Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration

Exploring the Role of Reactive Oxygen Species in the Pathogenesis and Pathophysiology of Alzheimer's and Parkinson's Disease and the Efficacy of Antioxidant Treatment

Alzheimer's (AD) and Parkinson's Disease (PD) are life-altering diseases that are characterised by progressive memory loss and motor dysfunction. The prevalence of AD and PD is predicted to continuously increase. Symptoms of AD and PD are primarily mediated by progressive neuron death and dysfunction in the hippocampus and substantia nigra. Central features that drive neurodegeneration are caspase activation, DNA fragmentation, lipid peroxidation, protein carbonylation, amyloid-β, and/or α-synuclein formation. Reactive oxygen species (ROS) increase these central features. Currently, there are limited therapeutic options targeting these mechanisms. Antioxidants reduce ROS levels by the induction of antioxidant proteins and direct neutralisation of ROS. This review aims to assess the effectiveness of antioxidants in reducing ROS and neurodegeneration. Antioxidants enhance major endogenous defences against ROS including superoxide dismutase, catalase, and glutathione. Direct neutralisation of ROS by antioxidants protects against ROS-induced cytotoxicity. The combination of Indirect and direct protective mechanisms prevents ROS-induced α-synuclein and/or amyloid-β formation. Antioxidants ameliorate ROS-mediated oxidative stress and subsequent deleterious downstream effects that promote apoptosis. As a result, downstream harmful events including neuron death, dysfunction, and protein aggregation are decreased. The protective effects of antioxidants in human models have yet to directly replicate the success seen in cell and animal models. However, the lack of diversity in antioxidants for clinical trials prevents a definitive answer if antioxidants are protective. Taken together, antioxidant treatment is a promising avenue in neurodegenerative disease therapy and subsequent clinical trials are needed to provide a definitive answer on the protective effects of antioxidants. No current treatment strategies have significant impact in treating advanced AD and PD, but new mimetics of endogenous mitochondrial antioxidant enzymes (Avasopasem Manganese, GC4419 AVA) may be a promising innovative option for decelerating neurodegenerative progress in the future at the mitochondrial level of OS.

Personalizing progressive changes to brain structure in Alzheimer's disease using normative modeling

INTRODUCTION

Neuroanatomical normative modeling captures individual variability in Alzheimer's disease (AD). Here we used normative modeling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD.

METHODS

Cortical and subcortical normative models were generated using healthy controls (n ≈ 58k). These models were used to calculate regional z scores in 3233 T1-weighted magnetic resonance imaging time-series scans from 1181 participants. Regions with z scores < -1.96 were classified as outliers mapped on the brain and summarized by total outlier count (tOC).

RESULTS

tOC increased in AD and in people with MCI who converted to AD and also correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of progression from MCI to AD. Brain outlier maps identified the hippocampus as having the highest rate of change.

DISCUSSION

Individual patients' atrophy rates can be tracked by using regional outlier maps and tOC.

HIGHLIGHTS

Neuroanatomical normative modeling was applied to serial Alzheimer's disease (AD) magnetic resonance imaging (MRI) data for the first time. Deviation from the norm (outliers) of cortical thickness or brain volume was computed in 3233 scans. The number of brain-structure outliers increased over time in people with AD. Patterns of change in outliers varied markedly between individual patients with AD. People with mild cognitive impairment whose outliers increased over time had a higher risk of progression from AD.

Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks

Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine. We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures. We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data. In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies.

Correlation between serum biomarkers, brain volume, and retinal neuronal loss in early-onset Alzheimer's disease

PURPOSE

To compare the peripapillary retinal nerve fiber layer (pRNFL), retinal nerve fiber layer (RNFL), and ganglion cell complex (GCC) thickness measurement in early-onset Alzheimer's disease (EOAD) and controls using spectral domain optical coherence tomography (SD-OCT). We also assessed the relationship between SD-OCT measurements and cognitive measures, serum biomarkers for Alzheimer's disease (AD), and cerebral microstructural volume.

METHODS

pRNFL, RNFL, and GCC thicknesses were measured in 43 EOAD and 42 controls using SD-OCT. Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) were used to assess cognitive status, magnetic resonance imaging (MRI) tool was used to quantify cerebral microstructural volume, and serum biomarkers were quantified from peripheral blood.

RESULTS

EOAD patients had thinner pRNFL (P < 0.001), RNFL (P = 0.008), and GCC (P = 0.018) thicknesses compared to controls after adjusting for multiple factors. pRNFL thickness correlated (P = 0.016) with serum t-tau level. Serum Aβ42 (P < 0.05) concentration correlated with RNFL thickness. Importantly, occipital lobe volume (P = 0.010) correlated with GCC thicknesses in EOAD patients.

CONCLUSION

Our findings suggest that retinal thickness may be useful markers for assessing neurodegenerative process in EOAD.

Associations of dietary patterns and longitudinal brain-volume change in Japanese community-dwelling adults: results from the national institute for longevity sciences-longitudinal study of aging

BACKGROUND

The association of dietary patterns and longitudinal changes in brain volume has rarely been investigated in Japanese individuals. We prospectively investigated this association in middle-aged and older Japanese community-dwelling adults.

METHODS

Data with a 2-year follow-up from the sixth wave (July 2008 to July 2010; baseline) to the seventh (July 2010 to July 2012; follow-up) of the National Institute for Longevity Sciences-Longitudinal Study of Aging project were analyzed. Dietary intake was assessed using a 3-day dietary record, and longitudinal volume changes (%) in the total gray matter (TGM), total white matter, and frontal, parietal, occipital, temporal, and insular lobes were assessed using 3-dimensional T1 magnetic resonance imaging scans. Multiple factor analysis and hierarchical clustering revealed sex-specific dietary patterns. Associations between dietary patterns and annual brain-volume changes (%) were evaluated using general linear models adjusted for age, apoprotein E genotype, body mass index, medical history, lifestyle behaviors, socioeconomic factors, and energy intake.

RESULTS

Among the 1636 participants (age: 40.3-89.2 years), three dietary patterns were determined for men (n = 815; Western; Vegetable-Fruit-Dairy; and Traditional Japanese diets) and women (n = 821; Western; Grain-Vegetable-Fruit; and Traditional Japanese diets). Compared to women following the Western diet, those on the Traditional Japanese diet had less TGM atrophy. Multivariable-adjusted β (95% confidence interval) of the annual change (%) of TGM was - 0.145 (-0.287 to -0.002; P = 0.047), which correlated with reduced parietal lobe atrophy. No association between dietary pattern and brain atrophy was observed in men.

CONCLUSIONS

Adherence to healthy dietary patterns, with higher consumption of whole grains, seafood, vegetables, fruits, mushrooms, soybean products, and green tea, potentially confers a protective effect against brain atrophy in middle-aged and older Japanese women but not in men. Further research to confirm these results and ascertain the underlying mechanisms is required. This study highlights the importance of sex-specific effects on the relationship between dietary patterns and brain health in diverse populations.

Personalising Alzheimer's Disease progression using brain atrophy markers

INTRODUCTION

Neuroanatomical normative modelling can capture individual variability in Alzheimer's Disease (AD). We used neuroanatomical normative modelling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD.

METHODS

Cortical thickness and subcortical volume neuroanatomical normative models were generated using healthy controls (n~58k). These models were used to calculate regional Z-scores in 4361 T1-weighted MRI time-series scans. Regions with Z-scores <-1.96 were classified as outliers and mapped on the brain, and also summarised by total outlier count (tOC).

RESULTS

Rate of change in tOC increased in AD and in people with MCI who converted to AD and correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of MCI progression to AD. Brain Z-score maps showed that the hippocampus had the highest rate of atrophy change.

CONCLUSIONS

Individual-level atrophy rates can be tracked by using regional outlier maps and tOC.

