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Mild cognitive impairment can represent a transitional state between normal ageing and Alzheimer's disease. Non-invasive diagnostic methods are needed to identify mild cognitive impairment individuals for early therapeutic interventions. Our objective was to determine whether automated magnetic resonance imaging-based measures could identify mild cognitive impairment individuals with a high degree of accuracy. Baseline volumetric T1-weighted magnetic resonance imaging scans of 313 individuals from two independent cohorts were examined using automated software tools to identify the volume and mean thickness of 34 neuroanatomic regions. The first cohort included 49 older controls and 48 individuals with mild cognitive impairment, while the second cohort included 94 older controls and 57 mild cognitive impairment individuals. Sixty-five patients with probable Alzheimer's disease were also included for comparison. For the discrimination of mild cognitive impairment, entorhinal cortex thickness, hippocampal volume and supramarginal gyrus thickness demonstrated an area under the curve of 0.91 (specificity 94%, sensitivity 74%, positive likelihood ratio 12.12, negative likelihood ratio 0.29) for the first cohort and an area under the curve of 0.95 (specificity 91%, sensitivity 90%, positive likelihood ratio 10.0, negative likelihood ratio 0.11) for the second cohort. For the discrimination of Alzheimer's disease, these three measures demonstrated an area under the curve of 1.0. The three magnetic resonance imaging measures demonstrated significant correlations with clinical and neuropsychological assessments as well as with cerebrospinal fluid levels of tau, hyperphosphorylated tau and abeta 42 proteins. These results demonstrate that automated magnetic resonance imaging measures can serve as an in vivo surrogate for disease severity, underlying neuropathology and as a non-invasive diagnostic method for mild cognitive impairment and Alzheimer's disease. [ABSTRACT FROM AUTHOR]

The clinical phenotype of Huntington's disease (HD) is far more complex and variable than depictions of it as a progressive movement disorder dominated by neostriatal pathology represent. The availability of novel neuro-imaging methods has enabled us to evaluate cerebral cortical changes in HD, which we have found to occur early and to be topographically selective. What is less clear, however, is how these changes influence the clinical expression of the disease. In this study, we used a high-resolution surface based analysis of in vivo MRI data to measure cortical thickness in 33 individuals with HD, spanning the spectrum of disease and 22 age- and sex-matched controls. We found close relationships between specific functional and cognitive measures and topologically specific cortical regions. We also found that distinct motor phenotypes were associated with discrete patterns of cortical thinning. The selective topographical associations of cortical thinning with clinical features of HD suggest that we are not simply correlating global worsening with global cortical degeneration. Our results indicate that cortical involvement contributes to important symptoms, including those that have been ascribed primarily to the striatum, and that topologically selective changes in the cortex might explain much of the clinical heterogeneity found in HD. Additionally, a significant association between regional cortical thinning and total functional capacity, currently the leading primary outcome measure used in neuroprotection trials for HD, establishes cortical MRI morphometry as a potential biomarker of disease progression. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) is considered to initiate cerebrovascular pathology, involving in the development of multiple forms of neurodegeneration. However, it is unknown the relationships between imaging marker of cerebrovascular injury (white matter hyperintensity, WMH), its load on white matter tract and disrupted brain dynamics with cognitive function in mild TBI (mTBI). MRI data and neuropsychological assessments were collected from 85 mTBI patients and 52 healthy controls. Between-group difference was conducted for the tract-specific WMH volumes, white matter integrity, and dynamic brain connectivity (i.e., fractional occupancies [%], dwell times [seconds], and state transitions). Regression analysis was used to examine associations between white matter damage, brain dynamics, and cognitive function. Increased WMH volumes induced by mTBI within the thalamic radiation and corpus callosum were highest among all tract fibers, and related with altered fractional anisotropy (FA) within the same tracts. Clustering identified two brain states, segregated state characterized by the sparse inter-independent component connections, and default mode network (DMN)-centered integrated state with strongly internetwork connections between DMN and other networks. In mTBI, higher WMH loads contributed to the longer dwell time and larger fractional occupancies in DMN-centered integrated state. Every 1 mL increase in WMH volume within the left thalamic radiation was associated with a 47% increase fractional occupancies, and contributed to 65.6 s delay in completion of cognitive processing speed test. Our study provided the first evidence for the structural determinants (i.e., small vessel lesions) that mediate the spatiotemporal brain dynamics to cognitive impairments in mTBI., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

White matter lesions (WMLs) are prevalent with aging, and higher WML burden has been observed in older adults with vascular diseases. While the physiology underlying the formation of WMLs is not known, various risk factors are associated with high WML burden. Here, we investigated the relationship between vascular risk factors and microvascular physiology (i.e., oxygen supply and oxygen extraction fraction [OEF]) and their association with WML burden. Forty-one typically aging adults (60-80 years) were classified into high or low vascular risk based on common modifiable vascular risk factors (hypertension, diabetes, hyperlipidemia, and overweight). These groups were subdivided into high or low WML burden. Differences in microvascular physiology (oxygen supply and OEF) were then compared between and within groups. Overall, OEF was significantly higher in the high vascular risk group compared to the low vascular risk group (p < 0.01). In the low vascular risk subgroup, OEF was uniquely lower in the individuals with high WML versus low WML burden (p = 0.02), despite no differences in oxygen supply between these subgroups (p = 0.87). The coupling of impaired OEF with the absence of compensatory physiology, such as elevated oxygen supply, may represent an important mechanism underlying WML burden in individuals with low vascular risk factors., Competing Interests: Declaration of conflicting interestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Meher R. Juttukonda receives research-related support from Siemens Healthineers. The other authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The human brain undergoes age-related microstructural alterations across the lifespan. Soma and Neurite Density Imaging (SANDI), a novel biophysical model of diffusion MRI, provides estimates of cell body (soma) radius and density, and neurite density in gray matter. The goal of this cross-sectional study was to assess the sensitivity of high-gradient diffusion MRI toward age-related alterations in cortical microstructure across the adult lifespan using SANDI. Seventy-two cognitively unimpaired healthy subjects (ages 19-85 years; 40 females) were scanned on the 3T Connectome MRI scanner with a maximum gradient strength of 300mT/m using a multi-shell diffusion MRI protocol incorporating 8 b-values and diffusion time of 19 ms. Intra-soma signal fraction obtained from SANDI model-fitting to the data was strongly correlated with age in all major cortical lobes (r = -0.69 to -0.60, FDR-p < 0.001). Intra-soma signal fraction (r = 0.48-0.63, FDR-p < 0.001) and soma radius (r = 0.28-0.40, FDR-p < 0.04) were significantly correlated with cortical volume in the prefrontal cortex, frontal, parietal, and temporal lobes. The strength of the relationship between SANDI metrics and age was greater than or comparable to the relationship between cortical volume and age across the cortical regions, particularly in the occipital lobe and anterior cingulate gyrus. In contrast to the SANDI metrics, all associations between diffusion tensor imaging (DTI) and diffusion kurtosis imaging metrics and age were low to moderate. These results suggest that high-gradient diffusion MRI may be more sensitive to underlying substrates of neurodegeneration in the aging brain than DTI and traditional macroscopic measures of neurodegeneration such as cortical volume and thickness., (© 2024 The Author(s). Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.)

