When, where, and how the corpus callosum changes in MCI and AD: a multimodal MRI study

Improved deep canonical correlation fusion approach for detection of early mild cognitive impairment

Detection of early mild cognitive impairment (EMCI) is clinically challenging as it involves subtle alterations in multiple brain sub-anatomic regions. Among different brain regions, the corpus callosum and lateral ventricles are primarily affected due to EMCI. In this study, an improved deep canonical correlation analysis (CCA) based framework is proposed to fuse magnetic resonance (MR) image features from lateral ventricular and corpus callosal structures for the detection of EMCI condition. For this, obtained structural MR images of healthy controls and EMCI subjects are preprocessed. Lateral ventricles and corpus callosum structures are segmented from these images and features are extracted. Extracted features from different brain structures are fused using non-linear orthogonal iteration-based deep CCA. Fused features are employed to differentiate healthy controls and EMCI condition using extreme learning machine classifier. Results indicate that fused callosal and ventricular features are able to detect EMCI. Improved deep CCA algorithm with tuned hyperparameters achieves the highest classifier performance with an F-score of 82.15%. The proposed framework is compared with state-of-the-art CCA approaches, and the results demonstrate its improved performance in EMCI detection. This highlights the potential of the proposed framework in the automated diagnosis of preclinical MCI conditions.

Alterations in corpus callosum subregions morphology and functional connectivity in patients with adult-onset hypothyroidism

BACKGROUND

Magnetic resonance imaging (MRI) brain abnormalities have been reported in the corpus callosum (CC) of patients with adult-onset hypothyroidism. However, no study has directly compared CC-specific morphological or functional alterations among subclinical hypothyroidism (SCH), overt hypothyroidism (OH), and healthy controls (HC). Moreover, the association of CC alterations with cognition and emotion is not well understood.

METHODS

Demographic data, clinical variables, neuropsychological scores, and MRI data of 152 participants (60 SCH, 37 OH, and 55 HC) were collected. This study investigated the clinical performance, morphological and functional changes of CC subregions across three groups. Moreover, a correlation analysis was performed to explore potential relationships between these factors.

RESULTS

Compared to HC, SCH and OH groups exhibited lower cognitive scores and higher depressive/anxious scores. Notably, rostrum and rostral body volume of CC was larger in the SCH group. Functional connectivity between rostral body, anterior midbody and the right precentral and dorsolateral superior frontal gyrus were increased in the SCH group. In contrast, the SCH and OH groups exhibited a decline in functional connectivity between splenium and the right angular gyrus. Within the SCH group, rostrum volume demonstrated a negative correlation with Montreal Cognitive Assessment and visuospatial/executive scores, while displaying a positive correlation with 24-item Hamilton Depression Rating Scale scores. In the OH group, rostral body volume exhibited a negative correlation with serum thyroid stimulating hormone levels, while a positive correlation with serum total thyroxine and free thyroxine levels.

CONCLUSIONS

This study suggests that patients with different stages of adult-onset hypothyroidism may exhibit different patterns of CC abnormalities. These findings offer new insights into the neuropathophysiological mechanisms in hypothyroidism.

The White Matter Integrity and Functional Connection Differences of Fornix (Cres)/Stria Terminalis in Individuals with Mild Cognitive Impairment Induced by Occupational Aluminum Exposure

Long-term aluminum (Al) exposure increases the risk of mild cognitive impairment (MCI). The aim of the present study was to investigate the neural mechanisms of Al-induced MCI. In our study, a total of 52 individuals with occupational Al exposure >10 years were enrolled and divided into two groups: MCI (Al-MCI) and healthy controls (Al-HC). Plasma Al concentrations and Montreal Cognitive Assessment (MoCA) score were collected for all participants. And diffusion tensor imaging and resting-state functional magnetic resonance imaging were used to examine changes of white matter (WM) and functional connectivity (FC). There was a negative correlation between MoCA score and plasma Al concentration. Compared with the Al-HC, fractional anisotropy value for the right fornix (cres)/stria terminalis (FX/ST) was higher in the Al-MCI. Furthermore, there was a difference in FC between participants with and without MCI under Al exposure. We defined the regions with differing FC as a "pathway," specifically the connectivity from the right temporal pole to the right FX/ST, then to the right sagittal stratum, and further to the right anterior cingulate and paracingulate gyri and right inferior frontal gyrus, orbital part. In summary, we believe that the observed differences in WM integrity and FC in the right FX/ST between participants with and without MCI under long-term Al exposure may represent the neural mechanisms underlying MCI induced by Al exposure.

Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods

Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.

Altered Callosal Morphology and Connectivity in Asymptomatic Carotid Stenosis

BACKGROUND

Carotid stenosis, even in the clinically asymptomatic stage, causes cognitive impairment, silent lesions, and hemispheric changes. The corpus callosum (CC) is crucial for hemispheric cortical integration and specialization.

PURPOSE

To examine if CC morphology and connectivity relate to cognitive decline and lesion burden in asymptomatic carotid stenosis (ACS).

STUDY TYPE

Retrospective, cross-sectional.

POPULATION

33 patients with unilaterally severe (70%) ACS and 28 demographically and comorbidity-matched controls. A publicly available healthy adult lifespan (ages between 18 and 80; n = 483) MRI dataset was also included.

FIELD STRENGTH/SEQUENCE

A 3.0 T; T1 MPRAGE and diffusion weighted gradient echo-planar imaging sequences.

ASSESSMENT

Structural MRI and multidomain cognitive data were obtained. Midsagittal CC area, circularity, thickness, integrity, and probabilistic tractography were calculated and correlated with cognitive tests and white matter hyperintensity. Fractional anisotropy, mean diffusivity (MD), and radial diffusivity were determined from DTI.

STATISTICAL TESTS

Independent two-sample t-tests, χ2 tests, Mann-Whitney U, locally weighted scatterplot smoothing (LOWESS) curve fit, and Pearson correlation. A P value < 0.05 was considered statistically significant.

RESULTS

Patients with ACS demonstrated significant reductions in callosal area, circularity, and thickness compared to controls. The callosal atrophy was significantly correlated with white matter hyperintensity size (r = -0.629, P < 0.001). Voxel-wise analysis of diffusion measures in the volumetric CC showed that ACS patients exhibited significantly lower fractional anisotropy and higher MD and radial diffusivity in the genu and splenium of the CC than controls. Further lifespan trajectory analysis showed that although the midsagittal callosal area, circularity, and thickness exhibited age-related decreases, the values in the ACS patients were significantly lower in all age groups.

DATA CONCLUSION

Midsagittal callosal atrophy and connectivity reflect the load of silent lesions and the severity of cognitive decline, respectively, suggesting that CC degeneration has potential to serve as an early marker in ACS.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Cerebrospinal fluid and blood exosomes as biomarkers for amyotrophic lateral sclerosis; a systematic review

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal motor neuron disease. Due to the limited knowledge about potential biomarkers that help in early diagnosis and monitoring disease progression, today's diagnoses are based on ruling out other diseases, neurography, and electromyography examination, which takes a time-consuming procedure.

METHODS

PubMed, ScienceDirect, and Web of Science were explored to extract articles published from January 2015 to June 2023. In the searching strategy following keywords were included; amyotrophic lateral sclerosis, biomarkers, cerebrospinal fluid, serum, and plama.

RESULTS

A total number of 6 studies describing fluid-based exosomal biomarkers were included in this study. Aggregated proteins including SOD1, TDP-43, pTDP-43, and FUS could be detected in the microvesicles (MVs). Moreover, TDP-43 and NFL extracted from plasma exosomes could be used as prognostic biomarkers. Also, downregulated miR-27a-3p detected through exoEasy Maxi and exoQuick Kit in the plasma could be measured as a diagnostic biomarker. Eventually, the upregulated level of CORO1A could be used to monitor disease progression.

CONCLUSION

Based on the results, each biomarker alone is insufficient to evaluate ALS. CNS-derived exosomes contain multiple ALS-related biomarkers (SOD1, TDP-43, pTDP-43, FUS, and miRNAs) that are detectable in cerebrospinal fluid and blood is a proper alternation. Exosome detecting kits listed as exoEasy, ExoQuick, Exo-spin, ME kit, ExoQuick Plus, and Exo-Flow, are helpful to reach this purpose.