Elevated plasma sulfides are associated with cognitive dysfunction and brain atrophy in human Alzheimer's disease and related dementias

Emerging evidence indicates that vascular stress is an important contributor to the pathophysiology of Alzheimer's disease and related dementias (ADRD). Hydrogen sulfide (H2S) and its metabolites (acid-labile (e.g., iron-sulfur clusters) and bound (e.g., per-, poly-) sulfides) have been shown to modulate both vascular and neuronal homeostasis. We recently reported that elevated plasma sulfides were associated with cognitive dysfunction and measures of microvascular disease in ADRD. Here we extend our previous work to show associations between elevated sulfides and magnetic resonance-based metrics of brain atrophy and white matter integrity. Elevated bound sulfides were associated with decreased grey matter volume, while increased acid labile sulfides were associated with decreased white matter integrity and greater ventricular volume. These findings are consistent with alterations in sulfide metabolism in ADRD which may represent maladaptive responses to oxidative stress.

Tau-targeting antisense oligonucleotide MAPTRx in mild Alzheimer's disease: a phase 1b, randomized, placebo-controlled trial

Tau plays a key role in Alzheimer's disease (AD) pathophysiology, and accumulating evidence suggests that lowering tau may reduce this pathology. We sought to inhibit MAPT expression with a tau-targeting antisense oligonucleotide (MAPTRx) and reduce tau levels in patients with mild AD. A randomized, double-blind, placebo-controlled, multiple-ascending dose phase 1b trial evaluated the safety, pharmacokinetics and target engagement of MAPTRx. Four ascending dose cohorts were enrolled sequentially and randomized 3:1 to intrathecal bolus administrations of MAPTRx or placebo every 4 or 12 weeks during the 13-week treatment period, followed by a 23 week post-treatment period. The primary endpoint was safety. The secondary endpoint was MAPTRx pharmacokinetics in cerebrospinal fluid (CSF). The prespecified key exploratory outcome was CSF total-tau protein concentration. Forty-six patients enrolled in the trial, of whom 34 were randomized to MAPTRx and 12 to placebo. Adverse events were reported in 94% of MAPTRx-treated patients and 75% of placebo-treated patients; all were mild or moderate. No serious adverse events were reported in MAPTRx-treated patients. Dose-dependent reduction in the CSF total-tau concentration was observed with greater than 50% mean reduction from baseline at 24 weeks post-last dose in the 60 mg (four doses) and 115 mg (two doses) MAPTRx groups. Clinicaltrials.gov registration number: NCT03186989 .

N-acetyl-aspartate and Myo-inositol as Markers of White Matter Microstructural Organization in Mild Cognitive Impairment: Evidence from a DTI-1H-MRS Pilot Study

We implemented a multimodal approach to examine associations between structural and neurochemical changes that could signify neurodegenerative processes related to mild cognitive impairment (MCI). Fifty-nine older adults (60-85 years; 22 MCI) underwent whole-brain structural 3T MRI (T1W, T2W, DTI) and proton magnetic resonance spectroscopy (¹H-MRS). The regions of interest (ROIs) for ¹H-MRS measurements were the dorsal posterior cingulate cortex, left hippocampal cortex, left medial temporal cortex, left primary sensorimotor cortex, and right dorsolateral prefrontal cortex. The findings revealed that subjects in the MCI group showed moderate to strong positive associations between the total N-acetylaspartate to total creatine and the total N-acetylaspartate to myo-inositol ratios in the hippocampus and dorsal posterior cingulate cortex and fractional anisotropy (FA) of WM tracts crossing these regions-specifically, the left temporal tapetum, right corona radiata, and right posterior cingulate gyri. In addition, negative associations between the myo-inositol to total creatine ratio and FA of the left temporal tapetum and right posterior cingulate gyri were observed. These observations suggest that the biochemical integrity of the hippocampus and cingulate cortex is associated with a microstructural organization of ipsilateral WM tracts originating in the hippocampus. Specifically, elevated myo-inositol might be an underlying mechanism for decreased connectivity between the hippocampus and the prefrontal/cingulate cortex in MCI.

Increased Hippocampal-Inferior Temporal Gyrus White Matter Connectivity following Donepezil Treatment in Patients with Early Alzheimer's Disease: A Diffusion Tensor Probabilistic Tractography Study

The incidence of Alzheimer's disease (AD) has been increasing each year, and a defective hippocampus has been primarily associated with an early stage of AD. However, the effect of donepezil treatment on hippocampus-related networks is unknown. Thus, in the current study, we evaluated the hippocampal white matter (WM) connectivity in patients with early-stage AD before and after donepezil treatment using probabilistic tractography, and we further determined the WM integrity and changes in brain volume. Ten patients with early-stage AD (mean age = 72.4 ± 7.9 years; seven females and three males) and nine healthy controls (HC; mean age = 70.7 ± 3.5 years; six females and three males) underwent a magnetic resonance (MR) examination. After performing the first MR examination, the patients received donepezil treatment for 6 months. The brain volumes and diffusion tensor imaging scalars of 11 regions of interest (the superior/middle/inferior frontal gyrus, the superior/middle/inferior temporal gyrus, the amygdala, the caudate nucleus, the hippocampus, the putamen, and the thalamus) were measured using MR imaging and DTI, respectively. Seed-based structural connectivity analyses were focused on the hippocampus. The patients with early AD had a lower hippocampal volume and WM connectivity with the superior frontal gyrus and higher mean diffusivity (MD) and radial diffusivity (RD) in the amygdala than HC (p < 0.05, Bonferroni-corrected). However, brain areas with a higher (or lower) brain volume and WM connectivity were not observed in the HC compared with the patients with early AD. After six months of donepezil treatment, the patients with early AD showed increased hippocampal-inferior temporal gyrus (ITG) WM connectivity (p < 0.05, Bonferroni-corrected).

Interpretable brain disease classification and relevance-guided deep learning

Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by humans. Heat mapping by deep Taylor decomposition revealed that (potentially misleading) image features even outside of the brain tissue are crucial for the classifier's decision. We propose a regularization technique to train convolutional neural network (CNN) classifiers utilizing relevance-guided heat maps calculated online during training. The method was applied using T1-weighted MR images from 128 subjects with Alzheimer's disease (mean age = 71.9 ± 8.5 years) and 290 control subjects (mean age = 71.3 ± 6.4 years). The developed relevance-guided framework achieves higher classification accuracies than conventional CNNs but more importantly, it relies on less but more relevant and physiological plausible voxels within brain tissue. Additionally, preprocessing effects from skull stripping and registration are mitigated. With the interpretability of the decision mechanisms underlying CNNs, these results challenge the notion that unprocessed T1-weighted brain MR images in standard CNNs yield higher classification accuracy in Alzheimer's disease than solely atrophy.

Can hippocampal subfield measures supply information that could be used to improve the diagnosis of Alzheimer's disease?

The diagnosis of Alzheimer's disease (AD) needs to be improved. We investigated if hippocampal subfield volume measured by structural imaging, could supply information, so that the diagnosis of AD could be improved. In this study, subjects were classified based on clinical, neuropsychological, and amyloid positivity or negativity using PET scans. Data from 478 elderly Korean subjects grouped as cognitively unimpaired β-amyloid-negative (NC), cognitively unimpaired β-amyloid-positive (aAD), mild cognitively impaired β-amyloid-positive (pAD), mild cognitively impaired-specific variations not due to dementia β-amyloid-negative (CIND), severe cognitive impairment β-amyloid-positive (ADD+) and severe cognitive impairment β-amyloid-negative (ADD-) were used. NC and aAD groups did not show significant volume differences in any subfields. The CIND did not show significant volume differences when compared with either the NC or the aAD (except L-HATA). However, pAD showed significant volume differences in Sub, PrS, ML, Tail, GCMLDG, CA1, CA4, HATA, and CA3 when compared with the NC and aAD. The pAD group also showed significant differences in the hippocampal tail, CA1, CA4, molecular layer, granule cells/molecular layer/dentate gyrus, and CA3 when compared with the CIND group. The ADD- group had significantly larger volumes than the ADD+ group in the bilateral tail, SUB, PrS, and left ML. The results suggest that early amyloid depositions in cognitive normal stages are not accompanied by significant bilateral subfield volume atrophy. There might be intense and accelerated subfield volume atrophy in the later stages associated with the cognitive impairment in the pAD stage, which subsequently could drive the progression to AD dementia. Early subfield volume atrophy associated with the β-amyloid burden may be characterized by more symmetrical atrophy in CA regions than in other subfields. We conclude that the hippocampal subfield volumetric differences from structural imaging show promise for improving the diagnosis of Alzheimer's disease.

