Dynamics of gray matter loss in Alzheimer's disease

Central nervous system sulfatide deficiency as a causal factor for bladder disorder in Alzheimer's disease

BACKGROUND

Despite being a brain disorder, Alzheimer's disease (AD) is often accompanied by peripheral organ dysregulations (e.g., loss of bladder control in late-stage AD), which highly rely on spinal cord coordination. However, the causal factor(s) for peripheral organ dysregulation in AD remain elusive.

METHODS

The central nervous system (CNS) is enriched in lipids. We applied quantitative shotgun lipidomics to determine lipid profiles of human AD spinal cord tissues. Additionally, a CNS sulfatide (ST)-deficient mouse model was used to study the lipidome, transcriptome and peripheral organ phenotypes of ST loss.

RESULTS

We observed marked myelin lipid reduction in the spinal cord of AD subjects versus cognitively normal individuals. Among which, levels of ST, a myelin-enriched lipid class, were strongly and negatively associated with the severity of AD. A CNS myelin-specific ST-deficient mouse model was used to further identify the causes and consequences of spinal cord lipidome changes. Interestingly, ST deficiency led to spinal cord lipidome and transcriptome profiles highly resembling those observed in AD, characterized by decline of multiple myelin-enriched lipid classes and enhanced inflammatory responses, respectively. These changes significantly disrupted spinal cord function and led to substantial enlargement of urinary bladder in ST-deficient mice.

CONCLUSIONS

Our study identified CNS ST deficiency as a causal factor for AD-like lipid dysregulation, inflammation response and ultimately the development of bladder disorders. Targeting to maintain ST levels may serve as a promising strategy for the prevention and treatment of AD-related peripheral disorders.

Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an "AD-NeuroScore," that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1-91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses. Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics. In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.

Evaluation of Brain-Body Health in Individuals With Common Neuropsychiatric Disorders

Importance

Physical health and chronic medical comorbidities are underestimated, inadequately treated, and often overlooked in psychiatry. A multiorgan, systemwide characterization of brain and body health in neuropsychiatric disorders may enable systematic evaluation of brain-body health status in patients and potentially identify new therapeutic targets.

Objective

To evaluate the health status of the brain and 7 body systems across common neuropsychiatric disorders.

Design, Setting, and Participants

Brain imaging phenotypes, physiological measures, and blood- and urine-based markers were harmonized across multiple population-based neuroimaging biobanks in the US, UK, and Australia, including UK Biobank; Australian Schizophrenia Research Bank; Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing; Alzheimer's Disease Neuroimaging Initiative; Prospective Imaging Study of Ageing; Human Connectome Project-Young Adult; and Human Connectome Project-Aging. Cross-sectional data acquired between March 2006 and December 2020 were used to study organ health. Data were analyzed from October 18, 2021, to July 21, 2022. Adults aged 18 to 95 years with a lifetime diagnosis of 1 or more common neuropsychiatric disorders, including schizophrenia, bipolar disorder, depression, generalized anxiety disorder, and a healthy comparison group were included.

Main Outcomes and Measures

Deviations from normative reference ranges for composite health scores indexing the health and function of the brain and 7 body systems. Secondary outcomes included accuracy of classifying diagnoses (disease vs control) and differentiating between diagnoses (disease vs disease), measured using the area under the receiver operating characteristic curve (AUC).

Results

There were 85 748 participants with preselected neuropsychiatric disorders (36 324 male) and 87 420 healthy control individuals (40 560 male) included in this study. Body health, especially scores indexing metabolic, hepatic, and immune health, deviated from normative reference ranges for all 4 neuropsychiatric disorders studied. Poor body health was a more pronounced illness manifestation compared to brain changes in schizophrenia (AUC for body = 0.81 [95% CI, 0.79-0.82]; AUC for brain = 0.79 [95% CI, 0.79-0.79]), bipolar disorder (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.57-0.58]), depression (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.58-0.58]), and anxiety (AUC for body = 0.63 [95% CI, 0.63-0.63]; AUC for brain = 0.57 [95% CI, 0.57-0.58]). However, brain health enabled more accurate differentiation between distinct neuropsychiatric diagnoses than body health (schizophrenia-other: mean AUC for body = 0.70 [95% CI, 0.70-0.71] and mean AUC for brain = 0.79 [95% CI, 0.79-0.80]; bipolar disorder-other: mean AUC for body = 0.60 [95% CI, 0.59-0.60] and mean AUC for brain = 0.65 [95% CI, 0.65-0.65]; depression-other: mean AUC for body = 0.61 [95% CI, 0.60-0.63] and mean AUC for brain = 0.65 [95% CI, 0.65-0.66]; anxiety-other: mean AUC for body = 0.63 [95% CI, 0.62-0.63] and mean AUC for brain = 0.66 [95% CI, 0.65-0.66).

Conclusions and Relevance

In this cross-sectional study, neuropsychiatric disorders shared a substantial and largely overlapping imprint of poor body health. Routinely monitoring body health and integrated physical and mental health care may help reduce the adverse effect of physical comorbidity in people with mental illness.

Apolipoprotein E ε4 accelerates the longitudinal cerebral atrophy in open access series of imaging studies-3 elders without dementia at enrollment

Introduction

Early studies have reported that APOE is strongly associated with brain atrophy and cognitive decline among healthy elders and Alzheimer's disease (AD). However, previous research has not directly outlined the modulation of APOE on the trajectory of cerebral atrophy with aging during the conversion from cognitive normal (CN) to dementia (CN2D).

Methods

This study tried to elucidate this issue from a voxel-wise whole-brain perspective based on 416 qualified participants from a longitudinal OASIS-3 neuroimaging cohort. A voxel-wise linear mixed-effects model was applied for detecting cerebrum regions whose nonlinear atrophic trajectories were driven by AD conversion and to elucidate the effect of APOE variants on the cerebral atrophic trajectories during the process.

Results

We found that CN2D participants had faster quadratically accelerated atrophy in bilateral hippocampi than persistent CN. Moreover, APOE ε4 carriers had faster-accelerated atrophy in the left hippocampus than ε4 noncarriers in both CN2D and persistent CN, and CN2D ε4 carriers an noncarriers presented a faster atrophic speed than CN ε4 carriers. These findings could be replicated in a sub-sample with a tough match in demographic information.

Discussion

Our findings filled the gap that APOE ε4 accelerates hippocampal atrophy and the conversion from normal cognition to dementia.

The relationship between long-term blood pressure variability and cortical thickness in older adults

High blood pressure variability (BPV) is a risk factor for cognitive decline and dementia, but its association with cortical thickness is not well understood. Here we use a topographical approach, to assess links between long-term BPV and cortical thickness in 478 (54% men at baseline) community dwelling older adults (70-88 years) from the ASPirin in Reducing Events in the Elderly NEURO sub-study. BPV was measured as average real variability, based on annual visits across three years. Higher diastolic BPV was significantly associated with reduced cortical thickness in multiple areas, including temporal (banks of the superior temporal sulcus), parietal (supramarginal gyrus, post-central gyrus), and posterior frontal areas (pre-central gyrus, caudal middle frontal gyrus), while controlling for mean BP. Higher diastolic BPV was associated with faster progression of cortical thinning across the three years. Diastolic BPV is an important predictor of cortical thickness, and trajectory of cortical thickness, independent of mean blood pressure. This finding suggests an important biological link in the relationship between BPV and cognitive decline in older age.

Metabolic Asymmetry Relates to Clinical Characteristics and Brain Network Abnormalities in Alzheimer's Disease

BACKGROUND

Metabolic asymmetry has been observed in Alzheimer's disease (AD), but different studies have inconsistent viewpoints.

OBJECTIVE

To analyze the asymmetry of cerebral glucose metabolism in AD and investigate its clinical significance and potential metabolic network abnormalities.

