Biology subjects

Tam, A.

Publications and source records attributed to Tam, A..

4 recordsLinked to original sources

bioRxiv2018

A signature of cognitive deficits and brain atrophy that is highly predictive of progression to Alzheimer’s dementia

Patients with mild cognitive impairment (MCI) are at risk of progressing to Alzheimers dementia, yet only a fraction of them do. We explore here whether a very high-risk MCI subgroup can be identified using cognitive assessments and structural neuroimaging. A multimodal signature of Alzheimers dementia was first extracted using machine learning tools in the ADNI1 sample, and was comprised of cognitive deficits across multiple domains as well as atrophy in temporal, parietal and occipital regions. We then validated the predictive value of this signature on two MCI cohorts. In ADNI1 (N=235), the presence of the signature predicted progression to dementia over three years with 80.4% positive predictive value, adjusted for a \"typical\" MCI baseline rate of 33% (95.6% specificity, 55.1% sensitivity). These results were replicated in ADNI2 (N=235), with 87.8% adjusted positive predictive value (96.7% specificity, 47.3% sensitivity). Our results demonstrate that, even for widely used markers, marked improvement in positive predictive value over the literature can be achieved by focusing on a subgroup of individuals with similar brain characteristics. The signature can be readily applied for the enrichment of clinical trials.

neuroscience↗

bioRxiv2017

Leveraging heterogeneity across multiple data sets increases accuracy of cell-mixture deconvolution and reduces biological and technical biases

In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix. We hypothesized that matrices created using only healthy samples from a single microarray platform would introduce biological and technical biases in deconvolution. We show presence of such biases in two existing matrices, IRIS and LM22, irrespective of the deconvolution method used. Here, we present immunoStates, a basis matrix built using 6160 samples with different disease states across 42 microarray platforms. We found that immunoStates significantly reduced biological and technical biases. We further show that cellular proportion estimates using immunoStates are consistently more correlated with measured proportions than IRIS and LM22, across all methods. Importantly, we found that different methods have virtually no effect once the basis matrix is chosen. Our results demonstrate the need and importance of incorporating biological and technical heterogeneity in a basis matrix for achieving consistently high accuracy.

bioinformatics↗

bioRxiv2017

Subtypes of functional brain connectivity as early markers of neurodegeneration in Alzheimer’s disease

HighlightsO_LIReliable functional brain network subtypes accompany cognitive impairment in AD\nC_LIO_LISymptom-related subtypes exist in the default-mode, limbic and salience networks\nC_LIO_LIA limbic subtype is associated with a familial risk of AD in healthy older adults\nC_LIO_LILimbic subtypes also associate with beta amyloid deposition and ApoE4\nC_LI\n\nIn BriefWe found reliable subtypes of functional brain connectivity networks in older adults, associated with AD-related clinical symptoms in patients as well as several AD risk factors/biomarkers in asymptomatic individuals.\n\nSummaryThe heterogeneity of brain degeneration has not been investigated yet for functional brain network connectivity, a promising biomarker of Alzheimers disease. We coupled cluster analysis with resting-state functional magnetic resonance imaging to discover connectivity subtypes in healthy older adults and patients with cognitive disorders related to Alzheimers disease, noting associations between subtypes and cognitive symptoms in the default-mode, limbic and salience networks. In an independent asymptomatic cohort with a family history of Alzheimers dementia, the connectivity subtypes had good test-retest reliability across all tested networks. We found that a limbic subtype was overrepresented in these individuals, which was previously associated with symptoms. Other limbic subtypes showed associations with cerebrospinal fluid A{beta}1-42 levels and ApoE4 genotype. Our results demonstrate the existence of reliable subtypes of functional brain networks in older adults and support future investigations in limbic connectivity subtypes as early biomarkers of Alzheimers degeneration.

neuroscience↗

bioRxiv2017

Resting-state network dysfunction in Alzheimer’s disease: a systematic review and meta-analysis

INTRODUCTIONWe performed a systematic review and meta-analysis of the Alzheimers disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and mild cognitive impairment (MCI), using resting-state functional magnetic resonance imaging (rsfMRI).\n\nMETHODSStudies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies.\n\nRESULTSThirty-four studies were included (1,363 participants, average 40 per study). Consistent alterations in connectivity were found in the default-mode, salience and limbic networks in patients with AD dementia, MCI, or in both groups. We also identified a bias in the literature towards specific examination of the default-mode network.\n\nDISCUSSIONConvergent evidence across the literature supports the use of resting-state connectivity as a biomarker of AD. The locations of consistent alterations suggest that metabolically expensive hub regions in the brain might be an early target of AD.

neuroscience↗