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Background: Carotid atherosclerosis is associated with cognitive impairment and dementia, though there is limited evidence of a direct link between carotid disease and amyloid-β (Aβ) burden. Objective: We studied the association of baseline and progressive carotid intima media thickness (CIMT) with Aβ on 11C-Pittsburgh Compound B (PiB) to determine if those with carotid atherosclerosis would have higher Aβ burden. Methods: We studied 47 participants from the Framingham Offspring cohort with carotid ultrasounds measuring CIMT at their 6th clinic examination (aged 49.5±5.7 years) and an average of 9.6 years later, and PiB imaging measuring Aβ on average 22.1 years post baseline. We used multivariate linear regression analyses to relate baseline, follow-up, mean, and progression of internal carotid artery (ICA) and common carotid artery (CCA) CIMT to Aβ in brain regions associated with Alzheimer’s disease (AD) and related dementias (ADRD), adjusting for age, sex, and other vascular risk factors. Results: Participants with higher mean ICA IMT had more Aβ in the precuneus (beta±standard error [β±SE]: 0.466±0.171 mm, p = 0.01) and the frontal, lateral, and retrosplenial regions (β±SE: 0.392±0.164 mm, p = 0.022) after adjusting for age, sex, vascular risk factors, and medication use. We did not find an association between any CCA IMT measures and Aβ or progression of ICA or CCA IMT and Aβ. Conclusion: Carotid atherosclerosis, as measured by ICA IMT, is associated with increased Aβ burden later in life. These findings support a link between vascular disease and AD/ADRD pathophysiology.

Age-related impairments in cognitive function occur in multiple animal species including humans and nonhuman primates. Humans and rhesus monkeys exhibit a similar pattern of cognitive decline beginning in middle age, particularly within the domain of executive function. The prefrontal cortex is the brain region most closely associated with mediating executive function. Previous studies in rhesus monkeys have demonstrated that normal aging leads to an increase in myelin degradation in the prefrontal regions that correlates with cognitive decline. This myelin deterioration is thought to result, at least in part, from the age-related emergence of chronic low levels of inflammation. One therapeutic that may arrest the deleterious effects of neuroinflammation is curcumin (CUR), the primary component of the spice turmeric. CUR has been shown to be a potent anti-inflammatory and antioxidant and improves performance on tasks for working memory and motor function. In the present study, middle-aged monkeys (12-21 years old) were given daily dietary supplementation of 500 mg of curcumin or vehicle over a period of 3-4 years. Here, we present data from a series of both object and spatial reversal tasks. Compared to vehicle, the CUR group showed enhanced performance on object, but not spatial reversal learning. These findings suggest that curcumin may improve specific aspects of executive function. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Age-related declines in cognitive abilities occur as early as middle-age in humans and rhesus monkeys. Specifically, performance by aged individuals on tasks of executive function (EF) and working memory (WM) is characterized by greater frequency of errors, shorter memory spans, increased frequency of perseverative responses, impaired use of feedback and reduced speed of processing. However, how aging precisely differentially impacts specific aspects of these cognitive functions and the distinct brain areas mediating cognition are not well understood. The prefrontal cortex (PFC) is known to mediate EF and WM and is an area that shows a vulnerability to age-related alterations in neuronal morphology. In the current study, we show that performance on EF and WM tasks exhibited significant changes with age and these impairments correlate with changes in biophysical properties of L3 pyramidal neurons in lateral LPFC (LPFC). Specifically, there was a significant age-related increase in excitability of Layer 3 LPFC pyramidal neurons, consistent with previous studies. Further, this age-related hyperexcitability of LPFC neurons was significantly correlated with age-related decline on a task of WM, but not an EF task. The current study characterizes age-related performance on tasks of WM and EF and provides insight into the neural substrates that may underlie changes in both WM and EF with age.

