Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease

Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.

The Health & Aging Brain among Latino Elders (HABLE) study methods and participant characteristics

INTRODUCTION

Mexican Americans remain severely underrepresented in Alzheimer's disease (AD) research. The Health & Aging Brain among Latino Elders (HABLE) study was created to fill important gaps in the existing literature.

METHODS

Community-dwelling Mexican Americans and non-Hispanic White adults and elders (age 50 and above) were recruited. All participants underwent comprehensive assessments including an interview, functional exam, clinical labs, informant interview, neuropsychological testing, and 3T magnetic resonance imaging (MRI) of the brain. Amyloid and tau positron emission tomography (PET) scans were added at visit 2. Blood samples were stored in the Biorepository.

RESULTS

Data was examined from n = 1705 participants. Significant group differences were found in medical, demographic, and sociocultural factors. Cerebral amyloid and neurodegeneration imaging markers were significantly different between Mexican Americans and non-Hispanic Whites.

DISCUSSION

The current data provide strong support for continued investigations that examine the risk factors for and biomarkers of AD among diverse populations.

Mask-Guided Convolutional Neural Network for Breast Tumor Prognostic Outcome Prediction on 3D DCE-MR Images

In this proof-of-concept work, we have developed a 3D-CNN architecture that is guided by the tumor mask for classifying several patient-outcomes in breast cancer from the respective 3D dynamic contrast-enhanced MRI (DCE-MRI) images. The tumor masks on DCE-MRI images were generated using pre- and post-contrast images and validated by experienced radiologists. We show that our proposed mask-guided classification has a higher accuracy than that from either the full image without tumor masks (including background) or the masked voxels only. We have used two patient outcomes for this study: (1) recurrence of cancer after 5 years of imaging and (2) HER2 status, for comparing accuracies of different models. By looking at the activation maps, we conclude that an image-based prediction model using 3D-CNN could be improved by even a conservatively generated mask, rather than overly trusting an unguided, blind 3D-CNN. A blind CNN may classify accurately enough, while its attention may really be focused on a remote region within 3D images. On the other hand, only using a conservatively segmented region may not be as good for classification as using full images but forcing the model's attention toward the known regions of interest.

Transfer learning-trained convolutional neural networks identify novel MRI biomarkers of Alzheimer's disease progression

INTRODUCTION

Genome-wide association studies (GWAS) for late onset Alzheimer's disease (AD) may miss genetic variants relevant for delineating disease stages when using clinically defined case/control as a phenotype due to its loose definition and heterogeneity.

METHODS

We use a transfer learning technique to train three-dimensional convolutional neural network (CNN) models based on structural magnetic resonance imaging (MRI) from the screening stage in the Alzheimer's Disease Neuroimaging Initiative consortium to derive image features that reflect AD progression.

RESULTS

CNN-derived image phenotypes are significantly associated with fasting metabolites related to early lipid metabolic changes as well as insulin resistance and with genetic variants mapped to candidate genes enriched for amyloid beta degradation, tau phosphorylation, calcium ion binding-dependent synaptic loss, APP-regulated inflammation response, and insulin resistance.

DISCUSSION

This is the first attempt to show that non-invasive MRI biomarkers are linked to AD progression characteristics, reinforcing their use in early AD diagnosis and monitoring.

The variability of MR axon radii estimates in the human white matter

The noninvasive quantification of axonal morphology is an exciting avenue for gaining understanding of the function and structure of the central nervous system. Accurate non-invasive mapping of micron-sized axon radii using commonly applied neuroimaging techniques, that is, diffusion-weighted MRI, has been bolstered by recent hardware developments, specifically MR gradient design. Here the whole brain characterization of the effective MR axon radius is presented and the inter- and intra-scanner test-retest repeatability and reproducibility are evaluated to promote the further development of the effective MR axon radius as a neuroimaging biomarker. A coefficient-of-variability of approximately 10% in the voxelwise estimation of the effective MR radius is observed in the test-retest analysis, but it is shown that the performance can be improved fourfold using a customized along-tract analysis.

Targeted systemic dendrimer delivery of CSF-1R inhibitor to tumor-associated macrophages improves outcomes in orthotopic glioblastoma

Glioblastoma is the most common and aggressive form of primary brain cancer, with median survival of 16-20 months and a 5-year survival rates of <5%. Recent advances in immunotherapies have shown that addressing the tumor immune profile by targeting the colony-stimulating factor 1 (CSF-1) signaling pathway of tumor-associated macrophages (TAMs) has the potential to improve glioblastoma therapy. However, such therapies have shown limited successes in clinical translation partially due to lack of specific cell targeting in solid tumors and systemic toxicity. In this study, we present a novel hydroxyl dendrimer-mediated immunotherapy to deliver CSF-1R inhibitor BLZ945 (D-BLZ) from systemic administration selectively to TAMs in glioblastoma brain tumors to repolarize the tumor immune environment in a localized manner. We show that conjugation of BLZ945 to dendrimers enables sustained release in intracellular and intratumor conditions. We demonstrate that a single systemic dose of D-BLZ targeted to TAMs decreases pro-tumor expression in TAMs and promotes cytotoxic T cell infiltration, resulting in prolonged survival and ameliorated disease burden compared to free BLZ945. Our results demonstrate that dendrimer-drug conjugates can facilitate specific, localized manipulation of tumor immune responses from systemic administration by delivering immunotherapies selectively to TAMs, thereby improving therapeutic efficacy while reducing off-target effects.

