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IntroductionBehavioral variant frontotemporal dementia (bvFTD) may present sporadically or due to an autosomal dominant mutation. Characterization of both forms will improve understanding of the generalizability of assessments and treatments.MethodsA total of 135 sporadic (s-bvFTD; mean age 63.3 years; 34% female) and 99 familial (f-bvFTD; mean age 59.9; 48% female) bvFTD participants were identified. f-bvFTD cases included 43 with known or presumed chromosome 9 open reading frame 72 (C9orf72) gene expansions, 28 with known or presumed microtubule-associated protein tau (MAPT) mutations, 14 with known progranulin (GRN) mutations, and 14 with a strong family history of FTD but no identified mutation.ResultsParticipants with f-bvFTD were younger and had earlier age at onset. s-bvFTD had higher total Neuropsychiatric Inventory Questionnaire (NPI-Q) scores due to more frequent endorsement of depression and irritability.Discussionf-bvFTD and s-bvFTD cases are clinically similar, suggesting the generalizability of novel biomarkers, therapies, and clinical tools developed in either form to the other.

IntroductionLeisure activities impact brain aging and may be prevention targets. We characterized how physical and cognitive activities relate to brain health for the first time in autosomal dominant frontotemporal lobar degeneration (FTLD).MethodsA total of 105 mutation carriers (C9orf72/MAPT/GRN) and 69 non-carriers reported current physical and cognitive activities at baseline, and completed longitudinal neurobehavioral assessments and brain magnetic resonance imaging (MRI) scans.ResultsGreater physical and cognitive activities were each associated with an estimated >55% slower clinical decline per year among dominant gene carriers. There was also an interaction between leisure activities and frontotemporal atrophy on cognition in mutation carriers. High-activity carriers with frontotemporal atrophy (-1 standard deviation/year) demonstrated >two-fold better cognitive performances per year compared to their less active peers with comparable atrophy rates.DiscussionActive lifestyles were associated with less functional decline and moderated brain-to-behavior relationships longitudinally. More active carriers "outperformed" brain volume, commensurate with a cognitive reserve hypothesis. Lifestyle may confer clinical resilience, even in autosomal dominant FTLD.

Depression is common in individuals infected with hepatitis C virus (HCV), and both depression and HCV infection are independently associated with insulin resistance (IR). To evaluate the relationship between depression and IR, among other factors, in an HCV-infected cohort. In this cross-sectional analysis, seventy-four non-type 2 diabetic, noncirrhotic, HCV-infected patients underwent comprehensive clinical, histologic and metabolic evaluation. IR was assessed directly with an insulin suppression test by measuring steady-state plasma glucose (SSPG) levels during continuous infusions of octreotide, glucose and insulin. Logistic regression modelling was used to evaluate predictors associated with depression. Thirty-nine (53%) patients were depressed, and 21 (54%) depressed patients were on at least one antidepressant. A higher estimated proportion of depressed patients were Caucasian (51% vs 20%, P = 0.005), unemployed (69% vs 49%, P = 0.07), heavier smokers (18 pack-years vs 13 pack-years, P = 0.07), on substance abuse therapy (16% vs 3%, P = 0.06) and had lower HDL levels (1.2 mmol/L vs 1.4 mmol/L, P = 0.01). The mean SSPG levels in depressed and nondepressed patients were 7.3 and 8.3 mmol/L (P = 0.45), respectively. In multipredictor adjusted analysis, only Caucasian race (OR 4.19, 95% CI 1.42-12.35, P = 0.009) and lower HDL (OR 0.95, 95% CI 0.89-0.99, P = 0.046) were associated with depression. In conclusion, although prevalent, depression was not associated with peripheral IR in this HCV-infected cohort. Attention to other modifiable factors associated with depression in the HCV-infected population is warranted.