Objective: Elevated entorhinal cortex (EC) tau in low beta-amyloid individuals can predict accumulation of pathology and cognitive decline. We compared the accuracy of magnetic resonance imaging (MRI)-derived locus coeruleus integrity, neocortical beta-amyloid burden by positron emission tomography (PET), and hippocampal volume in identifying elevated entorhinal tau signal in asymptomatic individuals who are considered beta-amyloid PET-negative., Methods: We included 188 asymptomatic individuals (70.78 ± 11.51 years, 58% female) who underwent 3T-MRI of the locus coeruleus, Pittsburgh compound-B (PiB), and Flortaucipir (FTP) PET. Associations between elevated EC tau and neocortical PiB, hippocampal volume, or locus coeruleus integrity were evaluated and compared using logistic regression and receiver operating characteristic analyses in the PiB- sample with a clinical dementia rating (CDR) of 0. Associations with clinical progression (CDR-sum-of-boxes) over a time span of 6 years were evaluated with Cox proportional hazard models., Results: We identified 26 (21%) individuals with high EC FTP in the CDR = 0/PiB- sample. Locus coeruleus integrity was a significantly more sensitive and specific predictor of elevated EC FTP (area under the curve [AUC] = 85%) compared with PiB (AUC = 77%) or hippocampal volume (AUC = 76%). Based on the Youden-index, locus coeruleus integrity obtained a sensitivity of 77% and 85% specificity. Using the resulting locus coeruleus Youden cut-off, lower locus coeruleus integrity was associated with a two-fold increase in clinical progression, including mild cognitive impairment., Interpretation: Locus coeruleus integrity has promise as a low-cost, non-invasive screening instrument to detect early cortical tau deposition and associated clinical progression in asymptomatic, low beta-amyloid individuals. ANN NEUROL 2024;96:650-661., (© 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Background: Changes in cerebral hemodynamics with aging are important for understanding age-related variation in neuronal health. While many prior studies have focused on gray matter, less is known regarding white matter due in part to measurement challenges related to the lower vascular density in white matter., Purpose: To investigate the impact of age and sex on white matter hemodynamics in a Human Connectome Project in Aging (HCP-A) cohort using tract-based spatial statistics (TBSS)., Study Type: Retrospective cross-sectional., Population: Six hundred seventy-eight typically aging individuals (381 female), aged 36-100 years., Field Strength/sequence: Multi-delay pseudo-continuous arterial spin labeling (ASL) and diffusion-weighted pulsed-gradient spin-echo echo planar imaging sequences at 3.0 T., Assessment: A skeleton of mean fractional anisotropy (FA) was produced using TBSS. This skeleton was used to project ASL-derived cerebral blood flow (CBF) and arterial transit time (ATT) measures onto white matter tracts., Statistical Tests: General linear models were applied to white matter FA, CBF, and ATT maps, while covarying for age and sex. Threshold-free cluster enhancement multiple comparisons correction was performed for the effects of age and sex, thresholded at P FWE  < 0.05. CBF, ATT, and FA were compared between sex for each tract using analysis of covariance, with multiple comparisons correction for the number of tracts at P FDR  < 0.05., Results: Significantly lower white matter CBF and significantly prolonged white matter ATTs were associated with older age. These effects were widespread across tracts for ATT. Significant (P FDR  < 0.05) sex differences in ATT were observed across all tracts, and significant sex differences in CBF were observed in all tracts except the bilateral uncinate fasciculus. Females demonstrated significantly higher CBF compared to males across the lifespan. Few tracts demonstrated significant sex differences in FA., Data Conclusion: This study identified significant sex- and age-associated differences in white matter hemodynamics across tracts., Evidence Level: 3 TECHNICAL EFFICACY: Stage 3., (© 2024 International Society for Magnetic Resonance in Medicine.)