Transcriptomic response of brain tissue to focused ultrasound-mediated blood-brain barrier disruption depends strongly on anesthesia

Focused ultrasound (FUS) mediated blood-brain barrier disruption (BBBD) targets the delivery of systemically-administered therapeutics to the central nervous system. Preclinical investigations of BBBD have been performed on different anesthetic backgrounds; however, the influence of the choice of anesthetic on the molecular response to BBBD is unknown, despite its potential to critically affect interpretation of experimental therapeutic outcomes. Here, using bulk RNA sequencing, we comprehensively examined the transcriptomic response of both normal brain tissue and brain tissue exposed to FUS-induced BBBD in mice anesthetized with either isoflurane with medical air (Iso) or ketamine/dexmedetomidine (KD). In normal murine brain tissue, Iso alone elicited minimal differential gene expression (DGE) and repressed pathways associated with neuronal signaling. KD alone, however, led to massive DGE and enrichment of pathways associated with protein synthesis. In brain tissue exposed to BBBD (1 MHz, 0.5 Hz pulse repetition frequency, 0.4 MPa peak-negative pressure), we systematically evaluated the relative effects of anesthesia, microbubbles, and FUS on the transcriptome. Of particular interest, we observed that gene sets associated with sterile inflammatory responses and cell-cell junctional activity were induced by BBBD, regardless of the choice of anesthesia. Meanwhile, gene sets associated with metabolism, platelet activity, tissue repair, and signaling pathways, were differentially affected by BBBD, with a strong dependence on the anesthetic. We conclude that the underlying transcriptomic response to FUS-mediated BBBD may be powerfully influenced by anesthesia. These findings raise considerations for the translation of FUS-BBBD delivery approaches that impact, in particular, metabolism, tissue repair, and intracellular signaling.

Cell-Laden Multiple-Step and Reversible 4D Hydrogel Actuators to Mimic Dynamic Tissue Morphogenesis

Shape-morphing hydrogels bear promising prospects as soft actuators and for robotics. However, they are mostly restricted to applications in the abiotic domain due to the harsh physicochemical conditions typically necessary to induce shape morphing. Here, multilayer hydrogel actuator systems are developed using biocompatible and photocrosslinkable oxidized, methacrylated alginate and methacrylated gelatin that permit encapsulation and maintenance of living cells within the hydrogel actuators and implement programmed and controlled actuations with multiple shape changes. The hydrogel actuators encapsulating cells enable defined self-folding and/or user-regulated, on-demand-folding into specific 3D architectures under physiological conditions, with the capability to partially bioemulate complex developmental processes such as branching morphogenesis. The hydrogel actuator systems can be utilized as novel platforms for investigating the effect of programmed multiple-step and reversible shape morphing on cellular behaviors in 3D extracellular matrix and the role of recapitulating developmental and healing morphogenic processes on promoting new complex tissue formation.

Wearable and Implantable Electroceuticals for Therapeutic Electrostimulations

Wearable and implantable electroceuticals (WIEs) for therapeutic electrostimulation (ES) have become indispensable medical devices in modern healthcare. In addition to functionality, device miniaturization, conformability, biocompatibility, and/or biodegradability are the main engineering targets for the development and clinical translation of WIEs. Recent innovations are mainly focused on wearable/implantable power sources, advanced conformable electrodes, and efficient ES on targeted organs and tissues. Herein, nanogenerators as a hotspot wearable/implantable energy-harvesting technique suitable for powering WIEs are reviewed. Then, electrodes for comfortable attachment and efficient delivery of electrical signals to targeted tissue/organ are introduced and compared. A few promising application directions of ES are discussed, including heart stimulation, nerve modulation, skin regeneration, muscle activation, and assistance to other therapeutic modalities.