Introduction We examined the ability of plasma hyperphosphorylated tau (p-tau)181 to detect cognitive impairment due to Alzheimer's disease (AD) independently and in combination with plasma total tau (t-tau) and neurofilament light (NfL). Methods Plasma samples were analyzed using the Simoa platform for 235 participants with normal cognition (NC), 181 with mild cognitive impairment due to AD (MCI), and 153 with AD dementia. Statistical approaches included multinomial regression and Gaussian graphical models (GGMs) to assess a network of plasma biomarkers, neuropsychological tests, and demographic variables. Results Plasma p-tau181 discriminated AD dementia from NC, but not MCI, and correlated with dementia severity and worse neuropsychological test performance. Plasma NfL similarly discriminated diagnostic groups. Unlike plasma NfL or t-tau, p-tau181 had a direct association with cognitive diagnosis in a bootstrapped GGM. Discussion These results support plasma p-tau181 for the detection of AD dementia and the use of blood-based biomarkers for optimal disease detection.

Background: Depressive symptoms predict increased risk for dementia decades before the emergence of cognitive symptoms. Studies in older adults provide preliminary evidence for an association between depressive symptoms and amyloid-β (Aβ) and tau accumulation. It is unknown if similar alterations are observed in midlife when preventive strategies may be most effective. Objective: The study aim was to evaluate the association between depressive symptoms and cerebral Aβ and tau in a predominately middle-aged cohort with examination of the apolipoprotein (APOE) ɛ4 allele as a moderator. Methods: Participants included 201 adults (mean age 53±8 years) who underwent 11C-Pittsburgh Compound B amyloid and 18F-Flortaucipir tau positron emission tomography (PET) imaging. Depressive symptoms were evaluated with the Center for Epidemiological Studies Depression Scale (CES-D) at the time of PET imaging, as well as eight years prior. Associations between depressive symptoms at both timepoints, as well as depression (CES-D≥16), with regional Aβ and tau PET retention were evaluated with linear regression adjusting for age and sex. Interactions with the APOE ɛ4 allele were explored. Results: Depressive symptoms and depression were not associated with PET outcomes in the overall sample. However, among APOE ɛ4 allele carriers, there was a significant cross-sectional association between depressive symptoms and increased tau PET uptake in the entorhinal cortex (β= 0.446, SE = 0.155, p = 0.006) and amygdala (β= 0.350, SE = 0.133, p = 0.012). Conclusion: Although longitudinal studies are necessary, the results suggest that APOE ɛ4 carriers with depressive symptoms may present with higher susceptibility to early tau accumulation in regions integral to affective regulation and memory consolidation.