METHODS

Standardized uptake value ratios (SUVRs) were obtained from 18F-FDG positron emission tomography (PET) images of all participants, and the asymmetry indices (AIs) were calculated according to the SUVRs. AD group was divided into left/right-dominant or bilateral symmetric hypometabolism (AD-L/AD-R or AD-BI) when more than half of the AIs of the 20 regions of interest (ROIs) were < -2SD, >2SD, or between±1SD. Differences in clinical features among the three AD groups were compared, and the abnormal network characteristics underlying metabolic asymmetry were explored.

RESULTS

In AD group, the proportions of AD-L, AD-R, and AD-BI were 28.4%, 17.9%, and 18.5%, respectively. AD-L/AD-R groups had younger age of onset and faster rate of cognitive decline than AD-BI group (p < 0.05). The absolute values of AIs in half of the 20 ROIs became higher at follow-up than at baseline (p < 0.05). Compared with those in AD-BI group, metabolic connection strength of network, global efficiency, cluster coefficient, degree centrality and local efficiency were lower, but shortest path length was longer in AD-L and AD-R groups (p < 0.05).

CONCLUSION

Asymmetric and symmetric hypometabolism may represent different clinical subtypes of AD, which may provide a clue for future studies on the heterogeneity of AD and help to optimize the design of clinical trials.

An Association Between Large Optic Cupping and Total and Regional Brain Volume: The Women's Health Initiative

PURPOSE

To investigate the relationships between optic nerve cupping and total and regional brain volumes.

DESIGN

Secondary analysis of randomized clinical trial data.

METHODS

Women 65 to 79 years of age without glaucoma with cup-to-disc ratio (CDR) measurements from the Women's Health Initiative (WHI) Sight Examination study and magnetic resonance imaging (MRI)-based total and regional brain volumes from the WHI Memory Study MRI-1 were included. Large CDR was defined as 0.6 or greater in either eye. Generalized estimating equation models were used to account for intra-brain correlations between the right and left sides. The final analysis was adjusted for demographic and clinical characteristics and for total brain volume (for regional analyses).

RESULTS

Final analyses included 471 women, with the mean age ± SD was 69.2 ± 3.6 years; 92.8% of the subjects were white. Of 471 women, 34 (7.2%) had large CDR. Controlling for total brain volume and for demographic and clinical characteristics, lateral ventricle volume was 3.01 cc larger for subjects with large CDR compared to those without large CDR (95% CI = 0.02 to 5.99; P = .048). Furthermore, frontal lobe volume was 4.78 cc lower for subjects with large CDR compared to those without (95% CI = -8.71, -0.84; P = 0.02), and occipital lobe volume was 1.86 cc lower for those with large CDR compared to those without (95% CI = -3.39, -0.3; P =.02).

CONCLUSIONS

Our analysis suggests that in women aged 65 years or more, large CDR is associated with lower relative total brain volume and absolute regional volume in the frontal and occipital lobes. Enlarged CDR in individuals without glaucoma may represent a sign of optic nerve and brain aging, although more longitudinal data are needed.

Molecular and neural roles of sodium-glucose cotransporter 2 inhibitors in alleviating neurocognitive impairment in diabetic mice

Diabetes causes a variety of molecular changes in the brain, making it a real risk factor for the development of cognitive dysfunction. Complex pathogenesis and clinical heterogeneity of cognitive impairment makes the efficacy of current drugs limited. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) gained our attention as drugs with potential beneficial effects on the CNS. In the present study, these drugs ameliorated the cognitive impairment associated with diabetes. Moreover, we verified whether SGLT2i can mediate the degradation of amyloid precursor protein (APP) and modulation of gene expression (Bdnf, Snca, App) involved in the control of neuronal proliferation and memory. The results of our research proved the participation of SGLT2i in the multifactorial process of neuroprotection. SGLT2i attenuate the neurocognitive impairment through the restoration of neurotrophin levels, modulation of neuroinflammatory signaling, and gene expression of Snca, Bdnf, and App in the brain of diabetic mice. The targeting of the above-mentioned genes is currently seen as one of the most promising and developed therapeutic strategies for diseases associated with cognitive dysfunction. The results of this work could form the basis of a future administration of SGLT2i in diabetics with neurocognitive impairment.

Two Routes to Alzheimer's Disease Based on Differential Structural Changes in Key Brain Regions

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder with homogenous disease patterns. Neuropathological changes precede symptoms by up to two decades making neuroimaging biomarkers a prime candidate for early diagnosis, prognosis, and patient stratification.

OBJECTIVE

The goal of the study was to discern intermediate AD stages and their precursors based on neuroanatomical features for stratifying patients on their progression through different stages.

METHODS

Data include grey matter features from 14 brain regions extracted from longitudinal structural MRI and cognitive data obtained from 1,017 healthy controls and AD patients of ADNI. AD progression was modeled with a Hidden Markov Model, whose hidden states signify disease stages derived from the neuroanatomical data. To tie the progression in brain atrophy to a behavioral marker, we analyzed the ADAS-cog sub-scores in the stages.

RESULTS

The optimal model consists of eight states with differentiable neuroanatomical features, forming two routes crossing once at a very early point and merging at the final state. The cortical route is characterized by early and sustained atrophy in cortical regions. The limbic route is characterized by early decrease in limbic regions. Cognitive differences between the two routes are most noticeable in the memory domain with subjects from the limbic route experiencing stronger memory impairments.

CONCLUSION

Our findings corroborate that more than one pattern of grey matter deterioration with several discernable stages can be identified in the progression of AD. These neuroanatomical subtypes are behaviorally meaningful and provide a door into early diagnosis of AD and prognosis of the disease's progression.

Human mini-brains for reconstituting central nervous system disorders

Neurological disorders in the central nervous system (CNS) are progressive and irreversible diseases leading to devastating impacts on patients' life as they cause cognitive impairment, dementia, and even loss of essential body functions. The development of effective medicines curing CNS disorders is, however, one of the most ambitious challenges due to the extremely complex functions and structures of the human brain. In this regard, there are unmet needs to develop simplified but physiopathologically-relevant brain models. Recent advances in the microfluidic techniques allow multicellular culture forming miniaturized 3D human brains by aligning parts of brain regions with specific cells serving suitable functions. In this review, we overview designs and strategies of microfluidics-based human mini-brains for reconstituting CNS disorders, particularly Alzheimer's disease (AD), Parkinson's disease (PD), traumatic brain injury (TBI), vascular dementia (VD), and environmental risk factor-driven dementia (ERFD). Afterward, the applications of the mini-brains in the area of medical science are introduced in terms of the clarification of pathogenic mechanisms and identification of promising biomarkers. We also present expanded model systems ranging from the CNS to CNS-connecting organ axes to study the entry pathways of pathological risk factors into the brain. Lastly, the advantages and potential challenges of current model systems are addressed with future perspectives.

Mode-based morphometry: A multiscale approach to mapping human neuroanatomy

Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes--eigenmodes--of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.

Altered functional coupling between the cerebellum and cerebrum in patients with amnestic mild cognitive impairment

Cognitive processing relies on the functional coupling between the cerebrum and cerebellum. However, it remains unclear how the 2 collaborate in amnestic mild cognitive impairment (aMCI) patients. With functional magnetic resonance imaging techniques, we compared cerebrocerebellar functional connectivity during the resting state (rsFC) between the aMCI and healthy control (HC) groups. Additionally, we distinguished coupling between functionally corresponding and noncorresponding areas across the cerebrum and cerebellum. The results demonstrated decreased rsFC between both functionally corresponding and noncorresponding areas, suggesting distributed deficits of cerebrocerebellar connections in aMCI patients. Increased rsFC was also observed, which were between functionally noncorresponding areas. Moreover, the increased rsFC was positively correlated with attentional scores in the aMCI group, and this effect was absent in the HC group, supporting that there exists a compensatory mechanism in patients. The current study contributes to illustrating how the cerebellum adjusts its coupling with the cerebrum in individuals with cognitive impairment.