Modularly Programmable Nanoparticle Vaccine Based on Polyethyleneimine for Personalized Cancer Immunotherapy

Nanoparticles (NPs) can serve as a promising vaccine delivery platform for improving pharmacological property and codelivery of antigens and adjuvants. However, NP-based vaccines are generally associated with complex synthesis and postmodification procedures, which pose technical and manufacturing challenges for tailor-made vaccine production. Here, modularly programmed, polyethyleneimine (PEI)-based NP vaccines are reported for simple production of personalized cancer vaccines. Briefly, PEI is conjugated with neoantigens by facile coupling chemistry, followed by electrostatic assembly with CpG adjuvants, leading to the self-assembly of nontoxic, sub-50 nm PEI NPs. Importantly, PEI NPs promote activation and antigen cross-presentation of antigen-presenting cells and cross-priming of neoantigen-specific CD8+ T cells. Surprisingly, after only a single intratumoral injection, PEI NPs with optimal PEGylation elicit as high as ≈30% neoantigen-specific CD8+ T cell response in the systemic circulation and sustain elevated CD8+ T cell response over 3 weeks. PEI-based nanovaccines exert potent antitumor efficacy against pre-established local tumors as well as highly aggressive metastatic tumors. PEI engineering for modular incorporation of neoantigens and adjuvants offers a promising strategy for rapid and facile production of personalized cancer vaccines.

Association of PET-based stages of amyloid deposition with neuropathological markers of Aβ pathology

OBJECTIVE

To determine if PET-based stages of regional amyloid deposition are associated with neuropathological phases of Aβ pathology.

METHODS

We applied data-driven regional frequency-based and a-priori striatum-based PET staging approaches to ante-mortem 18F-Florbetapir-PET scans of 30 cases from the Alzheimer's Disease Neuroimaging Initiative autopsy cohort, and used Bayesian regression analysis to study the associations of these in vivo amyloid stages with neuropathological Thal phases of regional Aβ plaque distribution and with semi-quantitative ratings of neocortical and striatal plaque densities.

RESULTS

Bayesian regression revealed extreme evidence for an association of both PET-based staging approaches with Thal phases, and these associations were about 44 times more likely for frequency-based stages and 89 times more likely for striatum-based stages than for global cortical 18F-Florbetapir-PET signal. Early (i.e., neocortical-only) PET-based amyloid stages also predicted the absence of striatal/diencephalic cored plaques. Receiver operating characteristics curves revealed highly accurate discrimination between low/high Thal phases and the presence/absence of regional plaques. The median areas under the curve were 0.99 for frequency-based staging (95% credibility interval 0.97-1.00), 0.93 for striatum-based staging (0.83-1.00), and 0.87 for global 18F-Florbetapir-PET signal (0.72-0.98).

INTERPRETATION

Our data indicate that both regional frequency- and striatum-based amyloid-PET staging approaches were superior to standard global amyloid-PET signal for differentiating between low and high degrees of regional amyloid pathology spread. Despite this, we found no evidence for the ability of either staging scheme to differentiate between low and moderate degrees of amyloid pathology which may be particularly relevant for early, preclinical stages of Alzheimer's disease.

Depth relationships and measures of tissue thickness in dorsal midbrain

Dorsal human midbrain contains two nuclei with clear laminar organization, the superior and inferior colliculi. These nuclei extend in depth between the superficial dorsal surface of midbrain and a deep midbrain nucleus, the periaqueductal gray matter (PAG). The PAG, in turn, surrounds the cerebral aqueduct (CA). This study examined the use of two depth metrics to characterize depth and thickness relationships within dorsal midbrain using the superficial surface of midbrain and CA as references. The first utilized nearest-neighbor Euclidean distance from one reference surface, while the second used a level-set approach that combines signed distances from both reference surfaces. Both depth methods provided similar functional depth profiles generated by saccadic eye movements in a functional MRI task, confirming their efficacy for delineating depth for superficial functional activity. Next, the boundaries of the PAG were estimated using Euclidean distance together with elliptical fitting, indicating that the PAG can be readily characterized by a smooth surface surrounding PAG. Finally, we used the level-set approach to measure tissue depth between the superficial surface and the PAG, thus characterizing the variable thickness of the colliculi. Overall, this study demonstrates depth-mapping schemes for human midbrain that enables accurate segmentation of the PAG and consistent depth and thickness estimates of the superior and inferior colliculi.

DNA Nanostructures and DNA-Functionalized Nanoparticles for Cancer Theranostics

In the last two decades, DNA has attracted significant attention toward the development of materials at the nanoscale for emerging applications due to the unparalleled versatility and programmability of DNA building blocks. DNA-based artificial nanomaterials can be broadly classified into two categories: DNA nanostructures (DNA-NSs) and DNA-functionalized nanoparticles (DNA-NPs). More importantly, their use in nanotheranostics, a field that combines diagnostics with therapy via drug or gene delivery in an all-in-one platform, has been applied extensively in recent years to provide personalized cancer treatments. Conveniently, the ease of attachment of both imaging and therapeutic moieties to DNA-NSs or DNA-NPs enables high biostability, biocompatibility, and drug loading capabilities, and as a consequence, has markedly catalyzed the rapid growth of this field. This review aims to provide an overview of the recent progress of DNA-NSs and DNA-NPs as theranostic agents, the use of DNA-NSs and DNA-NPs as gene and drug delivery platforms, and a perspective on their clinical translation in the realm of oncology.