Associations Between Sub-Threshold Amyloid-β Deposition, Cortical Volume, and Cognitive Function Modulated by APOE ɛ4 Carrier Status in Cognitively Normal Older Adults

Background:

There has been renewed interest in the deteriorating effects of sub-threshold amyloid-β (Aβ) accumulation in Alzheimer’s disease (AD). Despite evidence suggesting a synergistic interaction between the APOE ɛ4 allele and Aβ deposition in neurodegeneration, few studies have investigated the modulatory role of this allele in sub-threshold Aβ deposition during the preclinical phase.

Objective:

We aimed to explore the differential effect of the APOE ɛ4 carrier status on the association between sub-threshold Aβ deposition, cortical volume, and cognitive performance in cognitively normal older adults (CN).

Methods:

A total of 112 CN with sub-threshold Aβ deposition was included in the study. Participants underwent structural magnetic resonance imaging, [¹⁸F] flutemetamol PET-CT, and a neuropsychological battery. Potential interactions between APOE ɛ4 carrier status, Aβ accumulation, and cognitive function for cortical volume were assessed with whole-brain voxel-wise analysis.

Results:

We found that greater cortical volume was observed with higher regional Aβ deposition in the APOE ɛ4 carriers, which could be attributed to an interaction between the APOE ɛ4 carrier status and regional Aβ deposition in the posterior cingulate cortex/precuneus. Finally, the APOE ɛ4 carrier status-neuropsychological test score interaction demonstrated a significant effect on the gray matter volume of the left middle occipital gyrus.

Conclusion:

There might be a compensatory response to initiating Aβ in APOE ɛ4 carriers during the earliest AD stage. Despite its exploratory nature, this study offers some insight into recent interests concerning probabilistic AD modeling, focusing on the modulating role of the APOE ɛ4 carrier status during the preclinical period.

Brain ventricles, CSF and cognition: a narrative review

The brain ventricles are structures that have been related to cognition since antiquity. They are essential components in the development and maintenance of brain functions. The aging process runs with the enlargement of ventricles and is related to a less selective blood-cerebrospinal fluid barrier and then a more toxic cerebrospinal fluid environment. The study of brain ventricles as a biological marker of aging is promissing because they are structures easily identified in neuroimaging studies, present good inter-rater reliability, and measures of them can identify brain atrophy earlier than cortical structures. The ventricular system also plays roles in the development of dementia, since dysfunction in the clearance of beta-amyloid protein is a key mechanism in sporadic Alzheimer's disease. The morphometric and volumetric studies of the brain ventricles can help to distinguish between healthy elderly and persons with mild cognitive impairment (MCI) and dementia. Brain ventricle data may contribute to the appropriate allocation of individuals in groups at higher risk for MCI-dementia progression in clinical trials and to measuring therapeutic responses in these studies, as well as providing differential diagnosis, such as normal pressure hydrocephalus. Here, we reviewed the pathophysiology of healthy aging and cognitive decline, focusing on the role of the choroid plexus and brain ventricles in this process.

Neuroproteomics Chip-Based Mass Spectrometry and Other Techniques for Alzheimer´S Disease Biomarkers – Update

Background:

Alzheimer's disease (AD) is a progressive neurodegenerative disease of growing interest given that there is cognitive damage and symptom onset acceleration. Therefore, it is important to find AD biomarkers for early diagnosis, disease progression, and discrimination of AD and other diseases.

Objective:

To update the relevance of mass spectrometry for the identification of peptides and proteins involved in AD useful as discriminating biomarkers.

Methods:

Proteomics and peptidomics technologies that show the highest possible specificity and selectivity for AD biomarkers are analyzed, together with the biological fluids used. In addition to positron emission tomography and magnetic resonance imaging, MALDI-TOF mass spectrometry is widely used to identify proteins and peptides involved in AD. The use of protein chips in SELDI technology and electroblotting chips for peptides makes feasible small amounts (L) of samples for analysis.

Results:

Suitable biomarkers are related to AD pathology, such as intracellular neurofibrillary tangles; extraneuronal senile plaques; neuronal and axonal degeneration; inflammation and oxidative stress. Recently, peptides were added to the candidate list, which are not amyloid-b or tau fragments, but are related to coagulation, brain plasticity, and complement/neuroinflammation systems involving the neurovascular unit.

Conclusion:

The progress made in the application of mass spectrometry and recent chip techniques is promising for discriminating between AD, mild cognitive impairment, and matched healthy controls. The application of this technique to blood samples from patients with AD has shown to be less invasive and fast enough to determine the diagnosis, stage of the disease, prognosis, and follow-up of the therapeutic response.

Predicting diagnosis 4 years prior to Alzheimer's disease incident

This study employed a deep learning longitudinal model, graph convolutional and recurrent neural network (graph-CNN-RNN), on a series of brain structural MRI scans for AD prognosis. It characterized whole-brain morphology via incorporating longitudinal cortical and subcortical morphology and defined a probabilistic risk for the prediction of AD as a function of age prior to clinical diagnosis. The graph-CNN-RNN model was trained on half of the Alzheimer's Disease Neuroimaging Initiative dataset (ADNI, n = 1559) and validated on the other half of the ADNI dataset and the Open Access Series of Imaging Studies-3 (OASIS-3, n = 930). Our findings demonstrated that the graph-CNN-RNN can reliably and robustly diagnose AD at the accuracy rate of 85% and above across all the time points for both datasets. The graph-CNN-RNN predicted the AD conversion from 0 to 4 years before the AD onset at ∼80% of accuracy. The AD probabilistic risk was associated with clinical traits, cognition, and amyloid burden assessed using [18F]-Florbetapir (AV45) positron emission tomography (PET) across all the time points. The graph-CNN-RNN provided the quantitative trajectory of brain morphology from prognosis to overt stages of AD. Such a deep learning tool and the AD probabilistic risk have great potential in clinical applications for AD prognosis.

Alzheimer's Disease Seen through the Eye: Ocular Alterations and Neurodegeneration

Alzheimer’s Disease (AD) is one of the main neurodegenerative diseases worldwide. Unfortunately, AD shares many similarities with other dementias at early stages, which impedes an accurate premortem diagnosis. Therefore, it is urgent to find biomarkers to allow for early diagnosis of the disease. There is increasing scientific evidence highlighting the similarities between the eye and other structures of the CNS, suggesting that knowledge acquired in eye research could be useful for research and diagnosis of AD. For example, the retina and optic nerve are considered part of the central nervous system, and their damage can result in retrograde and anterograde axon degeneration, as well as abnormal protein aggregation. In the anterior eye segment, the aqueous humor and tear film may be comparable to the cerebrospinal fluid. Both fluids are enriched with molecules that can be potential neurodegenerative biomarkers. Indeed, the pathophysiology of AD, characterized by cerebral deposits of amyloid-beta (Aβ) and tau protein, is also present in the eyes of AD patients, besides numerous structural and functional changes observed in the structure of the eyes. Therefore, all this evidence suggests that ocular changes have the potential to be used as either predictive values for AD assessment or as diagnostic tools.

