Neuroimaging biomarkers of neurodegenerative diseases and dementia

Neurodegenerative disorders leading to dementia are common diseases that affect many older and some young adults. Neuroimaging methods are important tools for assessing and monitoring pathological brain changes associated with progressive neurodegenerative conditions. In this review, the authors describe key findings from neuroimaging studies (magnetic resonance imaging and radionucleotide imaging) in neurodegenerative disorders, including Alzheimer's disease (AD) and prodromal stages, familial and atypical AD syndromes, frontotemporal dementia, amyotrophic lateral sclerosis with and without dementia, Parkinson's disease with and without dementia, dementia with Lewy bodies, Huntington's disease, multiple sclerosis, HIV-associated neurocognitive disorder, and prion protein associated diseases (i.e., Creutzfeldt-Jakob disease). The authors focus on neuroimaging findings of in vivo pathology in these disorders, as well as the potential for neuroimaging to provide useful information for differential diagnosis of neurodegenerative disorders

Targeted neurogenesis pathway-based gene analysis identifies ADORA2A associated with hippocampal volume in mild cognitive impairment and Alzheimer's disease

poster abstractBackground: New neurons are generated throughout adulthood in the olfactory bulb and dentate gyrus of the hippocampus, and are incorporated into hippocampal networks during maintenance of neural circuits and in turn contribute to learning and memory. Numerous intrinsic and extrinsic factors such as growth factors, transcription factors, and cell cycle regulators control neural stem cells proliferation, differentiation, and maintenance into mature neurons. However, the genetic mechanisms controlling adult hippocampal neurogenesis remain unclear. We performed a gene-based association analysis of neurogenesis pathway-related candidate genes using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Methods: Neurogenesis-related genes were curated from existing databases (Qiagen RT2 Profiler PCR Arrays, GoGene and MANGO). The gene list was filtered by AD susceptibility genes from the Alzgene database (http://www.alzgene.org/) and large-scale GWAS (Lambert,et al. 2013, Nature). Caucasian non-Hispanic individuals (N=1,525) with AD or mild cognitive impairment (MCI) and cognitively normal older adults from the ADNI cohort with MRI and genotyping data were included. Gene-based association analysis of neurogenesis pathway-related candidate genes was performed. Baseline bilateral hippocampus and hippocampal subfield (CA regions and dentate gyrus) volumes were extracted from MRI and served as phenotypes. Gender, age, intracranial volume, MRI field strength, and diagnosis at scanning were entered as covariates. The empirical p value from permutation testing for each gene was adjusted for the number of significant SNPs in each gene. Results: ADORA2A was significantly associated with total hippocampal volume and hippocampal subfield volumes (p<0.001). For the most significant SNP (rs9608282) in ADORA2A, dosage of the minor allele (T) increased hippocampal volume. rs9608282 was also associated with composite memory score (p= 0.0076). Conclusion: ADORA2A-mediated control of neuroinflammation modulates adult neurogenesis and the inhibition of ADORA2A prevents Aβ-induced neurotoxicity. Targeted pathway-based genetic analysis combined with brain imaging endophenotypes appears promising to help elucidate disease pathophysiology and identify potential therapeutic targets. **Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/ uploads/how_to_apply/ADNI_Acknowledgement_List.pd

Traumatic Brain Injury and Age at Onset of Cognitive Impairment in Older Adults

There is a deficiency of knowledge regarding how traumatic brain injury (TBI) is associated with age at onset (AAO) of cognitive impairment in older adults. Participants with a TBI history were identified from the Alzheimer's disease neuroimaging initiative (ADNI 1/GO/2) medical history database. Using an analysis of covariance (ANCOVA) model, the AAO was compared between those with and without TBI, and potential confounding factors were controlled. The AAO was also compared between those with mild TBI (mTBI) and moderate or severe TBI (sTBI). Lastly, the effects of mTBI were analyzed on the AAO of participants with clinical diagnoses of either mild cognitive impairment (MCI) or Alzheimer's disease (AD). The AAO for a TBI group was 68.2 ± 1.1 years [95 % confidence interval (CI) 66.2–70.3, n = 62], which was significantly earlier than the AAO for the non-TBI group of 70.9 ± 0.2 years (95 % CI 70.5–71.4, n = 1197) (p = 0.013). Participants with mTBI history showed an AAO of 68.5 ± 1.1 years (n = 56), which was significantly earlier than the AAO for the non-TBI group (p = 0.032). Participants with both MCI and mTBI showed an AAO of 66.5 ± 1.3 years (95 % CI 63.9–69.1, n = 45), compared to 70.6 ± 0.3 years for the non-TBI MCI group (95 % CI 70.1–71.1, n = 935) (p = 0.016). As a conclusion, a history of TBI may accelerate the AAO of cognitive impairment by two or more years. These results were consistent with reports of TBI as a significant risk factor for cognitive decline in older adults, and TBI is associated with an earlier AAO found in patients with MCI or AD

