Testing for Network Specificity in Brain-Behavior Associations Using Ordinal Dominance Curves.

BackgroundPrevious studies have shown that synaptic biomarkers are increased in CSF in the preclinical stage of Alzheimer’s disease (AD). However, little is known about how CSF synaptic biomarkers associate with structural and functional brain measures in preclinical Alzheimer. The aim of this study was to test the association of CSF synaptic biomarkers with brain metabolism and structure in preclinical Alzheimer and explore whether these associations were modified by AD pathology.MethodWe studied 328 cognitively‐unimpaired individuals of the ALFA+ cohort. Participants underwent lumbar puncture, [18F]‐FDG PET scan and structural MRI. The following synaptic biomarkers were measured in CSF: neurogranin (NeuroToolKit [a panel of automated robust prototype immunoassays], Roche Diagnostics International Ltd), GAP‐43 (ELISA), SNAP‐25 and synaptotagmin‐1 (IP‐MS). We performed voxel‐wise analyses to test the association of each synaptic biomarker with brain metabolism and grey matter volume, using a threshold of p<0.005 and a minimum cluster size of 100 voxels. Participants were categorized by AT groups (A+ if CSF Aβ42/40<0.071 and T+ if CSF p‐tau>24pg/ml) and interaction terms with each synaptic biomarker were evaluated.Result212 (64.6%) participants were A‐T‐, 91 (27.7%) were A+T‐ and 25 (7.6%) were A+T+. To focus the study on the AD continuum, A‐T+ participants (n=12) were excluded. When assessing all participants, positive associations between each CSF synaptic biomarkers and FDG PET uptake and grey matter volume were found in brain areas including the caudate, the insula, cingulate gyrus and inferior temporal and parietal gyrus (Fig 1A). AT status modified the associations with both FDG PET uptake and grey matter volume. Compared to A‐T‐, A+T+ participants showed a positive association of CSF neurogranin, GAP‐43 and synaptotagmin‐1 with FDG PET uptake in the precuneus. Conversely, CSF synaptic biomarkers were negatively associated with grey matter volume with progressive AD pathology (A‐T‐>A+T‐>A+T+) in several brain regions including the cingulate gyrus and middle and superior frontal gyrus (Fig 1B).ConclusionIn preclinical Alzheimer, CSF synaptic biomarkers show associations with functional and structural brain measures, which are modified by AD pathology. Progressive AD pathology is associated with increased brain metabolism and decreased grey matter volume in several AD‐related brain regions.

BackgroundHuman neuroimaging evidence suggests that cardiovascular disease (CVD) risk may relate to functional and structural features of the brain. The present study tested whether combining functional and structural (multimodal) brain measures, derived from magnetic resonance imaging (MRI), would yield a multivariate brain biomarker that reliably predicts a subclinical marker of CVD risk, carotid-artery intima-media thickness (CA-IMT). MethodsNeuroimaging, cardiovascular, and demographic data were assessed in 324 midlife and otherwise healthy adults who were free of (a) clinical CVD and (b) use of medications for chronic illnesses (aged 30–51 years, 49% female). We implemented a prediction stacking algorithm that combined multimodal brain imaging measures and Framingham Risk Scores (FRS) to predict CA-IMT. We included imaging measures that could be easily obtained in clinical settings: resting state functional connectivity and structural morphology measures from T1-weighted images. ResultsOur models reliably predicted CA-IMT using FRS, as well as for several individual MRI measures; however, none of the individual MRI measures outperformed FRS. Moreover, stacking functional and structural brain measures with FRS did not boost prediction accuracy above that of FRS alone. ConclusionsCombining multimodal functional and structural brain measures through a stacking algorithm does not appear to yield a reliable brain biomarker of subclinical CVD, as reflected by CA-IMT.

Prior studies have demonstrated the existence of cognitively-defined subgroups among dementia free older adults, however, it is unclear whether such subgroups are characterized by distinct neuroimaging measures of brain function and structure. To address this gap, the current study used latent profile analysis (LPA) to identify cognitively-defined subgroups in a sample of 167 (mean age = 69 years) dementia-free older adults with cognitive testing, amyloid PET, and multimodal brain MRI scans. The cognitive test scores covered the domains of episodic memory, executive function, language, and visuospatial processing. Linear regression models tested the associations between subgroup membership and neuroimaging measures, adjusting for age, sex, and years of education. Based on the LPA, three cognitive subgroups were identified: (1) high-average cognition (n = 61, 36%), (2) average cognition (n = 88, 53%), and low-average cognition (n = 18, 11%). Compared to the high-average group, the low-average group had lower volumes in cortical regions sensitive to Alzheimer's disease, lower global white matter microstructural integrity measured by diffusion tensor imaging, and higher global white matter hyperintensity burden. There were no group differences in global PET amyloid burden. Additionally, the high-average group tended to have higher resting-state functional connectivity within large-scale cognitive networks than the other two groups. These results suggest that cognitively-defined subgroups among older adults without dementia are associated with several measures of brain structure and function. Evaluating brain structure/function differences among dementia-free older adults may help identify individuals at greatest risk for future cognitive decline.

