• Neuroinformatics
• Canadian Institutes of Health Research close

The purpose of this study was to determine whether white matter microstructure measured by diffusion magnetic resonance imaging (dMRI) provides independent information about baseline level or change in executive function (EF) or memory (MEM) in older adults with and without cognitive impairment. Longitudinal data was acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study from phases GO and 2 (2009–2015). ADNI participants included were diagnosed as cognitively normal (n = 46), early mild cognitive impairment (MCI) (n = 48), late MCI (n = 29), and dementia (n = 39) at baseline. We modeled the association between dMRI-based global white matter mean diffusivity (MD) and baseline level and change in EF and MEM composite scores, in models controlling for baseline bilateral hippocampal volume, regional cerebral FDG PET metabolism and global cerebral AV45 PET uptake. EF and MEM composite scores were measured at baseline, 6, 12, 24 and 36 months. In the baseline late MCI and dementia groups, greater global MD was associated with lesser baseline EF, but not EF change nor MEM baseline or change. As expected, lesser hippocampal volume and lesser FDG PET metabolism was associated with greater rates of EF and MEM decline. In ADNI-GO/2 participants, white matter integrity provided independent information about current executive function, but was not sensitive to future cognitive change. Since individuals experiencing executive function declines progress to dementia more rapidly than those with only memory impairment, better biomarkers of future executive function decline are needed. Highlights • In the ADNI cohort, MRI and PET predictors of baseline and change in executive function were tested. • Global mean diffusivity was associated with baseline, but not change in, executive function. • Diffusion MRI provides independent information on current executive function in older adults.

Mood and anxiety disorders are complex heterogeneous syndromes that manifest in dysfunctions across multiple brain regions, cell types, and circuits. Biomarkers using brain-wide activity patterns in humans have proven useful in distinguishing between disorder subtypes and identifying effective treatments. In order to improve biomarker identification, it is crucial to understand the basic circuitry underpinning brain-wide activity patterns. Leveraging a large repertoire of techniques, animal studies have examined roles of specific cell types and circuits in driving maladaptive behavior. Recent advances in multiregion recording techniques, data-driven analysis approaches, and machine-learning-based behavioral analysis tools can further push the boundary of animal studies and bridge the gap with human studies, to assess how brain-wide activity patterns encode and drive emotional behavior. Together, these efforts will allow identifying more precise biomarkers to enhance diagnosis and treatment.

Abstract. In the context of epilepsy monitoring, electroencephalography (EEG) remains the modality of choice. Functional near-infrared spectroscopy (fNIRS) is a relatively innovative modality that cannot only characterize hemodynamic profiles of seizures but also allow for long-term recordings. We employ deep learning methods to investigate the benefits of integrating fNIRS measures for seizure detection. We designed a deep recurrent neural network with long short-term memory units and subsequently validated it using the CHBMIT scalp EEG database—a compendium of 896 h of surface EEG seizure recordings. After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS measures. Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task, with low generalization errors and loss. False detection rates are generally low, with 11.8% and 5.6% for EEG and multimodal data, respectively. Employing multimodal neuroimaging, particularly EEG-fNIRS, in epileptic patients, can enhance seizure detection performance. Furthermore, the neural network model proposed and characterized herein offers a promising framework for future multimodal investigations in seizure detection and prediction.