Immunohistochemical evidence for adult human neurogenesis in health and disease

Postnatal and adult neurogenesis in the subventricular zone and subgranular zone of animals such as rodents and non-human primates has been observed with many different technical approaches. Since most techniques used in animals cannot be used in humans, the majority of human neurogenesis studies rely on postmortem immunohistochemistry. This technique is difficult in human tissue, due to poor and variable preservation of antigens and samples. Nevertheless, a survey of the literature reveals that most published studies provide evidence for childhood and adult neurogenesis in the human brain stem cell niches. There are some conflicting results even when assessing the same markers and when using the same antibodies. Focusing on immunohistochemical studies on post-mortem human sections, we discuss the relative robustness of the literature on adult neurogenesis. We also discuss the response of the subventricular and subgranular zones to human disease, showing that the two niches can respond differently and that the stage of disease impacts neurogenesis levels. Thus, we highlight strong evidence for adult human neurogenesis, discuss other work that did not find it, describe obstacles in analysis, and offer other approaches to evaluate the neurogenic potential of the subventricular and subgranular zones of Homo sapiens. This article is categorized under: Neurological Diseases > Stem Cells and Development Reproductive System Diseases > Stem Cells and Development.

Orientational changes of white matter fibers in Alzheimer's disease and amnestic mild cognitive impairment

White matter abnormalities represent early neuropathological events in neurodegenerative diseases such as Alzheimer's disease (AD), investigating these white matter alterations would likely provide valuable insights into pathological changes over the course of AD. Using a novel mathematical framework called "Director Field Analysis" (DFA), we investigated the geometric microstructural properties (i.e., splay, bend, twist, and total distortion) in the orientation of white matter fibers in AD, amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) individuals from the Alzheimer's Disease Neuroimaging Initiative 2 database. Results revealed that AD patients had extensive orientational changes in the bilateral anterior thalamic radiation, corticospinal tract, inferior and superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and uncinate fasciculus in comparison with CN. We postulate that these orientational changes of white matter fibers may be partially caused by the expansion of lateral ventricle, white matter atrophy, and gray matter atrophy in AD. In contrast, aMCI individuals showed subtle orientational changes in the left inferior longitudinal fasciculus and right uncinate fasciculus, which showed a significant association with the cognitive performance, suggesting that these regions may be preferential vulnerable to breakdown by neurodegenerative brain disorders, thereby resulting in the patients' cognitive impairment. To our knowledge, this article is the first to examine geometric microstructural changes in the orientation of white matter fibers in AD and aMCI. Our findings demonstrate that the orientational information of white matter fibers could provide novel insight into the underlying biological and pathological changes in AD and aMCI.

Global hippocampal and selective thalamic nuclei atrophy differentiate chronic TBI from Non-TBI

Traumatic brain injury (TBI) may increase susceptibility to neurodegenerative diseases later in life. One neurobiological parallel between chronic TBI and neurodegeneration may be accelerated aging and the nature of atrophy across subcortical gray matter structures. The main aim of the present investigation is to evaluate and rank the degree that subcortical gray matter atrophy differentiates chronic moderate-severe TBI from non-TBI participants by evaluating morphometric differences between groups. Forty individuals with moderate-severe chronic TBI (9.23 yrs from injury) and 33 healthy controls (HC) underwent high resolution 3D T1-weighted structural magnetic resonance imaging. Whole brain volume was classified into white matter, cortical and subcortical gray matter structures with hippocampi and thalami further segmented into subfields and nuclei, respectively. Extensive atrophy was observed across nearly all brain regions for chronic TBI participants. A series of multivariate logistic regression models identified subcortical gray matter structures of the hippocampus and thalamus as the most sensitive to differentiating chronic TBI from non-TBI participants (McFadden R² = .36, p < .001). Further analyses revealed the pattern of hippocampal atrophy to be global, occurring across nearly all subfields. The pattern of thalamic atrophy appeared to be much more selective and non-uniform, with largest between-group differences evident for nuclei bordering the ventricles. Subcortical gray matter was negatively correlated with time since injury (r = -.31, p = .054), while white matter and cortical gray matter were not. Cognitive ability was lower in the chronic TBI group (Cohen's d = .97, p = .003) and correlated with subcortical structures including the pallidum (r² = .23, p = .038), thalamus (r² = .36, p = .007) and ventral diencephalon (r² = .23, p = .036). These data may support an accelerated aging hypothesis in chronic moderate-severe TBI that coincides with a similar neuropathological profile found in neurodegenerative diseases.

Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs

Many neurological diseases are characterized by gradual deterioration of brain structure andfunction. Large longitudinal MRI datasets have revealed such deterioration, in part, by applying machine and deep learning to predict diagnosis. A popular approach is to apply Convolutional Neural Networks (CNN) to extract informative features from each visit of the longitudinal MRI and then use those features to classify each visit via Recurrent Neural Networks (RNNs). Such modeling neglects the progressive nature of the disease, which may result in clinically implausible classifications across visits. To avoid this issue, we propose to combine features across visits by coupling feature extraction with a novel longitudinal pooling layer and enforce consistency of the classification across visits in line with disease progression. We evaluate the proposed method on the longitudinal structural MRIs from three neuroimaging datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI, N=404), a dataset composed of 274 normal controls and 329 patients with Alcohol Use Disorder (AUD), and 255 youths from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). In allthree experiments our method is superior to other widely used approaches for longitudinal classification thus making a unique contribution towards more accurate tracking of the impact of conditions on the brain. The code is available at https://github.com/ouyangjiahong/longitudinal-pooling.

Gray Matter Atrophy in Amnestic Mild Cognitive Impairment: A Voxel-Based Meta-Analysis

Background: Voxel-based morphometry (VBM) has been widely used to investigate structural alterations in amnesia mild cognitive impairment (aMCI). However, inconsistent results have hindered our understanding of the exact neuropathology related to aMCI. Objectives: Our aim was to systematically review the literature reporting VBM on aMCI to elucidate consistent gray matter alterations, their functional characterization, and corresponding co-activation patterns. Methods: The PubMed, Web of Science, and EMBASE databases were searched for VBM studies on aMCI published from inception up to June 2020. Peak coordinates were extracted from clusters that showed significant gray matter differences between aMCI patients and healthy controls (HC). Meta-analysis was performed using seed-based d mapping with the permutation of subject images (SDM-PSI), a newly improved meta-analytic method. Functional characterization and task-based co-activation patterns using the BrainMap database were performed on significant clusters to explore their functional roles. Finally, VBM was performed based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to further support the findings. Results: A total of 31 studies with 681 aMCI patients and 837 HC were included in this systematic review. The aMCI group showed significant gray matter atrophy in the left amygdala and right hippocampus, which was consistent with results from the ADNI dataset. Functional characterization revealed that these regions were mainly associated with emotion, cognition, and perception. Further, meta-regression analysis demonstrated that gray matter atrophy in the left inferior frontal gyrus and the left angular gyrus was significantly associated with cognitive impairment in the aMCI group. Conclusions: The findings of gray matter atrophy in the left amygdala and right hippocampus are highly consistent and robust, and not only offer a better understanding of the underlying neuropathology but also provide accurate potential biomarkers for aMCI.

Accelerating Structural Degeneration in Temporal Regions and Their Effects on Cognition in Aging of MCI Patients

Age is the major risk factor for Alzheimer's disease (AD) and for mild cognitive impairment (MCI). However, there is limited evidence about MCI-specific aging-related simultaneous changes of the brain structure and their impact on cognition. We analyzed the brain imaging data from 269 subjects (97 MCI patients and 172 cognitively normal [CN] elderly) using voxel-based morphometry and tract-based spatial statistics procedures to explore the special structural pattern during aging. We found that the patients with MCI showed accelerated age-related reductions in gray matter volume in the left planum temporale, thalamus, and posterior cingulate gyrus. The similar age×group interaction effect was found in the fractional anisotropy of the bilateral parahippocampal cingulum white matter tract, which connects the temporal regions. Importantly, the age-related temporal gray matter and white matter alterations were more significantly related to performance in memory and attention tasks in MCI patients. The accelerated degeneration patterns in the brain structure provide evidence for different neural mechanisms underlying aging in MCI patients. Temporal structural degeneration may serve as a potential imaging marker for distinguishing the progression of the preclinical AD stage from normal aging.

