Finding of increased caudate nucleus in patients with Alzheimer's disease

Constructing hierarchical attentive functional brain networks for early AD diagnosis

Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature. This urges the need of representing FBN in a hierarchical manner for more effective analysis of the complementary diagnostic insights at different scales. To this end, this paper proposes to build hierarchical FBNs adaptively within the Transformer framework. Specifically, a sparse attention-based node-merging module is designed to work alongside the conventional network feature extraction modules in each layer. The proposed module generates coarser nodes for further FBN construction and analysis by combining fine-grained nodes. By stacking multiple such layers, a hierarchical representation of FBN can be adaptively learned in an end-to-end manner. The hierarchical structure can not only integrate the complementary information from multiscale FBN for joint analysis, but also reduce the model complexity due to decreasing node sizes. Moreover, this paper argues that the nodes defined by the existing atlases are not necessarily the optimal starting level to build FBN hierarchy and exploring finer nodes may further enrich the FBN representation. In this regard, each predefined node in an atlas is split into multiple sub-nodes, overcoming the scale limitation of the existing atlases. Extensive experiments conducted on various data sets consistently demonstrate the superior performance of the proposed method over the competing methods.

Association of Longitudinal Changes in Cerebral Microstructure with Cognitive Functioning in Breast Cancer Survivors after Adjuvant Chemotherapy

Background: Adjuvant chemotherapy for breast cancer might impact cognitive function and brain structure. Methods: In this study, we investigated the cerebral microstructural changes in breast cancer survivors after adjuvant chemotherapy and the correlation with cognitive function with both cross-sectional and longitudinal study designs. All participants underwent structural MRI. In total, we recruited 67 prechemotherapy patients (BB), 67 postchemotherapy patients (BA), and 77 healthy controls (BH). For the follow-up study, 28 participants in the BH and 28 in the BB groups returned for imaging and assessment (BHF, BBF). Voxel-based morphometry analysis was performed to evaluate differences in brain volume; vertex-based shape analysis was used to assess the shape alterations of subcortical regions. Moreover, multiple regression was applied to assess the association between the changes in neuropsychological assessment and brain volume. Results: The results showed brain volume reduction in the temporal and parietal gyrus in BB and BA patients. Among each group, we also found significant shape alterations in the caudate and thalamus. Volume reductions in the temporal regions and shape changes in the caudate and hippocampus were also observed in patients from time point 1 to time point 2 (postchemotherapy). An association between brain volume and cognitive performance was also found in the limbic system. Conclusions: Based on our findings, we can provide a better understanding of the cerebral structural changes in breast cancer survivors, establish a subsequent prediction model, and serve as a reference for subsequent treatment.

Multi-Ethnic Norms for Volumes of Subcortical and Lobar Brain Structures Measured by Neuro I: Ethnicity May Improve the Diagnosis of Alzheimer's Disease1

Background

We previously demonstrated the validity of a regression model that included ethnicity as a novel predictor for predicting normative brain volumes in old age. The model was optimized using brain volumes measured with a standard tool FreeSurfer.

Objective

Here we further verified the prediction model using newly estimated brain volumes from Neuro I, a quantitative brain analysis system developed for Korean populations.

Methods

Lobar and subcortical volumes were estimated from MRI images of 1,629 normal Korean and 786 Caucasian subjects (age range 59-89) and were predicted in linear regression from ethnicity, age, sex, intracranial volume, magnetic field strength, and scanner manufacturers.

Results

In the regression model predicting the new volumes, ethnicity was again a substantial predictor in most regions. Additionally, the model-based z-scores of regions were calculated for 428 AD patients and the matched controls, and then employed for diagnostic classification. When the AD classifier adopted the z-scores adjusted for ethnicity, the diagnostic accuracy has noticeably improved (AUC = 0.85, ΔAUC = + 0.04, D = 4.10, p < 0.001).

Conclusions

Our results suggest that the prediction model remains robust across different measurement tool, and ethnicity significantly contributes to the establishment of norms for brain volumes and the development of a diagnostic system for neurodegenerative diseases.

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links

In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.

Smaller grey matter volume in the central olfactory system in mild cognitive impairment

One of the major challenges in the diagnosis of Alzheimer's disease (AD) is to increase the specificity of the early diagnosis. While episodic memory impairment is a sensitive AD marker, other measures are needed to improve diagnostic specificity. A promising biomarker might be a cerebral atrophy of the central olfactory processing areas in the early stages of the disease since an impairment of olfactory identification is present at the clinical stage of AD. Our goal was therefore, (1) to evaluate the grey matter volume (GMV) of central olfactory processing regions in prodromal AD and (2) to assess its association with episodic memory. We included 34 cognitively normal healthy controls (HC), 92 individuals with subjective cognitive decline (SCD), and 40 with mild cognitive impairment (MCI). We performed regions of interest analysis (ROI) using two different approaches, allowing to extract GMV from (1) atlas-based anatomical ROIs and from (2) functional and non-functional subregions of these ROIs (olfactory ROIs and non-olfactory ROIs). Participants with MCI exhibited smaller olfactory ROIs GMV, including significant reductions in the piriform cortex, amygdala, entorhinal cortex, and left hippocampus compared to other groups (p ≤ 0.05, corrected). No significant effect was found regarding anatomical or non-olfactory ROIs GMV. The left hippocampus olfactory ROI GMV was correlated with episodic memory performance (p < 0.05 corrected). Limbic/medial-temporal olfactory processing areas are specifically atrophied at the MCI stage, and the degree of atrophy might predict cognitive decline in AD early stages.