Metabolic syndrome is a collection of health factors that increases risk for cardiovascular disease. A condition of aging, metabolic syndrome is associated with reduced brain network integrity, including functional connectivity alterations among the default mode, regions vulnerable to neurodegeneration. Prevalence of metabolic syndrome is elevated in younger populations including post-9/11 Veterans and individuals with posttraumatic stress disorder, but it is unclear whether metabolic syndrome affects brain function in earlier adulthood. Identifying early effects of metabolic syndrome on brain network integrity is critical, as these impacts could contribute to increased risk for cognitive disorders later in life for Veterans. The current study examined whether metabolic syndrome and its individual components were associated with default mode functional connectivity. We also explored the contribution of posttraumatic stress disorder and traumatic brain injury on these metabolic syndrome-brain relationships. Post-9/11 Veterans with combat deployment history (95 with and 325 without metabolic syndrome) underwent functional magnetic resonance imaging to capture seed-based resting-state functional connectivity within the default mode. The metabolic syndrome group demonstrated reduced positive functional connectivity between the posterior cingulate cortex seed and the bilateral superior frontal gyrus. Data-driven analyses demonstrated that metabolic syndrome components, particularly cholesterol and central adiposity, were associated with widespread reductions in default mode network connectivity. Functional connectivity was also reduced in participants with metabolic syndrome but without current posttraumatic stress disorder diagnosis and with traumatic brain injury history. These results suggest that metabolic syndrome disrupts resting-state functional connectivity decades earlier than prior work has shown., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: Poor sleep quality has been associated with changes in brain volume among veterans, particularly those who have experienced mild traumatic brain injury (mTBI) and post-traumatic stress disorder (PTSD). This study sought to investigate (1) whether poor sleep quality is associated with decreased cortical thickness in Iraq and Afghanistan war veterans, and (2) whether these associations differ topographically depending on the presence or absence of mTBI and PTSD., Methods: A sample of 440 post-9/11 era U.S. veterans enrolled in the Translational Research Center for Traumatic Brain Injury and Stress Disorders study at VA Boston, MA from 2010 to 2022 was included in the study. We examined the relationship between sleep quality, as measured by the Pittsburgh Sleep Quality Index (PSQI), and cortical thickness in veterans with mTBI (n = 57), PTSD (n = 110), comorbid mTBI and PTSD (n = 129), and neither PTSD nor mTBI (n = 144). To determine the topographical relationship between subjective sleep quality and cortical thickness in each diagnostic group, we employed a General Linear Model (GLM) at each vertex on the cortical mantle. The extent of topographical overlap between the resulting statistical maps was assessed using Dice coefficients., Results: There were no significant associations between PSQI and cortical thickness in the group without PTSD or mTBI (n = 144) or in the PTSD-only group (n = 110). In the mTBI-only group (n = 57), lower sleep quality was significantly associated with reduced thickness bilaterally in frontal, cingulate, and precuneus regions, as well as in the right parietal and temporal regions (β = -0.0137, P < 0.0005). In the comorbid mTBI and PTSD group (n = 129), significant associations were observed bilaterally in frontal, precentral, and precuneus regions, in the left cingulate and the right parietal regions (β = -0.0094, P < 0.0005). Interaction analysis revealed that there was a stronger relationship between poor sleep quality and decreased cortical thickness in individuals with mTBI (n = 186) compared to those without mTBI (n = 254) specifically in the frontal and cingulate regions (β = -0.0077, P < 0.0005)., Conclusions: This study demonstrates a significant relationship between poor sleep quality and lower cortical thickness primarily within frontal regions among individuals with both isolated mTBI or comorbid diagnoses of mTBI and PTSD. Thus, if directionality is established in longitudinal and interventional studies, it may be crucial to consider addressing sleep in the treatment of veterans who have sustained mTBI., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Metabolic syndrome has been associated with reduced brain white matter integrity in older individuals. However, less is known about how metabolic syndrome might impact white matter integrity in younger populations. This study examined metabolic syndrome-related global and regional white matter integrity differences in a sample of 537 post-9/11 Veterans. Metabolic syndrome was defined as ≥3 factors of: increased waist circumference, hypertriglyceridemia, low high-density lipoprotein cholesterol, hypertension, and high fasting glucose. T1 and diffusion weighted 3 T MRI scans were processed using the FreeSurfer image analysis suite and FSL Diffusion Toolbox. Atlas-based regions of interest were determined from a combination of the Johns Hopkins University atlas and a Tract-Based Spatial Statistics-based FreeSurfer WMPARC white matter skeleton atlas. Analyses revealed individuals with metabolic syndrome (n = 132) had significantly lower global fractional anisotropy than those without metabolic syndrome (n = 405), and lower high-density lipoprotein cholesterol levels was the only metabolic syndrome factor significantly related to lower global fractional anisotropy levels. Lobe-specific analyses revealed individuals with metabolic syndrome had decreased fractional anisotropy in frontal white matter regions compared with those without metabolic syndrome. These findings indicate metabolic syndrome is prevalent in this sample of younger Veterans and is related to reduced frontal white matter integrity. Early intervention for metabolic syndrome may help alleviate adverse metabolic syndrome-related brain and cognitive effects with age., (Published by Oxford University Press 2024.)

Cerebrovascular dysfunction is a significant contributor to Alzheimer's disease (AD) progression. AD mouse models show altered capillary morphology, density, and diminished blood flow in areas of tau and beta-amyloid accumulation. The purpose of this study was to examine alterations in vascular structure and their contributions to perfusion deficits in the hippocampus in AD and mild cognitive impairment (MCI). Seven individuals with AD and MCI (1 AD/6 MCI), nine cognitively intact older healthy adults, and seven younger healthy adults underwent pseudo-continuous arterial spin labeling (PCASL) and gradient-echo/spin-echo (GESE) dynamic susceptibility contrast (DSC) MRI. Cerebral blood flow (CBF), cerebral blood volume, relative vessel size index (rVSI), and mean vessel density were calculated from model fitting. Lower CBF from PCASL and SE DSC MRI was observed in the hippocampus of AD/MCI group. rVSI in the hippocampus of the AD/MCI group was larger than that of the two healthy groups (FDR- P  = 0.02). No difference in vessel density was detected between the groups. We also explored relationship of tau burden from 18 F-flortaucipir positron emission tomography and vascular measures from MRI. Tau burden was associated with larger vessel size and lower CBF in the hippocampus. We postulate that larger vessel size may be associated with vascular alterations in AD/MCI., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Autopsy studies indicated that the locus coeruleus (LC) accumulates hyperphosphorylated tau before allocortical regions in Alzheimer's disease. By combining in vivo longitudinal magnetic resonance imaging measures of LC integrity, tau positron emission tomography imaging and cognition with autopsy data and transcriptomic information, we examined whether LC changes precede allocortical tau deposition and whether specific genetic features underlie LC's selective vulnerability to tau. We found that LC integrity changes preceded medial temporal lobe tau accumulation, and together these processes were associated with lower cognitive performance. Common gene expression profiles between LC-medial temporal lobe-limbic regions map to biological functions in protein transport regulation. These findings advance our understanding of the spatiotemporal patterns of initial tau spreading from the LC and LC's selective vulnerability to Alzheimer's disease pathology. LC integrity measures can be a promising indicator for identifying the time window when individuals are at risk of disease progression and underscore the importance of interventions mitigating initial tau spread., (© 2024. The Author(s).)

