Advanced magnetic resonance imaging of neurodegenerative diseases

Surface-Tailored Nanoplatform for the Diagnosis and Management of Stroke: Current Strategies and Future Outlook

Stroke accounts for one of the top leading reasons for neurological mortality and morbidity around the globe. Both ischemic and hemorrhagic strokes lead to local hypoxia and are brought about by the occlusion or rupturing of the blood vessels. The events taking place after the onset of a stroke include membrane ion pump failure, calcium and glutamate-mediated excitotoxicity, increased ROS production causing DNA damage, mitochondrial dysfunction, oxidative stress, development of brain edema, and microvascular dysfunction. To date, tissue plasminogen activator (tPA) therapy and mechanical removal of blood clots are the only clinically available stroke therapies, approved by Food and Drug Administration (FDA). But because of the narrow therapeutic window of around 4.5 h for tPA therapy and complications like systemic bleeding and anaphylaxis, more clinical trials are ongoing in the same field. Therefore, using nanocarriers with diverse physicochemical properties is a promising strategy in treating and diagnosing stroke as they can efficiently bypass the tight blood-brain barrier (BBB) through mechanisms like receptor-mediated transcytosis and help achieve controlled and targeted drug delivery. In this review, we will mainly focus on the pathophysiology of stroke, BBB alterations following stroke, strategies to target BBB for stroke therapies, different types of nanocarriers currently being used for therapeutic intervention of stroke, and biomarkers as well as imaging techniques used for the detection and diagnosis of stroke.

Unveiling the future: Advancements in MRI imaging for neurodegenerative disorders

Neurodegenerative disorders represent a significant and growing global health challenge, necessitating continuous advancements in diagnostic tools for accurate and early detection. This work explores the recent progress in Magnetic Resonance Imaging (MRI) techniques and their application in the realm of neurodegenerative disorders. The introductory section provides a comprehensive overview of the study's background, significance, and objectives. Recognizing the current challenges associated with conventional MRI, the manuscript delves into advanced imaging techniques such as high-resolution structural imaging (HR-MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography-MRI (PET-MRI) fusion. Each technique is critically examined regarding its potential to address theranostic limitations and contribute to a more nuanced understanding of the underlying pathology. A substantial portion of the work is dedicated to exploring the applications of advanced MRI in specific neurodegenerative disorders, including Parkinson's disease, Alzheimer's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS). In addressing the future landscape, the manuscript examines technological advances, including the integration of machine learning and artificial intelligence in neuroimaging. The conclusion summarizes key findings, outlines implications for future research, and underscores the importance of these advancements in reshaping our understanding and approach to neurodegenerative disorders.

Understanding exosomes: Part 2-Emerging leaders in regenerative medicine

Exosomes are the smallest subset of extracellular signaling vesicles secreted by most cells with the ability to communicate with other tissues and cell types over long distances. Their use in regenerative medicine has gained tremendous momentum recently due to their ability to be utilized as therapeutic options for a wide array of diseases/conditions. Over 5000 publications are currently being published yearly on this topic, and this number is only expected to dramatically increase as novel therapeutic strategies continue to be developed. Today exosomes have been applied in numerous contexts including neurodegenerative disorders (Alzheimer's disease, central nervous system, depression, multiple sclerosis, Parkinson's disease, post-traumatic stress disorders, traumatic brain injury, peripheral nerve injury), damaged organs (heart, kidney, liver, stroke, myocardial infarctions, myocardial infarctions, ovaries), degenerative processes (atherosclerosis, diabetes, hematology disorders, musculoskeletal degeneration, osteoradionecrosis, respiratory disease), infectious diseases (COVID-19, hepatitis), regenerative procedures (antiaging, bone regeneration, cartilage/joint regeneration, osteoarthritis, cutaneous wounds, dental regeneration, dermatology/skin regeneration, erectile dysfunction, hair regrowth, intervertebral disc repair, spinal cord injury, vascular regeneration), and cancer therapy (breast, colorectal, gastric cancer and osteosarcomas), immune function (allergy, autoimmune disorders, immune regulation, inflammatory diseases, lupus, rheumatoid arthritis). This scoping review is a first of its kind aimed at summarizing the extensive regenerative potential of exosomes over a broad range of diseases and disorders.

Swallowing performance in older adults: Associated cognitive, neuroanatomical and demographic factors

BACKGROUND

Swallowing problems are frequently seen in older adults, especially in individuals with cognitive impairment (CI). The brain plays a crucial role in both cognition and swallowing. Using magnetic resonance imaging (MRI) data, researchers identified regions associated with swallowing. However, it is not yet fully elucidated which factors influence the swallowing performance in older adults.

OBJECTIVES

The current study investigated which factors, such as cognitive function, neuroanatomical factors (e.g., the cortical thickness and volume of specific brain regions) and demographical factors are associated with swallowing performance in older adults. Secondly, it was investigated whether there is a difference in neuroanatomical factors between individuals with and without CI.

RESEARCH DESIGN AND METHODS

In total, 15 CI individuals (73.1 ± 9.1 years; 46.7% male) and 48 non-CI controls (69.0 ± 5.1 years; 29.2% male) were included. The repetitive saliva swallowing test (RSST) was performed, and an MRI scan was acquired from the participants.

RESULTS

Multivariate linear regression analysis showed that the cortical thickness of the right supramarginal gyrus and female gender were positively associated, and a higher age was negatively associated with the RSST in older adults (p < .05). CI was not significantly associated with swallowing performance. Furthermore, it was found that the cortical volume differs more frequently between CI and non-CI than the cortical thickness.

CONCLUSION

A thinner cortex of the right supramarginal gyrus and being an older female are associated with poorer swallowing performance. Secondly, cortical volume was more often found to differ between CI and non-CI individuals than cortical thickness.

Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer's Disease

Alzheimer's disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for AD, such as neuroimaging approaches. Neuroimaging techniques, including positron emission tomography and magnetic resonance imaging, have revolutionized the field by providing valuable insights into the structural and functional alterations in the brains of individuals with AD. These imaging modalities enable the detection of early biomarkers such as amyloid-β plaques and tau protein tangles, facilitating early and precise diagnosis. Furthermore, the emerging technologies encompassing blood-based biomarkers and neurochemical profiling exhibit promising results in the identification of specific molecular signatures for AD. The integration of machine learning algorithms and artificial intelligence has enhanced the predictive capacity of these diagnostic tools when analyzing complex datasets. In this review article, we will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD. These advancements hold immense potential for early detection and intervention, thereby paving the way for personalized therapeutic strategies and ultimately augmenting the quality of life for individuals affected by AD.

Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases

Neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) present a major health burden to society. Changes in brain structure and cognition are generally only observed at the late stage of the disease. Although advanced magnetic resonance imaging (MRI) techniques such as diffusion imaging may allow identification of biomarkers at earlier stages of neurodegeneration, early diagnosis is still challenging. Magnetic resonance elastography (MRE) is a noninvasive MRI technique for studying the mechanical properties of tissues by measuring the wave propagation induced in the tissues using a purpose-built actuator. Here, we present a systematic review of preclinical and clinical studies in which MRE has been applied to study neurodegenerative diseases. Actuator systems for data acquisition, inversion algorithms for data analysis, and sample demographics are described and tissue stiffness measures obtained for the whole brain and internal structures are summarized. A total of six animal studies and eight human studies have been published. The animal studies refer to 123 experimental animals (68 AD and 55 PD) and 121 wild-type animals, while the human studies refer to 142 patients with neurodegenerative disease (including 56 AD and 17 PD) and 166 controls. The animal studies are consistent in the reporting of decreased stiffness of the hippocampal region in AD mice. However, in terms of disease progression, although consistent decreases in either storage modulus or shear modulus magnitude are reported for whole brain, there is variation in the results reported for the hippocampal region. The clinical studies are consistent in reports of a significant decrease in either whole brain storage modulus or shear modulus magnitude, in both AD and PD and with different brain structures affected in different neurodegenerative diseases. MRE studies of neurodegenerative diseases are still in their infancy, and in future it will be interesting to investigate potential relationships between brain mechanical properties and clinical measures, which may help elucidate the mechanisms underlying onset and progression of neurodegenerative diseases. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.

Disentangling the genetic overlap and causal relationships between primary open-angle glaucoma, brain morphology and four major neurodegenerative disorders

BACKGROUND

Primary open-angle glaucoma (POAG) is an optic neuropathy characterized by progressive degeneration of the optic nerve that leads to irreversible visual impairment. Multiple epidemiological studies suggest an association between POAG and major neurodegenerative disorders (Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and Parkinson's disease). However, the nature of the overlap between neurodegenerative disorders, brain morphology and glaucoma remains inconclusive.

METHOD

In this study, we performed a comprehensive assessment of the genetic and causal relationship between POAG and neurodegenerative disorders, leveraging genome-wide association data from studies of magnetic resonance imaging of the brain, POAG, and four major neurodegenerative disorders.

FINDINGS

This study found a genetic overlap and causal relationship between POAG and its related phenotypes (i.e., intraocular pressure and optic nerve morphology traits) and brain morphology in 19 regions. We also identified 11 loci with a significant local genetic correlation and a high probability of sharing the same causal variant between neurodegenerative disorders and POAG or its related phenotypes. Of interest, a region on chromosome 17 corresponding to MAPT, a well-known risk locus for Alzheimer's and Parkinson's disease, was shared between POAG, optic nerve degeneration traits, and Alzheimer's and Parkinson's diseases. Despite these local genetic overlaps, we did not identify strong evidence of a causal association between these neurodegenerative disorders and glaucoma.

INTERPRETATION

Our findings indicate a distinctive and likely independent neurodegenerative process for POAG involving several brain regions although several POAG or optic nerve degeneration risk loci are shared with neurodegenerative disorders, consistent with a pleiotropic effect rather than a causal relationship between these traits.

FUNDING

PG was supported by an NHMRC Investigator Grant (#1173390), SM by an NHMRC Senior Research Fellowship and an NHMRC Program Grant (APP1150144), DM by an NHMRC Fellowship, LP is funded by the NEIEY015473 and EY032559 grants, SS is supported by an NIH-Oxford Cambridge Fellowship and NIH T32 grant (GM136577), APK is supported by a UK Research and Innovation Future Leaders Fellowship, an Alcon Research Institute Young Investigator Award and a Lister Institute for Preventive Medicine Award.

A Systematic Review of PET Contrasted with MRI for Detecting Crossed Cerebellar Diaschisis in Patients with Neurodegenerative Diseases

There has not been extensive research into crossed cerebellar diaschisis (CCD) in neurodegenerative disorders. CCD is frequently detected using positron emission tomography (PET). However, advanced MRI techniques have come forth for the detection of CCD. The correct diagnosis of CCD is crucial for the care of neurological patients and those with neurodegenerative conditions. The purpose of this study is to determine whether PET can offer extra value over MRI or an advanced technique in MRI for detecting CCD in neurological conditions. We searched three main electronic databases from 1980 until the present and included only English and peer-reviewed journal articles. Eight articles involving 1246 participants met the inclusion criteria, six of which used PET imaging while the other two used MRI and hybrid imaging. The findings in PET studies showed decreased cerebral metabolism in the frontal, parietal, temporal, and occipital cortices, as on the opposite side of the cerebellar cortex. However, the findings in MRI studies showed decreased cerebellar volumes. This study concludes that PET is a common, accurate, and sensitive technique for detecting both crossed cerebellar and uncrossed basal ganglia as well as thalamic diaschisis in neurodegenerative diseases, while MRI is better for measuring brain volume. This study suggests that PET has a higher diagnostic value for diagnosing CCD compared to MRI, and that PET is a more valuable technique for predicting CCD.

The effects of repetitive transcranial magnetic stimulation and aerobic exercise on cognition, balance and functional brain networks in patients with Alzheimer's disease

The purpose of this study was to investigate the effects of high-frequency repetitive Transcranial Magnetic Stimulation (rTMS) and aerobic exercises (AE) in addition to the pharmacological therapy (PT) in Alzheimer's Disease (AD). Twenty-seven patients with AD aged ≥ 60 years were included in the study and divided into 3 groups (rTMS, AE and control). All groups received PT. rTMS group (n = 10) received 20 Hz rTMS over dorsolateral prefrontal cortex (dlPFC) bilaterally and AE group (n = 9) received the structured moderate-intensity AE for 5 consecutive days/week over 2 weeks. Control group (n = 8) only received PT. Cognition, balance, mobility, quality of life (QoL), and resting state functional brain activity were evaluated one week before and one week after the interventions. (ClinicalTrials.gov ID:NCT05102045). Significant improvements were found in executive functions, behavior, and QoL in the rTMS group, in balance and mobility in the AE group, and in the visual memory and behavior in the control group (p < 0.05). Significant differences were found in the behavior in favor of the rTMS group, and balance in favor of the AE group (p < 0.05). There was a significant increase in activation on middle temporal gyrus, intra calcarine, central opercular cortex, superior parietal lobule, and paracingulate cortex in Default Mode Network (DMN) in the rTMS group (p < 0.05). High-frequency rTMS over bilateral dlPFC may improve executive functions and behavior and lead to increased activation in DMN, structured moderate-intensity AE may improve balance and mobility, and PT may improve memory and behaviour compared to pretreatment in AD.