Background and ObjectivesLate neuropathologies of repetitive head impacts from contact sports can include chronic traumatic encephalopathy (CTE) and white matter degeneration. White matter hyperintensities (WMH) on fluid-attenuated inversion recovery (FLAIR) MRI scans are often viewed as microvascular disease from vascular risk, but might have unique underlying pathologies and risk factors in the setting of repetitive head impacts. We investigated the neuropathologic correlates of antemortem WMH in brain donors exposed to repetitive head impacts. The association between WMH and repetitive head impact exposure and informant-reported cognitive and daily function were tested.MethodsThis imaging–pathologic correlation study included symptomatic male decedents exposed to repetitive head impacts. Donors had antemortem FLAIR scans from medical records and were without evidence of CNS neoplasm, large vessel infarcts, hemorrhage, or encephalomalacia. WMH were quantified using log-transformed values for total lesion volume (TLV), calculated using the lesion prediction algorithm from the Lesion Segmentation Toolbox. Neuropathologic assessments included semiquantitative ratings of white matter rarefaction, cerebrovascular disease, hyperphosphorylated tau (p-tau) severity (CTE stage, dorsolateral frontal cortex), and β-amyloid (Aβ). Among football players, years of play was a proxy for repetitive head impact exposure. Retrospective informant-reported cognitive and daily function were assessed using the Cognitive Difficulties Scale (CDS) and Functional Activities Questionnaire (FAQ). Regression models controlled for demographics, diabetes, hypertension, and MRI resolution. Statistical significance was defined as p ≤ 0.05.ResultsThe sample included 75 donors: 67 football players and 8 nonfootball contact sport athletes or military veterans. Dementia was the most common MRI indication (64%). Fifty-three (70.7%) had CTE at autopsy. Log TLV was associated with white matter rarefaction (odds ratio [OR] 2.32, 95% confidence interval [CI] 1.03, 5.24; p = 0.04), arteriolosclerosis (OR 2.38, 95% CI 1.02, 5.52; p = 0.04), CTE stage (OR 2.58, 95% CI 1.17, 5.71; p = 0.02), and dorsolateral frontal p-tau severity (OR 3.03, 95% CI 1.32, 6.97; p = 0.01). There was no association with Aβ. More years of football play was associated with log TLV (unstandardized β 0.04, 95% CI 0.01, 0.06; p = 0.01). Greater log TLV correlated with higher FAQ (unstandardized β 4.94, 95% CI 0.42, 8.57; p = 0.03) and CDS scores (unstandardized β 15.35, 95% CI −0.27, 30.97; p = 0.05).DiscussionWMH might capture long-term white matter pathologies from repetitive head impacts, including those from white matter rarefaction and p-tau, in addition to microvascular disease. Prospective imaging–pathologic correlation studies are needed.Classification of EvidenceThis study provides Class IV evidence of associations between FLAIR white matter hyperintensities and neuropathologic changes (white matter rarefaction, arteriolosclerosis, p-tau accumulation), years of American football play, and reported cognitive symptoms in symptomatic brain donors exposed to repetitive head impacts.

Background and ObjectivesPrevious research has shown that elevated blood C-reactive protein (CRP) is associated with increased Alzheimer disease (AD) risk only in APOE ε4 allele carriers; the objective of this study was to examine the interactive effects of plasma CRP and APOE genotype on cognition and AD biomarkers.MethodsData from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were analyzed, including APOE genotype; plasma CRP concentrations; diagnostic status (i.e., mild cognitive impairment and dementia due to AD); Mini-Mental State Examination (MMSE) and Clinical Dementia Rating Dementia Staging Instrument scores; CSF concentrations of β-amyloid peptide (Aβ42), total tau (t-Tau) and phosphorylated tau (p-Tau); and amyloid (AV45) PET imaging. Multivariable regression analyses tested the associations between plasma CRP and APOE on cognitive and biomarker outcomes.ResultsAmong 566 ADNI participants, 274 (48.4%) had no, 222 (39.2%) had 1, and 70 (12.4%) had 2 APOE ε4 alleles. Among only participants who had 2 APOE ε4 alleles, elevated CRP was associated with lower MMSE score at baseline (β [95% confidence interval] −0.52 [−1.01, −0.12]) and 12-month follow-up (β −1.09 [−1.88, −0.17]) after adjustment for sex, age, and education. The interaction of 2 APOE ε4 alleles and elevated plasma CRP was associated with increased CSF levels of t-Tau (β = 11.21, SE 3.37, p < 0.001) and p-Tau (β = +2.74, SE 1.14, p < 0.01). Among those who had no APOE ε4 alleles, elevated CRP was associated with decreased CSF t-Tau and p-Tau. These effects were stronger at the 12-month follow-up.DiscussionCRP released during peripheral inflammation could be a mediator in APOE ε4–related AD neurodegeneration and serve as a drug target for AD.