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.

Brain proteomic analysis implicates actin filament processes and injury response in resilience to Alzheimer's disease

Resilience to Alzheimer's disease is an uncommon combination of high disease burden without dementia that offers valuable insights into limiting clinical impact. Here we assessed 43 research participants meeting stringent criteria, 11 healthy controls, 12 resilience to Alzheimer's disease and 20 Alzheimer's disease with dementia and analyzed matched isocortical regions, hippocampus, and caudate nucleus by mass spectrometry-based proteomics. Of 7115 differentially expressed soluble proteins, lower isocortical and hippocampal soluble Aβ levels is a significant feature of resilience when compared to healthy control and Alzheimer's disease dementia groups. Protein co-expression analysis reveals 181 densely-interacting proteins significantly associated with resilience that were enriched for actin filament-based processes, cellular detoxification, and wound healing in isocortex and hippocampus, further supported by four validation cohorts. Our results suggest that lowering soluble Aβ concentration may suppress severe cognitive impairment along the Alzheimer's disease continuum. The molecular basis of resilience likely holds important therapeutic insights.

Heritability of cognitive abilities and regional brain structures in middle-aged to elderly East Asians

This study examined the single-nucleotide polymorphism heritability and genetic correlations of cognitive abilities and brain structural measures (regional subcortical volume and cortical thickness) in middle-aged and elderly East Asians (Korean) from the Gwangju Alzheimer's and Related Dementias cohort study. Significant heritability was found in memory function, caudate volume, thickness of the entorhinal cortices, pars opercularis, superior frontal gyri, and transverse temporal gyri. There were 3 significant genetic correlations between (i) the caudate volume and the thickness of the entorhinal cortices, (ii) the thickness of the superior frontal gyri and pars opercularis, and (iii) the thickness of the superior frontal and transverse temporal gyri. This is the first study to describe the heritability and genetic correlations of cognitive and neuroanatomical traits in middle-aged to elderly East Asians. Our results support the previous findings showing that genetic factors play a substantial role in the cognitive and neuroanatomical traits in middle to advanced age. Moreover, by demonstrating shared genetic effects on different brain regions, it gives us a genetic insight into understanding cognitive and brain changes with age, such as aging-related cognitive decline, cortical atrophy, and neural compensation.

Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates

It is well documented that some brain regions, such as association cortices, caudate, and hippocampus, are particularly prone to age-related atrophy, but it has been hypothesized that there are individual differences in atrophy profiles. Here, we document heterogeneity in regional-atrophy patterns using latent-profile analysis of 1,482 longitudinal magnetic resonance imaging observations. The results supported a 2-group solution reflecting differences in atrophy rates in cortical regions and hippocampus along with comparable caudate atrophy. The higher-atrophy group had the most marked atrophy in hippocampus and also lower episodic memory, and their normal caudate atrophy rate was accompanied by larger baseline volumes. Our findings support and refine models of heterogeneity in brain aging and suggest distinct mechanisms of atrophy in striatal versus hippocampal-cortical systems.

Diagnosis of Alzheimer's disease using hypergraph p-Laplacian regularized multi-task feature learning

Multimodal data-based classification methods have been widely used in the diagnosis of Alzheimer's disease (AD) and have achieved better performance than single-modal-based methods. However, most classification methods based on multimodal data tend to consider only the correlation between different modal data and ignore the inherent non-linear higher-order relationships between similar data, which can improve the robustness of the model. Therefore, this study proposes a hypergraph p-Laplacian regularized multi-task feature selection (HpMTFS) method for AD classification. Specifically, feature selection for each modal data is considered as a distinct task and the common features of multimodal data are extracted jointly by group-sparsity regularizer. In particular, two regularization terms are introduced in this study, namely (1) a hypergraph p-Laplacian regularization term to retain higher-order structural information for similar data, and (2) a Frobenius norm regularization term to improve the noise immunity of the model. Finally, using a multi-kernel support vector machine to fuse multimodal features and perform the final classification. We used baseline sMRI, FDG-PET, and AV-45 PET imaging data from 528 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate our approach. Experimental results show that our HpMTFS method outperforms existing multimodal-based classification methods.

Landmark-dependent Navigation Strategy Declines across the Human Life-Span: Evidence from Over 37,000 Participants

Humans show a remarkable capacity to navigate various environments using different navigation strategies, and we know that strategy changes across the life span. However, this observation has been based on studies of small sample sizes. To this end, we used a mobile app-based video game (Sea Hero Quest) to test virtual navigation strategies and memory performance within a distinct radial arm maze level in over 37,000 participants. Players were presented with six pathways (three open and three closed) and were required to navigate to the three open pathways to collect a target. Next, all six pathways were made available and the player was required to visit the pathways that were previously unavailable. Both reference memory and working memory errors were calculated. Crucially, at the end of the level, the player was asked a multiple-choice question about how they found the targets (i.e., a counting-dependent strategy vs. a landmark-dependent strategy). As predicted from previous laboratory studies, we found the use of landmarks declined linearly with age. Those using landmark-based strategies also performed better on reference memory than those using a counting-based strategy. These results extend previous observations in the laboratory showing a decreased use of landmark-dependent strategies with age.