Recent efforts demonstrated the efficacy of identifying early-stage neuropathology of Alzheimer's disease (AD) through lumbar puncture cerebrospinal fluid assessment and positron emission tomography (PET) radiotracer imaging. These methods are effective yet are invasive, expensive, and not widely accessible. We extend and improve the multiscale structural mapping (MSSM) procedure to develop structural indicators of β-amyloid neuropathology in preclinical AD, by capturing both macrostructural and microstructural properties throughout the cerebral cortex using a structural MRI. We find that the MSSM signal is regionally altered in clear positive and negative cases of preclinical amyloid pathology (N = 220) when cortical thickness alone or hippocampal volume is not. It exhibits widespread effects of amyloid positivity across the posterior temporal, parietal, and medial prefrontal cortex, surprisingly consistent with the typical pattern of amyloid deposition. The MSSM signal is significantly correlated with amyloid PET in almost half of the cortex, much of which overlaps with regions where beta-amyloid accumulates, suggesting it could provide a regional brain 'map' that is not available from systemic markers such as plasma markers., Competing Interests: Declaration of Competing Interest In the past 36 months, outside the submitted work, B.C.D has received research support from NIH; consulted for Acadia, Alector, Arkuda, Biogen, Denali, Lilly, Merck, Novartis, Takeda, and Wave LifeSciences; performed editorial duties with payment for Elsevier (Neuroimage: Clinical and Cortex); and received royalties from Oxford University Press and Cambridge University Press. D.H.S has held leadership or fiduciary role in Niji Corp, Smart Ion, and Salat Research Consulting; and performed editorial duties with payment for Elsevier (Neuroimage and Neurobiology of Aging). S.Y.H has received research support from NIH and Siemens Healthineers; and consulted for Siemens Healthineers. The other authors declare that they have no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Cerebral perfusion is directly affected by systemic blood pressure, which has been shown to be negatively correlated with cerebral blood flow (CBF). The impact of aging on these effects is not fully understood., Purpose: To determine whether the relationship between mean arterial pressure (MAP) and cerebral hemodynamics persists throughout the lifespan., Study Type: Retrospective, cross-sectional study., Population: Six hundred and sixty-nine participants from the Human Connectome Project-Aging ranging between 36 and 100+ years and without a major neurological disorder., Field Strength/sequence: Imaging data was acquired at 3.0 Tesla using a 32-channel head coil. CBF and arterial transit time (ATT) were measured by multi-delay pseudo-continuous arterial spin labeling., Assessment: The relationships between cerebral hemodynamic parameters and MAP were evaluated globally in gray and white matter and regionally using surface-based analysis in the whole group, separately within different age groups (young: <60 years; younger-old: 60-79 years; oldest-old: ≥80 years)., Statistical Tests: Chi-squared, Kruskal-Wallis, ANOVA, Spearman rank correlation and linear regression models. The general linear model setup in FreeSurfer was used for surface-based analyses. P < 0.05 was considered significant., Results: Globally, there was a significant negative correlation between MAP and CBF in both gray (ρ = -0.275) and white matter (ρ = -0.117). This association was most prominent in the younger-old [gray matter CBF (β = -0.271); white matter CBF (β = -0.241)]. In surface-based analyses, CBF exhibited a widespread significant negative association with MAP throughout the brain, whereas a limited number of regions showed significant prolongation in ATT with higher MAP. The associations between regional CBF and MAP in the younger-old showed a different topographic pattern in comparison to young subjects., Data Conclusion: These observations further emphasize the importance of cardiovascular health in mid-to-late adulthood for healthy brain aging. The differences in the topographic pattern with aging indicate a spatially heterogeneous relationship between high blood pressure and CBF., Level of Evidence: 3 TECHNICAL EFFICACY STAGE: 3., (© 2023 International Society for Magnetic Resonance in Medicine.)

Adolescence represents a critical period of neural development during which binge drinking (BD) is prevalent. Though prior work has shown that white matter (WM) integrity is susceptible to damage from excessive alcohol intake in adults, the effect of early adolescent BD on WM health in adulthood remains unknown. Veterans with a history of BD onset before age 15 [n = 49; mean age = 31.8 years; early-onset adolescent binge drinkers (EBD)] and after age 15 [n = 290; mean age = 32.2 years; late-onset adolescent binge drinkers (LBD)] were studied with diffusion tensor imaging. Group differences in fractional anisotropy (FA; movement of water molecules along the WM) and mean diffusivity (MD; average movement of water molecules) were examined as indices of WM integrity using FreeSurfer and FMRIB Software Library (FSL) processing streams. Lower FA and higher MD are thought to represent degradations in WM integrity. A reference group (RG) of social drinkers with no history of BD (n = 31) was used to provide comparative normative data. We observed widespread decreased FA and increased MD in EBDs, compared to LBDs, as well as decreased FA in the pars triangularis, lateral orbitofrontal cortex, superior frontal cortex, isthmus cingulate, and genu and splenium of the corpus callosum EBDs also had lower WM integrity compared to the RG. Adults who initiated BD during early adolescence demonstrated decreased FA and increased MD throughout the frontostriatal circuits that mediate inhibitory control and thus may result in impulsive behavior and a predisposition for developing alcohol use disorder during adulthood., (© Published by Oxford University Press on behalf of Medical Council on Alcohol 2023.)

Several cardiovascular and metabolic indicators, such as cholesterol and blood pressure have been associated with altered neural and cognitive health as well as increased risk of dementia and Alzheimer's disease in later life. In this cross-sectional study, we examined how an aggregate index of cardiovascular and metabolic risk factor measures was associated with correlation-based estimates of resting-state functional connectivity (FC) across a broad adult age-span (36-90+ years) from 930 volunteers in the Human Connectome Project Aging (HCP-A). Increased (i.e., worse) aggregate cardiometabolic scores were associated with reduced FC globally, with especially strong effects in insular, medial frontal, medial parietal, and superior temporal regions. Additionally, at the network-level, FC between core brain networks, such as default-mode and cingulo-opercular, as well as dorsal attention networks, showed strong effects of cardiometabolic risk. These findings highlight the lifespan impact of cardiovascular and metabolic health on whole-brain functional integrity and how these conditions may disrupt higher-order network integrity., Competing Interests: Declaration of Competing Interest The authors report no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from resting-state functional magnetic resonance imaging. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Methods: Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estimated a hidden Markov model and the occupancy rate for each subject. Then, we investigated the link between CCN dFC features and AD progression. Also, we investigated the link between sFC and AD progression and compared its results with dFC results. Results: Progression of AD symptoms was associated with increases in connectivity within the middle frontal gyrus. Also, the very mild AD (vmAD) showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region and the rest of CCN (in dFC analysis). Also, we found that within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with vmAD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Conclusion: Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.