The Boston University-based Gulf War Illness Consortium (GWIC) is a multidisciplinary initiative developed to provide detailed understanding of brain and immune alterations that underlie Gulf War illness (GWI), the persistent multisymptom disorder associated with military service in the 1990–1991 Gulf War. The core GWIC case-control clinical study conducted in-depth brain and immune evaluation of 269 Gulf War veterans (223 GWI cases, 46 controls) at three U.S. sites that included clinical assessments, brain imaging, neuropsychological testing, and analyses of a broad range of immune and immunogenetic parameters. GWI cases were similar to controls on most demographic, military, and deployment characteristics although on average were two years younger, with a higher proportion of enlisted personnel vs. officers. Results of physical evaluation and routine clinical lab tests were largely normal, with few differences between GWI cases and healthy controls. However, veterans with GWI scored significantly worse than controls on standardized assessments of general health, pain, fatigue, and sleep quality and had higher rates of diagnosed conditions that included hypertension, respiratory and sinus conditions, gastrointestinal conditions, and current or lifetime depression and post-traumatic stress disorder. Among multiple deployment experiences/exposures reported by veterans, multivariable logistic regression identified just two significant GWI risk factors: extended use of skin pesticides in theater (adjusted OR = 3.25, p = 0.005) and experiencing mild traumatic brain injury during deployment (OR = 7.39, p = 0.009). Gulf War experiences associated with intense stress or trauma (e.g., participation in ground combat) were not associated with GWI. Data and samples from the GWIC project are now stored in a repository for use by GWI researchers. Future reports will present detailed findings on brain structure and function, immune function, and association of neuroimmune measures with characteristics of GWI and Gulf War service.

The goal of this study was to investigate whether alterations in cerebral microvasculature, as measured by cerebral blood volume (CBV), contribute to age- and hypertension-related impairments in cognitive function with a focus on executive function and memory. Data were collected on 19 male rhesus monkeys ranging from 6.4 to 21.6 years of age. Hypertension was induced through surgical coarctation of the thoracic aorta. We assessed whether performance on tasks of memory and executive function corresponded to CBV in either the hippocampus or prefrontal cortex. We found a relationship between duration of hypertension and CBV in the gray matter of the prefrontal cortex, but not the hippocampus. No relationships were found with the degree of hypertension or age. Increased prefrontal CBV was related to greater impairment in executive function while hippocampal CBV was not related to memory performance. These findings suggest that duration, but not severity, of hypertension or age are important factors underlying alterations in brain microvasculature and that executive function is more vulnerable than memory function. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Advances in molecular positron emission tomography (PET) have enabled anatomic tracking of brain pathology in longitudinal studies of normal aging and dementia, including assessment of the central model of Alzheimer's disease (AD) pathogenesis, according to which TAU pathology begins focally but expands catastrophically under the influence of amyloid-beta (A beta) pathology to mediate neurodegeneration and cognitive decline. Initial TAU deposition occurs many years before A beta in a specific area of the medial temporal lobe. Building on recent work that enabled focus of molecular PET measurements on specific TAU-vulnerable convolutional temporal lobe anatomy, we applied an automated anatomic sampling method to quantify TAU PET signal in 443 adult participants from several observational studies of aging and AD, spanning a wide range of ages, A beta burdens, and degrees of clinical impairment. We detected initial cortical emergence of tauopathy near the rhinal sulcus in clinically normal people and, in a subset with longitudinal 2-year follow-up data (n = 104), tracked A beta-associated spread of TAU from this site first to nearby neocortex of the temporal lobe and then to extratemporal regions. Greater rate of TAU spread was associated with baseline measures of both global A beta burden and medial temporal lobe TAU. These findings are consistent with clinicopathological correlation studies of Alzheimer's tauopathy and enable precise tracking of AD-related TAU progression for natural history studies and prevention therapeutic trials.

Spatial navigation is a cognitive skill fundamental to successful interaction with our environment, and aging is associated with weaknesses in this skill. Identifying mechanisms underlying individual differences in navigation ability in aged adults is important to understanding these age-related weaknesses. One understudied factor involved in spatial navigation is self-motion perception. Important to self-motion perception is optic flow–the global pattern of visual motion experienced while moving through our environment. A set of optic flow-sensitive (OF-sensitive) cortical regions was defined in a group of young (n = 29) and aged (n = 22) adults. Brain activity was measured in this set of OF-sensitive regions and control regions using functional magnetic resonance imaging while participants performed visual path integration (VPI) and turn counting (TC) tasks. Aged adults had stronger activity in RMT+ during both tasks compared to young adults. Stronger activity in the OF-sensitive regions LMT+ and RpVIP during VPI, not TC, was associated with greater VPI accuracy in aged adults. The activity strength in these two OF-sensitive regions measured during VPI explained 42% of the variance in VPI task performance in aged adults. The results of this study provide novel support for global motion processing as a mechanism underlying visual path integration in normal aging.