Subcortical brain structures and the risk of dementia in the Rotterdam Study

INTRODUCTION

Volumetric and morphological changes in subcortical brain structures are present in persons with dementia, but it is unknown if these changes occur prior to diagnosis.

METHODS

Between 2005 and 2016, 5522 Rotterdam Study participants (mean age: 64.4) underwent cerebral magnetic resonance imaging (MRI) and were followed for development of dementia until 2018. Volume and shape measures were obtained for seven subcortical structures.

RESULTS

During 12 years of follow-up, 272 dementia cases occurred. Mean volumes of thalamus (hazard ratio [HR] per standard deviation [SD] decrease 1.94, 95% confidence interval [CI]: 1.55-2.43), amygdala (HR 1.66, 95% CI: 1.44-1.92), and hippocampus (HR 1.64, 95% CI: 1.43-1.88) were strongly associated with dementia risk. Associations for accumbens, pallidum, and caudate volumes were less pronounced. Shape analyses identified regional surface changes in the amygdala, limbic thalamus, and caudate.

DISCUSSION

Structure of the amygdala, thalamus, hippocampus, and caudate is associated with risk of dementia in a large population-based cohort of older adults.

Increased functional connectivity patterns in mild Alzheimer's disease: A rsfMRI study

Background

Alzheimer's disease (AD) is the most common age-related neurodegenerative disorder. In view of our rapidly aging population, there is an urgent need to identify Alzheimer's disease (AD) at an early stage. A potential way to do so is by assessing the functional connectivity (FC), i.e., the statistical dependency between two or more brain regions, through novel analysis techniques.

Methods

In the present study, we assessed the static and dynamic FC using different approaches. A resting state (rs)fMRI dataset from the Alzheimer's disease neuroimaging initiative (ADNI) was used (n = 128). The blood-oxygen-level-dependent (BOLD) signals from 116 regions of 4 groups of participants, i.e., healthy controls (HC; n = 35), early mild cognitive impairment (EMCI; n = 29), late mild cognitive impairment (LMCI; n = 30), and Alzheimer's disease (AD; n = 34) were extracted and analyzed. FC and dynamic FC were extracted using Pearson's correlation, sliding-windows correlation analysis (SWA), and the point process analysis (PPA). Additionally, graph theory measures to explore network segregation and integration were computed.

Results

Our results showed a longer characteristic path length and a decreased degree of EMCI in comparison to the other groups. Additionally, an increased FC in several regions in LMCI and AD in contrast to HC and EMCI was detected. These results suggest a maladaptive short-term mechanism to maintain cognition.

Conclusion

The increased pattern of FC in several regions in LMCI and AD is observable in all the analyses; however, the PPA enabled us to reduce the computational demands and offered new specific dynamic FC findings.

Fitting Contralateral Neuroanatomical Asymmetry into the Amyloid Cascade Hypothesis

Alzheimer's Disease (AD) is the most common cause of dementia. Due to the progressive nature of the neurodegeneration associated with the disease, it is of clinical interest to achieve an early diagnosis of AD. In this study, we analyzed the viability of asymmetry-related measures as potential biomarkers to facilitate the early diagnosis of AD. These measures were obtained from MAPER-segmented MP-RAGE MRI studies available at the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and by analyzing these studies at the level of individual segmented regions. The temporal evolution of these measures was obtained and then analyzed by generating spline regression models. Data imputation was performed where missing information prevented the temporal analysis of each measure from being realized, using additional information provided by ADNI for each patient. The temporal evolution of these measures was compared to the evolution of other commonly used markers for the diagnosis of AD, such as cognitive function, concentrations of Phosphorylated-Tau, Amyloid-β, and structural MRI volumetry. The results of the regression models showed that asymmetry measures, in particular regions such as the parahippocampal gyrus, differentiated themselves temporally before most of the other evaluated biomarkers. Further studies are suggested to corroborate these results.

Biomarkers identification for Schizophrenia via VAE and GSDAE-based data augmentation

Deep learning has made great progress in analyzing MRI data, while the MRI data with high dimensional but small sample size (HDSSS) brings many limitations to biomarkers identification. Few-shot learning has been proposed to solve such problems and data augmentation is a typical method of it. The variational auto-encoder (VAE) is a generative method based on variational Bayesian inference that is used for data augmentation. Graph regularized sparse deep autoencoder (GSDAE) can reconstruct sparse samples and keep the manifold structure of data which will facilitate biomarkers selection greatly. To generate better HDSSS data for biomarkers identification, a data augmentation method based on VAE and GSDAE is proposed in this paper, termed GS-VDAE. Instead of utilizing the final products of GSDAE, our proposed model embeds the generation procedure into GSDAE for augmentation. In this way, the augmented samples will be rooted in the significant features extracted from the original samples, which can ensure the newly formed samples contain the most significant characteristics of the original samples. The classification accuracy of the samples generated directly from VAE is 0.74, while the classification accuracy of the samples generated from GS-VDAE is 0.84, which proves the validity of our model. Additionally, a regression feature selection method with truncated nuclear norm regularization is chosen for biomarkers selection. The biomarkers selection results of schizophrenia data reveal that the augmented samples obtained by our proposed method can get higher classification accuracy with less ranked features compared with original samples, which proves the validation of our model.