Altered blood flow in the human brain is characteristic of typical aging. However, numerous factors contribute to inter-individual variation in patterns of blood flow throughout the lifespan. To better understand the mechanisms behind such variation, we studied how sex and APOE genotype, a primary genetic risk factor for Alzheimer's disease (AD), influence associations between age and brain perfusion measures. We conducted a cross-sectional study of 562 participants from the Human Connectome Project - Aging (36 to >90 years of age). We found widespread associations between age and vascular parameters, where increasing age was associated with regional decreases in cerebral blood flow (CBF) and increases in arterial transit time (ATT). When grouped by sex and APOE genotype, interactions between group and age demonstrated that females had relatively greater CBF and lower ATT compared to males. Females carrying the APOEε4 allele showed the strongest association between CBF decline and ATT incline with age. This demonstrates that sex and genetic risk for AD modulate age-associated patterns of cerebral perfusion measures., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023. Published by Elsevier Inc.)

While genome wide association studies (GWASs) of Alzheimer's Disease (AD) in European (EUR) ancestry cohorts have identified approximately 83 potentially independent AD risk loci, progress in non-European populations has lagged. In this study, data from the Million Veteran Program (MVP), a biobank which includes genetic data from more than 650,000 US Veteran participants, was used to examine dementia genetics in an African descent (AFR) cohort. A GWAS of Alzheimer's disease and related dementias (ADRD), an expanded AD phenotype including dementias such as vascular and non-specific dementia that included 4012 cases and 18,435 controls age 60+ in AFR MVP participants was performed. A proxy dementia GWAS based on survey-reported parental AD or dementia (n = 4385 maternal cases, 2256 paternal cases, and 45,970 controls) was also performed. These two GWASs were meta-analyzed, and then subsequently compared and meta-analyzed with the results from a previous AFR AD GWAS from the Alzheimer's Disease Genetics Consortium (ADGC). A meta-analysis of common variants across the MVP ADRD and proxy GWASs yielded GWAS significant associations in the region of APOE (p = 2.48 × 10 - 101 ), in ROBO1 (rs11919682, p = 1.63 × 10 - 8 ), and RNA RP11-340A13.2 (rs148433063, p = 8.56 × 10 - 9 ). The MVP/ADGC meta-analysis yielded additional significant SNPs near known AD risk genes TREM2 (rs73427293, p = 2.95 × 10 - 9 ), CD2AP (rs7738720, p = 1.14 × 10 -9 ), and ABCA7 (rs73505251, p = 3.26 × 10 -10 ), although the peak variants observed in these genes differed from those previously reported in EUR and AFR cohorts. Of the genes in or near suggestive or genome-wide significant associated variants, nine (CDA, SH2D5, DCBLD1, EML6, GOPC, ABCA7, ROS1, TMCO4, and TREM2) were differentially expressed in the brains of AD cases and controls. This represents the largest AFR GWAS of AD and dementia, finding non-APOE GWAS-significant common SNPs associated with dementia. Increasing representation of AFR participants is an important priority in genetic studies and may lead to increased insight into AD pathophysiology and reduce health disparities., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Objectives: Radiological markers for cerebral small vessel disease (SVD) may have different biological underpinnings in their development. We attempted to categorize SVD burden by integrating white matter signal abnormalities (WMSA) features and secondary presence of lacunes, microbleeds, and enlarged perivascular spaces., Methods: Data were acquired from 610 older adults (aged > 40 years) who underwent brain magnetic resonance imaging exam as part of a health checkup. The WMSA were classified individually by the number and size of non-contiguous lesions, distribution, and contrast. Age-detrended lacunes, microbleeds, and enlarged perivascular space were quantified to further categorize individuals. Clinical and laboratory values were compared across the individual classes., Results: Class I was characterized by multiple, small, deep WMSA but a low burden of lacunes and microbleeds; class II had large periventricular WMSA and a high burden of lacunes and microbleeds; and class III had limited juxtaventricular WMSA and lacked lacunes and microbleeds. Class II was associated with older age, diabetes, and a relatively higher neutrophil-to-lymphocyte ratio. Smoking and higher uric acid levels were associated with an increased risk of class I., Conclusion: The heterogeneity of SVD was categorized into three classes with distinct clinical correlates. This categorization will improve our understanding of SVD pathophysiology, risk stratification, and outcome prediction., Key Points: • Classification of white matter signal abnormality (WMSA) features was associated with different characteristic of lacunes, microbleeds, and enlarged perivascular space and clinical variability. • Class I was characterized by multiple, small, deep WMSA but a low burden of lacunes and microbleeds. Class II had large periventricular WMSA and a high burden of lacunes and microbleeds. Class III had limited juxtaventricular WMSA and lacked lacunes and microbleeds. • Class II was associated with older age, diabetes, and higher neutrophil-to-lymphocyte ratio. Smoking and higher uric acid levels were associated with an increased risk of class I., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Primary Objective: Despite a high prevalence of intimate partner violence (IPV) and its lasting impacts on individuals, particularly women, very little is known about how IPV may impact the brain. IPV is known to frequently result in traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD). In this overview of literature, we examined literature related to neuroimaging in women with IPV experiences between the years 2010-2021., Research Design: Literature overview., Methods and Procedures: A total of 17 studies were included in the review, which is organized into each imaging modality, including magnetic resonance imaging (structural, diffusion, and functional MRI), Electroencephalography (EEG), proton magnetic resonance spectroscopy (pMRS), and multimodal imaging., Main Outcomes and Results: Research has identified changes in brain regions associated with cognition, emotion, and memory. Howeverto date, it is difficult to disentangle the unique contributions of TBI and PTSD effects of IPV on the brain. Furthermore, experimental design elements differ considerably among studies., Conclusions: The aim is to provide an overview of existing literature to determine commonalities across studies and to identify remaining knowledge gaps and recommendations for implementing future imaging studies with individuals who experience IPV.