Computer assisted diagnosis of Alzheimer's disease using statistical likelihood-ratio test

The purpose of this work is to present a computer assisted diagnostic tool for radiologists in their diagnosis of Alzheimer's disease. A statistical likelihood-ratio procedure from signal detection theory was implemented in the detection of Alzheimer's disease. The probability density functions of the likelihood ratio were constructed by using medial temporal lobe (MTL) volumes of patients with Alzheimer's disease (AD) and normal controls (NC). The volumes of MTL as well as other anatomical regions of the brains were calculated by the FreeSurfer software using T1 weighted MRI images. The MRI images of AD and NC were downloaded from the database of Alzheimer's disease neuroimaging initiative (ADNI). A separate dataset of minimal interval resonance imaging in Alzheimer's disease (MIRIAD) was used for diagnostic testing. A sensitivity of 89.1% and specificity of 87.0% were achieved for the MIRIAD dataset which are better than the 85% sensitivity and specificity achieved by the best radiologists without input of other patient information.

Towards early detection of neurodegenerative diseases: A gut feeling

The gastrointestinal tract communicates with the nervous system through a bidirectional network of signaling pathways called the gut-brain axis, which consists of multiple connections, including the enteric nervous system, the vagus nerve, the immune system, endocrine signals, the microbiota, and its metabolites. Alteration of communications in the gut-brain axis is emerging as an overlooked cause of neuroinflammation. Neuroinflammation is a common feature of the pathogenic mechanisms involved in various neurodegenerative diseases (NDs) that are incurable and debilitating conditions resulting in progressive degeneration and death of neurons, such as in Alzheimer and Parkinson diseases. NDs are a leading cause of global death and disability, and the incidences are expected to increase in the following decades if prevention strategies and successful treatment remain elusive. To date, the etiology of NDs is unclear due to the complexity of the mechanisms of diseases involving genetic and environmental factors, including diet and microbiota. Emerging evidence suggests that changes in diet, alteration of the microbiota, and deregulation of metabolism in the intestinal epithelium influence the inflammatory status of the neurons linked to disease insurgence and progression. This review will describe the leading players of the so-called diet-microbiota-gut-brain (DMGB) axis in the context of NDs. We will report recent findings from studies in model organisms such as rodents and fruit flies that support the role of diets, commensals, and intestinal epithelial functions as an overlooked primary regulator of brain health. We will finish discussing the pivotal role of metabolisms of cellular organelles such as mitochondria and peroxisomes in maintaining the DMGB axis and how alteration of the latter can be used as early disease makers and novel therapeutic targets.

Inter-hemispherical comparison of tau-pathology in the human temporal lobe

Alzheimer's disease (AD) is characterized histopathologically by hyperphosphorylated and aggregated Tau and amyloid plaques. While the latter appear in a less stringent way throughout the brain in the course of the disease, the former evolve in a highly predictable pattern as described by Braak and Braak (1991). It is, however, not clear if this pattern develops simultaneously in both hemispheres. In this study, we therefore compared Tau-pathology of both hemispheres of the same individual in 36 consecutive brain donations as they arrived in our brain bank. 26 exhibited little differences, in eight cases left hemisphere was clearly more affected and in two cases the right hemisphere. Thus, cases with evident interhemispheric Tau-pathology do exist and interhemispheric comparison in such cases may help to identify driving forces in the progression of AD.

Cortical thinning in chemotherapy-treated older long-term breast cancer survivors

Cognitive decline is an increasing issue for cancer survivors, especially for older adults, as chemotherapy affects brain structure and function. The purpose of this single center study was to evaluate alterations in cortical thickness and cognition in older long-term survivors of breast cancer who had been treated with chemotherapy years ago. In this prospective cohort study, we enrolled 3 groups of women aged ≥ 65 years with a history of stage I-III breast cancer who had received adjuvant chemotherapy 5 to 15 years ago (chemotherapy group, C +), age-matched women with breast cancer but no chemotherapy (no-chemotherapy group, C-) and healthy controls (HC). All participants underwent brain magnetic resonance imaging and neuropsychological testing with the NIH Toolbox Cognition Battery at time point 1 (TP1) and again at 2 years after enrollment (time point 2 (TP2)). At TP1, there were no significant differences in cortical thickness among the 3 groups. Longitudinally, the C + group showed cortical thinning in the fusiform gyrus (p = 0.006, effect size (d) = -0.60 [ -1.86, -0.66]), pars triangularis (p = 0.026, effect size (d) = -0.43 [-1.68, -0.82]), and inferior temporal lobe (p = 0.026, effect size (d) = -0.38 [-1.62, -0.31]) of the left hemisphere. The C + group also showed decreases in neuropsychological scores such as the total composite score (p = 0.01, effect size (d) = -3.9726 [-0.9656, -6.9796], fluid composite score (p = 0.03, effect size (d) = -4.438 [-0.406, -8.47], and picture vocabulary score (p = 0.04, effect size (d) = -3.7499 [-0.0617, -7.438]. Our results showed that cortical thickness could be a candidate neuroimaging biomarker for cancer-related cognitive impairment and accelerated aging in older long-term cancer survivors.

Multi-tasking deep network for tinnitus classification and severity prediction from multimodal structural MR images

Objective:Subjective tinnitus is an auditory phantom perceptual disorder without an objective biomarker. Fast and efficient diagnostic tools will advance clinical practice by detecting or confirming the condition, tracking change in severity, and monitoring treatment response. Motivated by evidence of subtle anatomical, morphological, or functional information in magnetic resonance images of the brain, we examine data-driven machine learning methods for joint tinnitus classification (tinnitus or no tinnitus) and tinnitus severity prediction.Approach:We propose a deep multi-task multimodal framework for tinnitus classification and severity prediction using structural MRI (sMRI) data. To leverage complementary information multimodal neuroimaging data, we integrate two modalities of three-dimensional sMRI-T1 weighted (T1w) and T2 weighted (T2w) images. To explore the key components in the MR images that drove task performance, we segment both T1w and T2w images into three different components-cerebrospinal fluid, grey matter and white matter, and evaluate performance of each segmented image.Main results:Results demonstrate that our multimodal framework capitalizes on the information across both modalities (T1w and T2w) for the joint task of tinnitus classification and severity prediction.Significance:Our model outperforms existing learning-based and conventional methods in terms of accuracy, sensitivity, specificity, and negative predictive value.

Decoding the radiomic features of dorsolateral prefrontal cortex in individuals with accelerated cortical changes: implications for personalized transcranial magnetic stimulation

Purpose

Image-guided transcranial magnetic stimulation (TMS) is an emerging research field in neuroscience and rehabilitation medicine. Cortical morphometry, as a radiomic phenotype of aging, plays a vital role in developing personalized TMS model, yet few studies are afoot to examine the aging effects on region-specific morphometry and use it in the estimation of TMS-induced electric fields. Our study was aimed to investigate the radiomic features of bilateral dorsolateral prefrontal cortex (DLPFC) and quantify the TMS-induced electric fields during aging.

Approach

Baseline, 1-year and 3-year structural magnetic resonance imaging (MRI) scans from normal aging (NA) adults ( n = 32 ) and mild cognitive impairment (MCI) converters ( n = 22 ) were drawn from the Open Access Series of Imaging Studies. The quantitative measures of radiomics included cortical thickness, folding, and scalp-to-cortex distance. Realistic head models were developed to simulate the impacts of radiomic features on TMS-induced E-fields using the finite-element method.

Results

A pronounced aging-related decrease was found in the gyrification of left DLPFC in MCI converters ( t = 2.21 , p = 0.035 ), which could predict the decline of global cognition at 3-year follow up. Along with the decreased gyrification in left DLPFC, the magnitude of TMS-induced E-fields was rapidly decreased in MCI converters ( t = 2.56 , p = 0.018 ).

Conclusions

MRI-informed radiomic features of the treatment targets have significant effects on the intensity and distribution of the stimulation-induced electric fields in prodromal dementia patients. Our findings highlight the importance of region-specific radiomics when conducting the transcranial brain stimulation in individuals with accelerated cortical changes, such as Alzheimer's disease.