[Brain MRI-Based Artificial Intelligence Software in Patients with Neurodegenerative Diseases: Current Status]

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.

Updated Review of the Evidence Supporting the Medical and Legal Use of NeuroQuant® and NeuroGage® in Patients With Traumatic Brain Injury

Over 40 years of research have shown that traumatic brain injury affects brain volume. However, technical and practical limitations made it difficult to detect brain volume abnormalities in patients suffering from chronic effects of mild or moderate traumatic brain injury. This situation improved in 2006 with the FDA clearance of NeuroQuant®, a commercially available, computer-automated software program for measuring MRI brain volume in human subjects. More recent strides were made with the introduction of NeuroGage®, commercially available software that is based on NeuroQuant® and extends its utility in several ways. Studies using these and similar methods have found that most patients with chronic mild or moderate traumatic brain injury have brain volume abnormalities, and several of these studies found-surprisingly-more abnormal enlargement than atrophy. More generally, 102 peer-reviewed studies have supported the reliability and validity of NeuroQuant® and NeuroGage®. Furthermore, this updated version of a previous review addresses whether NeuroQuant® and NeuroGage® meet the Daubert standard for admissibility in court. It concludes that NeuroQuant® and NeuroGage® meet the Daubert standard based on their reliability, validity, and objectivity. Due to the improvements in technology over the years, these brain volumetric techniques are practical and readily available for clinical or forensic use, and thus they are important tools for detecting signs of brain injury.

Structural and Functional Abnormalities of Olfactory-Related Regions in Subjective Cognitive Decline, Mild Cognitive Impairment, and Alzheimer's Disease

BACKGROUND

Odor identification (OI) dysfunction is an early marker of Alzheimer's disease (AD), but it remains unclear how olfactory-related regions change from stages of subjective cognitive decline (SCD) and mild cognitive impairment (MCI) to AD dementia.

METHODS

Two hundred and sixty-nine individuals were recruited in the present study. The olfactory-related regions were defined as the regions of interest, and the grey matter volume (GMV), low-frequency fluctuation, regional homogeneity (ReHo), and functional connectivity (FC) were compared for exploring the changing pattern of structural and functional abnormalities across AD, MCI, SCD, and normal controls.

RESULTS

From the SCD, MCI to AD groups, the reduced GMV, increased low-frequency fluctuation, increased ReHo, and reduced FC of olfactory-related regions became increasingly severe, and only the degree of reduced GMV of hippocampus and caudate nucleus clearly distinguished the 3 groups. SCD participants exhibited reduced GMV (hippocampus, etc.), increased ReHo (caudate nucleus), and reduced FC (hippocampus-hippocampus and hippocampus-parahippocampus) in olfactory-related regions compared with normal controls. Additionally, reduced GMV of the bilateral hippocampus and increased ReHo of the right caudate nucleus were associated with OI dysfunction and global cognitive impairment, and they exhibited partially mediated effects on the relationships between OI and global cognition across all participants.

CONCLUSION

Structural and functional abnormalities of olfactory-related regions present early with SCD and deepen with disease severity in the AD spectrum. The hippocampus and caudate nucleus may be the hub joining OI and cognitive function in the AD spectrum.

[Modulatory effect of 2-arachidonoylglycerol on voltage-gated sodium currents in rat caudate nucleus neurons with kainic acid-induced injury]

OBJECTIVE

To investigate the modulatory effect of 2-arachidonoylglycerol (2-AG) on voltage-gated sodium currents(VGSCs) in rat caudate nucleus (CN) neurons with kainic acid (KA)-induced injury and explore the molecular mechanism underlying the neuroprotective effect of 2-AG.

METHODS

Primary cultures of CN neurons isolated from neonatal SD rats were treated with KA, 2-AG+KA, RIM (a CB1 receptor antagonist) +2-AG+KA, or vehicle only (as control).After 7 days in primary culture, the neurons were treated with corresponding agents for 12 h (RIM and 2-AG were added at the same time; KA was added 30 min later) before recording of current density changes, current-voltage characteristics, activation and inactivation kinetics of VGSCs (I_Na) using whole-cell patch clamp technique.

RESULTS

In cultured CN neurons, KA significantly increased current density of VGSCs (P=0.009) as compared with vehicle treatment.KA also produced a hyperpolarizing shift in the activation curve of I_Na and significantly increased the absolute value of V_1/2 for activation (P=0.008).Addition of 2-AG in the culture medium obviously prevented KA-induced increase of I_Na (P=0.009) and hyperpolarizing shift in the activation curve of I_Na, and significantly reduced the value of V_1/2 for activation(P=0.009)in a CB1 receptor-dependent manner.2-AG alone did not affect the density, activation or deactivation of VGSCs in rat CN neurons.