Alzheimer's disease (AD) is characterized neuropathologically by β-amyloid (Aβ) plaques, hyperphosphorylated tau neurofibrillary tangles, and neurodegeneration, which lead to a phenotypically heterogeneous cognitive-behavioral dementia syndrome. Our understanding of how these neuropathological and neurodegeneration biomarkers relate to each other is still evolving. A relatively new approach to measuring structural brain change, gray matter to white matter signal intensity ratio (GWR), quantifies the signal contrast between these tissue compartments, and has emerged as a promising marker of AD-related neurodegeneration. We sought to validate GWR as a novel MRI biomarker of neurodegeneration in 29 biomarker positive individuals across the atypical syndromic spectrum of AD. Bivariate correlation analyses revealed that GWR was associated with cortical thickness, tau PET, and amyloid PET, with GWR showing a larger magnitude of abnormality than cortical thickness. We also found that combining GWR, cortical thickness, and amyloid PET better explained observed tau PET signal than using these modalities alone, suggesting that the three imaging biomarkers contribute independently and synergistically to explaining the variance in the distribution of tau pathology. We conclude that GWR is a uniquely sensitive in vivo marker of neurodegenerative change that reflects pathological mechanisms which may occur prior to cortical atrophy. By using all of these imaging biomarkers of AD together, we may be better able to capture, and possibly predict, AD neuropathologic changes in vivo. We hope that such an approach will ultimately contribute to better endpoints to evaluate the efficacy of therapeutic interventions as we move toward an era of disease-modifying treatments for this devastating disease., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Apolipoprotein E (APOE) polymorphic alleles are genetic factors associated with Alzheimer's disease (AD) risk. Although previous studies have explored the link between AD genetic risk and static functional network connectivity (sFNC), to the best of our knowledge, no previous studies have evaluated the association between dynamic FNC (dFNC) and AD genetic risk. Here, we examined the link between sFNC, dFNC, and AD genetic risk with a data-driven approach. We used rs-fMRI, demographic, and APOE data from cognitively normal individuals (N = 886) between 42 and 95 years of age (mean = 70 years). We separated individuals into low, moderate, and high-risk groups. Using Pearson correlation, we calculated sFNC across seven brain networks. We also calculated dFNC with a sliding window and Pearson correlation. The dFNC windows were partitioned into three distinct states with k-means clustering. Next, we calculated the proportion of time each subject spent in each state, called occupancy rate or OCR and frequency of visits. We compared both sFNC and dFNC features across individuals with different genetic risks and found that both sFNC and dFNC are related to AD genetic risk. We found that higher AD risk reduces within-visual sensory network (VSN) sFNC and that individuals with higher AD risk spend more time in a state with lower within-VSN dFNC. We also found that AD genetic risk affects whole-brain sFNC and dFNC in women but not men. In conclusion, we presented novel insights into the links between sFNC, dFNC, and AD genetic risk., Competing Interests: Declaration of Competing Interest Elizabeth Mormino provides consulting services for Neurotrack and Eli Lilly. David Salat is a founder and has equity interest in Niji Corp. The remaining authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

White matter lesions (WML) have been linked to cognitive decline in aging as well as in Alzheimer's disease. While hypoperfusion is frequently considered a cause of WMLs due to the resulting reduction in oxygen availability to brain tissue, such reductions could also be caused by impaired oxygen exchange. Here, we tested the hypothesis that venous hyperintense signal (VHS) in arterial spin labeling (ASL) magnetic resonance imaging (MRI) may represent a marker of impaired oxygen extraction in aging older adults. In participants aged 60-80 years (n = 30), we measured cerebral blood flow and VHS with arterial spin labeling, maximum oxygen extraction fraction (OEF max ) with dynamic susceptibility contrast, and WML volume with T 1 -weighted MRI. We found a significant interaction between OEF max and VHS presence on WML volume ( p  = 0.02), where lower OEF max was associated with higher WML volume in participants with VHS, and higher OEF max was associated with higher WML volume in participants without VHS. These results indicate that VHS in perfusion-weighted ASL data may represent a distinct cerebrovascular aging pattern involving oxygen extraction inefficiency as well as hypoperfusion.

Veterans who deployed in support of Operation Enduring Freedom (OEF), Iraqi Freedom (OIF), and New Dawn (OND) commonly experience severe psychological trauma, often accompanied by physical brain trauma resulting in mild traumatic brain injury (mTBI). Prior studies of individuals with posttraumatic stress disorder (PTSD) have revealed alterations in brain structure, accelerated cellular aging, and impacts on cognition following exposure to severe psychological trauma and potential interactive effects of military-related mTBI. To date, however, little is known how such deployment-related trauma changes with time and age of injury of the affected veteran. In this study, we explored changes in cortical thickness, volume, and surface area after an average interval of approximately 2 years in a cohort of 254 OEF/OIF/OND Veterans ranging in age from 19 to 67 years. Whole-brain vertex-wise analyses revealed that veterans who met criteria for severe PTSD (Clinician-Administered PTSD Scale ≥60) at baseline showed greater negative longitudinal changes in cortical thickness, volume, and area over time. Analyses also revealed a significant severe-PTSD by age interaction on cortical measures with severe-PTSD individuals exhibiting accelerated cortical degeneration with increasing age. Interaction effects of comorbid military-related mTBI within the severe-PTSD group were also observed in several cortical regions. These results suggest that those exhibiting severe PTSD symptomatology have accelerated atrophy that is exacerbated with increasing age and history of mTBI., (Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Human Brain Mapping published by Wiley Periodicals LLC.)