Brain Gray Matter Volume Mediated the Correlation Between Plasma P-Tau and Cognitive Function of Early Alzheimer's Disease in China: A Cross-Sectional Observational Study

BACKGROUND

The primary manifestations of Alzheimer's disease (AD) include cognitive decline and brain gray matter volume (GMV) atrophy. Recent studies have found that plasma phosphorylated-tau (p-tau) concentrations perform better in diagnosing, differentiating, and monitoring the progression of AD. However, the correlation between plasma p-tau, GMV, and cognition remains unclear.

OBJECTIVE

To investigate whether GMV plays a mediating role in the association between plasma p-tau concentrations and cognition.

METHODS

In total, 99 participants (47 patients with AD and 52 cognitively unimpaired [CU] individuals) were included. All participants underwent neuropsychological assessments, laboratory examinations, and magnetic resonance imaging scans. Plasma p-tau217 and p-tau181 concentrations were measured using an enzyme-linked immunosorbent assay kit. Voxel-based morphometry was performed to assess participants' brain GMV. Partial correlation and mediation analyses were conducted in AD group.

RESULTS

Plasma p-tau concentrations were significantly higher in the AD group than in the CU group. Patients with AD had significant brain GMV atrophy in the right hippocampus, bilateral middle temporal gyrus, and right inferior temporal gyrus. In the AD group, there were significant correlations between plasma p-tau217 concentrations, GMV, and Mini-Mental State Examination (MMSE) scores. Brain GMV of the right hippocampus mediated the association between plasma p-tau217 concentrations and MMSE scores. A significant correlation between plasma p-tau181 and MMSE scores was not identified.

CONCLUSION

The findings indicate that p-tau217 is a promising biomarker for central processes affecting brain GMV and cognitive function. This may provide potential targets for future intervention and treatment of tau-targeting therapies in the early stages of AD.

Impact of Eating a Carbohydrate-Restricted Diet on Cortical Atrophy in a Cross-Section of Amyloid Positive Patients with Alzheimer's Disease: A Small Sample Study

BACKGROUND

A carbohydrate-restricted diet aimed at lowering insulin levels has the potential to slow Alzheimer's disease (AD). Restricting carbohydrate consumption reduces insulin resistance, which could improve glucose uptake and neural health. A hallmark feature of AD is widespread cortical thinning; however, no study has demonstrated that lower net carbohydrate (nCHO) intake is linked to attenuated cortical atrophy in patients with AD and confirmed amyloidosis.

OBJECTIVE

We tested the hypothesis that individuals with AD and confirmed amyloid burden eating a carbohydrate-restricted diet have thicker cortex than those eating a moderate-to-high carbohydrate diet.

METHODS

A total of 31 patients (mean age 71.4±7.0 years) with AD and confirmed amyloid burden were divided into two groups based on a 130 g/day nCHO cutoff. Cortical thickness was estimated from T1-weighted MRI using FreeSurfer. Cortical surface analyses were corrected for multiple comparisons using cluster-wise probability. We assessed group differences using a two-tailed two-independent sample t-test. Linear regression analyses using nCHO as a continuous variable, accounting for confounders, were also conducted.

RESULTS

The lower nCHO group had significantly thicker cortex within somatomotor and visual networks. Linear regression analysis revealed that lower nCHO intake levels had a significant association with cortical thickness within the frontoparietal, cingulo-opercular, and visual networks.

CONCLUSIONS

Restricting carbohydrates may be associated with reduced atrophy in patients with AD. Lowering nCHO to under 130 g/day would allow patients to follow the well-validated MIND diet while benefiting from lower insulin levels.

Evaluation of 18F-flutemetamol amyloid PET image analysis parameters on the effect of verubecestat on brain amlyoid load in Alzheimer's disease

PURPOSE

The BACE inhibitor verubecestat was previously found to reduce amyloid load as assessed by ¹⁸F-flutemetamol positron emission tomography (PET) composite cortical standard uptake value ratio (SUVr) in patients with mild-to-moderate Alzheimer's disease (AD) in a substudy of the EPOCH trial. Here, we report on additional analyses relevant to the EPOCH PET data, to help inform on the use of PET for assessing amlyloid load in AD clinical trials.

PROCEDURES

The analyses addressed (1) identification of an optimal ¹⁸F-flutemetamol reference region, (2) determination of the threshold to characterize the magnitude of the longitudinal change, and (3) the impact of partial volume correction (PVC). Pons and subcortical white matter were evaluated as reference regions. The SUVr cutoffs and final reference region choice were determined using 162 ¹⁸F-flutemetamol PET scans from the AIBL dataset. ¹⁸F-flutemetamol SUVrs were computed at baseline and at Week 78 in EPOCH participants who received verubecestat 12 mg (n = 14), 40 mg (n = 20), or placebo (n = 20). Drug effects on amyloid load were computed using either Meltzer (MZ), or symmetric geometric transfer matrix (SGTM) PVC and compared to uncorrected data.

RESULTS

The optimal subcortical white matter and pons SUVr cutoffs were determined to be 0.69 and 0.62, respectively. The effect size to detect longitudinal change was higher for subcortical white matter (1.20) than pons (0.45). Hence, subcortical white matter was used as the reference region for the EPOCH PET substudy. In EPOCH, uncorrected baseline SUVr values correlated strongly with MZ PVC (r² = 0.94) and SGTM PVC (r² = 0.92) baseline SUVr values, and PVC did not provide improvement for evaluating treatment effects on amyloid load at Week 78. No change from baseline was observed in the placebo group at Week 78, whereas a 0.02 and a 0.04 decrease in SUVr were observed in the 12 mg and 40 mg arms, with the latter representing a 22% reduction in the amyloid load above the detection threshold.

CONCLUSIONS

Treatment-related ¹⁸F-flutemetamol longitudinal changes in AD clinical trials can be quantified using a subcortical white matter reference region without PVC.

CLINICAL TRIAL REGISTRATION

clinicaltrials.gov NCT01739348.

Racial discrimination associates with lower cingulate cortex thickness in trauma-exposed black women

Racial discrimination (RD) has been consistently linked to adverse brain health outcomes. These may be due in part to RD effects on neural networks involved with threat appraisal and regulation; RD has been linked to altered activity in the rostral anterior cingulate cortex (rACC) and structural decrements in the anterior cingulum bundle and hippocampus. In the present study, we examined associations of RD with cingulate, hippocampus and amygdala gray matter morphology in a sample of trauma-exposed Black women. Eighty-one Black women aged 19-62 years were recruited as part of an ongoing study of trauma. Participants completed assessments of RD, trauma exposure, and posttraumatic stress disorder (PTSD), and underwent T1-weighted anatomical imaging. Cortical thickness, surface area and gray matter volume were extracted from subregions of cingulate cortex, and gray matter volume was extracted from amygdala and hippocampus, and entered into partial correlation analyses that included RD and other socio-environmental variables. After correction for multiple comparisons and accounting for variance associated with other stressors and socio-environmental factors, participants with more RD exposure showed proportionally lower cortical thickness in the left rACC, caudal ACC, and posterior cingulate cortex (ps < = 0.01). These findings suggest that greater experiences of RD are linked to compromised cingulate gray matter thickness. In the context of earlier findings indicating that RD produces increased response in threat neurocircuitry, our data suggest that RD may increase vulnerability for brain health problems via cingulate cortex alterations. Further research is needed to elucidate biological mechanisms for these changes.

VEP Score of a Left Eye Had Predictive Values for Achieving NEDA-3 over Ten Years in Patients with Multiple Sclerosis

BACKGROUND

The aim of this study was to determine the predictive value of visual evoked potentials (VEPs) in patients with relapsing-remitting multiple sclerosis (RRMS) in achieving no evidence of disease activity-3 (NEDA-3) during up to 10 years of first-line immunomodulatory therapy and to determine whether the lateralization of optic nerve damage may have prognostic significance concerning clinical disability and response to therapy.