The human functional connectome in neurodegenerative diseases: relationship to pathology and clinical progression

INTRODUCTION

Neurodegenerative diseases can be considered as 'disconnection syndromes,' in which a communication breakdown prompts cognitive or motor dysfunction. Mathematical models applied to functional resting-state MRI allow for the organization of the brain into nodes and edges, which interact to form the functional brain connectome.

AREAS COVERED

The authors discuss the recent applications of functional connectomics to neurodegenerative diseases, from preclinical diagnosis, to follow up along with the progressive changes in network organization, to the prediction of the progressive spread of neurodegeneration, to stratification of patients into prognostic groups, and to record responses to treatment. The authors searched PubMed using the terms 'neurodegenerative diseases' AND 'fMRI' AND 'functional connectome' OR 'functional connectivity' AND 'connectomics' OR 'graph metrics' OR 'graph analysis.' The time range covered the past 20 years.

EXPERT OPINION

Considering the great pathological and phenotypical heterogeneity of neurodegenerative diseases, identifying a common framework to diagnose, monitor and elaborate prognostic models is challenging. Graph analysis can describe the complexity of brain architectural rearrangements supporting the network-based hypothesis as unifying pathogenetic mechanism. Although a multidisciplinary team is needed to overcome the limit of methodologic complexity in clinical application, advanced methodologies are valuable tools to better characterize functional disconnection in neurodegeneration.

Coupling analysis between functional and structural brain networks in Alzheimer's disease

The coupling between functional and structural brain networks is difficult to clarify due to the complicated alterations in gray matter and white matter for the development of Alzheimer's disease (AD). A cohort of 112 participants [normal control group (NC, 62 cases), mild cognitive impairment group (MCI, 31 cases) and AD group (19 cases)], was recruited in our study. The brain networks of rsfMRI functional connectivity (rsfMRI-FC) and diffusion tensor imaging structural connectivity (DTI-SC) across the three groups were constructed, and their correlations were evaluated by Pearson's correlation analyses and multiple comparison with Bonferroni correction. Furthermore, the correlations between rsfMRI-SC/DTI-FC coupling and four neuropsychological scores of mini-mental state examination (MMSE), clinical dementia rating-sum of boxes (CDR-SB), functional activities questionnaire (FAQ) and montreal cognitive assessment (MoCA) were inferred by partial correlation analyses, respectively. The results demonstrated that there existed significant correlation between rsfMRI-FC and DTI-SC (p < 0.05), and the coupling of rsfMRI-FC/DTI-SC showed negative correlation with MMSE score (p < 0.05), positive correlations with CDR-SB and FAQ scores (p < 0.05), and no correlation with MoCA score (p > 0.05). It was concluded that there existed FC/SC coupling and varied network characteristics for rsfMRI and DTI, and this would provide the clues to understand the underlying mechanisms of cognitive deficits of AD.

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.

3D hemisphere-based convolutional neural network for whole-brain MRI segmentation

Whole-brain segmentation is a crucial pre-processing step for many neuroimaging analyses pipelines. Accurate and efficient whole-brain segmentations are important for many neuroimage analysis tasks to provide clinically relevant information. Several recently proposed convolutional neural networks (CNN) perform whole brain segmentation using individual 2D slices or 3D patches as inputs due to graphical processing unit (GPU) memory limitations, and use sliding windows to perform whole brain segmentation during inference. However, these approaches lack global and spatial information about the entire brain and lead to compromised efficiency during both training and testing. We introduce a 3D hemisphere-based CNN for automatic whole-brain segmentation of T1-weighted magnetic resonance images of adult brains. First, we trained a localization network to predict bounding boxes for both hemispheres. Then, we trained a segmentation network to segment one hemisphere, and segment the opposing hemisphere by reflecting it across the mid-sagittal plane. Our network shows high performance both in terms of segmentation efficiency and accuracy (0.84 overall Dice similarity and 6.1 mm overall Hausdorff distance) in segmenting 102 brain structures. On multiple independent test datasets, our method demonstrated a competitive performance in the subcortical segmentation task and a high consistency in volumetric measurements of intra-session scans.

Parkinson's Disease: A Nanotheranostic Approach Targeting Alpha-Synuclein Aggregation

Parkinson's disease (PD) is one of the most common neurodegenerative disorders that is implicated in aging populations. As numerous developed nations are experiencing progressively aging populations today, there is a heightened propensity for the occurrence of PD cases. Alpha-synuclein (α-syn) aggregation has been considered to be the pivotal mechanism leading to PD pathogenesis. Thus, early diagnostic and therapeutic strategies targeting the misfolded α-syn protein can potentially improve the prognosis of PD. With rapid advancements in nanotechnology in the last decade, effective solutions to various neurodegenerative and oncological diseases have been suggested. This review will explore the current innovations in nanotechnology that target the α-syn aggregation pathway, and reinstate the promise they hold as effective early diagnostic and therapeutic solutions to PD.

Stem Cell-Derived Exosomes: a New Strategy of Neurodegenerative Disease Treatment

Short-term symptomatic treatment and dose-dependent side effects of pharmacological treatment for neurodegenerative diseases have forced the medical community to seek an effective treatment for this serious global health threat. Therapeutic potential of stem cell for treatment of neurodegenerative disorders was identified in 1980 when fetal nerve tissue was used to treat Parkinson's disease (PD). Then, extensive studies have been conducted to develop this treatment strategy for neurological disease therapy. Today, stem cells and their secretion are well-known as a therapeutic environment for the treatment of neurodegenerative diseases. This new paradigm has demonstrated special characteristics related to this treatment, including neuroprotective and neurodegeneration, remyelination, reduction of neural inflammation, and recovery of function after induced injury. However, the exact mechanism of stem cells in repairing nerve damage is not yet clear; exosomes derived from them, an important part of their secretion, are introduced as responsible for an important part of such effects. Numerous studies over the past few decades have evaluated the therapeutic potential of exosomes in the treatment of various neurological diseases. In this review, after recalling the features and therapeutic history, we will discuss the latest stem cell-derived exosome-based therapies for these diseases.

