimer's disease cortical thickness index predicts likely pathology and clinical progression in mild cognitive impairment." Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 10 (2018): 301-310.
19. Dickerson, Bradford C., et al. "The cortical signature of Alzheimer's disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals." Cerebral cortex 19.3 (2008): 497-510.
20. Douaud, Gwenaëlle, et al. "DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease." Neuroimage 55.3 (2011): 880-890.
21. Zhang, Y., et al. "Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease." Neurology 68.1 (2007): 13-19.
22. Lo, Chun-Yi, et al. "Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer's disease." Journal of Neuroscience 30.50 (2010): 16876-16885.