METHODS

In a retrospective study, a total of 83 patients (53 female and 30 male) with RRMS participated. The average age of patients was 38.31 ± 9.01. Patients were followed for 2, 5 or 10 years. VEPs were measured at the beginning of the follow-up and after many years of monitoring. Data on optical neuritis (ON) were obtained from medical history. The degree of disability was estimated by the neurologist (independent rater), and magnetic resonance (MR) imaging of the endocranium was performed with gadolinium contrasts. Achieving NEDA-3 is considered a favorable outcome of treatments.

RESULTS

Among those treated, 19 (22.9%) reached NEDA-3, while 64 (77.1%) did not reach NEDA-3. The values of the evoked potential (EP) score for the left eye (r = 0.008, odds ratio (OR) = 0.344 (0.156-0.757)) and latency for the left eye (r = 0.042, OR = 0.966 (0.934-0.999)) at the onset of disease were predictive factors for achieving NEDA-3.

CONCLUSIONS

A normal VEP at the beginning of RRMS increases the chance of reaching NEDA-3 by about six times.

Interhemispheric Connectivity in Idiopathic Cervical Dystonia and Spinocerebellar Ataxias: A Transcranial Magnetic Stimulation Study

BACKGROUND AND RATIONALE

Hyperkinetic movement disorders represent a heterogeneous group of diseases, different from a genetic and clinical perspective. In the past, neurophysiological approaches provided different, sometimes contradictory findings, pointing to an impaired cortical inhibition as a common electrophysiological marker. Our aim was to evaluate changes in interhemispheric communication in patients with idiopathic cervical dystonia (ICD) and spinocerebellar ataxias (SCAs).

MATERIALS AND METHODS

Eleven patients with ICD, 7 with genetically confirmed SCA2 or SCA3, and 10 healthy volunteers were enrolled. The onset latency and duration of the ipsilateral silent period (iSPOL and iSPD, respectively), as well as the so-called transcallosal conduction time (TCT), were then recorded from the abductor pollicis brevis of the right side using an 8-shaped focal coil with wing diameters of 70 mm; all these parameters were evaluated and compared among groups. In SCAs, changes in neurophysiological measures were also correlated to the mutational load.

RESULTS

iSPD was significantly shorter in patients with SCA2 and SCA3, when compared both to control and ICD (P < .0001); iSPOL and TCT were prolonged in SCAs patients (P < .001). Changes in iSPD, iSPOL, and TCT in SCAs are significantly correlated with the mutational load (P = .01, P = .02, and P = .002, respectively).

DISCUSSION

This is the first study to assess changes in interhemispheric communication in patients with SCAs and ICD, using a transcranial magnetic stimulation protocol. Together with previous data in Huntington's disease, we suggest that these changes may underlie, at least in part, a common disease mechanism of polyglutamine disorders.

Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification

Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks.

Polygenic score for Alzheimer’s disease identifies differential atrophy in hippocampal subfield volumes

Hippocampal subfield atrophy is a prime structural change in the brain, associated with cognitive aging and neurodegenerative diseases such as Alzheimer’s disease. Recent developments in genome-wide association studies (GWAS) have identified genetic loci that characterize the risk of hippocampal volume loss based on the processes of normal and abnormal aging. Polygenic risk scores are the genetic proxies mimicking the genetic role of the pre-existing vulnerabilities of the underlying mechanisms influencing these changes. Discriminating the genetic predispositions of hippocampal subfield atrophy between cognitive aging and neurodegenerative diseases will be helpful in understanding the disease etiology. In this study, we evaluated the polygenic risk of Alzheimer’s disease (AD PGRS) for hippocampal subfield atrophy in 1,086 individuals (319 cognitively normal (CN), 591 mild cognitively impaired (MCI), and 176 Alzheimer’s disease dementia (ADD)). Our results showed a stronger association of AD PGRS effect on the left hemisphere than on the right hemisphere for all the hippocampal subfield volumes in a mixed clinical population (CN+MCI+ADD). The subfields CA1, CA4, hippocampal tail, subiculum, presubiculum, molecular layer, GC-ML-DG, and HATA showed stronger AD PGRS associations with the MCI+ADD group than with the CN group. The subfields CA3, parasubiculum, and fimbria showed moderately higher AD PGRS associations with the MCI+ADD group than with the CN group. Our findings suggest that the eight subfield regions, which were strongly associated with AD PGRS are likely involved in the early stage ADD and a specific focus on the left hemisphere could enhance the early prediction of ADD.

Brain MRI Biomarkers to Predict Cognitive Decline in Older People with Alzheimer's Disease

BACKGROUND

Structural magnetic resonance imaging markers predicting symptomatic progression at the individual level can be highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD). However, the correlation between baseline MRI findings and AD progression has not been fully established.

OBJECTIVE

To explore the correlation between baseline MRI findings and AD progression.

METHODS

Brain volumetric measures were applied to differentiate the patients at risk of fast deterioration in AD. We included 194 AD patients with a 24-month follow-up: 65 slow decliners, 63 normal decliners, and 66 fast decliners categorized by changes in Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog). ANOVA analyses were used to identify baseline brain atrophy between groups. Logistic regressions were further performed to explore the relative merits of AD resemblance structural atrophy index (AD-RAI) and individual regional volumetric measures in prediction of disease progression.

RESULTS

Atrophy in the temporal and insular lobes was associated with fast cognitive decline over 24 months. Smaller volumes of temporal and insular lobes in the left but not the right brain were associated with fast cognitive decline. Baseline AD-RAI predicted fast versus slow progression of cognitive decline (odds ratio 3.025 (95% CI: 1.064-8.600), high versus low, AUC 0.771). Moreover, AD-RAI was significantly lower among slow decliners when compared with normal decliners (p = 0.039).

CONCLUSION

AD-RAI on MRI showed potential in identifying clinical AD patients at risk of accelerated cognitive decline.

A fuzzy membership based comparison of the grey matter (GM) in cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) using brain images

Grey matter (GM) in human brain contains most of the important cells covering the regions involved in neurophysiological operations such as memory, emotions, decision making, etc. Alzheimer’s disease (AD) is a neurological disease that kills the brain cells in regions which are mostly involved in the neurophysiological operations. Mild Cognitive Impairment (MCI) is a stage between Cognitively Normal (CN) and AD, where a significant cognitive declination can be observed. The destruction of brain cells causes a reduction in the size of GM. Evaluation of changes in GM, may help in studying the overall brain transformations and accurate classification of different stages of AD. In this work, firstly skull of brain images is stripped for 5 different slices, then segmentation of GM is performed. Finally, the average number of pixels in grey region and the average atrophy in grey pixels per year is calculated and compared amongst CN, MCI, and AD patients of various ages and genders. It is observed that, for some subjects (in some particular ages) from different dementia stages, pattern of GM changes is almost identical. To solve this issue, we have used the concept of fuzzy membership functions to classify the dementia stages more accurately. It is observed from the comparison that average difference in the number of pixels between CN and MCI= 10.01%, CN and AD= 19.63%, MCI and AD= 10.72%. It can be also observed from the comparison that, the average atrophy in grey matter per year in CN= 1.92%, MCI= 3.13%, and AD= 4.33%.

Identifying Cognitive Impairment in Elderly Using Coupling Functions Between Cerebral Oxyhemoglobin and Arterial Blood Pressure

Background

This study aimed to assess brain oxygenation status and cerebral autoregulation function in subjects with cognitive dysfunction.

Methods

The Montreal Cognitive Assessment (MoCA) was applied to divide the subjects into three groups: cognitive impairment (Group CI, 72.50 ± 10.93 y), mild cognitive impairment (Group MCI, 72.02 ± 9.90 y), and normal cognition (Group NC, 70.72 ± 7.66 y). Near-infrared spectroscopy technology and a non-invasive blood pressure device were used to simultaneously measure changes in cerebral tissue oxygenation signals in the bilateral prefrontal lobes (LPFC/RPFC) and arterial blood pressure (ABP) signals from subjects in the resting state (15 min). The coupling between ABP and cerebral oxyhemoglobin concentrations (Δ [O2Hb]) was calculated in very-low-frequency (VLF, 0.02-0.07 Hz) and low-frequency (LF, 0.07-0.2 Hz) bands based on the dynamical Bayesian inference approach. Pearson correlation analyses were used to study the relationships between MoCA scores, tissue oxygenation index, and strength of coupling function.