Numerous studies have proposed that periodontitis is a potential risk factor for Alzheimer's disease. However, the association between periodontitis and brain normal cognition in aged and elderly individuals (NCs) is unclear. Such a link could provide clues to Alzheimer's disease development and strategies for early prevention. To explore the associations between periodontal condition and metrics of both brain structure and function among NCs with the help of multimodal magnetic resonance imaging (MRI). High-resolution T1-weighted structural data, resting-state functional-MRI data, and measures of periodontal condition were collected from 40 NCs. Cortical volume, thickness, and area as well as regional homogeneity were calculated with the aid of DPABISurf software. Correlation analyses were then conducted between each imaging metric and periodontal index. Consistent negative correlations were observed between severity of periodontitis (mild, moderate, severe) and cortical volume, area, and thickness, not only in brain regions that took charge of primary function but also in brain regions associated with advanced cognition behavior. Among participants with mild attachment loss (AL) and a shallow periodontal pocket depth (PPD), periodontal index was positively correlated with most measures of brain structure and function, while among participants with severe AL and deep PPD, periodontal index was negatively correlated with measures of brain structure and function (all p < .005 for each hemisphere). Our results demonstrate that periodontitis is associated with widespread changes in brain structure and function among middle-aged and elderly adults without signs of cognitive decline, which might be a potential risk factor for brain damage.

Sex and sobriety: Human brain structure and function in AUD abstinence

Objective: Blood pressure (BP) has been extensively studied with respect to brain structure and function; however, findings differ depending on the BP component in consideration, and whether brachial or central BP is used. We assessed associations between detailed measures of brain structure and function with comprehensive central and peripheral BP measures. Furthermore, mediation analyses investigated potential macro- and microvascular mechanistic pathways. Design and method: 1438 individuals (69.7 ± 6.2 years) from a community-based, tri-ethnic cohort underwent vascular, cognitive and MRI-based structural brain measures. BP measures included central (cSBP (Pulsecor)), peripheral systolic BP (pSBP), diastolic BP (DBP), brachial (bPP) and central pulse pressure (cPP), and mean arterial pressure. Cognitive assessments included global function (CSID), executive function and memory. For brain structure, hippocampal volume was our key measure. Potential macro- and microvascular mediators included: pulse wave velocity (cfPWV), carotid intima-media thickness, retinopathy, white matter hyperintensities, infarcts and estimated glomerular filtration rate (eGFR). Multivariable regression analyses assessed associations of BP components with cognitive function scores and brain volumes, adjusted for age, sex and ethnicity as well as macro- and microvascular risk factors. Results: Table 1 shows associations between vascular measures and measures of brain structure and function (data are β±SE (z-score)). After adjusting for age, sex and ethnicity, cSBP and pSBP were negatively associated with memory, while DBP was positively associated with hippocampal volume. cPP was negatively associated with memory, executive function and hippocampal volume, while bPP was negatively associated with all outcome variables. The strongest association was observed between cPP/bPP and brain structure/function. Associations between cPP and bPP measures with brain structure and function were similar. cfPWV was negatively associated with hippocampal volume. Stroke, large infarct and eGFR were associated with all cognitive outcome variables. Stroke and eGFR were positively associated with hippocampal volume. Furthermore, these associations were not mediated by macro- or microvascular disease.Conclusions: These results suggest a direct association between increased PP and decline in brain structure and function, implying that older individuals with suboptimal PP control may be at increased risk of developing cognitive impairment. Measuring PP offers mechanistic information above and beyond conventional BP measures.

Imaging genetics studies with large samples have identified many genes associated with brain functions and structures, but little is known about genes associated with brain functional and structural network properties. The current genome-wide association study examined graph theory measures of brain structural and functional networks with 497 healthy Chinese participants (17–28 years). Four genes (TGFB3, LGI1, TSPAN18, and FAM155A) were identified to be significantly associated with functional network global efficiency, and two (NLRP6 and ICE2) with structural network global efficiency. Meta-analysis of structural and functional brain network property confirmed the four functional-related genes and revealed two more (RBFOX1 and WWOX). They were reported to be significantly associated with regional brain structural or functional measurements in the UK Biobank project; and showed differential gene expression level between low and high structure–function coupling regions according to Allen Human Brain Atlas gene expression data. Taken together, our results suggest that brain structural and functional networks had shared and unique genetic bases, consistent with the notion of many-to-many structure–function coupling of the brain.