High-resolution, relational, resonance-based, electroencephalic mirroring (HIRREM) improves symptoms and autonomic function for insomnia: A randomized, placebo-controlled clinical trial

INTRODUCTION

Effective insomnia interventions that also address autonomic dysregulation are lacking. We evaluate high-resolution, relational, resonance-based, electroencephalic mirroring (HIRREM® ), in a randomized, controlled clinical trial. HIRREM is a noninvasive, closed-loop, allostatic, acoustic stimulation neurotechnology, to support self-optimization of brain rhythms.

METHODS

One hundred and seven adults (mean age 45.7, SD ± 5.6, 73 women), with Insomnia Severity Index (ISI) scores of ≥15, received ten, 90-min sessions of HIRREM, with tones linked to brainwaves (LB, 56), or random tones not linked to brainwaves (NL, 51), as an active, sham placebo. Outcomes were obtained at enrollment (V1), 1-7 days (V2), 8-10 weeks (V3), and 16-18 weeks (V4) after intervention. Primary outcome was differential change in ISI from V1 to V3. Secondary measures assessed depression (BDI), anxiety (BAI), quality of life (EQ-5D), and a sleep diary. Ten minute recordings of HR and BP allowed analysis of heart rate variability (HRV) and baroreflex sensitivity (BRS).

RESULTS

Of 107 randomized, 101 completed the intervention. Intention-to-treat analysis (107) of change from V1 to V3 revealed a mean reduction of ISI in NL of -4.93 (SE ± 0.76) points, with additional, significant reduction of -2.05 points (0.74) in LB (total reduction of -6.98, p = .045). Additional reduction of -2.30 points (0.76) was still present in the LB at V4 (p = .058). Total ISI reduction from V1 to V4 was -5.90 points for NL and -7.93 points in LB. There were group differences (p < .05) for multiple HRV and BRS measures (rMSSD, SDNN, HF alpha, and Seq ALL), as well as total sleep time, sleep onset latency, and sleep efficiency. There were no serious adverse events.

CONCLUSIONS

Results of this controlled clinical trial showed clinically relevant reduction of insomnia symptoms with HIRREM, over, and above an active, sham control, with associated, durable improvement in autonomic cardiovascular regulation.

Discordant Alzheimer's neurodegenerative biomarkers and their clinical outcomes

OBJECTIVE

In the 2018 ATN framework, Alzheimer's neurodegenerative biomarkers comprised cerebrospinal fluid (CSF) total tau, 18 F-fluorodeoxyglucose-positron emission tomography, and brain atrophy. We aimed to assess the clinical outcomes of having discordant Alzheimer's neurodegenerative biomarkers.

METHODS

A total of 721 non-demented individuals from the Alzheimer's Disease Neuroimaging Initiative database were included and then further categorized into concordant-negative, discordant, and concordant-positive groups. Demographic distributions of the groups were compared. Longitudinal changes in clinical outcomes and risk of conversion were assessed using linear mixed-effects models and multivariate Cox proportional hazard models, respectively.

RESULTS

Discordant group was intermediate to concordant-negative and concordant-positive groups in terms of APOE ε4 positivity, CSF amyloid-beta, and phosphorylated tau. Compared with concordant-negative group, discordant group deteriorated faster in cognitive scores (Mini-Mental State Examination, the Clinical Dementia Rating Scale-Sum of Boxes, and the Functional Activities Questionnaire) and demonstrated greater rates of atrophy in brain structures (hippocampus, entorhinal cortex, and whole brain), and concordant-positive group performed worse over time than discordant group. Moreover, the risk of cognitive decline increased from concordant-negative to discordant to concordant-positive. The results from longitudinal analyses were validated in A+T+, cognitively normal, and mild cognitive impairment individuals, and were also validated by applying different cutoffs and neurodegenerative biomarkers.

INTERPRETATION

Discordant neurodegenerative status denotes a stage of cognitive function which is intermediate between concordant-negative and concordant-positive. Identification of discordant cases would provide insights into intervention and new therapy approaches, particularly in A+T+ individuals. Moreover, this work may be a complement to the ATN scheme.

Nonreciprocal Magnetic Coupling Using Nonlinear Meta-Atoms

Breaking Lorentz reciprocity is fundamental to an array of functional radiofrequency (RF) and optical devices, such as isolators and circulators. The application of external excitation, such as magnetic fields and spatial-temporal modulation, has been employed to achieve nonreciprocal responses. Alternatively, nonlinear effects may also be employed to break reciprocity in a completely passive fashion. Herein, a coupled system comprised of linear and nonlinear meta-atoms that achieves nonreciprocity based on the coupling and frequency detuning of its constituent meta-atoms is presented. An analytical model is developed based on the coupled mode theory (CMT) in order to design and optimize the nonreciprocal meta-atoms in this coupled system. Experimental demonstration of an RF isolator is performed, and the contrast between forward and backward propagation approximates 20 dB. Importantly, the use of the CMT model developed herein enables a generalizable capacity to predict the limitations of nonlinearity-based nonreciprocity, thereby facilitating the development of novel approaches to breaking Lorentz reciprocity. The CMT model and implementation scheme presented in this work may be deployed in a wide range of applications, including integrated photonic circuits, optical metamaterials, and metasurfaces, among others.