Results

In the interval VLF, Group CI (p = 0.001) and Group MCI (p = 0.013) exhibited significantly higher coupling strength from ABP to Δ [O2Hb] in the LPFC than Group NC. In the interval LF, coupling strength from ABP to Δ [O2Hb] in the LPFC was significantly higher in Group CI than in Group NC (p = 0.001). Pearson correlation results showed that MoCA scores had a significant positive correlation with the tissue oxygenation index and a significant negative correlation with the coupling strength from ABP to Δ [O2Hb].

Conclusion

The significantly increased coupling strength may be evidence of impaired cerebral autoregulation function in subjects with cognitive dysfunction. The Pearson correlation results suggest that indicators of brain oxygenation status and cerebral autoregulation function can reflect cognitive function. This study provides insights into the mechanisms underlying the pathophysiology of cognitive impairment and provides objective indicators for screening cognitive impairment in the elderly population.

Aberrant Cross-Tissue Functional Connectivity in Alzheimer’s Disease: Static, Dynamic, and Directional Properties

Background: BOLD signals in the gray matter (GM) and white matter (WM) are tightly coupled. However, our understanding of the cross-tissue functional network in Alzheimer’s disease (AD) is limited. Objective: We investigated the changes of cross-tissue functional connectivity (FC) metrics for the GM regions susceptible to AD damage. Methods: For each GM region in the default mode (DMN) and limbic networks, we obtained its low-order static FC with any WM region, and the high-order static FC between any two WM regions based on their FC pattern similarity with multiple GM regions. The dynamic and directional properties of cross-tissue FC were then acquired, specifically for the regional pairs whose low- or high-order static FCs showed significant differences between AD and normal control (NC). Moreover, these cross-tissue FC metrics were correlated with voxel-based GM volumes and MMSE in all participants. Results: Compared to NC, AD patients showed decreased low-order static FCs between the intra-hemispheric GM-WM pairs (right ITG-right fornix; left MoFG-left posterior corona radiata), and increased low-order static, dynamic, and directional FCs between the inter-hemispheric GM-WM pairs (right MTG-left superior/posterior corona radiata). The high-order static and directional FCs between the left cingulate bundle-left tapetum were increased in AD, based on their FCs with the GMs of DMN. Those decreased and increased cross-tissue FC metrics in AD had opposite correlations with memory-related GM volumes and MMSE (positive for the decreased and negative for the increased). Conclusion: Cross-tissue FC metrics showed opposite changes in AD, possibly as useful neuroimaging biomarkers to reflect neurodegenerative and compensatory mechanisms.

Time course of lesion-induced atrophy in multiple sclerosis

BACKGROUND AND PURPOSE

White matter (WM) tract disruption impacts volume loss in connected deep gray matter (DGM) over 5 years in people with multiple sclerosis (PwMS). However, the timeline of this phenomenon remains poorly characterized.

MATERIALS AND METHODS

Annual serial MRI for 181 PwMS was retrospectively analyzed from a 10-year clinical trial database. Annualized thalamic atrophy, DGM atrophy, and disruption of connected WM tracts were measured. For time series analysis, ~700 epochs were collated using a sliding 5-year window, and regression models predicting 1-year atrophy were applied to characterize the influence of new tract disruption from preceding years, while controlling for whole brain atrophy and other relevant factors.

RESULTS

Disruptions of WM tracts connected to the thalamus were significantly associated with thalamic atrophy 1 year later (β: 0.048-0.103). This effect was not observed for thalamic tract disruption concurrent with the time of atrophy nor for thalamic tract disruption preceding the atrophy by 2-4 years. Similarly, disruptions of white matter tracts connected to the DGM were significantly associated with DGM atrophy 1 year later (β: 0.078-0.111), but not for tract disruption concurrent with, nor preceding the atrophy by 2-4 years.

CONCLUSION

Increased rates of thalamic and DGM atrophy were restricted to 1 year following newly developed disruption in connected WM tracts. In research and clinical settings, additional gray matter atrophy may be expected 1 year following new lesion growth in connected white matter.

Molecular Signatures of Mitochondrial Complexes Involved in Alzheimer’s Disease via Oxidative Phosphorylation and Retrograde Endocannabinoid Signaling Pathways

Objective

The inability to intervene in Alzheimer's disease (AD) forces the search for promising gene-targeted therapies. This study was aimed at exploring molecular signatures and mechanistic pathways to improve the diagnosis and treatment of AD.

Methods

Microarray datasets were collected to filter differentially expressed genes (DEGs) between AD and nondementia controls. Weight gene correlation network analysis (WGCNA) was employed to analyze the correlation of coexpression modules with AD phenotype. A global regulatory network was established and then visualized using Cytoscape software to determine hub genes and their mechanistic pathways. Receiver operating characteristic (ROC) analysis was conducted to estimate the diagnostic performance of hub genes in AD prediction.

Results

A total of 2,163 DEGs from 13,049 background genes were screened in AD relative to nondementia controls. Among the six coexpression modules constructed by WGCNA, DEGs of the key modules with the strongest correlation with AD were extracted to build a global regulatory network. According to the Maximal Clique Centrality (MCC) method, five hub genes associated with mitochondrial complexes were chosen. Further pathway enrichment analysis of hub genes, such as oxidative phosphorylation and retrograde endocannabinoid signaling, was identified. According to the area under the curve (AUC) of about 70%, each hub gene exhibited a good diagnostic performance in predicting AD.

Conclusions

Our findings highlight the perturbation of mitochondrial complexes underlying AD onset, which is mediated by molecular signatures involved in oxidative phosphorylation (COX5A, NDUFAB1, SDHB, UQCRC2, and UQCRFS1) and retrograde endocannabinoid signaling (NDUFAB1) pathways.

The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects

Research on segmentation of the hippocampus in magnetic resonance images through deep learning convolutional neural networks (CNNs) shows promising results, suggesting that these methods can identify small structural abnormalities of the hippocampus, which are among the earliest and most frequent brain changes associated with Alzheimer disease (AD). However, CNNs typically achieve the highest accuracy on datasets acquired from the same domain as the training dataset. Transfer learning allows domain adaptation through further training on a limited dataset. In this study, we applied transfer learning on a network called spatial warping network segmentation (SWANS), developed and trained in a previous study. We used MR images of patients with clinical diagnoses of mild cognitive impairment (MCI) and AD, segmented by two different raters. By using transfer learning techniques, we developed four new models, using different training methods. Testing was performed using 26% of the original dataset, which was excluded from training as a hold‐out test set. In addition, 10% of the overall training dataset was used as a hold‐out validation set. Results showed that all the new models achieved better hippocampal segmentation quality than the baseline SWANS model (p_s < .001), with high similarity to the manual segmentations (mean dice [best model] = 0.878 ± 0.003). The best model was chosen based on visual assessment and volume percentage error (VPE). The increased precision in estimating hippocampal volumes allows the detection of small hippocampal abnormalities already present in the MCI phase (SD = [3.9 ± 0.6]%), which may be crucial for early diagnosis.

Crossed cerebellar diaschisis in Alzheimer's disease

BACKGROUND

Crossed cerebellar diaschisis (CCD) is characterized by hypometabolism and hypoperfusion on molecular imaging in the cerebellum due to a supratentorial lesion on the contralateral side. CCD is a well-established phenomenon in acute or subacute conditions such as infarction but it has been less well described in chronic conditions such as neurodegenerative dementias. Here, we investigate CCD in a large sample of 830 people meeting research criteria for Alzheimer's disease (AD) using [18F]fluorodeoxyglucose-positron emission tomography (FDG-PET).