Diffusion Weighted Imaging is extremely important for the diagnosis of probable sporadic Jakob-Creutzfeldt disease, the most common human prion disease. Although visual assessment of DWI MRI is critical diagnostically, a more objective, quantifiable approach might more precisely identify the precise pattern of brain involvement. Furthermore, a quantitative, systematic tracking of MRI changes occurring over time might provide insights regarding the underlying histopathological mechanisms of human prion disease and provide information useful for clinical trials. The purposes of this study were: 1) to describe quantitatively the average cross-sectional pattern of reduced mean diffusivity, fractional anisotropy, atrophy and T1 relaxation in the gray matter (GM) in sporadic Jakob-Creutzfeldt disease, 2) to study changes in mean diffusivity and atrophy over time and 3) to explore their relationship with clinical scales. Twenty-six sporadic Jakob-Creutzfeldt disease and nine control subjects had MRIs on the same scanner; seven sCJD subjects had a second scan after approximately two months. Cortical and subcortical gray matter regions were parcellated with Freesurfer. Average cortical thickness (or subcortical volume), T1-relaxiation and mean diffusivity from co-registered diffusion maps were calculated in each region for each subject. Quantitatively on cross-sectional analysis, certain brain regions were preferentially affected by reduced mean diffusivity (parietal, temporal lobes, posterior cingulate, thalamus and deep nuclei), but with relative sparing of the frontal and occipital lobes. Serial imaging, surprisingly showed that mean diffusivity did not have a linear or unidirectional reduction over time, but tended to decrease initially and then reverse and increase towards normalization. Furthermore, there was a strong correlation between worsening of patient clinical function (based on modified Barthel score) and increasing mean diffusivity.

Objective There is increasing evidence that altered glutamate (Glu) homeostasis is involved in the pathophysiology of multiple sclerosis (MS). The aim of this study was to evaluate the in vivo effects of excess brain Glu on neuroaxonal integrity measured by N-acetylaspartate (NAA), brain volume, and clinical outcomes in a large, prospectively followed cohort of MS subjects. Methods We used multivoxel spectroscopy at 3T to longitudinally estimate Glu and NAA concentrations from large areas of normal-appearing white and gray matter (NAWM and GM) in MS patients (n = 343) with a mean follow-up time of 5 years. Using linear mixed-effects models, Glu was examined as a predictor of NAA decline, annualized percentage brain volume change, and evolution of clinical outcomes (Multiple Sclerosis Functional Composite [MSFC], Paced Auditory Serial Addition Test-3 [PASAT], and Expanded Disability Status Scale). Glu/NAA ratio was tested as a predictor of brain volume loss and clinical outcomes. Results Baseline Glu[NAWM] was predictive of accelerated longitudinal decline in NAA[GM] (-0.06mM change in NAA[GM]/yr for each unit increase in Glu; p = 0.004). The sustained elevation of Glu[NAWM] was predictive of a loss of 0.28mM/yr in NAA[NAWM] (p < 0.001) and 0.15mM/yr in NAA[GM] (p = 0.056). Each 10% increase in Glu/NAA [NAWM] was associated with a loss of 0.33% brain volume/yr (p = 0.001), 0.009 standard deviations/yr in MSFC z-score (p < 0.001), and 0.17 points/yr on the PASAT (p < 0.001). Interpretation These results indicate that higher Glu concentrations increase the rate of NAA decline, and higher Glu/NAA[NAWM] ratio increases the rate of decline of brain volume, MSFC, and PASAT. This provides evidence of a relationship between brain Glu and markers of disease progression in MS. © 2014 American Neurological Association.