CONCLUSION

In excitotoxic events, endogenous 2-AG can offer neuroprotection by modulating VGSCs in the CN neurons through a CB1 receptor-dependent pathway.

Altered properties of brain white matter structural networks in patients with nasopharyngeal carcinoma after radiotherapy

Previous neuroimaging studies revealed radiation-induced brain injury in patients with nasopharyngeal carcinoma (NPC) in the years after radiotherapy (RT). These injuries may be associated with structural and functional alterations. However, differences in the brain structural connectivity of NPC patients at different times after RT, especially in the early-delayed period, remain unclear. We acquired diffusion tensor imaging (DTI) data from three groups of NPC patients, 25 in the pre-RT (before RT) group, 22 in the early-delayed (1-6 months) period (post-RT-ED) group, and 33 in the late-delayed (>6 months) period (post-RT-LD) group. Then, we constructed brain white matter (WM) structural networks and used graph theory to compare their between-group differences. The NPC patients in the post-RT-ED group showed decreased global properties when compared with the pre-RT group. We also detected the nodes with between-group differences in nodal parameters. The nodes that differed between the post-RT-ED and pre-RT groups were mainly located in the default mode (DMN) and central executive networks (CEN); those that differed between the post-RT-LD and pre-RT groups were located in the limbic system; and those that differed between the post-RT-LD and post-RT-ED groups were mainly in the DMN. These findings may indicate that radiation-induced brain injury begins in the early-delayed period and that a reorganization strategy begins in the late-delayed period. Our findings may provide new insight into the pathogenesis of radiation-induced brain injury in normal-appearing brain tissue from the network perspective.

Systematic Review: Quantitative Susceptibility Mapping (QSM) of Brain Iron Profile in Neurodegenerative Diseases

Iron has been increasingly implicated in the pathology of neurodegenerative diseases. In the past decade, development of the new magnetic resonance imaging technique, quantitative susceptibility mapping (QSM), has enabled for the more comprehensive investigation of iron distribution in the brain. The aim of this systematic review was to provide a synthesis of the findings from existing QSM studies in neurodegenerative diseases. We identified 80 records by searching MEDLINE, Embase, Scopus, and PsycInfo databases. The disorders investigated in these studies included Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Wilson's disease, Huntington's disease, Friedreich's ataxia, spinocerebellar ataxia, Fabry disease, myotonic dystrophy, pantothenate-kinase-associated neurodegeneration, and mitochondrial membrane protein-associated neurodegeneration. As a general pattern, QSM revealed increased magnetic susceptibility (suggestive of increased iron content) in the brain regions associated with the pathology of each disorder, such as the amygdala and caudate nucleus in Alzheimer's disease, the substantia nigra in Parkinson's disease, motor cortex in amyotrophic lateral sclerosis, basal ganglia in Huntington's disease, and cerebellar dentate nucleus in Friedreich's ataxia. Furthermore, the increased magnetic susceptibility correlated with disease duration and severity of clinical features in some disorders. Although the number of studies is still limited in most of the neurodegenerative diseases, the existing evidence suggests that QSM can be a promising tool in the investigation of neurodegeneration.

Expert Opinions and Recommendations for the Clinical Use of Quantitative Analysis Software for MRI-Based Brain Volumetry

치매를 비롯한 퇴행성 신경 질환의 초기 진단에 자기공명영상을 이용한 뇌 위축 평가와 정량적 용적 분석이 중요하다. 뇌 위축의 시각적 평가는 주관적으로 평가자에 따라 다른 결과를 보여주기 때문에, 객관적인 결과를 제공하면서 임상 적용도 가능한 소프트웨어의 수요와 개발이 늘어나고 있다. 이러한 임상용 소프트웨어의 실제 임상 적용은 영상 검사의 표준화가 선행되어야 하고, 개발된 소프트웨어의 검증이 반드시 필요하다. 따라서 대한신경두경부영상의학회는 뇌용적 분석 임상용 소프트웨어의 임상적 활용에 대한 의견을 제시하기 위해 전문위원회를 구성하고 현재까지 발표된 연구를 정리하였다. 그리고, 정량화 분석을 위한 영상 검사의 표준화 및 소프트웨어의 임상 적용에 대한 전문가 의견을 제시하기 위하여 공동 작업을 수행하였다. 본 종설에서는 뇌 자기공명영상의 정량화 분석의 필요성 및 배경, 정량화 분석을 위한 임상용 소프트웨어의 소개 및 기존의 표준품(reference standard)과의 진단능 비교, 영상 획득의 표준화, 분석 및 평가의 표준화, 소프트웨어의 임상 적용에 대한 전문가 의견, 제한점 및 대처 방법 등 대한신경두경부영상의학회의 전문가 권고안을 소개하는 것이 목적이다.

Altered Functional Connectivity Strength of Primary Visual Cortex in Subjects with Diabetic Retinopathy

OBJECTIVE

The purpose of the study was to find the differences in intrinsic functional connectivity (FC) patterns of the primary visual area (V1) among diabetic retinopathy (DR), diabetes mellitus (DM), and healthy controls (HCs) applying resting-state functional magnetic resonance imaging (rs-fMRI).