Detection of memory- and learning-related brain connectivity changes following trace eyeblink-conditioning using resting-state functional magnetic resonance imaging in the awake rabbit

Animal imaging studies have the potential to further establish resting-state fMRI (rs-fMRI) and enable its validation for clinical use. The rabbit subjects used in this work are an ideal model system for studying learning and behavior and are also an excellent established test subject for awake scanning given their natural tolerance for restraint. We found that analysis of rs-fMRI conducted on a cohort of rabbits undergoing eyeblink conditioning can reveal functional brain connectivity changes associated with learning, and that rs-fMRI can be used to capture differences between subjects with different levels of cognitive performance. rs-fMRI sessions were conducted on a cohort of rabbits before and after trace eyeblink conditioning. MRI results were analyzed using independent component analysis (ICA) and network analysis. Behavioral data were collected with standard methods using an infrared reflective sensor aimed at the cornea to detect blinks. Behavioral results were analyzed, and a median split was used to create two groups of rabbits based on their performance. The cohort of rabbits undergoing eyeblink conditioning exhibited increased functional connectivity in the cingulate cortex, retrosplenial cortex, and thalamus consistent with brain reorganization associated with increased learning. Differences in the striatum and right cerebellum were also identified between rabbits in the top or bottom halves of the group as measured by the behavioral assay. Thus, rs-fMRI can provide not only a tool to detect and monitor functional brain changes associated with learning, but also to discriminate between different levels of cognitive performance.

Multiparametric magnetic resonance imaging and positron emission tomography findings in neurodegenerative diseases: Current status and future directions

Neurodegenerative diseases (NDDs) are characterized by progressive neuronal loss, leading to dementia and movement disorders. NDDs broadly include Alzheimer's disease, frontotemporal lobar degeneration, parkinsonian syndromes, and prion diseases. There is an ever-increasing prevalence of mild cognitive impairment and dementia, with an accompanying immense economic impact, prompting efforts aimed at early identification and effective interventions. Neuroimaging is an essential tool for the early diagnosis of NDDs in both clinical and research settings. Structural, functional, and metabolic imaging modalities, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are widely available. They show encouraging results for diagnosis, monitoring, and treatment response evaluation. The current review focuses on the complementary role of various imaging modalities in relation to NDDs, the qualitative and quantitative utility of newer MRI techniques, novel radiopharmaceuticals, and integrated PET/MRI in the setting of NDDs.

A Role of PET/MR Imaging in Dementia?

Since many years, magnetic resonance imaging (MRI) and positron emission tomography (PET) have a prominent role in neurodegenerative disorders and dementia, not only in a research setting but also in a clinical setting. For several decades, information from both modalities is combined ranging from individual visual assessments to fully integrating all images. Several tools are available to coregister images from MRI and PET and to covisualize these images. When studying neurodegenerative disorders with PET it is important to perform a partial volume correction and this can be done using the structural information obtained by MRI. With the advent of PET/MR, the question arises in how far this hybrid imaging modality is an added value compared to combining PET and MRI data from two separate modalities. One issue in PET/MR is still not yet completely settled, that is, the attenuation correction. This is of less importance for visual assessments but it can become an issue when combining data from PET/CT and PET/MR scanners in multicenter studies or when using cut-off values to classify patients. Simultaneous imaging has clearly some advantages: for the patient it is beneficial to have only one scan session instead of two but also in cases in which PET data are related to functional of physiological data acquired with MRI (such as functional MRI or arterial spin labeling). However, the most important benefit is currently the more integrated use of PET and MRI. This is also possible with separate measurements but requires more streamlining of the whole process. In that case coregistration of images is mandatory. It needs to be determined in which cases simultaneous PET/MRI leads to new insights or improved diagnosis compared to multimodal imaging using dedicated scanners.

An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRI

Purpose: To develop and validate an integrative nomogram based on white matter (WM) radiomics biomarkers and nonmotor symptoms for the identification of early-stage Parkinson's disease (PD).

Methods: The brain magnetic resonance imaging (MRI) and clinical characteristics of 336 subjects, including 168 patients with PD, were collected from the Parkinson's Progress Markers Initiative (PPMI) database. All subjects were randomly divided into training and test sets. According to the baseline MRI scans of patients in the training set, the WM was segmented to extract the radiomic features of each patient and develop radiomics biomarkers, which were then combined with nonmotor symptoms to build an integrative nomogram using machine learning. Finally, the diagnostic accuracy and reliability of the nomogram were evaluated using a receiver operating characteristic curve and test data, respectively. In addition, we investigated 58 patients with atypical PD who had imaging scans without evidence of dopaminergic deficit (SWEDD) to verify whether the nomogram was able to distinguish patients with typical PD from patients with SWEDD. A decision curve analysis was also performed to validate the clinical practicality of the nomogram.

Results: The area under the curve values of the integrative nomogram for the training, testing and verification sets were 0.937, 0.922, and 0.836, respectively; the specificity values were 83.8, 88.2, and 91.38%, respectively; and the sensitivity values were 84.6, 82.4, and 70.69%, respectively. A significant difference in the number of patients with PD was observed between the high-risk group and the low-risk group based on the nomogram (P < 0.05).

Conclusion: This integrative nomogram is a new potential method to identify patients with early-stage PD.

Predicting the progression of Parkinson's disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole-brain white matter

PURPOSE

This study aimed to develop and validate a radiomics model based on whole-brain white matter and clinical features to predict the progression of Parkinson disease (PD).

METHODS

PD patient data from the Parkinson's Progress Markers Initiative (PPMI) database was evaluated. Seventy-two PD patients with disease progression, as measured by the Hoehn-Yahr Scale (HYS) (stage 1-5), and 72 PD patients with stable PD were matched by sex, age, and category of HYS and included in the current study. Each individual's T₁ -weighted MRI scans at the baseline timepoint were segmented to isolate whole-brain white matter for radiomics feature extraction. The total dataset was divided into a training and test set according to subject serial number. The size of the training dataset was reduced using the maximum relevance minimum redundancy (mRMR) algorithm to construct a radiomics signature using machine learning. Finally, a joint model was constructed by incorporating the radiomics signature and clinical progression scores. The test data were then used to validate the prediction models, which were evaluated based on discrimination, calibration, and clinical utility.