Protein contributions to brain atrophy acceleration in Alzheimer's disease and primary age-related tauopathy

Alzheimer's disease is characterized by the presence of amyloid-β and tau deposition in the brain, hippocampal atrophy and increased rates of hippocampal atrophy over time. Another protein, TAR DNA binding protein 43 (TDP-43) has been identified in up to 75% of cases of Alzheimer's disease. TDP-43, tau and amyloid-β have all been linked to hippocampal atrophy. TDP-43 and tau have also been linked to hippocampal atrophy in cases of primary age-related tauopathy, a pathological entity with features that strongly overlap with those of Alzheimer's disease. At present, it is unclear whether and how TDP-43 and tau are associated with early or late hippocampal atrophy in Alzheimer's disease and primary age-related tauopathy, whether either protein is also associated with faster rates of atrophy of other brain regions and whether there is evidence for protein-associated acceleration/deceleration of atrophy rates. We therefore aimed to model how these proteins, particularly TDP-43, influence non-linear trajectories of hippocampal and neocortical atrophy in Alzheimer's disease and primary age-related tauopathy. In this longitudinal retrospective study, 557 autopsied cases with Alzheimer's disease neuropathological changes with 1638 ante-mortem volumetric head MRI scans spanning 1.0-16.8 years of disease duration prior to death were analysed. TDP-43 and Braak neurofibrillary tangle pathological staging schemes were constructed, and hippocampal and neocortical (inferior temporal and middle frontal) brain volumes determined using longitudinal FreeSurfer. Bayesian bivariate-outcome hierarchical models were utilized to estimate associations between proteins and volume, early rate of atrophy and acceleration in atrophy rates across brain regions. High TDP-43 stage was associated with smaller cross-sectional brain volumes, faster rates of brain atrophy and acceleration of atrophy rates, more than a decade prior to death, with deceleration occurring closer to death. Stronger associations were observed with hippocampus compared to temporal and frontal neocortex. Conversely, low TDP-43 stage was associated with slower early rates but later acceleration. This later acceleration was associated with high Braak neurofibrillary tangle stage. Somewhat similar, but less striking, findings were observed between TDP-43 and neocortical rates. Braak stage appeared to have stronger associations with neocortex compared to TDP-43. The association between TDP-43 and brain atrophy occurred slightly later in time (∼3 years) in cases of primary age-related tauopathy compared to Alzheimer's disease. The results suggest that TDP-43 and tau have different contributions to acceleration and deceleration of brain atrophy rates over time in both Alzheimer's disease and primary age-related tauopathy.

Cerebral Small Vessel Disease

Cerebral small vessel disease (CSVD) represents a cluster of various vascular disorders with different pathological backgrounds. The advanced vasculature net of cerebral vessels, including small arteries, capillaries, arterioles and venules, is usually affected. Processes of oxidation underlie the pathology of CSVD, promoting the degenerative status of the epithelial layer. There are several classifications of cerebral small vessel diseases; some of them include diseases such as Binswanger’s disease, leukoaraiosis, cerebral microbleeds (CMBs) and lacunar strokes. This paper presents the characteristics of CSVD and the impact of the current knowledge of this topic on the diagnosis and treatment of patients.

Accelerated brain ageing in sepsis survivors with cognitive long-term impairment

In the last years, cognitive impairment was emphasized to be a prominent long-term sequelae of sepsis. The level of cognitive impairment is comparable with that in mild cognitive impairment (MCI) patients. Whether sepsis survivors also show a comparable brain atrophy is still unclear. For the analysis of brain atrophy, a novel method named brain age gap estimation (BrainAGE) was used. In this analysis approach, an algorithm identifies age-specific atrophy across the whole brain and calculates a BrainAGE score in years. In case of accelerated brain atrophy, the BrainAGE score is increased in comparison to the healthy age reference group, indicating a difference in estimated chronological age. 20 survivors of severe sepsis (longer than 2 years post sepsis) with persistent cognitive deficits were investigated with a battery of neuropsychological tests. Their MRI images were compared to an age- and sex-matched control group. Sepsis survivors showed a significant higher BrainAGE score of 4.5 years compared to healthy controls. We also found a close relationship between the BrainAGE score and severity of cognitive impairment (a higher BrainAGE score was associated with more severe cognitive impairment). Consequently, sepsis survivors with persistent cognitive impairment showed an accelerated brain ageing, which was closely associated with the severity of cognitive impairment (similar to MCI patients).

Microglial activation and tau burden predict cognitive decline in Alzheimer's disease

Tau pathology, neuroinflammation, and neurodegeneration are key aspects of Alzheimer's disease. Understanding whether these features predict cognitive decline, alone or in combination, is crucial to develop new prognostic measures and enhanced stratification for clinical trials. Here, we studied how baseline assessments of in vivo tau pathology (measured by 18F-AV-1451 PET), neuroinflammation (measured by 11C-PK11195 PET) and brain atrophy (derived from structural MRI) predicted longitudinal cognitive changes in patients with Alzheimer's disease pathology. Twenty-six patients (n = 12 with clinically probable Alzheimer's dementia and n = 14 with amyloid-positive mild cognitive impairment) and 29 healthy control subjects underwent baseline assessment with 18F-AV-1451 PET, 11C-PK11195 PET, and structural MRI. Cognition was examined annually over the subsequent 3 years using the revised Addenbrooke's Cognitive Examination. Regional grey matter volumes, and regional binding of 18F-AV-1451 and 11C-PK11195 were derived from 15 temporo-parietal regions characteristically affected by Alzheimer's disease pathology. A principal component analysis was used on each imaging modality separately, to identify the main spatial distributions of pathology. A latent growth curve model was applied across the whole sample on longitudinal cognitive scores to estimate the rate of annual decline in each participant. We regressed the individuals' estimated rate of cognitive decline on the neuroimaging components and examined univariable predictive models with single-modality predictors, and a multi-modality predictive model, to identify the independent and combined prognostic value of the different neuroimaging markers. Principal component analysis identified a single component for the grey matter atrophy, while two components were found for each PET ligand: one weighted to the anterior temporal lobe, and another weighted to posterior temporo-parietal regions. Across the whole-sample, the single-modality models indicated significant correlations between the rate of cognitive decline and the first component of each imaging modality. In patients, both stepwise backward elimination and Bayesian model selection revealed an optimal predictive model that included both components of 18F-AV-1451 and the first (i.e. anterior temporal) component for 11C-PK11195. However, the MRI-derived atrophy component and demographic variables were excluded from the optimal predictive model of cognitive decline. We conclude that temporo-parietal tau pathology and anterior temporal neuroinflammation predict cognitive decline in patients with symptomatic Alzheimer's disease pathology. This indicates the added value of PET biomarkers in predicting cognitive decline in Alzheimer's disease, over and above MRI measures of brain atrophy and demographic data. Our findings also support the strategy for targeting tau and neuroinflammation in disease-modifying therapy against Alzheimer's disease.