Light-Responsive Inorganic Biomaterials for Biomedical Applications

Light-responsive inorganic biomaterials are an emerging class of materials used for developing noninvasive, noncontact, precise, and controllable medical devices in a wide range of biomedical applications, including photothermal therapy, photodynamic therapy, drug delivery, and regenerative medicine. Herein, a range of biomaterials is discussed, including carbon-based nanomaterials, gold nanoparticles, graphite carbon nitride, transition metal dichalcogenides, and up-conversion nanoparticles that are used in the design of light-responsive medical devices. The importance of these light-responsive biomaterials is explored to design light-guided nanovehicle, modulate cellular behavior, as well as regulate extracellular microenvironments. Additionally, future perspectives on the clinical use of light-responsive biomaterials are highlighted.

Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer's disease

INTRODUCTION

Loss of entorhinal cortex (EC) layer II neurons represents the earliest Alzheimer's disease (AD) lesion in the brain. Research suggests differing functional roles between two EC subregions, the anterolateral EC (aLEC) and the posteromedial EC (pMEC).

METHODS

We use joint label fusion to obtain aLEC and pMEC cortical thickness measurements from serial magnetic resonance imaging scans of 775 ADNI-1 participants (219 healthy; 380 mild cognitive impairment; 176 AD) and use linear mixed-effects models to analyze longitudinal associations among cortical thickness, disease status, and cognitive measures.

RESULTS

Group status is reliably predicted by aLEC thickness, which also exhibits greater associations with cognitive outcomes than does pMEC thickness. Change in aLEC thickness is also associated with cerebrospinal fluid amyloid and tau levels.

DISCUSSION

Thinning of aLEC is a sensitive structural biomarker that changes over short durations in the course of AD and tracks disease severity-it is a strong candidate biomarker for detection of early AD.

Terminology and concepts for the characterization of in vivo MR spectroscopy methods and MR spectra: Background and experts' consensus recommendations

With a 40-year history of use for in vivo studies, the terminology used to describe the methodology and results of magnetic resonance spectroscopy (MRS) has grown substantially and is not consistent in many aspects. Given the platform offered by this special issue on advanced MRS methodology, the authors decided to describe many of the implicated terms, to pinpoint differences in their meanings and to suggest specific uses or definitions. This work covers terms used to describe all aspects of MRS, starting from the description of the MR signal and its theoretical basis to acquisition methods, processing and to quantification procedures, as well as terms involved in describing results, for example, those used with regard to aspects of quality, reproducibility or indications of error. The descriptions of the meanings of such terms emerge from the descriptions of the basic concepts involved in MRS methods and examinations. This paper also includes specific suggestions for future use of terms where multiple conventions have emerged or coexisted in the past.

Nanoengineered Light-Activatable Polybubbles for On-Demand Therapeutic Delivery

Vaccine coverage is severely limited in developing countries due to inefficient protection of vaccine functionality as well as lack of patient compliance to receive the additional booster doses. Thus, there is an urgent need to design a thermostable vaccine delivery platform that also enables release of the bolus after predetermined time. Here, the formation of injectable and light-activatable polybubbles for vaccine delivery is reported. In vitro studies show that polybubbles enable delayed burst release, irrespective of cargo types, namely small molecule and antigen. The extracorporeal activation of polybubbles is achieved by incorporating near-infrared (NIR)-sensitive gold nanorods (AuNRs). Interestingly, light-activatable polybubbles can be used for on-demand burst release of cargo. In vitro, ex vivo, and in vivo studies demonstrate successful activation of AuNR-loaded polybubbles. Overall, the light-activatable polybubble technology can be used for on-demand delivery of various therapeutics including small molecule drugs, immunologically relevant protein, peptide antigens, and nucleic acids.