RESULTS

Based on the overall data, the areas under curve (AUCs) of the joint model, signature and Unified Parkinson Disease Rating Scale III PD rating score were 0.836, 0.795, and 0.550, respectively. Furthermore, the sensitivities were 0.805, 0.875, and 0.292, respectively, and the specificities were 0.722, 0.697, and 0.861, respectively. In addition, the predictive accuracy of the model was 0.827, the sensitivity was 0.829 and the specificity was 0.702 for stage-1 PD. For stage-2 PD, the predictive accuracy of the model was 0.854, the sensitivity was 0.960, and the specificity was 0.600.

CONCLUSION

Our results provide evidence that conventional structural MRI can predict the progression of PD. This work also supports the use of a simple radiomics signature built from whole-brain white matter features as a useful tool for the assessment and monitoring of PD progression.

Molecular and Imaging Biomarkers in Alzheimer's Disease: A Focus on Recent Insights

Alzheimer’s disease (AD) is the most common neurodegenerative disease among the elderly, affecting millions of people worldwide and clinically characterized by a progressive and irreversible cognitive decline. The rapid increase in the incidence of AD highlights the need for an easy, efficient and accurate diagnosis of the disease in its initial stages in order to halt or delay the progression. The currently used diagnostic methods rely on measures of amyloid-β (Aβ), phosphorylated (p-tau) and total tau (t-tau) protein levels in the cerebrospinal fluid (CSF) aided by advanced neuroimaging techniques like positron emission tomography (PET) and magnetic resonance imaging (MRI). However, the invasiveness of these procedures and the high cost restrict their utilization. Hence, biomarkers from biological fluids obtained using non-invasive methods and novel neuroimaging approaches provide an attractive alternative for the early diagnosis of AD. Such biomarkers may also be helpful for better understanding of the molecular mechanisms underlying the disease, allowing differential diagnosis or at least prolonging the pre-symptomatic stage in patients suffering from AD. Herein, we discuss the advantages and limits of the conventional biomarkers as well as recent promising candidates from alternative body fluids and new imaging techniques.

Virtual reality and real-time neurofeedback functional MRI: a breakthrough in foreseeing Alzheimer's disease?

This scientific commentary refers to ‘Earliest amyloid and tau deposition modulate the influence of limbic networks during closed-loop hippocampal downregulation’ by Skouras etal. (doi:10.1093/brain/awaa011).

A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features

Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.

Determining the number of states in dynamic functional connectivity using cluster validity indexes

BACKGROUND

Clustering analysis is employed in brain dynamic functional connectivity (dFC) to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several cluster validity index (CVI) methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined.

NEW METHOD

Currently employed indexes do not provide a crisp answer on what is the best number of clusters. In addition, there is a lack of CVI testing in the context of dFC data. This work tests a comprehensive set of twenty four cluster validity indexes applied to addiction data and suggest the best ones for clustering dynamic functional connectivity.

RESULTS

Out of the twenty four considered CVIs, Davies-Bouldin and Ray-Turi were the most suitable methods to find the number of clusters in both simulation and real data. The solution for these two CVIs is to find a local minimum critical point, which can be automated using computational algorithms.

COMPARISON WITH EXISTING METHODS

Elbow-Criterion, Silhouette and GAP-Statistic methods have been widely used in dFC studies. These methods are included among the tested CVIs where the performances of all twenty four CVIs are compared.

CONCLUSIONS

Davies-Bouldin and Ray-Turi CVIs showed better performance among a group of twenty four CVIs in determining the number of clusters to use in dFC analysis.

White matter alterations in early Parkinson's disease: role of motor symptom lateralization

INTRODUCTION

Parkinson's disease (PD) is a motor disorder that initially presents with unilateral symptoms. Widespread white matter (WM) alterations have been reported since the early stages of the disease. The aim of this study was to investigate WM alterations in right-dominant and left-dominant symptom PD patients (RPD and LPD, respectively) with respect to healthy controls (HC) by diffusion-weighted magnetic resonance imaging (MRI).

METHODS

Thirty-eight subjects participated in this study: 12 RPD (median H&Y [IQR] = 1.5 [1.1-2], median UPDRS III [IQR] = 23 [7.8-25]), 9 LPD (median H&Y [IQR] = 1.5 [1-2.5], median UPDRS III [IQR] = 17 [12-22]), and 17 HC. All the participants were scanned on a 1.5-T MRI scanner. Maps of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were computed for all the subjects. Tract-based spatial statistics (TBSS) was performed for each diffusion parameter, to test WM differences between RPD, LPD, and HC (ANCOVA design). Family-wise error (FWE) correction was performed and p values lower than 0.05 were considered significant.

RESULTS

No significant FA and RD differences were observed between RPD, LPD, and HC. Significantly increased MD and AD were observed in RPD with respect to HC within widespread WM regions, bilaterally. Conversely, no significant WM alterations were detected in LPD.

CONCLUSION

WM integrity was found to be significantly altered in RPD but not in LPD, suggesting that LPD profile may be associated to more favorable prognosis. Since clinical laterality onset may affect the extent of WM integrity changes, it should be taken into account in neuroimaging studies investigating PD.

Effects of iso-α-acids, the hop-derived bitter components in beer, on the MRI-based Brain Healthcare Quotient in healthy middle-aged to older adults

Aim

Neurological disorders are a major public health issue worldwide and are often associated with structural changes in the brain. We have previously demonstrated that iso‐α‐acids (IAAs), the hop‐derived bitter components in beer, improve memory impairment in aged and Alzheimer's disease mouse models. In this study, we evaluated the effects of IAA intake on the brain structure in healthy middle‐aged to older adults. This study was conducted under the Impulsing Paradigm Change through Disruptive Technologies Program (ImPACT) study launched by the Cabinet office of Japan.

Method

This study employed an open‐labeled, single‐arm, before and after design. Healthy middle‐aged to older adults consumed a beverage containing IAAs (3 mg/190 mL) for 4 weeks.Recently developed magnetic resonance imaging‐based brain health indicators were used to evaluate the following brain conditions: the Brain Healthcare Quotient (BHQ) based on gray matter volume (GM‐BHQ) and white matter fractional anisotropy (FA‐BHQ).