MATERIALS AND METHODS

This study is based on FDG-PET data collected at baseline as part of two large-scale Phase III clinical trials of a novel tau aggregation inhibitor medication, methylthioninium in mild to moderate AD participants. Quantification of FDG-PET hypometabolism was carried out using standardized uptake value ratio (SUVR), with the pons as the comparison region. SUVR was compared in different regions of interest between the right and left hemispheres of the brain and cerebellum in people with mild AD (Mini-Mental State Examination score ≥ 20).

RESULTS

Comparison of SUVR in different brain regions demonstrated significant differences in the temporal, occipital and cerebellar cortices. Right and left asymmetry was noted with lower SUVR in the left temporal and occipital regions, whereas SUVR was lower in the right side of the cerebellum.

CONCLUSION

Here, we found robust evidence of CCD in a large sample of people with AD, a chronic neurodegenerative condition. The presence of this phenomenon in AD opens up a new avenue of research in AD pathogenesis and has the potential to change future diagnostic and therapeutic strategies.

Music affects functional brain connectivity and is effective in the treatment of neurological disorders

In a million years, under the pressure of natural selection, hominins have acquired the abilities for vocal learning, music, and language. Music is a relevant human activity, highly effective in enhancing sociality, is a universal experience common to all known human cultures, although it varies in rhythmic and melodic complexity. It has been part of human life since the beginning of our history, or almost, and it strengthens the mother-baby relation even within the mother's womb. Music engages multiple cognitive functions, and promotes attention, concentration, imagination, creativity, elicits memories and emotions, and stimulates imagination, and harmony of movement. It changes the chemistry of the brain, by inducing the release of neurotransmitters and hormones (dopamine, serotonin, and oxytocin) and activates the reward and prosocial systems. In addition, music is also used to develop new therapies necessary to alleviate severe illness, especially neurological disorders, and brain injuries.

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.

Simultaneous relaxometry and morphometry of human brain structures with 3D magnetic resonance fingerprinting: a multicenter, multiplatform, multifield-strength study

Relaxation times and morphological information are fundamental magnetic resonance imaging-derived metrics of the human brain that reflect the status of the underlying tissue. Magnetic resonance fingerprinting (MRF) enables simultaneous acquisition of T1 and T2 maps inherently aligned to the anatomy, allowing whole-brain relaxometry and morphometry in a single scan. In this study, we revealed the feasibility of 3D MRF for simultaneous brain structure-wise morphometry and relaxometry. Comprehensive test-retest scan analyses using five 1.5-T and three 3.0-T systems from a single vendor including different scanner types across 3 institutions demonstrated that 3D MRF-derived morphological information and relaxation times are highly repeatable at both 1.5 T and 3.0 T. Regional cortical thickness and subcortical volume values showed high agreement and low bias across different field strengths. The ability to acquire a set of regional T1, T2, thickness, and volume measurements of neuroanatomical structures with high repeatability and reproducibility facilitates the ability of longitudinal multicenter imaging studies to quantitatively monitor changes associated with underlying pathologies, disease progression, and treatments.

Predictors of subjective cognitive deficits in patients with mild cognitive impairment

BACKGROUND

Detailed examination of cognitive deficits in patients with mild cognitive impairment (MCI) yields substantial diagnostic and prognostic value, specifically with respect to memory. Magnitude and characteristics of subjective cognitive deficits, however, often receive less attention in this population at risk for developing dementia.

METHODS

We investigated predictors of subjective cognitive deficits in patients with MCI, using a detailed assessment for such impairments associated with different cognitive domains, as well as demographic and clinical variables including magnetic resonance imaging data.

RESULTS

The strongest predictor for subjective memory deficits was depressed mood, whereas subjective performance issues associated with attention or executive functions also corresponded to measurable impairments in the respective cognitive domains. Reduced hippocampal thickness and hemispheric entorhinal cortex thickness asymmetry were associated with objective memory impairment but not with subjective deficits or symptoms of depression.

CONCLUSIONS

Whereas low objective memory performance and reduced cortical thickness within medial temporal lobe subregions could be associated with neurodegeneration, greater subjective memory deficits in patients with MCI may indicate psychological burden.

Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data

The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.

Relation of middle cerebral artery flow velocity and risk of cognitive decline: A prospective community-based study

OBJECTIVES

Hemodynamic parameters measured by the Transcranial Doppler Ultrasound (TCD) are related to cognitive impairment in many cross-sectional studies, but the longitudinal evidence is scarce. In this study, we aim to verify the association between flow velocity of Middle Cerebral Artery (MCA) and the longitudinal cognitive decline in community dwelling older adults.

MATERIALS AND METHODS

Participants were administered TCD examination at the baseline. The Peak Systolic Velocity (PSV), Mean Flow Velocity (MFV), and Pulsatility Index (PI) of MCA segments on left middle (LmMCA), left proximal (LpMCA), right middle (RmMCA), and right proximal (RpMCA) were obtained. Mini-mental state examination (MMSE) were conducted at both baseline and follow-up.

RESULTS

One hundred and thirteen participants without dementia were followed up for 6.3 years in average. The mean annual rate of decline in the MMSE score was 0.15 (min to max: -1.0 to 1.2). LpMCA PSV (β = -0.0034, r = -0.231, P = 0.022) and LpMCA MFV (β = -0.0049, r = -0.217, P = 0.031) were inversely associated with annual rate of decline in the MMSE score after adjusting for age, gender, education year, APOE ε4, obesity, hypertension, diabetes mellitus, stroke, and coronary heart disease.

CONCLUSIONS

Blood flow velocity of left proximal MCA was inversely related to global cognitive decline. Cerebral blood flow velocity may impact the cognitive function.

Association between polygenic risk for Alzheimer's disease, brain structure and cognitive abilities in UK Biobank

Previous studies testing associations between polygenic risk for late-onset Alzheimer's disease (LOAD-PGR) and brain magnetic resonance imaging (MRI) measures have been limited by small samples and inconsistent consideration of potential confounders. This study investigates whether higher LOAD-PGR is associated with differences in structural brain imaging and cognitive values in a relatively large sample of non-demented, generally healthy adults (UK Biobank). Summary statistics were used to create PGR scores for n = 32,790 participants using LDpred. Outcomes included 12 structural MRI volumes and 6 concurrent cognitive measures. Models were adjusted for age, sex, body mass index, genotyping chip, 8 genetic principal components, lifetime smoking, apolipoprotein (APOE) e4 genotype and socioeconomic deprivation. We tested for statistical interactions between APOE e4 allele dose and LOAD-PGR vs. all outcomes. In fully adjusted models, LOAD-PGR was associated with worse fluid intelligence (standardised beta [β] = -0.080 per LOAD-PGR standard deviation, p = 0.002), matrix completion (β = -0.102, p = 0.003), smaller left hippocampal total (β = -0.118, p = 0.002) and body (β = -0.069, p = 0.002) volumes, but not other hippocampal subdivisions. There were no significant APOE x LOAD-PGR score interactions for any outcomes in fully adjusted models. This is the largest study to date investigating LOAD-PGR and non-demented structural brain MRI and cognition phenotypes. LOAD-PGR was associated with smaller hippocampal volumes and aspects of cognitive ability in healthy adults and could supplement APOE status in risk stratification of cognitive impairment/LOAD.

A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.

Agreement Between Automated and Manual MRI Volumetry in Alzheimer's Disease: A Systematic Review and Meta-Analysis

BACKGROUND

Automated magnetic resonance imaging (MRI) volumetry is a promising tool to evaluate regional brain volumes in dementia and especially Alzheimer's disease (AD).

PURPOSE

To compare automated methods and the gold standard manual segmentation in measuring regional brain volumes on MRI across healthy controls, patients with mild cognitive impairment, and patients with dementia due to AD.

STUDY TYPE

Systematic review and meta-analysis.

DATA SOURCES

MEDLINE, Embase, and PsycINFO were searched through October 2021.

FIELD STRENGTH

1.0 T, 1.5 T, or 3.0 T.

ASSESSMENT

Two review authors independently identified studies for inclusion and extracted data. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).