Currently, there is great interest in making neuroimaging widely accessible and thus expanding the sampling population for better understanding and preventing diseases. The use of wearable health devices has skyrocketed in recent years, allowing continuous assessment of physiological parameters in patients and research cohorts. While most health wearables monitor the heart, lungs and skeletal muscles, devices targeting the brain are currently lacking. To promote brain health in the general population, we developed a novel, low-cost wireless cerebral oximeter called FlexNIRS. The device has 4 LEDs and 3 photodiode detectors arranged in a symmetric geometry, which allows for a self-calibrated multi-distance method to recover cerebral hemoglobin oxygenation (SO 2 ) at a rate of 100 Hz. The device is powered by a rechargeable battery and uses Bluetooth Low Energy (BLE) for wireless communication. We developed an Android application for portable data collection and real-time analysis and display. Characterization tests in phantoms and human participants show very low noise (noise-equivalent power <70 fW/√Hz) and robustness of SO 2 quantification in vivo. The estimated cost is on the order of $50/unit for 1000 units, and our goal is to share the device with the research community following an open-source model. The low cost, ease-of-use, smart-phone readiness, accurate SO 2 quantification, real time data quality feedback, and long battery life make prolonged monitoring feasible in low resource settings, including typically medically underserved communities, and enable new community and telehealth applications., Competing Interests: Declaration of Competing Interest DHS has a financial interest in Niji Corp, a company developing a suite of home/remote usage digital biomarker tools for the identification and assessment of individuals with Alzheimer's disease. MAF has a financial interest in 149 Medical, Inc., a company developing diffuse correlation spectroscopy technology for assessing and monitoring cerebral blood flow in newborn infants. DHS and MAF interests were reviewed and are managed by Massachusetts General Hospital and Mass General Brigham in accordance with their conflict of interest policies., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying functional integration between brain networks. In a conventional dFNC pipeline, a clustering stage to summarize the connectivity patterns that are transiently but reliably realized over the course of a scanning session. However, identifying the right number of clusters (or states) through a conventional clustering criterion computed by running the algorithm repeatedly over a large range of cluster numbers is time-consuming and requires substantial computational power even for typical dFNC datasets, and the computational demands become prohibitive as datasets become larger and scans longer. Here we developed a new dFNC pipeline based on a two-step clustering approach to analyze large dFNC data without having access to huge computational power., Methods: In the proposed dFNC pipeline, we implement two-step clustering. In the first step, we randomly use a sub-sample dFNC data and identify several sets of states at different model orders. In the second step, we aggregate all dFNC states estimated from all iterations in the first step and use this to identify the optimum number of clusters using the elbow criteria. Additionally, we use this new reduced dataset and estimate a final set of states by performing a second kmeans clustering on the aggregated dFNC states from the first k-means clustering. To validate the reproducibility of results in the new pipeline, we analyzed four dFNC datasets from the human connectome project (HCP)., Results: We found that both conventional and proposed dFNC pipelines generate similar brain dFNC states across all four sessions with more than 99% similarity. We found that the conventional dFNC pipeline evaluates the clustering order and finds the final dFNC state in 275 min, while this process takes only 11 min for the proposed dFNC pipeline. In other words, the new pipeline is 25 times faster than the traditional method in finding the optimum number of clusters and finding the final dFNC states. We also found that the new method results in better clustering quality than the conventional approach ( p < 0.001). We show that the results are replicated across four different datasets from HCP., Conclusion: We developed a new analytic pipeline that facilitates the analysis of large dFNC datasets without having access to a huge computational power source. We validated the reproducibility of the result across multiple datasets., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Sendi, Salat, Miller and Calhoun.)

Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporal properties of FNC among brain networks by putting them into distinct states using the clustering method. The computational cost of clustering dFNCs has become a significant practical barrier given the availability of enormous neuroimaging datasets. To this end, we developed a new dFNC pipeline to analyze large dFNC data without accessing hug processing capacity. We validated our proposed pipeline and compared it with the standard one using a publicly available dataset. We found that both standard and iSparse kmeans generate similar dFNC states while our approach is 27 times faster than the traditional method in finding the optimum number of clusters and creating better clustering quality.

Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain., (Copyright © 2022. Published by Elsevier Inc.)

White matter signal abnormalities (WMSA), either hypo- or hyperintensities in MRI imaging, are considered a proxy of cerebrovascular pathology and contribute to, and modulate, the clinical presentation of Alzheimer's disease (AD), with cognitive dysfunction being apparent at lower levels of amyloid and/or tau pathology when lesions are present. To what extent the topography of cortical thinning associated with AD may be explained by WMSA remains unclear. Cortical thickness group difference maps and subgroup analyses show that the effect of WMSA on cortical thickness in cognitively normal participants has a higher overlap with the canonical pattern of AD, compared to AD participants. (Age and sex-matched group of 119 NC (AV45 PET negative, CDR = 0) versus 119 participants with AD (AV45 PET-positive, CDR > 0.5). The canonical patterns of cortical atrophy thought to be specific to Alzheimer's disease are strongly linked to cerebrovascular pathology supporting a reinterpretation of the classical models of AD suggesting that a part of the typical AD pattern is due to co-localized cortical loss before the onset of AD., (Copyright © 2022. Published by Elsevier Inc.)

Objective: More than one-third of women in the United States experience intimate partner violence (IPV) in their lifetime, increasing their risk for traumatic brain injury (TBI). Despite the prevalence of TBI among IPV survivors, research is sparse in comparison with parallel populations (eg, military, accidents, sports). This pilot study aimed to provide a preliminary investigation of the effect of TBI on brain morphometry and resting-state functional connectivity in women who experience IPV., Participants: A total of 45 community-dwelling women survivors of IPV who screened positive for posttraumatic stress disorder (PTSD)., Design: Participants completed comprehensive assessments of trauma exposure, PTSD, TBI history, and brain neurological health. Twenty-three participants (51.1%) met diagnostic criteria for lifetime TBI. Of these, 15 participants experienced 1 or more TBIs resulting from IPV. The remaining participants experienced TBI from non-IPV exposures (eg, sports/motor vehicle accident). Surface-based neuroimaging analyses were performed to examine group differences in cortical thickness and in functional connectivity of amygdala and isthmus cingulate seeds to examine emotion regulation and the default mode network, respectively., Main Measures: Boston Assessment of Traumatic Brain Injury-Lifetime for Intimate Partner Violence (BAT-L/IPV); Clinician Administered PTSD Scale (CAPS); structural and functional neuroimaging., Results: History of lifetime TBI in women IPV survivors was associated with differences in cortical thickness as well as functional connectivity between the isthmus cingulate seed and a variety of regions, including superior parietal and frontal cortices. Individuals with IPV-related TBI showed lower cortical thickness in the right paracentral gyrus than individuals with TBI from other non-IPV etiologies., Conclusion: Significant differences in brain structure and connectivity were observed in individuals with IPV and TBI. A lower mean cortical thickness of the paracentral gyrus was associated with TBI due to IPV than TBI from other etiologies. Although preliminary, findings from this pilot study present a step toward identifying potential mechanisms by which IPV and TBI secondary to IPV impact brain health in women., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