Age at Injury is Associated with the Long-Term Cognitive Outcome of Traumatic Brain Injuries

Abstract Introduction The association between age at injury (AAI) and long-term cognitive outcome of traumatic brain injuries (TBI) is debatable. Methods Eligible participants with a history of TBI from Alzheimer's Disease Neuroimaging Initiative were divided into a childhood TBI (cTBI) group (the AAI ≤ 21 years old) and an adult TBI (aTBI) group (the AAI > 21 years old). Results The cTBI group has a higher Everyday Cognition total score than the aTBI group. All perceived cognitive functions are worse for the cTBI group than for the aTBI group except memory. By contrast, the cTBI group has higher assessment scores on either the Boston Naming Test or Rey Auditory Verbal Learning Test than the aTBI group. Discussion The AAI is associated with the long-term cognitive outcomes in older adults with a history of TBI

Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

Motivation Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted. Results We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression. Availability and implementation The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA. Supplementary information Supplementary data are available at Bioinformatics online

Tau Imaging in Alzheimer's Disease Diagnosis and Clinical Trials

In vivo imaging of the tau protein has the potential to aid in quantitative diagnosis of Alzheimer's disease, corroborate or dispute the amyloid hypothesis, and demonstrate biomarker engagement in clinical drug trials. A host of tau positron emission tomography agents have been designed, validated, and tested in humans. Several agents have characteristics approaching the ideal imaging tracer with some limitations, primarily regarding off-target binding. Dozens of clinical trials evaluating imaging techniques and several pharmaceutical trials have begun to integrate tau imaging into their protocols

Identification of discriminative imaging proteomics associations in Alzheimer's Disease via a novel sparse correlation model

Brain imaging and protein expression, from both cerebrospinal fluid and blood plasma, have been found to provide complementary information in predicting the clinical outcomes of Alzheimer's disease (AD). But the underlying associations that contribute to such a complementary relationship have not been previously studied yet. In this work, we will perform an imaging proteomics association analysis to explore how they are related with each other. While traditional association models, such as Sparse Canonical Correlation Analysis (SCCA), can not guarantee the selection of only disease-relevant biomarkers and associations, we propose a novel discriminative SCCA (denoted as DSCCA) model with new penalty terms to account for the disease status information. Given brain imaging, proteomic and diagnostic data, the proposed model can perform a joint association and multi-class discrimination analysis, such that we can not only identify disease-relevant multimodal biomarkers, but also reveal strong associations between them. Based on a real imaging proteomic data set, the empirical results show that DSCCA and traditional SCCA have comparable association performances. But in a further classification analysis, canonical variables of imaging and proteomic data obtained in DSCCA demonstrate much more discrimination power toward multiple pairs of diagnosis groups than those obtained in SCCA

Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics

Brain imaging genetics studies the genetic basis of brain structures and functions via integrating both genotypic data such as single nucleotide polymorphism (SNP) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few of QTs and are not designed for feature selection from a large number of QTs; while existing SCCA methods typically employ only one modality of QTs to study its association with SNPs. Both MTL and SCCA encounter computational challenges as the number of SNPs increases. In this paper, combining the merits of MTL and SCCA, we propose a novel multi-task SCCA (MTSCCA) learning framework to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. Using the G2,1-norm regularization, MTSCCA treats all SNPs in the same group together to enforce sparsity at the group level. The l2,1-norm penalty is used to jointly select features across multiple tasks for SNPs, and across multiple modalities for QTs. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains improved performance regarding both correlation coefficients and canonical weights patterns. In addition, our method runs very fast and is easy-to-implement, and thus could provide a powerful tool for genome-wide brain-wide imaging genetic studies