STATISTICAL TESTS

Standardized mean differences (SMD; Hedges' g) were pooled using random-effects meta-analysis with robust variance estimation. Subgroup analyses were undertaken to explore potential sources of heterogeneity. Sensitivity analyses were conducted to examine the impact of the within-study correlation between effect estimates on the meta-analysis results.

RESULTS

Seventeen studies provided sufficient data to evaluate the hippocampus, lateral ventricles, and parahippocampal gyrus. The pooled SMD for the hippocampus, lateral ventricles, and parahippocampal gyrus were 0.22 (95% CI -0.50 to 0.93), 0.12 (95% CI -0.13 to 0.37), and -0.48 (95% CI -1.37 to 0.41), respectively. For the hippocampal data, subgroup analyses suggested that the pooled SMD was invariant across clinical diagnosis and field strength. Subgroup analyses could not be conducted on the lateral ventricles data and the parahippocampal gyrus data due to insufficient data. The results were robust to the selected within-study correlation value.

DATA CONCLUSION

While automated methods are generally comparable to manual segmentation for measuring hippocampal, lateral ventricle, and parahippocampal gyrus volumes, wide 95% CIs and large heterogeneity suggest that there is substantial uncontrolled variance. Thus, automated methods may be used to measure these regions in patients with AD but should be used with caution.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns

Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date, subtyping of MCI has been based primarily on cognitive measures, often resulting in indistinct boundaries between subgroups and limited validity. Here, we introduce a subtyping method for MCI based solely upon brain atrophy. We train a deep learning model to differentiate between Alzheimer's disease (AD) and cognitively normal (CN) subjects based on whole-brain MRI features. We then deploy the trained model to classify MCI subjects based on whole-brain gray matter resemblance to AD-like or CN-like patterns. We subsequently validate the subtyping approach using cognitive, clinical, fluid biomarker, and molecular imaging data. Overall, the results suggest that atrophy patterns in MCI are sufficiently heterogeneous and can thus be used to subtype individuals into biologically and clinically meaningful subgroups.

Electrophysiological Biomarkers of Epileptogenicity in Alzheimer's Disease

Cortical network hyperexcitability is an inextricable feature of Alzheimer’s disease (AD) that also might accelerate its progression. Seizures are reported in 10–22% of patients with AD, and subclinical epileptiform abnormalities have been identified in 21–42% of patients with AD without seizures. Accurate identification of hyperexcitability and appropriate intervention to slow the compromise of cognitive functions of AD might open up a new approach to treatment. Based on the results of several studies, epileptiform discharges, especially those with specific features (including high frequency, robust morphology, right temporal location, and occurrence during awake or rapid eye movement states), frequent small sharp spikes (SSSs), temporal intermittent rhythmic delta activities (TIRDAs), and paroxysmal slow wave events (PSWEs) recorded in long-term scalp electroencephalogram (EEG) provide sufficient sensitivity and specificity in detecting cortical network hyperexcitability and epileptogenicity of AD. In addition, magnetoencephalogram (MEG), foramen ovale (FO) electrodes, and computational approaches help to find subclinical seizures that are invisible on scalp EEGs. We performed a comprehensive analysis of the aforementioned electrophysiological biomarkers of AD-related seizures.

Neuroimaging Biomarkers Predicting the Efficacy of Multimodal Rehabilitative Intervention in the Alzheimer's Dementia Continuum Pathology

In this work we aimed to identify neural predictors of the efficacy of multimodal rehabilitative interventions in AD-continuum patients in the attempt to identify ideal candidates to improve the treatment outcome. Subjects in the AD continuum who participated in a multimodal rehabilitative treatment were included in the analysis [n = 82, 38 Males, mean age = 76 ± 5.30, mean education years = 9.09 ± 3.81, Mini Mental State Examination (MMSE) mean score = 23.31 ± 3.81]. All subjects underwent an MRI acquisition (1.5T) at baseline (T0) and a neuropsychological evaluation before (T0) and after intervention (T1). All subjects underwent an intensive multimodal cognitive rehabilitation (8–10 weeks). The MMSE and Neuropsychiatric Inventory (NPI) scores were considered as the main cognitive and behavioral outcome measures, and Delta change scores (T1–T0) were categorized in Improved (ΔMMSE > 0; ΔNPI < 0) and Not Improved (ΔMMSE ≤ 0; ΔNPI ≥ 0). Logistic Regression (LR) and Random Forest classification models were performed including neural markers (Medial Temporal Brain; Posterior Brain (PB); Frontal Brain (FB), Subcortical Brain indexes), neuropsychological (MMSE, NPI, verbal fluencies), and demographical variables (sex, age, education) at baseline. More than 50% of patients showed a positive effect of the treatment (ΔMMSE > 0: 51%, ΔNPI < 0: 52%). LR model on ΔMMSE (Improved vs. Not Improved) indicate a predictive role for MMSE score (p = 0.003) and PB index (p = 0.005), especially the right PB (p = 0.002) at baseline. The Random Forest analysis correctly classified 77% of cognitively improved and not improved AD patients. Concerning the NPI, LR model on ΔNPI (Improved vs. Not Improved) showed a predictive role of sex (p = 0.002), NPI (p = 0.005), PB index (p = 0.006), and FB index (p = 0.039) at baseline. The Random Forest reported a classification accuracy of 86%. Our data indicate that cognitive and behavioral status alone are not sufficient to identify best responders to a multidomain rehabilitation treatment. Increased neural reserve, especially in the parietal areas, is also relevant for the compensatory mechanisms activated by rehabilitative treatment. These data are relevant to support clinical decision by identifying target patients with high probability of success after rehabilitative programs on cognitive and behavioral functioning.

Brain aging mechanisms with mechanical manifestations

Brain aging is a complex process that affects everything from the subcellular to the organ level, begins early in life, and accelerates with age. Morphologically, brain aging is primarily characterized by brain volume loss, cortical thinning, white matter degradation, loss of gyrification, and ventricular enlargement. Pathophysiologically, brain aging is associated with neuron cell shrinking, dendritic degeneration, demyelination, small vessel disease, metabolic slowing, microglial activation, and the formation of white matter lesions. In recent years, the mechanics community has demonstrated increasing interest in modeling the brain's (bio)mechanical behavior and uses constitutive modeling to predict shape changes of anatomically accurate finite element brain models in health and disease. Here, we pursue two objectives. First, we review existing imaging-based data on white and gray matter atrophy rates and organ-level aging patterns. This data is required to calibrate and validate constitutive brain models. Second, we review the most critical cell- and tissue-level aging mechanisms that drive white and gray matter changes. We focuse on aging mechanisms that ultimately manifest as organ-level shape changes based on the idea that the integration of imaging and mechanical modeling may help identify the tipping point when normal aging ends and pathological neurodegeneration begins.

Bilingualism contributes to reserve and working memory efficiency: Evidence from structural and functional neuroimaging

This study compared brain and behavioral outcomes for monolingual and bilingual older adults who reported no cognitive or memory problems on three types of memory that typically decline in older age, namely, working memory (measured by n-back), item, and associative recognition. The results showed that bilinguals were faster on the two-back working memory task than monolinguals but used a set of frontostriatal regions less than monolinguals. There was no group difference on an item/associative recognition task. In brain structure, gray matter volume and white matter integrity (fractional anisotropy) were generally lower in bilinguals than in monolinguals, but bilinguals had better white matter integrity than monolinguals in the bilateral superior corona radiata and better gray matter density in the left inferior temporal gyrus. These regions may help preserve bilinguals' executive functions despite generally more significant atrophy throughout the brain than monolinguals in that these structures contribute to efficient communication between executive frontal regions and subcortical motor regions, and perceptual pathways. Reliable negative correlations between brain structure and age were only observed in bilinguals, and to the extent that bilinguals (but not monolinguals) had better brain structure, their performance was enhanced. Collectively, the findings provide evidence for reserve in bilingual older adults.