The study of complex brain networks, where structural or functional connections are evaluated to create an interconnected representation of the brain, has grown tremendously over the past decade. Many of the statistical network science tools for analyzing brain networks have been developed for cross-sectional studies and for the analysis of static networks. However, with both an increase in longitudinal study designs and an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for longitudinal brain network analysis are needed. We propose a paradigm for longitudinal brain network analysis over patient cohorts, with the key challenge being the adaptation of Stochastic Actor-Oriented Models to the neuroscience setting. Stochastic Actor-Oriented Models are designed to capture network dynamics representing a variety of influences on network change in a continuous-time Markov chain framework. Network dynamics are characterized through both endogenous (i.e. network related) and exogenous effects, where the latter include mechanisms conjectured in the literature. We outline an application to the resting-state functional magnetic resonance imaging setting with data from the Alzheimer’s Disease Neuroimaging Initiative study. We draw illustrative conclusions at the subject level and make a comparison between elderly controls and individuals with Alzheimer’s disease.

Background Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer’s disease (AD) classification performance. Methods T1-weighted brain MRI scans from 151 participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n = 107) and the National Alzheimer’s Coordinating Center (NACC, n = 565) were used for model validation. Results The 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets. Conclusion This study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality.

Background: The Framingham Stroke Risk Profile (FSRP) was created in 1991 to estimate 10-year risk of stroke. It was revised in 2017 (rFSRP) to reflect the modern data on vascular risk factors and stroke risk. Objective: This study examined the association between the rFSRP and cognitive and brain aging outcomes among participants from the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS). Methods: Cross-sectional rFSRP was computed at baseline for 19,309 participants (mean age = 72.84, SD = 8.48) from the NACC-UDS [9,697 (50.2%) normal cognition, 4,705 (24.4%) MCI, 4,907 (25.4%) dementia]. Multivariable linear, logistic, or ordinal regressions examined the association between the rFSRP and diagnostic status, neuropsychological test performance, CDR® Sum of Boxes, as well as total brain volume (TBV), hippocampal volume (HCV), and log-transformed white matter hyperintensities (WMH) for an MRI subset (n = 1,196). Models controlled for age, sex, education, racial identity, APOE ɛ4 status, and estimated intracranial volume for MRI models. Results: The mean rFSRP probability was 10.42% (min = 0.50%, max = 95.71%). Higher rFSRP scores corresponded to greater CDR Sum of Boxes (β= 0.02, p = 0.028) and worse performance on: Trail Making Test A (β= 0.05, p

BackgroundGenerative adversarial networks (GAN) can generate images of improved quality but their ability to augment image-based classification tasks is not fully explored.PurposeWe evaluated if a modified GAN can learn from MRI scans of multiple magnetic field strength to enhance Alzheimer’s disease (AD) classification performance.Materials and methodsT1-weighted brain MRI scans from 151 participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), who underwent both 1.5 Tesla (1.5T) and 3 Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (1.5T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Data from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n=107), and the National Alzheimer’s Coordinating Center (NACC, n=565) was used for model validation.ResultsThe mean quality of the generated (1.5T*) images was consistently higher than the 1.5T images, as measured using SNR, BRISQUE and NIQE on the validation datasets. The 1.5T*-based FCN classifier performed better than the FCN model trained using the 1.5T scans.Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940 and from 0.870 to 0.907 on the ADNI test, AIBL and NACC datasets, respectively.ConclusionThis study demonstrates that GAN frameworks can be constructed to simultaneously improve image quality and augment classification performance.Key pointsOur proposed generative adversarial network used 1.5 and 3 Tesla MR scans of the brain to generate images of improved quality, as estimated using no-reference image quality algorithms.Classification models of Alzheimer’s disease risk developed using the generated images had higher classification performance than the models developed using the original 1.5 Tesla scans.