Network localization of clinical, cognitive, and neuropsychiatric symptoms in Alzheimer's disease

There is both clinical and neuroanatomical variability at the single-subject level in Alzheimer's disease, complicating our understanding of brain-behaviour relationships and making it challenging to develop neuroimaging biomarkers to track disease severity, progression, and response to treatment. Prior work has shown that both group-level atrophy in clinical dementia syndromes and complex neurological symptoms in patients with focal brain lesions localize to brain networks. Here, we use a new technique termed 'atrophy network mapping' to test the hypothesis that single-subject atrophy maps in patients with a clinical diagnosis of Alzheimer's disease will also localize to syndrome-specific and symptom-specific brain networks. First, we defined single-subject atrophy maps by comparing cortical thickness in each Alzheimer's disease patient versus a group of age-matched, cognitively normal subjects across two independent datasets (total Alzheimer's disease patients = 330). No more than 42% of Alzheimer's disease patients had atrophy at any given location across these datasets. Next, we determined the network of brain regions functionally connected to each Alzheimer's disease patient's location of atrophy using seed-based functional connectivity in a large (n = 1000) normative connectome. Despite the heterogeneity of atrophied regions at the single-subject level, we found that 100% of patients with a clinical diagnosis of Alzheimer's disease had atrophy functionally connected to the same brain regions in the mesial temporal lobe, precuneus cortex, and angular gyrus. Results were specific versus control subjects and replicated across two independent datasets. Finally, we used atrophy network mapping to define symptom-specific networks for impaired memory and delusions, finding that our results matched symptom networks derived from patients with focal brain lesions. Our study supports atrophy network mapping as a method to localize clinical, cognitive, and neuropsychiatric symptoms to brain networks, providing insight into brain-behaviour relationships in patients with dementia.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year.

OBJECTIVES

To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI.

SEARCH METHODS

On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches.

SELECTION CRITERIA

We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter.

DATA COLLECTION AND ANALYSIS

Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available.

MAIN RESULTS

We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate.

AUTHORS' CONCLUSIONS

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

Long-standing multiple sclerosis neurodegeneration: volumetric magnetic resonance imaging comparison to Parkinson's disease, mild cognitive impairment, Alzheimer's disease, and elderly healthy controls

Multiple sclerosis (MS) exhibits neurodegeneration driven disability progression. We compared the extent of neurodegeneration among 112 long-standing MS patients, 37 Parkinson's disease (PD) patients, 34 amnestic mild cognitive impairment (aMCI) patients, 37 Alzheimer's disease (AD) patients, and 184 healthy controls. 3T MRI volumes of whole brain (WBV), white matter (WMV), gray matter (GMV), cortical (CV), deep gray matter (DGM), and nuclei-specific volumes of thalamus, caudate, putamen, globus pallidus, and hippocampus were derived with SIENAX and FIRST software. Аge and sex-adjusted analysis of covariance was used. WBV was not significantly different between diseases. MS had significantly lower WMV compared to other disease groups (p < 0.021). Only AD had smaller GMV and CV when compared to MS (both p < 0.001). MS had smaller DGM volume than PD and aMCI (p < 0.001 and p = 0.026, respectively) and lower thalamic volume when compared to all other neurodegenerative diseases (p < 0.008). Long-standing MS exhibits comparable global atrophy with lower WMV and thalamic volume when compared to other classical neurodegenerative diseases.

Prospective longitudinal atrophy in Alzheimer's disease correlates with the intensity and topography of baseline tau-PET

β-Amyloid plaques and tau-containing neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease (AD) and are thought to play crucial roles in a neurodegenerative cascade leading to dementia. Both lesions can now be visualized in vivo using positron emission tomography (PET) radiotracers, opening new opportunities to study disease mechanisms and improve patients' diagnostic and prognostic evaluation. In a group of 32 patients at early symptomatic AD stages, we tested whether β-amyloid and tau-PET could predict subsequent brain atrophy measured using longitudinal magnetic resonance imaging acquired at the time of PET and 15 months later. Quantitative analyses showed that the global intensity of tau-PET, but not β-amyloid-PET, signal predicted the rate of subsequent atrophy, independent of baseline cortical thickness. Additional investigations demonstrated that the specific distribution of tau-PET signal was a strong indicator of the topography of future atrophy at the single patient level and that the relationship between baseline tau-PET and subsequent atrophy was particularly strong in younger patients. These data support disease models in which tau pathology is a major driver of local neurodegeneration and highlight the relevance of tau-PET as a precision medicine tool to help predict individual patient's progression and design future clinical trials.

Pathways of Prevention: A Scoping Review of Dietary and Exercise Interventions for Neurocognition

Alzheimer's disease and related dementias (ADRD) represent an increasingly urgent public health concern, with an increasing number of baby boomers now at risk. Due to a lack of efficacious therapies among symptomatic older adults, an increasing emphasis has been placed on preventive measures that can curb or even prevent ADRD development among middle-aged adults. Lifestyle modification using aerobic exercise and dietary modification represents one of the primary treatment modalities used to mitigate ADRD risk, with an increasing number of trials demonstrating that exercise and dietary change, individually and together, improve neurocognitive performance among middle-aged and older adults. Despite several optimistic findings, examination of treatment changes across lifestyle interventions reveals a variable pattern of improvements, with large individual differences across trials. The present review attempts to synthesize available literature linking lifestyle modification to neurocognitive changes, outline putative mechanisms of treatment improvement, and discuss discrepant trial findings. In addition, previous mechanistic assumptions linking lifestyle to neurocognition are discussed, with a focus on potential solutions to improve our understanding of individual neurocognitive differences in response to lifestyle modification. Specific recommendations include integration of contemporary causal inference approaches for analyzing parallel mechanistic pathways and treatment-exposure interactions. Methodological recommendations include trial multiphase optimization strategy (MOST) design approaches that leverage individual differences for improved treatment outcomes.

Sentence comprehension in patients with dementia of the Alzheimer's type

Sentence comprehension is diminished in patients with dementia of the Alzheimer’s type (DAT). However, the underlying reason for such deficits is still not entirely clear. The Syntactic Deficit Hypothesis attributes sentence comprehension deficits in DAT patients to the impairment in syntactic ability, whereas the Processing Resource Deficit Hypothesis proposes that sentence comprehension deficits are the result of working memory deficiency. This study investigated the deficits in sentence comprehension in Chinese-speaking DAT patients with different degrees of severity using sentence-picture matching tasks. The study revealed a significant effect of syntactic complexity in patients and healthy controls, but the effect was stronger in patients than in healthy controls. When working memory demand was minimized, the effect of syntactic complexity was only significant in patients with moderate DAT, but not in healthy controls or those with mild DAT. The findings suggest that in patients with mild DAT, working memory decline was the major source of sentence comprehension difficulty and in patients with moderate DAT, working memory decline and syntactic impairment jointly contributed to the impairments in sentence comprehension. The source of sentence comprehension deficits varied with degree of dementia severity.

Low-dose ladostigil for mild cognitive impairment: A phase 2 placebo-controlled clinical trial

OBJECTIVE

Ladostigil reduces oxidative stress and microglial activation in aging rats. We assessed its safety and potential efficacy in a 3-year, randomized, double-blind, placebo-controlled phase 2 clinical trial in patients with mild cognitive impairment (MCI) and medial temporal lobe atrophy.

METHODS

Patients 55 to 85 years of age with MCI, Clinical Dementia Rating (CDR) score of 0.5, Mini-Mental State Examination (MMSE) score >24, Wechsler Memory Scale-Revised Verbal Paired Associates I score ≤18, and Medial Temporal Lobe Atrophy Scale score >1 were stratified by APOE ε4 genotype and randomly assigned (1:1) to ladostigil 10 mg/d or placebo. Primary outcomes were safety and onset of Alzheimer disease dementia. Secondary endpoints were Neuropsychological Test Battery (NTB) composite, Disability Assessment in Dementia (DAD), and Geriatric Depression Scale (GDS) scores. Exploratory outcomes were NTB component, CDR, and MMSE scores. Biomarkers included MRI-derived whole-brain, hippocampus, and entorhinal cortex volumes.