Proximal femoral (hip) strength computed by subject-specific CT scan-based finite element (FE) models has been explored as an improved measure for identifying subjects at risk of hip fracture. However, to our knowledge, no published study has reported the effect of loading condition on the association between incident hip fracture and hip strength. In the present study, we performed a nested age- and sex-matched case-control study in the Age Gene/Environment Susceptibility (AGES) Reykjavik cohort. Baseline (pre-fracture) quantitative CT (QCT) scans of 5500 older male and female subjects were obtained. During 4-7years follow-up, 51 men and 77 women sustained hip fractures. Ninety-seven men and 152 women were randomly selected as controls from a pool of age- and sex-matched subjects. From the QCT data, FE models employing nonlinear material properties computed FE-strength of the left hip of each subject in loading from a fall onto the posterolateral (FPL), posterior (FP) and lateral (FL) aspects of the greater trochanter (patent pending). For comparison, FE strength in stance loading (FStance) and total femur areal bone mineral density (aBMD) were also computed. For all loading conditions, the reductions in strength associated with fracture in men were more than twice those in women (p≤0.01). For fall loading specifically, posterolateral loading in men and posterior loading in women were most strongly associated with incident hip fracture. After adjusting for aBMD, the association between FP and fracture in women fell short of statistical significance (p=0.08), indicating that FE strength provides little advantage over aBMD for identifying female hip fracture subjects. However, in men, after controlling for aBMD, FPL was 424N (11%) less in subjects with fractures than in controls (p=0.003). Thus, in men, FE models of posterolateral loading include information about incident hip fracture beyond that in aBMD.

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. Here, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study, described in Part I (Concepts and Applications), was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT. [ABSTRACT FROM AUTHOR]

A previous study (Haley RW, Marshall WW, McDonald GG, Daugherty MA, Petty F, Fleckenstein JL: Brain abnormalities in Gulf War syndrome: evaluation with 1H MR spectroscopy. Radiology 2000; 215: 807-817) suggested that individuals with Gulf War Illness (GWI) had reduced quantities of the neuronal marker N-acetylaspartate (NAA) in the basal ganglia and pons. This study aimed to determine whether NAA is reduced in these regions and to investigate correlations with other possible causes of GWI, such as psychological response to stress in a large cohort of Gulf War veterans. Individuals underwent tests to determine their physical and psychological health and to identify veterans with (n=81) and without (n=97) GWI. When concentrations of NAA and ratios of NAA to creatine- and choline-containing metabolites were measured in the basal ganglia and pons, no significant differences were found between veterans with or without GWI, suggesting that GWI is not associated with reduced NAA in these regions. Veterans with GWI had significantly higher rates of post-traumatic stress disorder, supporting the idea that GWI symptoms are stress related. [ABSTRACT FROM AUTHOR]

Objective: Stromal tissue surrounding breast tumors can harbor abnormalities that predict increased risk of recurrence. The purpose of this study was to evaluate low-level contrast enhancement patterns in normal appearing breast stroma surrounding tumors using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). A secondary objective was to investigate whether enhancement patterns varied with distance from tumor and whether proximity-dependent enhancement was associated with recurrence-free survival following neoadjuvant chemotherapy (NACT). Materials and Methods: Sixty-three patients with locally-advanced breast cancer were imaged with DCE-MRI before (V1) and after one cycle (V2) of adriamycin-cytoxan (AC) therapy. Normal-appearing stromal tissue on MR images was defined as fibroglandular tissue outside of tumor regions. Fibroglandular tissue was segmented from pre-contrast T1-weighted images using a fuzzy C-means clustering method1. Tumor regions were identified using a percent enhancement threshold of 70% in the first post-contrast image (PE1). Distance from tumor (proximity) was defined for each stromal voxel as the minimum three-dimensional distance from any tumor voxel. Stromal enhancement was characterized by calculating mean PE and mean signal enhancement ratio (SER = PE1/PE2) in distance shells of 5 mm, from 0 to 40 mm outside of tumor tissue. Global PE and SER were calculated by averaging all stromal voxels from 5 to 40 mm from tumor. Proximity-dependent PE and SER were analyzed using a linear mixed effects model and Cox proportional hazards model for recurrence-free survival. Results: The mixed effects model displayed a decreasing radial trend in PE at both V1 (estimated mean = -0.39 per mm, 95% CI (-0.50, -0.29), p < 0.0001) and V2 (estimated mean = -0.34 per mm, 95% CI (-0.56, -0.13), p = 0.002). The trend was less pronounced in SER and did not achieve statistical significance (V1, p = 0.12; V2 p = 0.11). For the recurrent group, there was a marginal trend of lower global SER at V1 (estimated difference of -0.07, 95% CI (-0.13, 0.01) two-tail Wilcoxon-Mann-Whitney U-test, p = 0.11) but not at V2 (estimated difference in location of -0.04, 95% CI (-0.11, 0.26), p = 0.4). There was no evidence for a clear difference in PE for recurrent verses non-recurrent patients at either visit. Survival analysis with average PE or SER as predictors showed that although PE provided no detectable predictive power at V1 and V2, the SER hazard ratio estimates for each unit decrease in SER was marginally statistically significant at 11.5, 95% CI (0.85, 155), p = 0.07 for V1; but was not so for V2: estimate = 0.85, 95% CI (0.04, 20), p = 0.9. Conclusions: These findings show that stromal tissue outside the primary tumor can be quantitatively characterized by DCE-MRI, and suggest that stromal enhancement measurements may be worth exploring as a potential predictor of recurrence/disease-free survival following NACT. [ABSTRACT FROM AUTHOR]