We examined baseline and longitudinal associations between plasma neurofilament light (NfL) and total tau (t-tau), and the clinical presentation of Alzheimer's disease (AD). A total of 579 participants (238, normal cognition [NC]; 185, mild cognitive impairment [MCI]; 156, AD dementia) had baseline blood draws; 82% had follow-up evaluations. Plasma samples were analyzed for NfL and t-tau using Simoa technology. Baseline plasma NfL was higher in AD dementia than MCI (standardized mean difference = 0.55, 95% CI: 0.37–0.73) and NC (standardized mean difference = 0.68, 95% CI: 0.49–0.88), corresponded to Clinical Dementia Rating scores (OR = 1.94, 95% CI: 1.35–2.79]), and correlated with all neuropsychological tests (r's = 0.13–0.42). Longitudinally, NfL did not predict diagnostic conversion but predicted decline on 3/10 neuropsychological tests. Baseline plasma t-tau was higher in AD dementia than NC with a small effect (standardized mean difference = 0.33, 95% CI: 0.10–0.57) but not MCI. t-tau did not statistically significant predict any longitudinal outcomes. Plasma NfL may be useful for the detection of AD dementia and monitoring of disease progression. In contrast, there was minimal evidence in support of plasma t-tau.

The goal of this study was to examine whether hippocampal volume or resting-state functional connectivity (rsFC) patterns are associated with subjective memory decline (SMD) in cognitively normal aged adults. Magnetic resonance imaging data from 53 participants (mean age: 71.9 years) of the Boston University Alzheimer’s Disease Center registry were used in this cross-sectional study. Separate analyses treating SMD as a binary and continuous variable were performed. Subfield volumes were generated using FreeSurfer v6.0, and rsFC strength between the head and body of the hippocampus and the rest of the brain was calculated. Decreased left whole hippocampal volume and weaker rsFC strength between the right body of the hippocampus and the default mode network (DMN) were found in SMD+. Cognitive Change Index score was not correlated with volumetric measures but was inversely correlated with rsFC strength between the right body of the hippocampus and 6 brain networks, including the DMN, task control, and attentional networks. These findings suggest that hippocampal rsFC patterns reflect the current state of SMD in cognitively normal adults and may reflect subtle memory changes that standard neuropsychological tests are unable to capture.

Progress in neurodevelopmental brain research has been achieved through the use of animal models. Such models not only help understanding biological changes that govern brain development, maturation and aging, but are also essential for identifying possible mechanisms of neurodevelopmental and age-related chronic disorders, and to evaluate possible interventions with potential relevance to human disease. Genetic relationship of rhesus monkeys to humans makes those animals a great candidate for such models. With the typical lifespan of 25 years, they undergo cognitive maturation and aging that is similar to this observed in humans. Quantitative structural neuroimaging has been proposed as one of the candidate in vivo biomarkers for tracking white matter brain maturation and aging. While lifespan trajectories of white matter changes have been mapped in humans, such knowledge is not available for nonhuman primates. Here, we analyze and model lifespan trajectories of white matter microstructure using in vivo diffusion imaging in a sample of 44 rhesus monkeys. We report quantitative parameters (including slopes and peaks) of lifespan trajectories for 8 individual white matter tracts. We show different trajectories for cellular and extracellular microstructural imaging components that are associated with white matter maturation and aging, and discuss similarities and differences between those in humans and rhesus monkeys, the importance of our findings, and future directions for the field. Significance Statement: Quantitative structural neuroimaging has been proposed as one of the candidate in vivo biomarkers for tracking brain maturation and aging. While lifespan trajectories of structural white matter changes have been mapped in humans, such knowledge is not available for rhesus monkeys. We present here results of the analysis and modeling of the lifespan trajectories of white matter microstructure using in vivo diffusion imaging in a sample of 44 rhesus monkeys (age 4-27). We report and anatomically map lifespan changes related to cellular and extracellular microstructural components that are associated with white matter maturation and aging.