RESULTS

Two hundred ten patients from 15 sites in Austria, Germany, and Israel were randomly allocated to placebo (107 patients) or ladostigil (103 patients). After 36 months, 21 of 103 patients on placebo and 14 of 99 patients receiving ladostigil progressed to Alzheimer disease (log-rank test p = 0.162). There were no significant effects on the NTB composite, DAD, or GDS score. Whole-brain and hippocampus volumes decreased more in the placebo than in the ladostigil group (whole brain, p = 0.025, Cohen d = 0.43; hippocampus, p = 0.043, d = 0.43). Serious adverse events were reported by 28 of 107 patients treated with placebo and 26 of 103 with ladostigil.

CONCLUSION

Ladostigil was safe and well tolerated but did not delay progression to dementia. Its association with reduced brain and hippocampus volume loss suggests a potential effect on atrophy.

CLINICALTRIALSGOV IDENTIFIER

NCT01429623.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that for patients with MCI and medial temporal lobe atrophy, ladostigil did not significantly decrease the risk of the development of Alzheimer disease.

[Neurocognitive aging and cognitive disorders]

Aging is the leading risk factor of the most common cognitive disorders, primarily Alzheimer's disease and vascular dementia. These diseases have a progressive course and pathological underlying processes develop long before the onset of clinical signs of cognitive impairment. In the context of current trends in population aging and the steady increase in the number of patients with dementia, the search for factors contributing to the acceleration of neurocognitive aging processes, as well as factors with a modifying effect on these processes, is a key element in the effective management of elderly patients.

Atrophy subtypes in prodromal Alzheimer's disease are associated with cognitive decline

Alzheimer's disease is a heterogeneous disorder. Understanding the biological basis for this heterogeneity is key for developing personalized medicine. We identified atrophy subtypes in Alzheimer's disease dementia and tested whether these subtypes are already present in prodromal Alzheimer's disease and could explain interindividual differences in cognitive decline. First we retrospectively identified atrophy subtypes from structural MRI with a data-driven cluster analysis in three datasets of patients with Alzheimer's disease dementia: discovery data (dataset 1: n = 299, age = 67 ± 8, 50% female), and two independent external validation datasets (dataset 2: n = 181, age = 66 ± 7, 52% female; dataset 3: n = 227, age = 74 ± 8, 44% female). Subtypes were compared on clinical, cognitive and biological characteristics. Next, we classified prodromal Alzheimer's disease participants (n = 603, age = 72 ± 8, 43% female) according to the best matching subtype to their atrophy pattern, and we tested whether subtypes showed cognitive decline in specific domains. In all Alzheimer's disease dementia datasets we consistently identified four atrophy subtypes: (i) medial-temporal predominant atrophy with worst memory and language function, older age, lowest CSF tau levels and highest amount of vascular lesions; (ii) parieto-occipital atrophy with poor executive/attention and visuospatial functioning and high CSF tau; (iii) mild atrophy with best cognitive performance, young age, but highest CSF tau levels; and (iv) diffuse cortical atrophy with intermediate clinical, cognitive and biological features. Prodromal Alzheimer's disease participants classified into one of these subtypes showed similar subtype characteristics at baseline as Alzheimer's disease dementia subtypes. Compared across subtypes in prodromal Alzheimer's disease, the medial-temporal subtype showed fastest decline in memory and language over time; the parieto-occipital subtype declined fastest on executive/attention domain; the diffuse subtype in visuospatial functioning; and the mild subtype showed intermediate decline in all domains. Robust atrophy subtypes exist in Alzheimer's disease with distinct clinical and biological disease expression. Here we observe that these subtypes can already be detected in prodromal Alzheimer's disease, and that these may inform on expected trajectories of cognitive decline.

Differential medial temporal lobe and default-mode network functional connectivity and morphometric changes in Alzheimer's disease

We report group level differential detection of medial temporal lobe resting-state functional connectivity disruption and morphometric changes in the transition from cognitively normal to early mild cognitive impairment in an age-, education- and gender-matched 105 subjects Alzheimer's Disease Neuroimaging Initiative dataset. In mild Alzheimer's Disease, but not early mild cognitive impairment, characteristic brain atrophy was detected in FreeSurfer estimates of subcortical and hippocampal subfield volumes and cortical thinning. By contrast, functional connectivity analysis detected earlier significant changes. In early mild cognitive impairment these changes involved medial temporal lobe regions of transentorhinal, perirhinal and entorhinal cortices (associated with the earliest stages of neurofibrillary changes in Alzheimer's Disease), hippocampus, parahippocampal gyrus and temporal pole, and cortical regions comprising or co-activated with the default-mode network, including rostral and medial prefrontal cortex, anterior cingulate cortex, precuneus and inferior temporal cortex. Key findings include: a) focal, bilaterally symmetric spatial organization of affected medial temporal lobe regions; b) mutual hyperconnectivity involving ventral medial temporal lobe structures (temporal pole, uncus); c) dorsal medial temporal lobe hypoconnectivity with anterior and posterior midline default-mode network nodes; and d) a complex pattern of transient and persistent changes in hypo- and hyper-connectivity across Alzheimer's Disease stages. These findings position medial temporal lobe resting state functional connectivity as a candidate biomarker of an Alzheimer's Disease pathophysiological cascade, potentially in advance of clinical biomarkers, and coincident with biomarkers of the earliest stages of Alzheimer's neuropathology.

A Review and Hypothesized Model of the Mechanisms That Underpin the Relationship Between Inflammation and Cognition in the Elderly

Age is associated with increased risk for several disorders including dementias, cardiovascular disease, atherosclerosis, obesity, and diabetes. Age is also associated with cognitive decline particularly in cognitive domains associated with memory and processing speed. With increasing life expectancies in many countries, the number of people experiencing age-associated cognitive impairment is increasing and therefore from both economic and social terms the amelioration or slowing of cognitive aging is an important target for future research. However, the biological causes of age associated cognitive decline are not yet, well understood. In the current review, we outline the role of inflammation in cognitive aging and describe the role of several inflammatory processes, including inflamm-aging, vascular inflammation, and neuroinflammation which have both direct effect on brain function and indirect effects on brain function via changes in cardiovascular function.

Language in Amnestic Mild Cognitive Impairment and Dementia of Alzheimer’s Type: Quantitatively or Qualitatively Different?

Background/Aims: The purpose of this study was to explore language differences between individuals diagnosed with amnestic mild cognitive impairment multiple domain (aMCIm) and those with probable Alzheimer’s disease, with a goal of (i) characterizing the language profile of aMCIm and (ii) determining whether the profiles of dementia of Alzheimer’s type (DAT) and aMCIm individuals are on a continuum of one diagnostic entity or represent two distinct ­cognitive disorders. Methods: Language data from 28 patients with consensus diagnosis of aMCIm and DAT derived from a retrospective chart review were compared to that of healthy controls. Results: A non-parametric statistic established that there was no significant difference between groups in age, years of education or duration of symptoms and that expressive language was found to be relatively intact in both patient groups. In contrast, both groups exhibited significant impairments on receptive language tests and on linguistically complex tasks that rely on episodic memory and executive functions. Individuals with aMCIm and DAT present with configurations of language features that are largely in parallel to each other and reflect predominantly quantitative differences. Conclusion: Language tests provide an important contribution to the diagnostic process in their capacity to identify language impairments at an early stage. Understanding the nature of language decline is critically important to the intervention process as this information would inform cognitive intervention approaches aimed at promoting quality of life in people living with MCI and dementia.

Association of corneal nerve fiber measures with cognitive function in dementia

Objectives

Corneal confocal microscopy (CCM) is a noninvasive ophthalmic technique that identifies corneal nerve degeneration in a range of peripheral neuropathies and in patients with multiple sclerosis, Parkinson's disease, and amyotrophic lateral sclerosis. We sought to determine whether there is any association of corneal nerve fiber measures with cognitive function and functional independence in patients with MCI and dementia.