Results

In total, 25 subjects were recruited, and GM‐BHQ and FA‐BHQ were measured before and after intervention. In all subjects, no significant differences in GM‐BHQ and FA‐BHQ were observed. In subjects aged ≥ 60 years (mean 54.5; standard deviation 3.9) (n = 8), GM‐BHQ was significantly increased 4 weeks after intervention compared with that before intervention.

Conclusion

Intake of beverages containing IAAs might affect brain aging, particularly in healthy older adults, which may prevent the development of neurological disorders. Future studies employing more robust designs can elucidate the effects of IAAs on GM‐BHQ and cognitive functions.

This is the first clinical trial evaluating the effects of intake of bitter component of beer, iso‐alpha‐acid, on brain structure. The brain structure before and after intervention was measured by recently developed magnetic resonance imaging‐based method. The gray matter volume of older adults was improved by the intake of iso‐alpha‐acid.

Selection of Efficient Clustering Index to Estimate the Number of Dynamic Brain States from Functional Network Connectivity

Clustering analysis is employed in brain dynamic functional connectivity to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined. In this work we examine the use of the Davies-Bouldin clustering validity index via simulation and real data analysis. Currently employed indexes, such as the Silhouette index, do not provide an effective estimation requiring the use of an elbow criterion. All elbow criteria rely on users experience and introduce uncertainty into the estimation. We demonstrate the feasibility of using the Davies-Bouldin index as a method delivering a unique discrete response to provide automated selection of the number of clusters.

The power of sample size through a multi-scanner approach in MR neuroimaging regression analysis: evidence from Alzheimer's disease with and without depression

The inconsistency of volumetric results often seen in MR neuroimaging studies can be partially attributed to small sample sizes and variable data analysis approaches. Increased sample size through multi-scanner studies can tackle the former, but combining data across different scanner platforms and field-strengths may introduce a variability factor capable of masking subtle statistical differences. To investigate the sample size effect on regression analysis between depressive symptoms and grey matter volume (GMV) loss in Alzheimer's disease (AD), a retrospective multi-scanner investigation was conducted. A cohort of 172 AD patients, with or without comorbid depressive symptoms, was studied. Patients were scanned with different imaging protocols in four different MRI scanners operating at either 1.5 T or 3.0 T. Acquired data were uniformly analyzed using the computational anatomy toolbox (CAT12) of the statistical parametric mapping (SPM12) software. Single- and multi-scanner regression analyses were applied to identify the anatomical pattern of correlation between GM loss and depression severity. A common anatomical pattern of correlation between GMV loss and increased depression severity, mostly involving sensorimotor areas, was identified in all patient subgroups imaged in different scanners. Analysis of the pooled multi-scanner data confirmed the above finding employing a more conservative statistical criterion. In the retrospective multi-scanner setting, a significant correlation was also exhibited for temporal and frontal areas. Increasing the sample size by retrospectively pooling multi-scanner data, irrespective of the acquisition platform and parameters employed, can facilitate the identification of anatomical areas with a strong correlation between GMV changes and depression symptoms in AD patients.

White matter microstructure in fetal alcohol spectrum disorders: A systematic review of diffusion tensor imaging studies

Diffusion tensor imaging (DTI) has revolutionized our understanding of the neural underpinnings of alcohol teratogenesis. This technique can detect alterations in white matter in neurodevelopmental disorders, such as fetal alcohol spectrum disorder (FASD). Using Prisma guidelines, we identified 23 DTI studies conducted on individuals with prenatal alcohol exposure (PAE). These studies confirm the widespread nature of brain damage in PAE by reporting diffusivity alterations in commissural, association, and projection fibers; and in relation to increasing cognitive impairment. Reduced integrity in terms of lower fractional anisotropy (FA) and higher mean diffusivity (MD) and radial diffusivity (RD) is reported more consistently in the corpus callosum, cerebellar peduncles, cingulum, and longitudinal fasciculi connecting frontal and temporoparietal regions. Although these interesting results provide insight into FASD neuropathology, it is important to investigate the clinical diversity of this disorder for better treatment options and prediction of progression. The aim of this review is to provide a summary of different patterns of neural structure between PAE and typically developed individuals. We further discuss the association of alterations in diffusivity with demographic features and symptomatology of PAE. With the accumulated knowledge of the neural correlates of FASD presenting symptoms, a comprehensive understanding of the heterogeneity in FASD will potentially improve the disease management and will highlight the diagnostic challenges and potential areas of future research avenues, where neural markers may be beneficial.

Resting-state Functional MRI in Parkinsonian Syndromes

Background

Functional MRI (fMRI) has been widely used to study abnormal patterns of functional connectivity at rest in patients with movement disorders such as idiopathic Parkinson's disease (PD) and atypical parkinsonisms.

Methods

This manuscript provides an educational review of the current use of resting-state fMRI in the field of parkinsonian syndromes.

Results

Resting-state fMRI studies have improved the current knowledge about the mechanisms underlying motor and non-motor symptom development and progression in movement disorders. Even if its inclusion in clinical practice is still far away, resting-state fMRI has the potential to be a promising biomarker for early disease detection and prediction. It may also aid in differential diagnosis and monitoring brain responses to therapeutic agents and neurorehabilitation strategies in different movement disorders.

Conclusions

There is urgent need to identify and validate prodromal biomarkers in PD patients, to perform further studies assessing both overlapping and disease-specific fMRI abnormalities among parkinsonian syndromes, and to continue technical advances to fully realize the potential of fMRI as a tool to monitor the efficacy of chronic therapies.

A 12-Week Cycling Training Regimen Improves Upper Limb Functions in People With Parkinson's Disease

Background: It has been proposed that physical exercise can help improve upper limb functions in Parkinson’s disease (PD) patients; yet evidence for this hypothesis is limited.

Objective: To assess the effects of aerobic exercise training (AET) on general upper limb functions in sedentary people with PD and healthy adults (HA).

Methods: Two groups, 19 PD patients (Hoehn & Yahr ≤ 2) and 20 HA, matched on age and sedentary level, followed a 3-month stationary bicycle AET regimen. We used the kinematic theory framework to characterize and quantify the different motor control commands involved in performing simple upper-limb movements as drawing lines. Repeated measures ANCOVA models were used to assess the effect of AET in each group, as well as the difference between groups following the training regimen.