Aims Gulf War Illness (GWI), a chronic debilitating disorder characterized by fatigue, joint pain, cognitive, gastrointestinal, respiratory, and skin problems, is currently diagnosed by self-reported symptoms. The Boston Biorepository, Recruitment, and Integrative Network (BBRAIN) is the collaborative effort of expert Gulf War Illness (GWI) researchers who are creating objective diagnostic and pathobiological markers and recommend common data elements for GWI research. Main methods BBRAIN is recruiting 300 GWI cases and 200 GW veteran controls for the prospective study. Key data and biological samples from prior GWI studies are being merged and combined into retrospective datasets. They will be made available for data mining by the BBRAIN network and the GWI research community. Prospective questionnaire data include general health and chronic symptoms, demographics, measures of pain, fatigue, medical conditions, deployment and exposure histories. Available repository biospecimens include blood, plasma, serum, saliva, stool, urine, human induced pluripotent stem cells and cerebrospinal fluid. Key findings To date, multiple datasets have been merged and combined from 15 participating study sites. These data and samples have been collated and an online request form for repository requests as well as recommended common data elements have been created. Data and biospecimen sample requests are reviewed by the BBRAIN steering committee members for approval as they are received. Significance The BBRAIN repository network serves as a much needed resource for GWI researchers to utilize for identification and validation of objective diagnostic and pathobiological markers of the illness.

The aim of this study was to describe imaging markers of decision-making under uncertain conditions in normal individuals, in order to provide baseline activity to compare to impaired decision-making in pathological states. In this cross-sectional study, 19 healthy subjects ages 18–35 completed a novel decision-making card-matching task using a Phillips T3 Scanner and a 32-channel head coil. Functional data were collected in six functional runs. In one condition of the task, the participant was certain of the rule to apply to match the cards; in the other condition, the participant was uncertain. We performed cluster-based comparison of the two conditions using FSL fMRI Expert Analysis Tool and network-based analysis using MATLAB. The uncertain > certain comparison yielded three clusters—a midline cluster that extended through the midbrain, the thalamus, bilateral prefrontal cortex, the striatum, and bilateral parietal/occipital clusters. The certain > uncertain comparison yielded bilateral clusters in the insula, parietal and temporal lobe, as well as a medial frontal cluster. A larger, more connected functional network was found in the uncertain condition. The involvement of the insula, parietal cortex, temporal cortex, ventromedial prefrontal cortex, and orbitofrontal cortex of the certain condition reinforces the notion that certainty is inherently rewarding. For the uncertain condition, the involvement of the prefrontal cortex, parietal cortex, striatum, thalamus, amygdala, and hippocampal involvement was expected, as these are areas involved in resolving uncertainty and rule updating. The involvement of occipital cortical involvement and midbrain involvement may be attributed to increased visual attention and increased motor control.

Studies of both humans and non-human primates have demonstrated that aging is typically characterized by a decline in cognition that can occur as early as the fifth decade of life. Age-related changes in working memory are particularly evident and mediated, in part, by the prefrontal cortex, an area known to evidence age-related changes in myelin that is attributed to inflammation. In recent years, several nutraceuticals, including curcumin, by virtue of their anti-inflammatory and antioxidant effects, have received considerable attention as potential treatments for age-related cognitive decline and inflammation. Accordingly, we assessed for the first time in a non-human primate model of normal aging the efficacy of dietary intervention using the natural phenol curcumin to ameliorate the effects of aging on spatial working and recognition memory. Results revealed that monkeys receiving daily administration of curcumin over 14–18 months demonstrated a greater improvement in performance on repeated administration of a task of spatial working memory compared to monkeys that received a control substance.