The recently described biological framework of Alzheimer's disease (AD) emphasizes three types of pathology to characterize this disorder, referred to as the 'amyloid/tau/neurodegeneration' (A-T-N) status. The 'neurodegenerative' component is typically defined by atrophy measures derived from structural magnetic resonance imaging (MRI) such as hippocampal volume. Neurodegeneration measures from imaging are associated with disease symptoms and prognosis. Thus, sensitive image-based quantification of neurodegeneration in AD has an important role in a range of clinical and research operations. Although hippocampal volume is a sensitive metric of neurodegeneration, this measure is impacted by several clinical conditions other than AD and therefore lacks specificity. In contrast, selective regional cortical atrophy, known as the 'cortical signature of AD' provides greater specificity to AD pathology. Although atrophy is apparent even in the preclinical stages of the disease, it is possible that increased sensitivity to degeneration could be achieved by including tissue microstructural properties in the neurodegeneration measure. However, to facilitate clinical feasibility, such information should be obtainable from a single, short, noninvasive imaging protocol. We propose a multiscale MRI procedure that advances prior work through the quantification of features at both macrostructural (morphometry) and microstructural (tissue properties obtained from multiple layers of cortex and subcortical white matter) scales from a single structural brain image (referred to as 'multi-scale structural mapping'; MSSM). Vertex-wise partial least squares (PLS) regression was used to compress these multi-scale structural features. When contrasting patients with AD to cognitively intact matched older adults, the MSSM procedure showed substantially broader regional group differences including areas that were not statistically significant when using cortical thickness alone. Further, with multiple machine learning algorithms and ensemble procedures, we found that MSSM provides accurate detection of individuals with AD dementia (AUROC = 0.962, AUPRC = 0.976) and individuals with mild cognitive impairment (MCI) that subsequently progressed to AD dementia (AUROC = 0.908, AUPRC = 0.910). The findings demonstrate the critical advancement of neurodegeneration quantification provided through multiscale mapping. Future work will determine the sensitivity of this technique for accurately detecting individuals with earlier impairment and biomarker positivity in the absence of impairment., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Given the difficulty in factoring out typical age effects from subtle Alzheimer's disease (AD) effects on brain structure, identification of very early, as well as younger preclinical "at-risk" individuals has unique challenges. We examined whether age-correction procedures could be used to better identify individuals at very early potential risk from adults who did not have any existing cognitive diagnosis. First, we obtained cross-sectional age effects for each structural feature using data from a selected portion of the Human Connectome Project Aging (HCP-A) cohort. After age detrending, we weighted AD structural deterioration with patterns quantified from data of the Alzheimer's Disease Neuroimaging Initiative. Support vector machine was then used to classify individuals with brains that most resembled atrophy in AD across the entire HCP-A sample. Additionally, we iteratively adjusted the pipeline by removing individuals classified as AD-like from the HCP-A cohort to minimize atypical brain structural contributions to the age detrending. The classifier had a mean cross-validation accuracy of 94.0% for AD recognition. It also could identify mild cognitive impairment with more severe AD-specific biomarkers and worse cognition. In an independent HCP-A cohort, 8.8% were identified as AD-like, and they trended toward worse cognition. An "AD risk" score derived from the machine learning models also significantly correlated with cognition. This work provides a proof of concept for the potential to use structural brain imaging to identify asymptomatic individuals at young ages who show structural brain patterns similar to AD and are potentially at risk for a future clinical disorder., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Brain age gap, the difference between an individual's brain predicted age and their chronological age, is used as a biomarker of brain disease and aging. To date, although previous studies used structural magnetic resonance imaging (MRI) data to predict brain age, less work has used functional network connectivity (FNC) estimated from functional MRI to predict brain age and its association with Alzheimer's disease progression. This study used FNC estimated from 951 normal cognitive functions (NCF) individuals aged 42-95 years to train a support vector regression (SVR) to predict brain age. In the next step, we tested the trained model on two unseen datasets, including NCF and mild dementia (MD) subjects with similar age distribution (between 50-80 years old, N=70). The mean brain age gap for the NCF and MD groups was -2.25 and 2.08, respectively. We also found a significant difference between the brain age gap of NCF and MD groups. This piece of evidence introduces the brain age gap estimated from FNC as a biomarker of Alzheimer's disease progression.

Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression. Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database. Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices. Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. Impact statement This work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from diffusion-weighted magnetic resonance imaging, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and Alzheimer's disease.

The objective of this study is to examine whether metabolic syndrome (MetS), the clustering of 3 or more cardiovascular risk factors, disrupts the resting-state functional connectivity (FC) of the large-scale cortical brain networks. Resting-state functional magnetic resonance imaging data were collected from seventy-eight middle-aged and older adults living with and without MetS (27 MetS; 51 non-MetS). FC maps were derived from the time series of intrinsic activity in the large-scale brain networks by correlating the spatially averaged time series with all brain voxels using a whole-brain seed-based FC approach. Participants with MetS showed hyperconnectivity across the core brain regions with evidence of loss of modularity when compared with non-MetS individuals. Furthermore, patterns of higher between-network MetS-related effects were observed across most of the seed regions in both right and left hemispheres. These findings indicate that MetS is associated with altered intrinsic communication across core neural networks and disrupted between-network connections across the brain due to the co-occurring vascular risk factors in MetS., (Copyright © 2021 Elsevier Inc. All rights reserved.)