Background and Objectives: TMEM106B has been proposed as a modifier of disease risk in FTLD-TDP, particularly in GRN pathogenic variant carriers. Furthermore, TMEM106B has been investigated as a disease modifier in the context of healthy aging and across multiple neurodegenerative diseases. The objective of this study was to evaluate and compare the effect of TMEM106B on gray matter volume and cognition in each of the common genetic FTD groups and in patients with sporadic FTD., Methods: Participants were enrolled through the ARTFL/LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) study, which includes symptomatic and presymptomatic individuals with a pathogenic variant in C9orf72, GRN, MAPT, VCP, TBK1, TARDBP, symptomatic nonpathogenic variant carriers, and noncarrier family controls. All participants were genotyped for the TMEM106B rs1990622 SNP. Cross-sectionally, linear mixed-effects models were fitted to assess an association between TMEM106B and genetic group interaction with each outcome measure (gray matter volume and UDS3-EF for cognition), adjusting for education, age, sex, and CDR+NACC-FTLD sum of boxes. Subsequently, associations between TMEM106B and each outcome measure were investigated within the genetic group. For longitudinal modeling, linear mixed-effects models with time by TMEM106B predictor interactions were fitted., Results: The minor allele of TMEM106B rs1990622, linked to a decreased risk of FTD, associated with greater gray matter volume in GRN pathogenic variant carriers under the recessive dosage model (N = 82, beta = 3.25, 95% CI [0.37-6.19], p = 0.034). This was most pronounced in the thalamus in the left hemisphere (beta = 0.03, 95% CI [0.01-0.06], p = 0.006), with a retained association when considering presymptomatic GRN pathogenic variant carriers only (N = 42, beta = 0.03, 95% CI [0.01-0.05], p = 0.003). The minor allele of TMEM106B rs1990622 also associated with greater cognitive scores among all C9orf72 pathogenic variant carriers (N = 229, beta = 0.36, 95% CI [0.05-0.066], p = 0.021) and in presymptomatic C9orf72 pathogenic variant carriers (N = 106, beta = 0.33, 95% CI [0.03-0.63], p = 0.036), under the recessive dosage model., Discussion: We identified associations of TMEM106B with gray matter volume and cognition in the presence of GRN and C9orf72 pathogenic variants. The association of TMEM106B with outcomes of interest in presymptomatic GRN and C9orf72 pathogenic variant carriers could additionally reflect TMEM106B's effect on divergent pathophysiologic changes before the appearance of clinical symptoms.