Background Deterministic diffusion tractography obtained from high angular resolution diffusion imaging (HARDI) requires user-defined quantitative anisotropy (QA) thresholds. Most studies employ a common threshold across all subjects even though there is a strong degree of individual variation within groups. We sought to explore whether it would be beneficial to use individual thresholds in order to accommodate individual variance. To do this, we conducted two independent experiments. Method First, tractography of the arcuate fasciculus and network connectivity measures were examined in a sample of 14 healthy participants. Second, we assessed the effects of QA threshold on group differences in network connectivity measures between healthy young (n = 19) and old (n = 14) individuals. Results The results of both experiments were significantly influenced by QA threshold. Common thresholds set too high failed to produce sufficient reconstructions in most subjects, thus decreasing the likelihood of detecting meaningful group differences. On the other hand, common thresholds set too low resulted in spurious reconstructions, providing deleterious results. Comparison with existing methods Subject specific thresholds acquired using our QA threshold selection method (QATS) appeared to provide the most meaningful networks while ensuring that data from all subjects contributed to the analyses. Conclusions Together, these results support the use of a subject-specific threshold to ensure that data from all subjects are included in the analyses being conducted.

The introduction of mild cognitive impairment (MCI) as a diagnostic category adds to the challenges of diagnosing Alzheimer’s Disease (AD). No single marker has been proven to accurately categorize patients into their respective diagnostic groups. Thus, previous studies have attempted to develop fused predictors of AD and MCI. These studies have two main limitations. Most do not simultaneously consider all diagnostic categories and provide suboptimal fused representations using the same set of modalities for prediction of all classes. In this work, we present a combined framework, cascaded multiview canonical correlation (CaMCCo), for fusion and cascaded classification that incorporates all diagnostic categories and optimizes classification by selectively combining a subset of modalities at each level of the cascade. CaMCCo is evaluated on a data cohort comprising 149 patients for whom neurophysiological, neuroimaging, proteomic and genomic data were available. Results suggest that fusion of select modalities for each classification task outperforms (mean AUC = 0.92) fusion of all modalities (mean AUC = 0.54) and individual modalities (mean AUC = 0.90, 0.53, 0.71, 0.73, 0.62, 0.68). In addition, CaMCCo outperforms all other multi-class classification methods for MCI prediction (PPV: 0.80 vs. 0.67, 0.63).

To describe structural network differences in individuals with mild cognitive impairment (MCI) with high versus low executive abilities, as reflected by measures of white matter connectivity using diffusion tensor imaging (DTI). This was a retrospective, cross-sectional study. Of the 128 participants from the Alzheimer’s Disease Neuroimaging Initiative database who had both a DTI scan as well as a diagnosis of MCI, we used an executive function score to classify the top 15 scoring patients as high executive ability, and the bottom-scoring 16 patients as low executive ability. Using a regions-of-interest-based analysis, we constructed networks and calculated graph theory measures on the constructed networks. We used automated tractography in order to compare differences in major white matter tracts. The high executive ability group yielded greater network size, density and clustering coefficient. The high executive ability group reflected greater fractional anisotropy bilaterally in the inferior and superior longitudinal fasciculi. The network measures of the high executive ability group demonstrated greater white matter integrity. This suggests that white matter reserve may confer greater protection of executive abilities. Loss of this reserve may lead to greater impairment in the progression to Alzheimer’s disease dementia. • The MCI high executive ability group yielded a larger network. • The MCI high executive ability group had greater FA in numerous tracts. • White matter reserve may confer greater protection of executive abilities. • Loss of executive reserve may lead to greater impairment in AD dementia.

Imaging plays a crucial role in the initial evaluation of moderate and severe Traumatic Brain Injury (TBI) patients. Non- Contrast Head CT (NHCT) has long been the initial step of imaging