Abstract Introduction Deteriorating brain glucose metabolism precedes the clinical onset of Alzheimer's disease (AD) and appears to contribute to its etiology. Ketone bodies, mainly β-hydroxybutyrate and acetoacetate, are the primary alternative brain fuel to glucose. Some reports suggest that brain ketone metabolism is unchanged in AD but, to our knowledge, no such data are available for MCI. Objective To compare brain energy metabolism (glucose and acetoacetate) and some brain morphological characteristics in cognitively healthy older adult controls (CTL), mild cognitive impairment (MCI) and early AD. Methods 24 CTL, 20 MCI and 19 AD of similar age and metabolic phenotype underwent a dual-tracer PET and MRI protocol. The uptake rate constants and cerebral metabolic rate of glucose ( K Glu , CMR Glu ) and acetoacetate ( K AcAc , CMR AcAc ) were evaluated with PET using [ 18 F]-fluorodeoxyglucose ([ 18 F]-FDG), a glucose analogue, and [ 11 C]-acetoacetate ([ 11 C]-AcAc), a ketone PET tracer. Regional brain volume and cortical thickness were evaluated by T1-weighted MRI. Results In AD compared to CTL, CMR Glu was ~ 11% lower in the frontal, parietal, temporal lobes and in the cingulate gyrus ( p K Glu was ~ 15% lower in these same regions and also in subcortical regions. In MCI compared to CTL, ~ 7% glucose hypometabolism was present in the cingulate gyrus. Neither regional nor whole brain CMR AcAc or K AcAc were significantly different between CTL and MCI or AD. Reduced gray matter volume and cortical thinning were widespread in AD compared to CTL, whereas, in MCI compared to CTL, volumes were reduced only in the temporal cortex and cortical thinning was most apparent in temporal and cingulate regions. Discussion This quantitative kinetic PET and MRI imaging protocol for brain glucose and acetoacetate metabolism confirms that the brain undergoes structural atrophy and lower brain energy metabolism in MCI and AD and demonstrates that the deterioration in brain energy metabolism is specific to glucose. These results suggest that a ketogenic intervention to increase energy availability for the brain is warranted in an attempt to delay further cognitive decline by compensating for the brain glucose deficit in MCI and AD.

In this study we used functional magnetic resonance imaging to investigate age-related changes in large-scale brain functional networks during memory encoding and recognition in 12 younger and 16 older adults. For each participant, functional brain networks were constructed by computing temporal correlation matrices of 90 brain regions and analyzed using graph theoretical approaches. We found the age-related changes mainly in the long-range connections with widespread reductions associated with aging in the fronto-temporal and temporo-parietal regions, and a few age-related increases in the posterior parietal regions. Graph theoretical analysis revealed that the older adults had longer path lengths linking different regions in the functional brain networks as compared to the younger adults. Further analysis indicated that the increases in shortest path length in the networks were combined with the loss of long-range connections. Finally, we showed that for older adults, frontal areas played reduced roles in the network (reduced regional centrality), whereas several default-mode regions played increased roles relative to younger subjects (increased regional centrality). Together, our results suggest that normal aging is associated with disruption of large-scale brain systems during the performance of memory tasks, which provides novel insights into the understanding of age-related decline in multiple cognitive functions.

Additional file 1. Supplementary methods.

The present work investigated whether polyamines play a role in the control of the innate immune response in the brain. The first evidence that these molecules may be involved in such a process was based on the robust increase in the expression of the first and rate-limiting enzyme of biosynthesis of polyamines during immune stimuli. Indeed, systemic lipopolysaccharide (LPS) administration increased ornithine decarboxylase (ODC) mRNA and protein within neurons and microglia across the mouse central nervous system (CNS). This treatment was also associated with a robust and transient transcriptional activation of genes encoding pro-inflammatory cytokines and toll-like receptor 2 (TLR2) in microglial cells. The endotoxin increased the cerebral activity of ODC, which was abolished by a suicide inhibitor of ODC. The decrease in putrescine levels largely prevented the ability of LPS to trigger tumor necrosis factor α and TLR2 gene transcription in the mouse brain. In contrast, expression of both transcripts was clearly exacerbated in response to intracerebral spermine infusion. Finally, inhibition of polyamine synthesis abolished neurodegeneration and increased the survival rate of mice exposed to a model of severe innate immune reaction in the CNS. Thus, polyamines have a major impact on the neuronal integrity and cerebral homeostasis during immune insults.

Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834 ± 0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781 ± 0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30AG010129, K01 AG030514, and the Dana Foundation.