PATIENTS AND METHODS

Thirty-five subjects with DR (18 males and 17 females), 22 DM (10 males and 12 females) and 38 HCs (16 males and 22 females) matched for sex, age, and education underwent rs-fMRI scanning. Seed-based FC analysis was performed to find the alterations in the intrinsic FC patterns of V1 in DR compared with DM and HCs.

RESULTS

The study found that DR patients had a significant lower FC between the bilateral calcarine (CAL)/left lingual gyrus (LING) (BA 17/18) and the left V1, and between the bilateral CAL/left LING (BA 17/18) and the right V1 compared with the HCs. Meanwhile, patients with DR exhibited higher FC strength between the left V1 and the bilateral Caudate/Olfactory/Orbital superior frontal gyrus (OSFG), and between the bilateral Caudate/Olfactory/OSFG (BA 3/4/6) and the right V1. Compared with DM group, patients with DR showed increased FC strength between the right CAL (BA 17/18) and the right V1. DM group exhibited lower FC strength between the left fusiform and the left V1, and between the bilateral CAL and the right V1 when compared with HCs. Moreover, DM group was observed to have higher FC strength between the left superior frontal gyrus and the left V1.

CONCLUSION

Our findings indicated that DR patients exhibited FC disruptions between V1 and higher visual regions at rest, which may reflect the aberrant information communication in the V1 area of DR individuals. The findings offer important insights into the neuromechanism of vision disorder in DR patients.

An update on magnetic resonance imaging markers in AD

The purpose of the present review is to provide an update of the available recent scientific literature on the use of magnetic resonance imaging (MRI) in Alzheimer's disease (AD). MRI is playing an increasingly important role in the characterization of the AD signatures, which can be useful in both the diagnostic process and monitoring of disease progression. Furthermore, this technique is unique in assessing brain structure and function and provides a deep understanding of in vivo evolution of cerebral pathology. In the reviewing process, we established a priori criteria and we thoroughly searched the very recent scientific literature (January 2018-March 2020) for relevant articles on this topic. In summary, we selected 73 articles out of 1654 publications retrieved from PubMed. Based on this selection, this review summarizes the recent application of MRI in clinical trials, defining the predementia stages of AD, the clinical utility of MRI, proposal of novel biomarkers and brain regions of interest, and assessing the relationship between MRI and cognitive features, risk and protective factors of AD. Finally, the value of a multiparametric approach in clinical and preclinical stages of AD is discussed.

Negative correlation between grey matter in the hippocampus and caudate nucleus in healthy aging

Neurobiological changes that occur with aging include a reduction in function and volume of the hippocampus. These changes were associated with corresponding memory deficits in navigation tasks. However, navigation can involve different strategies that are dependent on the hippocampus and caudate nucleus. The proportion of people using hippocampus-dependent spatial strategies decreases across the lifespan. As such, the decrease in spatial strategies, and corresponding increase in caudate nucleus-dependent response strategies with age, may play a role in the observed neurobiological changes in the hippocampus. Furthermore, we previously showed a negative correlation between grey matter in the hippocampus and caudate nucleus/striatum in mice, young adults, and in individuals diagnosed with Alzheimer's disease. As such, we hypothesized that this negative relationship between the two structures would be present during normal aging. The aim of the current study was to investigate this gap in the literature by studying the relationship between grey matter in the hippocampus and caudate nucleus of the striatum, in relation to each other and to navigation strategies, during healthy aging. Healthy older adults (N = 39) were tested on the Concurrent Spatial Discrimination Learning Task (CSDLT), a virtual radial task that dissociates between spatial and response strategies. A regression of strategies against structural MRIs showed for the first time in older adults that the response strategy was associated with higher amounts of grey matter in the caudate nucleus. As expected, the spatial strategy correlated with grey matter in the hippocampus, which was negatively correlated with grey matter in the caudate nucleus. Interestingly, a sex difference emerged showing that among older adult response learners, women have the least amount of grey matter in the hippocampus, which is a known risk for Alzheimer's disease. This difference was absent among spatial learners. These results are discussed in the context of the putative protective role of spatial memory against grey matter loss in the hippocampus, especially in women.

The Long-Term Effects of Acupuncture on Hippocampal Functional Connectivity in aMCI with Hippocampal Atrophy: A Randomized Longitudinal fMRI Study

Background. Acupuncture has been used to treat amnestic mild cognitive impairment (aMCI) for many years in China. However, the long-term effects of continuous acupuncture treatment remained unclear. Objective. We aimed to explore the long-term effects of continuous acupuncture treatment on hippocampal functional connectivity (FC) in aMCI. Methods. Fifty healthy control (HC) participants and 28 aMCI patients were recruited for resting-state functional magnetic resonance imaging (fMRI) at baseline. The 28 aMCI patients were then divided into the aMCI acupuncture group, which received acupuncture treatment for 6 months, and the aMCI control group, which received no intervention. All aMCI patients completed the second resting-state fMRI scanning after 6 months of acupuncture treatment. Analysis based on the region of interest and two-way analysis of covariance were both used to explore the long-term effects of acupuncture on cognition change and hippocampal FC in aMCI. Results. Compared to HC, aMCI showed decreased right hippocampal FC with the right inferior/middle temporal gyrus (ITG/MTG), left amygdala, and the right fusiform and increased FC with bilateral caudates at baseline. After acupuncture treatment, the right hippocampal FC with right ITG/MTG enhanced significantly in the aMCI acupuncture group, but continued to decrease in the aMCI control group. Whole brain FC analysis showed enhanced right hippocampal FC with the right ITG and the left MTG in the aMCI acupuncture group relative to the aMCI control group. Furthermore, FC strength of the right hippocampus with right ITG at baseline was negatively correlated with the changes in memory scores of aMCI acupuncture patients. Conclusion. Acupuncture treatment could alleviate the progression of cognitive decline and could enhance hippocampal FC with ITG and MTG in aMCI that may be associated with resilience to resistant against neurodegeneration. The findings provided a better understanding of the long-term effects of acupuncture treatment and confirmed the therapeutic role of acupuncture in aMCI.