Methods

In this study, 76 nondiabetic participants with MCI (n = 30), dementia (n = 26), and healthy age‐matched controls (n = 20) underwent assessment of cognitive and physical function and CCM.

Results

There was a progressive reduction in corneal nerve fiber density (CNFD), branch density (CNBD), and fiber length (CNFL) (P < 0.0001) in patients with MCI and dementia compared to healthy controls. Adjusted for confounders, all three corneal nerve fiber measures were significantly associated with cognitive function (P < 0.05) and functional independence (P < 0.01) in MCI and dementia. The area under the ROC curve to distinguish MCI with CNFD, CNBD, and CNFL was 69.1%, 73.2%, and 73.0% and for dementia it was 84.8%, 84.2%, and 86.2%, respectively.

Interpretation

CCM demonstrates corneal nerve fiber loss, which is associated with a decline in cognitive function and functional independence in patients with MCI and dementia.

Task-enhanced arterial spin labeled perfusion MRI predicts longitudinal neurodegeneration in mild cognitive impairment

Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease (AD), but is also recognized to be a heterogeneous condition. Biomarkers that predict AD progression in MCI are of clinical significance because they can be used to better identify appropriate candidates for therapeutic intervention studies. It has been hypothesized that comparing to structural measurements, functional ones may be more sensitive to early disease abnormalities and the sensitivity could be further enhanced when combined with cognitive task, a "brain stress test." In this study, we investigated the value of regional cerebral blood flow (CBF), measured by arterial spin labeled perfusion MRI (ASL MRI) during a memory-encoding task, in predicting the estimated rate of hippocampal atrophy, an established marker of AD progression. Thirty-one amnestic MCI patients (20 male and 11 female; age: 70.9 ± 6.5 years, range from 56 to 83 years; mini mental status examination: 27.8 ± 1.8) and 42 normal control subjects (13 male and 29 female; age: 70.6 ± 8.8 years, range from 55 to 88 years; mini mental status examination: 29.1 ± 1.2) were included in this study. We compared the predictive value of CBF during task to CBF during rest and structural volumetry. Both region-of-interest and voxelwise analyses showed that baseline CBF measurements during task (strongest effect in fusiform gyrus, region-of-interest analysis statistics: r = 0.56, p = .003), but not resting ASL MRI or structural volumetry, were correlated with the estimated rate of hippocampal atrophy in amnestic MCI patients. Further, stepwise linear regression demonstrated that resting ASL MRI and volumetry did not provide complementary information in prediction. These results support the notion that physiologic measures during a cognitive challenge may increase the ability to detect subtle functional changes that predict progression. As such, ASL MRI could have important utility in stratifying candidates for AD treatment trials.

MR approaches in neurodegenerative disorders

Neurodegenerative disease is the umbrella term which refers to a range of clinical conditions causing degeneration of neurons within the central nervous system leading to loss of brain function and eventual death. The most prevalent of these is Alzheimer's disease (AD), which affects approximately 50 million people worldwide and is predicted to reach 75 million by 2030. Neurodegenerative diseases can only be fully diagnosed at post mortem by neuropathological assessment of the type and distribution of protein deposits which characterise each different condition, but there is a clear role for imaging technologies in aiding patient diagnoses in life. Magnetic resonance imaging (MRI) and spectroscopy (MRS) techniques have been applied to study these conditions for many years. In this review, we consider the range of MR-based measurements and describe the findings in AD, but also contrast these with the second most common dementia, dementia with Lewy bodies (DLB). The most definitive observation is the major structural brain changes seen in AD using conventional T1-weighted (T1w) MRI, where medial temporal lobe structures are notably atrophied in most symptomatic patients with AD, but often preserved in DLB. Indeed these findings are sufficiently robust to have been incorporated into clinical diagnostic criteria. Diffusion tensor imaging (DTI) reveals widespread changes in tissue microstructure, with increased mean diffusivity and decreased fractional anisotropy reflecting the degeneration of the white matter structures. There are suggestions that there are subtle differences between AD and DLB populations. At the metabolic level, atrophy-corrected MRS demonstrates reduced density of healthy neurons in brain areas with altered perfusion and in regions known to show higher deposits of pathogenic proteins. As studies have moved from patients with advanced disease and clear dysfunction to patients with earlier presentation such as with mild cognitive impairment (MCI), which in some represents the first signs of their ensuing dementia, the ability of MRI to detect differences has been weaker and further work is still required, ideally in much larger cohorts than previously studied. The vast majority of imaging research in dementia populations has been univariate with respect to the MR-derived parameters considered. To date, none of these measurements has uniquely replicated the patterns of tissue involvement seen by neuropathology, and the ability of MR techniques to deliver a non-invasive diagnosis eludes us. Future opportunities may lie in combining MR and nuclear medicine approaches (position emission tomography, PET) to provide a more complete view of structural and metabolic changes. Such developments will require multi-variate analyses, possibly combined with artificial intelligence or deep learning algorithms, to enhance our ability to combine the array of image-derived information, genetic, gender and lifestyle factors.

Registration-based methods applied to serial high-resolution T1-weighted magnetic resonance imaging for the assessment of brain volume changes in anorexia nervosa of the restricting type

We aimed to determine whether variation in the body mass index (BMI)—a marker of anorexia nervosa (AN) severity—is associated with brain volume changes longitudinally estimated using registration-based methods on serial high-resolution T1-weighted magnetic resonance images (MRI). Fifteen female patients (mean age = 21 years; standard deviation [SD] = 5.7; range: 15–33 years) with the diagnosis of AN of the restricting type (AN-r)—according to the Diagnostic and Statistic Manual of Mental Disorders, 5^th edition criteria—underwent T1-weighted MRI at baseline and after a mean follow-up period of 11 months (SD = 6.4). We used the brain boundary shift integral (BSI) and the ventricular BSI (VBSI) to estimate volume changes after registering voxels of follow-up onto baseline MRI. Very significant and strong correlations were found between BMI variation and the brain BSI, as well as between BMI variation and the VBSI. After adjustment for age at onset, duration of illness, and the BMI rate of change before baseline MRI, the statistical significance of both associations persisted. Registration-based methods on serial MRI represent an additional tool to estimate AN severity, because they provide measures of brain volume change strongly associated with BMI variation.

Radiomic Features of Hippocampal Subregions in Alzheimer's Disease and Amnestic Mild Cognitive Impairment

Alzheimer's disease (AD) is characterized by progressive dementia, especially in episodic memory, and amnestic mild cognitive impairment (aMCI) is associated with a high risk of developing AD. Hippocampal atrophy/shape changes are believed to be the most robust magnetic resonance imaging (MRI) markers for AD and aMCI. Radiomics, a method of texture analysis, can quantitatively examine a large set of features and has previously been successfully applied to evaluate imaging biomarkers for AD. To test whether radiomic features in the hippocampus can be employed for early classification of AD and aMCI, 1692 features from the caudal and head parts of the bilateral hippocampus were extracted from 38 AD patients, 33 aMCI patients and 45 normal controls (NCs). One way analysis of variance (ANOVA) showed that 111 features exhibited statistically significant group differences (P < 0.01, Bonferroni corrected). Among these features, 98 were significantly correlated with Mini-Mental State Examination (MMSE) scores in AD and aMCI subjects (P < 0.01). The support vector machine (SVM) model demonstrated that radiomic features allowed us to distinguish AD from NC with an accuracy of 86.75% (specificity = 88.89% and sensitivity = 84.21%) and an area under curve (AUC) of 0.93. In conclusion, these findings provide evidence showing that radiomic features are beneficial in detecting early cognitive decline, and SVM classification analysis provides encouraging evidence for using hippocampal radiomic features as a potential biomarker for clinical applications in AD.