Results: At baseline, PD individuals had a larger antagonist response, a longer elapsed time between the visual stimulus and the end of the movement, and a longer time of displacement of the stylus than the HA. Following the 12-week AET, PD participants showed significant decreases of the agonist and antagonist commands, as well as the antagonist response spread. A significant group ^∗ session interaction effect was observed for the agonist command and the response spread of the antagonist command, suggesting a significant change for these two parameters only in PD patients following the AET. Among the differences observed at baseline, only the difference for the time of movement remained after AET.

Conclusion: A 3-month AET has a significant positive impact on the capacity to draw lines in a more efficiency way, in PD patients, indicating an improvement in the upper limb motor function.

Olfactory Testing in Frontotemporal Dementia: A Literature Review

Frontotemporal dementia (FTD) is a heterogeneous disorder featuring language impairment, personality changes, and executive defects, often due to the frontotemporal lobar degeneration (FTLD). Both FTD and FTLD are often associated with olfactory impairment, early biomarker for neurodegeneration, which can be evaluated with different techniques, among which low-cost olfactory tests are widely used. Therefore, we conducted a review of the literature focusing on papers published between January 1, 2007, and June 12, 2017, investigating the usefulness of olfactory testing in FTD/FTLD. A general decrease in the olfactory identification ability was seen in most of the articles and, taken together with a preserved odor discrimination, reveals a higher order impairment, possibly linked to cognitive decrease or language impairments, and not to a specific deficit of the olfactory system. This evidence could represent a useful add-on to the current literature, increasing the diagnostic value of olfactory assessment, particularly in cases where differential diagnosis is difficult.

Functional MRI in Idiopathic Parkinson's Disease

Functional MRI (fMRI) has been widely used to study abnormal patterns of brain connectivity at rest and activation during a variety of tasks in patients with idiopathic Parkinson's disease (PD). fMRI studies in PD have led to a better understanding of many aspects of the disease including both motor and non-motor symptoms. Although its translation into clinical practice is still at an early stage, fMRI measures hold promise for multiple clinical applications in PD, including the early detection, predicting future change in clinical status, and as a marker of alterations in brain physiology related to neurotherapeutic agents and neurorehabilitative strategies.

Technical note: can resting state functional MRI assist in routine clinical diagnosis?

Despite some differences in clinical presentation, it is often difficult to differentiate between dementia with Lewy bodies (DLB), clinical Alzheimer’s dementia (AD) and Parkinson’s disease dementia. However, differentiation can be crucial, especially as patients with DLB characteristically have a hypersensitivity to most antiemetic and neuroleptic drugs as they affect the cholinergic and dopaminergic system, potentially leading to life-threatening catatonia, loss of cognitive function and muscle rigidity. The aim of this study is to evaluate if resting state (RS) functional MRI (fMRI) can be used in routine practice on a 1.5 T scanner to differentiate between AD and DLB on an individual basis. We age- and gender-matched a known DLB patient with an AD patient and a human control (HC). Individual independent component analysis was carried out. Region of interest seeds were chosen from the midcingulate and insula regions. Functional connectivity from insula to midcingulate and within the midcingulate network (part of the Salience network) was lower in DLB than AD or HC. RS-fMRI on a 1.5 T scanner, in a routine clinical setting, detected abnormal functional connectivity patterns and allowed differentiation of DLB and AD in a routine clinical setting. This is the first evaluation of RS-fMRI in a routine clinical setting. It shows that incorporating RS-fMRI into the clinical scanning protocol can assist in early diagnosis and likely assist in monitoring the natural history of the disease or disease modifying treatments.

Gray/White Matter Contrast in Parkinson's Disease

Gray/white matter contrast (GWC) decreases with aging and has been found to be a useful MRI biomarker in Alzheimer’s disease (AD), but its utility in Parkinson’s disease (PD) patients has not been investigated. The aims of the study were to test whether GWC is sensitive to aging changes in PD patients, if PD patients differ from healthy controls (HCs) in GWC, and whether the use of GWC data would improve the sensitivity of cortical thickness analyses to differentiate PD patients from controls. Using T1-weighted structural images, we obtained individual cortical thickness and GWC values from a sample of 90 PD patients and 27 controls. Images were processed with the automated FreeSurfer stream. GWC was computed by dividing the white matter (WM) by the gray matter (GM) values and projecting the ratios onto a common surface. The sample characteristics were: 52 patients and 14 controls were males; mean age of 64.4 ± 10.6 years in PD and 64.7 ± 8.6 years in controls; 8.0 ± 5.6 years of disease evolution; 15.6 ± 9.8 UPDRS; and a range of 1.5–3 in Hoehn and Yahr (H&Y) stage. In both PD and controls we observed significant correlations between GWC and age involving almost the entire cortex. When applying a stringent cluster-forming threshold of p < 0.0001, the correlation between GWC and age also involved the entire cortex in the PD group; in the control group, the correlation was found in the parahippocampal gyrus and widespread frontal and parietal areas. The GWC of PD patients did not differ from controls’, whereas cortical thickness analyses showed thinning in temporal and parietal cortices in the PD group. Cortical thinning remained unchanged after adjusting for GWC. GWC is a very sensitive measure for detecting aging effects, but did not provide additional information over other parameters of atrophy in PD.

Non-invasive imaging modalities to study neurodegenerative diseases of aging brain

The aim of this article is to highlight current approaches for imaging elderly brain, indispensable for cognitive neuroscience research with emphasis on the basic physical principles of various non-invasive neuroimaging techniques. The first part of this article presents a quick overview of the primary non-invasive neuroimaging modalities used by cognitive neuroscientists such as transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), electroencephalography (EEG), magnetoencephalography (MEG), single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance spectroscopic imaging (MRSI), Profusion imaging, functional magnetic resonance imaging (fMRI), near infrared spectroscopy (NIRS) and diffusion tensor imaging (DTI) along with tractography and connectomics. The second part provides a comprehensive overview of different multimodality imaging techniques for various cognitive neuroscience studies of aging brain.

Effects of pharmacological and nonpharmacological treatments on brain functional magnetic resonance imaging in Alzheimer's disease and mild cognitive impairment: a critical review

BACKGROUND

A growing number of pharmacological and nonpharmacological trials have been performed to test the efficacy of approved or experimental treatments in Alzheimer disease (AD) and mild cognitive impairment (MCI). In this context, functional magnetic resonance imaging (fMRI) may be a good candidate to detect brain changes after a short period of treatment.