The striatum, the hippocampus, and short-term memory binding: Volumetric analysis of the subcortical grey matter's role in mild cognitive impairment

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Hippocampal atrophy plays no role in short-term memory binding.

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The globus pallidus could be part of the brain network supporting binding.

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Total brain atrophy does not correlate with striatal grey matter atrophy in MCI.

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Striatal grey matter atrophy reflects in total brain atrophy in controls.

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Hippocampal and parahippocampal volumes correlate in MCI and controls.

Background

Deficits in short-term memory (STM) binding are a distinguishing feature of preclinical stages leading to Alzheimer's disease (AD). However, the neuroanatomical correlates of conjunctive STM binding are largely unexplored. Here we examine the possible association between the volumes of hippocampi, parahippocampal gyri, and grey matter within the subcortical structures – all found to have foci that seemingly correlate with basic daily living activities in AD patients - with cognitive tests related to conjunctive STM binding.

Materials and methods

Hippocampal, thalamic, parahippocampal and corpus striatum volumes were semi-automatically quantified in brain magnetic resonance images from 25 cognitively normal people and 21 patients with Mild Cognitive Impairment (MCI) at high risk of AD progression, who undertook a battery of cognitive tests and the short-term memory binding test. Associations were assessed using linear regression models and group differences were assessed using the Mann-Whitney U test.

Results

Hippocampal and parahippocampal gyrus volumes differed between MCI and control groups. Although the grey matter volume in the globus pallidus (r = -0.71, p < 0.001) and parahippocampal gyry (r = -0.63, p < 0.05) correlated with a STM binding task in the MCI group, only the former remained associated with STM binding deficits in MCI patients, after correcting for age, gender and years of education (β = -0.56,P = 0.042) although with borderline significance.

Conclusions

Loss of hippocampal volume plays no role in the processing of STM binding. Structures within the basal ganglia, namely the globus pallidus, could be part of the extrahippocampal network supporting binding. Replication of this study in large samples is now needed.

Bayesian Scalar on Image Regression With Nonignorable Nonresponse

Medical imaging has become an increasingly important tool in screening, diagnosis, prognosis, and treatment of various diseases given its information visualization and quantitative assessment. The aim of this article is to develop a Bayesian scalar-on-image regression model to integrate high-dimensional imaging data and clinical data to predict cognitive, behavioral, or emotional outcomes, while allowing for nonignorable missing outcomes. Such a nonignorable nonresponse consideration is motivated by examining the association between baseline characteristics and cognitive abilities for 802 Alzheimer patients enrolled in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1), for which data are partially missing. Ignoring such missing data may distort the accuracy of statistical inference and provoke misleading results. To address this issue, we propose an imaging exponential tilting model to delineate the data missing mechanism and incorporate an instrumental variable to facilitate model identifiability followed by a Bayesian framework with Markov chain Monte Carlo algorithms to conduct statistical inference. This approach is validated in simulation studies where both the finite sample performance and asymptotic properties are evaluated and compared with the model with fully observed data and that with a misspecified ignorable missing mechanism. Our proposed methods are finally carried out on the ADNI1 dataset, which turns out to capture both of those clinical risk factors and imaging regions consistent with the existing literature that exhibits clinical significance. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

The effect of white matter hyperintensities on regional brain volumes and white matter microstructure, a population-based study in HUNT

Even though age-related white matter hyperintensities (WMH) begin to emerge in middle age, their effect on brain micro- and macrostructure in this age group is not fully elucidated. We have examined how presence of WMH and load of WMH affect regional brain volumes and microstructure in a validated, representative general population sample of 873 individuals between 50 and 66 years. Presence of WMH was determined as Fazakas grade ≥1. WMH load was WMH volume from manual tracing of WMHs divided on intracranial volume. The impact of age appropriate WMH (Fazakas grade 1) on the brain was also investigated. Major novel findings were that even the age appropriate WMH group had widespread macro- and microstructural changes in gray and white matter, showing that the mere presence of WMH, not just WMH load is an important clinical indicator of brain health. With increasing WMH load, structural changes spread centrifugally. Further, we found three major patterns of FA and MD changes related to increasing WMH load, demonstrating a heterogeneous effect on white matter microstructure, where distinct patterns were found in the proximity of the lesions, in deep white matter and in white matter near the cortex. This study also raises several questions about the onset of WMH related pathology, in particular, whether some of the aberrant brain structural and microstructural findings are present before the emergence of WMH. We also found, similar to other studies, that WMH risk factors had low explanatory power for WMH, making it unclear which factors lead to WMH.

Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI

Using the Pearson correlation coefficient to constructing functional brain network has been evidenced to be an effective means to diagnose different stages of mild cognitive impairment (MCI) disease. In this study, we investigated the efficacy of a classification framework to distinguish early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI) by using the effective features derived from functional brain network of three frequency bands (full-band: 0.01-0.08 Hz; slow-4: 0.027-0.08 Hz; slow-5: 0.01-0.027 Hz) at Rest. Graphic theory was performed to calculate and analyze the relationship between changes in network connectivity. Subsequently, three different algorithms [minimal redundancy maximal relevance (mRMR), sparse linear regression feature selection algorithm based on stationary selection (SS-LR), and Fisher Score (FS)] were applied to select the features of network attributes, respectively. Finally, we used the support vector machine (SVM) with nested cross validation to classify the samples into two categories to obtain unbiased results. Our results showed that the global efficiency, the local efficiency, and the average clustering coefficient were significantly higher in the slow-5 band for the LMCI-EMCI comparison, while the characteristic path length was significantly longer under most threshold values. The classification results showed that the features selected by the mRMR algorithm have higher classification performance than those selected by the SS-LR and FS algorithms. The classification results obtained by using mRMR algorithm in slow-5 band are the best, with 83.87% accuracy (ACC), 86.21% sensitivity (SEN), 81.21% specificity (SPE), and the area under receiver operating characteristic curve (AUC) of 0.905. The present results suggest that the method we proposed could effectively help diagnose MCI disease in clinic and predict its conversion to Alzheimer's disease at an early stage.

Glucocerebrosidase regulators SCARB2 and TFEB are up-regulated in Lewy body disease brain

Mutations in the glucocerebrosidase (GCase) gene (GBA) and GCase deficiency are major risk factors for Lewy body diseases. Decreased GCase activity enhances alpha-synuclein aggregation and disease development. Lysosomal integral membrane protein type 2, encoded by SCARB2, binds GCase targeting it to lysosomes and transcription factor EB (Tfeb) regulates lysosomal proteostasis. Our aim was to find out if GCase deficiency in Lewy body diseases is accompanied by SCARB2 and TFEB deregulation at the transcriptional level involving alternative splicing as well. Relative mRNA expression of two SCARB2 and two TFEB transcripts was studied by real-time PCR in post-mortem brain samples of cases with pure Lewy body pathology (LBP), cases with concomitant LBP and Alzheimer disease-like pathology, and controls. TFEB expression was increased in the temporal cortex and caudate nucleus of LBP cases, and SCARB2 was differentially expressed. Female-gender associated overexpression of all transcripts was found in the caudate nucleus, and disease duration associated TFEB expression changes in the temporal cortex. SCARB2 and TFEB expression correlated negatively with GBA mRNA expression in the temporal cortex. Our findings show disease-specific deregulation of TFEB and SCARB2 expression affecting alternative promoter usage and alternative splicing in Lewy body diseases.

Two strategies used to solve a navigation task: A different use of the hippocampus by males and females? A preliminary study in rats

There is abundant research (both in rodents and in humans) showing that males and females often use different types of information in spatial navigation. Males prefer geometry as a source of information, whereas females tend to focus on landmarks (which are often near to a goal objects). However, when considering the role of the hippocampus, the research focuses primarily on males only. In the present study, based on Rodríguez, Torres, Mackintosh, and Chamizo’s (2010, Experiment 2) navigation protocol, we conducted two experiments, one with males and another with females, in order to tentatively evaluate the role of the dorsal hippocampus in the acquisition of two tasks: one based on landmark learning and the alternate one on local pool-geometry learning. Both when landmark learning and when geometry learning, Sham male rats learned significantly faster than Lesion male animals. This was not the case with female rats in geometry learning. These results suggest that the dorsal hippocampus could play an important role in males only.

Using a memory systems lens to view the effects of estrogens on cognition: Implications for human health

Understanding the organizing and activating effects of gonadal steroids on adult physiology can guide insight into sex differences in and hormonal influences on health and disease, ranging from diabetes and other metabolic disorders, emotion and stress regulation, substance abuse, pain perception, immune function and inflammation, to cognitive function and dysfunction accompanying neurological disorders. Because the brain is highly sensitive to many forms of estrogens, it is not surprising that many adult behaviors, including cognitive function, are modulated by estrogens. Estrogens are known for their facilitating effects on learning and memory, but it is becoming increasingly clear that they also can impair learning and memory of some classes of tasks and may do so through direct actions on specific neural systems. This review takes a multiple memory systems approach to understanding how estrogens can at the same time enhance hippocampus-sensitive place learning and impair striatum-sensitive response learning by exploring the role estrogen receptor signaling may play in the opposing cognitive effects of estrogens. Accumulating evidence suggests that neither receptor subtype nor the timing of treatment, i.e. rapid vs slow, explain the bidirectional effects of estrogens on different types of learning. New findings pointing to neural metabolism and the provision of energy substrates by astrocytes as a candidate mechanism for cognitive enhancement and impairment are discussed.