Machine learning algorithms are a promising automated candidate that can help mitigate the growing need for dementia experts. Despite the substantial development in MRI-based machine learning analyses, case misclassification is a universal finding, yet the reasons behind misclassification are poorly understood. We implemented a multi-class classification approach that uses relevance vector machine and logistic classification to classify research participants based on their whole-brain T1-weighted MRI scans. A total of 468 participants from seven diagnostic classes were included: 144 healthy controls, 84 Alzheimer's disease, 108 behavioral variant frontotemporal dementia (bvFTD), 30 semantic variant primary progressive aphasia (svPPA), 30 non-fluent variant primary progressive aphasia (nfvPPA), 30 corticobasal syndrome (CBS), and 42 progressive supranuclear palsy syndrome (PSPS). We compared the algorithm's diagnostic accuracy against the clinical, pathological, genetic, and quantitative imaging data. The exact neurodegenerative syndrome was predicted in 71% of the cases, the neurodegenerative disease spectrum was predicted in 80% of the cases, and the algorithm distinguished controls from any dementia in 85% of the cases. The algorithm showed high performance in diagnosing healthy controls, moderate performance in diagnosing AD, bvFTD, and svPPA, and low performance in diagnosing CBS, nfvPPA, and PSPS. Based on the quantitative imaging data, most of the misclassified neurodegenerative cases had minimal atrophy and brain volumes comparable to healthy controls. In AD, early-onset AD cases with minimal brain atrophy represented most of the misclassified cases. In bvFTD, FTD genetic mutation carriers (predominantly C9orf72 repeat expansion), FTD phenocopy, patients meeting only possible bvFTD criteria represented most misclassified cases. Case misclassification in machine learning studies in neurodegenerative diseases results from neurodegenerative disease heterogeneity and the limitations of structural MRI's ability to capture the whole gamut of biological changes. Larger and more inclusive datasets that are representative of population biologic heterogeneity are needed to train better machine learning techniques, and a margin of error is expected and should be acceptable, like the uncertainty of a clinical diagnosis by a dementia expert.

Objectives: Cognitive and behavioral phenomena define behavioral variant frontotemporal dementia (bvFTD), but neuropsychiatric symptoms (NPS) outside the core criteria are common throughout the illness. Identifying how NPS cluster in bvFTD may clarify the underlying neurobiology of bvFTD-related NPS and guide development of therapies., Methodology: Participants (N=354) with sporadic and genetic bvFTD were enrolled in the ARTFL LEFFTDS Longitudinal Frontotemporal Lobar Degeneration Consortium. Dementia stage was defined as early (CDR ® plus NACC FTLD ≤ 1) or advanced (CDR ® plus NACC FTLD ≥ 1). Baseline and annual follow-up visit data were analyzed to compare NPS across stages of bvFTD. Psychiatric states were captured using the Neuropsychiatric Inventory-Questionnaire and Clinician Judgement of Symptoms. Polychoric cluster analysis was used to describe NPS clusters., Results: NPS were highly prevalent (≥ 90%) in early and late bvFTD. Four NPS clusters were identified based on magnitude of factor loadings: affective, disinhibited, compulsive, and psychosis. Neuropsychiatric symptoms fluctuated across visits. In the affective cluster, depression and anxiety showed the least visit-to-visit stability. In the disinhibited cluster, elation showed the least stability. Symptoms in the psychosis and compulsive clusters (hallucinations, delusions, obsessions/compulsions, and hyperorality) were largely stable, persisting from visit-to-visit in more than 50% of cases., Conclusion: NPS in bvFTD are frequent and cluster into four discrete groups in bvFTD. These clusters may result from specific neural network disruptions that could serve as targets for future interventions. The fluctuating nature of NPS in bvFTD suggests that they are not reliable markers of disease progression or stage.

Delirium is a detrimental mental condition often seen in older, hospitalized patients and is currently hard to predict. In this study, we leverage electronic health records (EHR) to identify 7,492 UCSF patients and 19,417 UC health system patients with an inpatient delirium diagnosis and the same number of control patients without delirium. We found significant associations between comorbidities or laboratory values and an inpatient delirium diagnosis, including metabolic abnormalities and psychiatric diagnoses. Some associations were sex-specific, including dementia subtypes and infections. We further explored the associations with anemia and bipolar disorder by conducting longitudinal analyses from the time of first diagnosis to development of delirium, demonstrating a significant relationship across time. Finally, we show that an inpatient delirium diagnosis leads to increased risk of mortality. These results demonstrate the powerful application of the EHR to shed insights into prior diagnoses and laboratory values that could help predict development of inpatient delirium and the importance of sex when making these assessments., Competing Interests: Competing interests: The authors declare no competing interests.