MAIN BODY

This critical review aimed to identify and discuss the available studies that have tested the efficacy of pharmacological and nonpharmacological treatments in AD and MCI cases using task-based or resting-state fMRI measures as primary outcomes. A PubMed-based literature search was performed with the use of the three macro-areas: 'disease', 'type of MRI', and 'type of treatment'. Each contribution was individually reviewed according to the Cochrane Collaboration's tool for assessing risk of bias. Study limitations were systematically detected and critically discussed. We selected 34 pharmacological and 13 nonpharmacological articles. According to the Cochrane Collaboration's tool for assessing risk of bias, 40% of these studies were randomized but only a few described clearly the randomization procedure, 36% declared the blindness of participants and personnel, and only 21% reported the blindness of outcome assessment. In addition, 28% of the studies presented more than 20% drop-outs at short- and/or long-term assessments. Additional common shortcomings of the reviewed works were related to study design, patient selection, sample size, choice of outcome measures, management of drop-out cases, and fMRI methods.

CONCLUSION

There is an urgent need to obtain efficient treatments for AD and MCI. fMRI is powerful enough to detect even subtle changes over a short period of treatment; however, the soundness of methods should be improved to enable meaningful data interpretation.

Update on Vascular Cognitive Impairment Associated with Subcortical Small-Vessel Disease

Subcortical small-vessel disease (SSVD) is a disorder well characterized from the clinical, imaging, and neuropathological viewpoints. SSVD is considered the most prevalent ischemic brain disorder, increasing in frequency with age. Vascular risk factors include hypertension, diabetes, hyperlipidemia, elevated homocysteine, and obstructive sleep apnea. Ischemic white matter lesions are the hallmark of SSVD; other pathological lesions include arteriolosclerosis, dilatation of perivascular spaces, venous collagenosis, cerebral amyloid angiopathy, microbleeds, microinfarcts, lacunes, and large infarcts. The pathogenesis of SSVD is incompletely understood but includes endothelial changes and blood-brain barrier alterations involving metalloproteinases, vascular endothelial growth factors, angiotensin II, mindin/spondin, and the mammalian target of rapamycin pathway. Metabolic and genetic conditions may also play a role but hitherto there are few conclusive studies. Clinical diagnosis of SSVD includes early executive dysfunction manifested by impaired capacity to use complex information, to formulate strategies, and to exercise self-control. In comparison with Alzheimer's disease (AD), patients with SSVD show less pronounced episodic memory deficits. Brain imaging has advanced substantially the diagnostic tools for SSVD. With the exception of cortical microinfarcts, all other lesions are well visualized with MRI. Diagnostic biomarkers that separate AD from SSVD include reduction of cerebrospinal fluid amyloid-β (Aβ)42 and of the ratio Aβ42/Aβ40 often with increased total tau levels. However, better markers of small-vessel function of intracerebral blood vessels are needed. The treatment of SSVD remains unsatisfactory other than control of vascular risk factors. There is an urgent need of finding targets to slow down and potentially halt the progression of this prevalent, but often unrecognized, disorder.

White matter tract alterations in Parkinson's disease patients with punding

OBJECTIVE

To assess brain white matter tract alterations in patients with Parkinson's disease and punding (PD-punding) compared with controls and PD cases without any impulsive-compulsive behaviour.

METHODS

Forty-nine PD patients (21 PD-punding and 28 PD with no impulsive-compulsive behaviours) and 28 controls were consecutively recruited. Clinical, cognitive and psychopathological evaluations were performed. Diffusion tensor MRI metrics of the main white matter tracts were assessed using a tractography approach.

RESULTS

Compared with controls, both PD groups showed white matter microstructural alterations of the left pedunculopontine tract and splenium of the corpus callosum. PD-punding patients showed a further damage to the right pedunculopontine tract and uncinate fasciculus, genu of the corpus callosum, and left parahippocampal tract relative to controls. When adjusting for depression and/or apathy severity, a greater damage of the genu of the corpus callosum and the left pedunculopontine tract was found in PD-punding compared with patients with no impulsive-compulsive behaviours.

CONCLUSIONS

PD-punding is associated with a disconnection between midbrain, limbic and white matter tracts projecting to the frontal cortices. These alterations are at least partially independent of their psychopathological changes. Diffusion tensor MRI is a powerful tool for understanding the neural substrates underlying punding in PD.

Imaging Biomarkers in Alzheimer's Disease: A Practical Guide for Clinicians

Although recent developments in imaging biomarkers have revolutionized the diagnosis of Alzheimer’s disease at early stages, the utility of most of these techniques in clinical setting remains unclear. The aim of this review is to provide a clear stepwise algorithm on using multitier imaging biomarkers for the diagnosis of Alzheimer’s disease to be used by clinicians and radiologists for day-to-day practice. We summarized the role of most common imaging techniques and their appropriate clinical use based on current consensus guidelines and recommendations with brief sections on acquisition and analysis techniques for each imaging modality. Structural imaging, preferably MRI or alternatively high resolution CT, is the essential first tier of imaging. It improves the accuracy of clinical diagnosis and excludes other potential pathologies. When the results of clinical examination and structural imaging, assessed by dementia expert, are still inconclusive, functional imaging can be used as a more advanced option. PET with ligands such as amyloid tracers and ¹⁸F-fluorodeoxyglucose can improve the sensitivity and specificity of diagnosis particularly at the early stages of the disease. There are, however, limitations in using these techniques in wider community due to a combination of lack of facilities and expertise to interpret the findings. The role of some of the more recent imaging techniques including tau imaging, functional MRI, or diffusion tensor imaging in clinical practice, remains to be established in the ongoing and future studies.

APOE moderates compensatory recruitment of neuronal resources during working memory processing in healthy older adults

The APOE ε4 allele increases the risk for sporadic Alzheimer's disease and modifies brain activation patterns of numerous cognitive domains. We assessed cognitively intact older adults with a letter n-back task to determine if previously observed increases in ε4 carriers' working-memory-related brain activation are compensatory such that they serve to maintain working memory function. Using multiple regression models, we identified interactions of APOE variant and age in bilateral hippocampus independently from task performance: ε4 carriers only showed a decrease in activation with increasing age, suggesting high sensitivity of fMRI data for detecting changes in Alzheimer's disease-relevant brain areas before cognitive decline. Moreover, we identified ε4 carriers to show higher activations in task-negative medial and task-positive inferior frontal areas along with better performance under high working memory load relative to non-ε4 carriers. The increased frontal recruitment is compatible with models of neuronal compensation, extends on existing evidence, and suggests that ε4 carriers require additional neuronal resources to successfully perform a demanding working memory task.