Gray matter volume and estimated brain age gap are not linked with sleep-disordered breathing

Alzheimer's disease (AD) and sleep-disordered breathing (SDB) are prevalent conditions with a rising burden. It is suggested that SDB may contribute to cognitive decline and advanced aging. Here, we assessed the link between self-reported SDB and gray matter volume in patients with AD, mild cognitive impairment (MCI) and healthy controls (HCs). We further investigated whether SDB was associated with advanced brain aging. We included a total of 330 participants, divided based on self-reported history of SDB, and matched across diagnoses for age, sex and presence of the Apolipoprotein E4 allele, from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Gray-matter volume was measured using voxel-wise morphometry and group differences in terms of SDB, cognitive status, and their interaction were assessed. Further, using an age-prediction model fitted on gray-matter data of external datasets, we predicted study participants' age from their structural images. Cognitive decline and advanced age were associated with lower gray matter volume in various regions, particularly in the bilateral temporal lobes. Brains age was well predicted from the morphological data in HCs and, as expected, elevated in MCI and particularly in AD subjects. However, there was neither a significant difference between regional gray matter volume in any diagnostic group related to the SDB status, nor in SDB-by-cognitive status interaction. Moreover, we found no difference in estimated chronological age gap related to SDB, or by-cognitive status interaction. Contrary to our hypothesis, we were not able to find a general or a diagnostic-dependent association of SDB with either gray-matter volumetric or brain aging.

Inflammatory pathway analytes predicting rapid cognitive decline in MCI stage of Alzheimer's disease

OBJECTIVE

To determine the inflammatory analytes that predict clinical progression and evaluate their performance against biomarkers of neurodegeneration.

METHODS

A longitudinal study of MCI-AD patients in a Discovery cohort over 15 months, with replication in the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI cohort over 36 months. Fifty-three inflammatory analytes were measured in the CSF and plasma with a RBM multiplex analyte platform. Inflammatory analytes that predict clinical progression on Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) and Mini Mental State Exam scores were assessed in multivariate regression models. To provide context, key analyte results in ADNI were compared against biomarkers of neurodegeneration, hippocampal volume, and CSF neurofilament light (NfL), in receiver operating characteristic (ROC) analyses evaluating highest quartile of CDR-SB change over two years (≥3 points).

RESULTS

Cerebrospinal fluid inflammatory analytes in relation to cognitive decline were best described by gene ontology terms, natural killer cell chemotaxis, and endothelial cell apoptotic process and in plasma, extracellular matrix organization, blood coagulation, and fibrin clot formation described the analytes. CSF CCL2 was most robust in predicting rate of cognitive change and analytes that correlated to CCL2 suggest IL-10 pathway dysregulation. The ROC curves for ≥3 points change in CDR-SB over 2 years when comparing baseline hippocampal volume, CSF NfL, and CCL2 were not significantly different.

INTERPRETATION

Baseline levels of immune cell chemotactic cytokine CCL2 in the CSF and IL-10 pathway dysregulation impact longitudinal cognitive and functional decline in MCI-AD. CCL2's utility appears comparable to biomarkers of neurodegeneration in predicting rapid decline.

Fluorogen activating protein toolset for protein trafficking measurements

Throughout the past decade the use of fluorogen activating proteins (FAPs) has expanded with several unique reporter dyes that support a variety of methods to specifically quantify protein trafficking events. The platform's capabilities have been demonstrated in several systems and shared for widespread use. This review will highlight the current FAP labeling techniques for protein traffic measurements and focus on the use of the different designed fluorogenic dyes for selective and specific labeling applications.

Effect of apolipoprotein E ε4 allele on the progression of cognitive decline in the early stage of Alzheimer's disease

INTRODUCTION

Possession of the apolipoprotein E (APO E) ε4 allele advances amyloid β (Aβ) deposition and symptomatic onset of Alzheimer's disease (AD), whereas its effect on the rate of cognitive decline remained controversial. We examined the effects of APOE ε4 allele on cognition in biomarker-confirmed late mild cognitive impairment (LMCI) and mild AD subjects in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) and North American ADNI (NA-ADNI).

METHODS

The "early AD" (ie, combined LMCI and mild AD) cohort of 649 subjects from J-ADNI and NA-ADNI were selected based on positivity of Aβ confirmed by amyloid positron emission tomography (PET) or cerebrospinal fluid testing. The rates of cognitive decline in the Mini Mental State Examination (MMSE), the Clinical Dementia Rating Sum of Boxes (CDR-SB), and the Alzheimer's Disease Assessment Scale-cognitive subscale 13 (ADAS-Cog) from baseline were examined using mixed-effects model. The effect of ε4 on time to conversion to dementia was also analyzed in LMCI using the Kaplan-Meier estimator and log-rank test.

RESULTS

The rates of cognitive decline were not significantly different between ε4 carriers and ε4 non-carriers in the total early AD cohort, which were affected neither by region nor by the number of ε4 alleles. In LMCI, ε4 carriers showed almost the same progression rates as ε4 non-carriers, except for a significantly faster decline in MMSE (P = .0282). Time to conversion to demenita was not significantly different between ε4 carriers and ε4 non-carriers. In ε4-positive mild AD, the rates of decline in MMSE (P = .003) and CDR-SB (P = .0071) were slower than those in ε4 non-carriers.