Optimal brain development is context-dependent: How socioeconomic status moderates brain-behavior relationships in cognitive and academic development.

Social determinants of brain health & brain changes across the human lifespan

While there has been considerable progress in conducting trials of aphasia therapy, there is no consistent evidence for long-term benefits of aphasia treatment, suggesting the need to reconsider current approaches. There are also no accurate methods for determining the amount, type and timing of aphasia therapy that should be provided for an individual. At the same time, there has been increasing interest in applying various principles of neuroplasticity to aphasia treatment and using measures of brain structure and function to predict recovery. This article will consider the potential of neuroplasticity principles and neurobiological predictors to improve our current approach to aphasia rehabilitation and optimise outcomes. Reviewing these principles highlights some of the challenges of translating animal model-based principles and emphases the need to also consider relevant theories of human learning. While considerable progress has been made in considering neurobiological principles and using measures of brain structure and function to predict recovery, there is significant work required to achieve the full potential of this neurobiological approach to aphasia management.

Within-individual coupling between measures of brain structure and function evolves in development and may underlie differential risk for neuropsychiatric disorders. Despite increasing interest in the development of structure–function relationships, rigorous methods to quantify and test individual differences in coupling remain nascent. In this article, we explore and address gaps in approaches for testing and spatially localizing individual differences in intermodal coupling, including a new method, called CEIDR (Cluster Enhancement for testing Individual Differences in (r)). CEIDR controls false positives in individual differences in intermodal correlations that arise from mean and variance heterogeneity and improves statistical power by adopting adaptive cluster enhancement. Through a comparison across different approaches to testing individual differences in intermodal coupling, we delineate subtle differences in the hypotheses they test, which may ultimately lead researchers to arrive at different results. Finally, we illustrate these differences in two applications to brain development using data from the Philadelphia Neurodevelopmental Cohort.

Within-individual coupling between measures of brain structure and function evolves in development and may underlie differential risk for neuropsychiatric disorders. Despite increasing interest in the development of structure-function relationships, rigorous methods to quantify and test individual differences in coupling remain nascent. In this article, we explore and address gaps in approaches for testing and spatially localizing individual differences in intermodal coupling, including a new method, called CEIDR. CEIDR controls false positives in individual differences in intermodal correlations that arise from mean and variance heterogeneity and improves statistical power by adopting adaptive cluster enhancement. Through a comparison across different approaches to testing individual differences in intermodal coupling, we delineate subtle differences in the hypotheses they test, which may ultimately lead researchers to arrive at different results. Finally, we illustrate these differences in two applications to brain development using data from the Philadelphia Neurodevelopmental Cohort.

The neurobiologic effects of cannabis, commonly referred to as ‘marijuana’ (MJ), have been studied for decades. The impact of recreational MJ use on cognition and measures of brain function and structure is outlined, and variables influencing study results are discussed, including age of the consumer, patterns of MJ use, variations in MJ potency, and the presence of additional cannabinoids. Although evidence suggests that chronic, heavy recreational MJ use is related to cognitive decrements and neural changes, particularly when use begins in adolescence, findings from studies of recreational MJ users may not be applicable to medical marijuana (MMJ) patients given differences in demographic variables, product selection, and reasons for use. Although additional research is needed to fully understand the impact of MJ and individual cannabinoids on the brain, current findings are beginning to inform public policy, including considerations for age limits, potential limits for some cannabinoids, and guidelines for use. However, barriers continue to impede researchers’ ability to conduct studies that will guide policy change and provide vital information to consumers and patients regarding best practices and safest methods for use. The need for information is critical, as legalization of MJ for medical and recreational use is increasingly widespread.

Cerebrovascular hemodynamics association with brain structure and function in Multiple Sclerosis

Sharing in embryology and function between the eye and brain has led to interest in whether assessments of the eye reflect brain changes seen in neurodegeneration. We aimed to examine the associations between measures of retinal layer thickness using optical coherence tomography (OCT) and multimodal measures of brain structure and function. Using a convenient sample of twins discordant for type 2 diabetes, we performed cognitive testing, structural brain MRI (tissue volumetry), diffusion tensor imaging (white matter microstructure), and arterial spin labelling (cerebral blood flow). OCT images were recorded and retinal thickness maps generated. We used mixed level modelling to examine the relationship between retinal layer thicknesses and brain measures. We enrolled 35 people (18 pairs, mean age 63.8 years, 63% female). Ganglion cell layer thickness was positively associated with memory, speed, gray matter volume, and altered mean diffusivity. Ganglion cell layer thickness was strongly positively associated with regional cerebral blood flow. We found only a limited number of associations between other retinal layer thickness and measures of brain structure or function. Ganglion cell layer thickness showed consistent associations with a range of brain measures suggesting it may have utility as a marker for future dementia risk.