Background: Frontotemporal lobar degeneration (FTLD) is a leading cause of dementia in individuals aged <65 years. Several challenges to conducting in-person evaluations in FTLD illustrate an urgent need to develop remote, accessible, and low-burden assessment techniques. Studies of unobtrusive monitoring of at-home computer use in older adults with mild cognitive impairment show that declining function is reflected in reduced computer use; however, associations with smartphone use are unknown., Objective: This study aims to characterize daily trajectories in smartphone battery use, a proxy for smartphone use, and examine relationships with clinical indicators of severity in FTLD., Methods: Participants were 231 adults (mean age 52.5, SD 14.9 years; n=94, 40.7% men; n=223, 96.5% non-Hispanic White) enrolled in the Advancing Research and Treatment of Frontotemporal Lobar Degeneration (ARTFL study) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS study) Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Mobile App study, including 49 (21.2%) with mild neurobehavioral changes and no functional impairment (ie, prodromal FTLD), 43 (18.6%) with neurobehavioral changes and functional impairment (ie, symptomatic FTLD), and 139 (60.2%) clinically normal adults, of whom 55 (39.6%) harbored heterozygous pathogenic or likely pathogenic variants in an autosomal dominant FTLD gene. Participants completed the Clinical Dementia Rating plus National Alzheimer's Coordinating Center Frontotemporal Lobar Degeneration Behavior and Language Domains (CDR+NACC FTLD) scale, a neuropsychological battery; the Neuropsychiatric Inventory; and brain magnetic resonance imaging. The ALLFTD Mobile App was installed on participants' smartphones for remote, passive, and continuous monitoring of smartphone use. Battery percentage was collected every 15 minutes over an average of 28 (SD 4.2; range 14-30) days. To determine whether temporal patterns of battery percentage varied as a function of disease severity, linear mixed effects models examined linear, quadratic, and cubic effects of the time of day and their interactions with each measure of disease severity on battery percentage. Models covaried for age, sex, smartphone type, and estimated smartphone age., Results: The CDR+NACC FTLD global score interacted with time on battery percentage such that participants with prodromal or symptomatic FTLD demonstrated less change in battery percentage throughout the day (a proxy for less smartphone use) than clinically normal participants (P<.001 in both cases). Additional models showed that worse performance in all cognitive domains assessed (ie, executive functioning, memory, language, and visuospatial skills), more neuropsychiatric symptoms, and smaller brain volumes also associated with less battery use throughout the day (P<.001 in all cases)., Conclusions: These findings support a proof of concept that passively collected data about smartphone use behaviors associate with clinical impairment in FTLD. This work underscores the need for future studies to develop and validate passive digital markers sensitive to longitudinal clinical decline across neurodegenerative diseases, with potential to enhance real-world monitoring of neurobehavioral change., (©Emily W Paolillo, Kaitlin B Casaletto, Annie L Clark, Jack C Taylor, Hilary W Heuer, Amy B Wise, Sreya Dhanam, Mark Sanderson-Cimino, Rowan Saloner, Joel H Kramer, John Kornak, Walter Kremers, Leah Forsberg, Brian Appleby, Ece Bayram, Andrea Bozoki, Danielle Brushaber, R Ryan Darby, Gregory S Day, Bradford C Dickerson, Kimiko Domoto-Reilly, Fanny Elahi, Julie A Fields, Nupur Ghoshal, Neill Graff-Radford, Matthew G H Hall, Lawrence S Honig, Edward D Huey, Maria I Lapid, Irene Litvan, Ian R Mackenzie, Joseph C Masdeu, Mario F Mendez, Carly Mester, Toji Miyagawa, Georges Naasan, Belen Pascual, Peter Pressman, Eliana Marisa Ramos, Katherine P Rankin, Jessica Rexach, Julio C Rojas, Lawren VandeVrede, Bonnie Wong, Zbigniew K Wszolek, Bradley F Boeve, Howard J Rosen, Adam L Boxer, Adam M Staffaroni, ALLFTD Consortium. Originally published in JMIR Aging (https://aging.jmir.org), 26.06.2024.)