DISCUSSION

The APOE ε4 allele had little effect on the rates of cognitive decline in the overall biomarker-confirmed early AD, regardless of region and number of ε4 alleles, with a slight variability in different clinical stages, the ε4 allele being slightly accelerative in LMCI, while decelerative in mild AD.

Large-scale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging

Major depressive disorder (MDD) is a serious mental illness characterized by dysfunctional connectivity among distributed brain regions. Previous connectome studies based on functional magnetic resonance imaging (fMRI) have focused primarily on undirected functional connectivity and existing directed effective connectivity (EC) studies concerned mostly task-based fMRI and incorporated only a few brain regions. To overcome these limitations and understand whether MDD is mediated by within-network or between-network connectivities, we applied spectral dynamic causal modeling to estimate EC of a large-scale network with 27 regions of interests from four distributed functional brain networks (default mode, executive control, salience, and limbic networks), based on large sample-size resting-state fMRI consisting of 100 healthy subjects and 100 individuals with first-episode drug-naive MDD. We applied a newly developed parametric empirical Bayes (PEB) framework to test specific hypotheses. We showed that MDD altered EC both within and between high-order functional networks. Specifically, MDD is associated with reduced excitatory connectivity mainly within the default mode network (DMN), and between the default mode and salience networks. In addition, the network-averaged inhibitory EC within the DMN was found to be significantly elevated in the MDD. The coexistence of the reduced excitatory but increased inhibitory causal connections within the DMNs may underlie disrupted self-recognition and emotional control in MDD. Overall, this study emphasizes that MDD could be associated with altered causal interactions among high-order brain functional networks.

APOE4 moderates effects of cortical iron on synchronized default mode network activity in cognitively healthy old-aged adults

INTRODUCTION

Apolipoprotein E ε4 (APOE4)-related genetic risk for sporadic Alzheimer's disease is associated with an early impairment of cognitive brain networks. The current study determines relationships between APOE4 carrier status, cortical iron, and cortical network-functionality.

METHODS

Sixty-nine cognitively healthy old-aged individuals (mean age [SD] 66.1 [± 7.2] years; Mini-Mental State Exam [MMSE] 29.3 ± 1.1) were genotyped for APOE4 carrier-status and received 3 Tesla magnetic resonance imaging (MRI) for blood oxygen level-dependent functional magnetic resonance imaging (MRI) at rest, three-dimensional (3D)-gradient echo (six echoes) for cortical gray-matter, non-heme iron by quantitative susceptibility mapping, and 18F-flutemetamol positron emission tomography for amyloid-β.

RESULTS

A spatial pattern consistent with the default mode network (DMN) could be identified by independent component analysis. DMN activity was enhanced in APOE4 carriers and related to cortical iron burden. APOE4 and cortical iron synergistically interacted with DMN activity. Secondary analysis revealed a positive, APOE4 associated, relationship between cortical iron and DMN connectivity.

DISCUSSION

Our findings suggest that APOE4 moderates effects of iron on brain functionality prior to manifestation of cognitive impairment.

Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks

Hippocampal volumetry is a critical biomarker of aging and dementia, and it is widely used as a predictor of cognitive performance; however, automated hippocampal segmentation methods are limited because the algorithms are (a) not publicly available, (b) subject to error with significant brain atrophy, cerebrovascular disease and lesions, and/or (c) computationally expensive or require parameter tuning. In this study, we trained a 3D convolutional neural network using 259 bilateral manually delineated segmentations collected from three studies, acquired at multiple sites on different scanners with variable protocols. Our training dataset consisted of elderly cases difficult to segment due to extensive atrophy, vascular disease, and lesions. Our algorithm, (HippMapp3r), was validated against four other publicly available state-of-the-art techniques (HippoDeep, FreeSurfer, SBHV, volBrain, and FIRST). HippMapp3r outperformed the other techniques on all three metrics, generating an average Dice of 0.89 and a correlation coefficient of 0.95. It was two orders of magnitude faster than some of the tested techniques. Further validation was performed on 200 subjects from two other disease populations (frontotemporal dementia and vascular cognitive impairment), highlighting our method's low outlier rate. We finally tested the methods on real and simulated "clinical adversarial" cases to study their robustness to corrupt, low-quality scans. The pipeline and models are available at: https://hippmapp3r.readthedocs.ioto facilitate the study of the hippocampus in large multisite studies.

Phage display as a tool for vaccine and immunotherapy development

Bacteriophages, or phages, are viruses that specifically infect bacteria and coopt the cellular machinery to create more phage proteins, eventually resulting in the release of new phage particles. Phages are heavily utilized in bioengineering for applications ranging from tissue engineering scaffolds to immune signal delivery. Of specific interest to vaccines and immunotherapies, phages have demonstrated an ability to activate both the innate and adaptive immune systems. The genome of these viral particles can be harnessed for DNA vaccination, or the surface proteins can be exploited for antigen display. More specifically, genes that encode an antigen of interest can be spliced into the phage genome, allowing antigenic proteins or peptides to be displayed by fusion to phage capsid proteins. Phages therefore present antigens to immune cells in a highly ordered and repetitive manner. This review discusses the use of phage with adjuvanting activity as antigen delivery vehicles for vaccination against infectious disease and cancer.

Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer's dementia score

18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) enables in-vivo capture of the topographic metabolism patterns in the brain. These images have shown great promise in revealing the altered metabolism patterns in Alzheimer's disease (AD). The AD pathology is progressive, and leads to structural and functional alterations that lie on a continuum. There is a need to quantify the altered metabolism patterns that exist on a continuum into a simple measure. This work proposes a 3D convolutional neural network with residual connections that generates a probability score useful for interpreting the FDG-PET images along the continuum of AD. This network is trained and tested on images of stable normal control and stable Dementia of the Alzheimer's type (sDAT) subjects, achieving an AUC of 0.976 via repeated fivefold cross-validation. An independent test set consisting of images in between the two extreme ends of the DAT spectrum is used to further test the generalization performance of the network. Classification performance of 0.811 AUC is achieved in the task of predicting conversion of mild cognitive impairment to DAT for conversion time of 0-3 years. The saliency and class activation maps, which highlight the regions of the brain that are most important to the classification task, implicate many known regions affected by DAT including the posterior cingulate cortex, precuneus, and hippocampus.

Longitudinal increases in structural connectome segregation and functional connectome integration are associated with better recovery after mild TBI

Traumatic brain injury damages white matter pathways that connect brain regions, disrupting transmission of electrochemical signals and causing cognitive and emotional dysfunction. Connectome-level mechanisms for how the brain compensates for injury have not been fully characterized. Here, we collected serial MRI-based structural and functional connectome metrics and neuropsychological scores in 26 mild traumatic brain injury subjects (29.4 ± 8.0 years, 20 males) at 1 and 6 months postinjury. We quantified the relationship between functional and structural connectomes using network diffusion (ND) model propagation time, a measure that can be interpreted as how much of the structural connectome is being utilized for the spread of functional activation, as captured via the functional connectome. Overall cognition showed significant improvement from 1 to 6 months (t25 = -2.15, p = .04). None of the structural or functional global connectome metrics was significantly different between 1 and 6 months, or when compared to 34 age- and gender-matched controls (28.6 ± 8.8 years, 25 males). We predicted longitudinal changes in overall cognition from changes in global connectome measures using a partial least squares regression model (cross-validated R2 = .27). We observe that increased ND model propagation time, increased structural connectome segregation, and increased functional connectome integration were related to better cognitive recovery. We interpret these findings as suggesting two connectome-based postinjury recovery mechanisms: one of neuroplasticity that increases functional connectome integration and one of remote white matter degeneration that increases structural connectome segregation. We hypothesize that our inherently multimodal measure of ND model propagation time captures the interplay between these two mechanisms.

Automated segmentation of medial temporal lobe subregions on in vivo T1-weighted MRI in early stages of Alzheimer's disease

Medial temporal lobe (MTL) substructures are the earliest regions affected by neurofibrillary tangle pathology-and thus are promising biomarkers for Alzheimer's disease (AD). However, automatic segmentation of the MTL using only T1-weighted (T1w) magnetic resonance imaging (MRI) is challenging due to the large anatomical variability of the MTL cortex and the confound of the dura mater, which is commonly segmented as gray matter by state-of-the-art algorithms because they have similar intensity in T1w MRI. To address these challenges, we developed a novel atlas set, consisting of 15 cognitively normal older adults and 14 patients with mild cognitive impairment with a label explicitly assigned to the dura, that can be used by the multiatlas automated pipeline (Automatic Segmentation of Hippocampal Subfields [ASHS-T1]) for the segmentation of MTL subregions, including anterior/posterior hippocampus, entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36, and parahippocampal cortex on T1w MRI. Cross-validation experiments indicated good segmentation accuracy of ASHS-T1 and that the dura can be reliably separated from the cortex (6.5% mislabeled as gray matter). Conversely, FreeSurfer segmented majority of the dura mater (62.4%) as gray matter and the degree of dura mislabeling decreased with increasing disease severity. To evaluate its clinical utility, we applied the pipeline to T1w images of 663 ADNI subjects and significant volume/thickness loss is observed in BA35, ERC, and posterior hippocampus in early prodromal AD and all subregions at later stages. As such, the publicly available new atlas and ASHS-T1 could have important utility in the early diagnosis and monitoring of AD and enhancing brain-behavior studies of these regions.

A concise and persistent feature to study brain resting-state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative

Alzheimer's disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of noninvasive neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large-scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but also worse is the discard of potential important information. To address this issue, we propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1,024 region-of-interests. The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI and much lower than normal controls, providing empirical evidence for decreased functional integration in AD dementia and MCI.