Template-based graph registration network for boosting the diagnosis of brain connectivity disorders

Brain graphs are powerful representations to explore the biological roadmaps of the human brain in its healthy and disordered states. Recently, a few graph neural networks (GNNs) have been designed for brain connectivity synthesis and diagnosis. However, such non-Euclidean deep learning architectures might fail to capture the neural interactions between different brain regions as they are trained without guidance from any prior biological template-i.e., template-free learning. Here we assume that using a population-driven brain connectional template (CBT) that captures well the connectivity patterns fingerprinting a given brain state (e.g., healthy) can better guide the GNN training in its downstream learning task such as classification or regression. To this aim we design a plug-in graph registration network (GRN) that can be coupled with any conventional graph neural network (GNN) so as to boost its learning accuracy and generalizability to unseen samples. Our GRN is a graph generative adversarial network (gGAN), which registers brain graphs to a prior CBT. Next, the registered brain graphs are used to train typical GNN models. Our GRN can be integrated into any GNN working in an end-to-end fashion to boost its prediction accuracy. Our experiments showed that GRN remarkably boosted the prediction accuracy of four conventional GNN models across four neurological datasets.

Connectivity alterations underlying the breakdown of pseudoneglect: New insights from healthy and pathological aging

A right-hemisphere dominance for visuospatial attention has been invoked as the most prominent neural feature of pseudoneglect (i.e., the leftward visuospatial bias exhibited in neurologically healthy individuals) but the neurophysiological underpinnings of such advantage are still controversial. Previous studies investigating visuospatial bias in multiple-objects visual enumeration reported that pseudoneglect is maintained in healthy elderly and amnesic mild cognitive impairment (aMCI), but not in Alzheimer’s disease (AD). In this study, we aimed at investigating the neurophysiological correlates sustaining the rearrangements of the visuospatial bias along the progression from normal to pathological aging. To this aim, we recorded EEG activity during an enumeration task and analyzed intra-hemispheric fronto-parietal and inter-hemispheric effective connectivity adopting indexes from graph theory in patients with mild AD, patients with aMCI, and healthy elderly controls (HC). Results revealed that HC showed the leftward bias and stronger fronto-parietal effective connectivity in the right as compared to the left hemisphere. A breakdown of pseudoneglect in patients with AD was associated with both the loss of the fronto-parietal asymmetry and the reduction of inter-hemispheric parietal interactions. In aMCI, initial alterations of the attentional bias were associated with a reduction of parietal inter-hemispheric communication, but not with modulations of the right fronto-parietal connectivity advantage, which remained intact. These data provide support to the involvement of fronto-parietal and inter-parietal pathways in the leftward spatial bias, extending these notions to the complex neurophysiological alterations characterizing pathological aging.

Corpus callosum anatomical changes in Alzheimer patients and the effect of acetylcholinesterase inhibitors on corpus callosum morphometry

The Corpus Callosum (CC) is an important structure that includes the majority of fibers connecting the two brain hemispheres. Several neurodegenerative diseases may alter CC size and morphology leading to its atrophy and malfunction which may play a role in the pathological manifestations found in these diseases. The purpose of the current study is to determine any possible changes in CC size in patients suffering from Alzheimer’s disease. The Study also investigated the effect of acetylcholinesterase inhibitors (AChEIs) on the size of CC and its association with improvement in the Alzheimer disease severity scores. Midsagittal size of CC were recorded prospectively from 439 routine T1-weighted MRI brain images in normal individuals. The internal skull surface was measured to calculate CC/ internal skull surface ratio. Two groups of patients were studied: 300 (150 male / 150 female) were healthy subjects and 130 (55 males / 75 females) had Alzheimer disease. Out of the 130 Alzheimer disease pateints, 70 patients were treated with Donepezil or Rivastigmine or both. The size of the CC was measured based on T1-weighted MRI images after the treatment to investigate any possible improvement in CC size. The mean surface area of CC in controls was 6.53±1.105 cm². There was no significant difference between males and females (P < 0.627), and CC/ internal skull surface ratio was 4.41±0.77%. Patients with mild or severe Alzheimer disease showed a significant reduction in CC size compared to healthy controls. Treating mild Alzheimer patients with either Donepezil or Rivastigmine exerts a comparable therapeutic effect in improving the CC size. There was more improvement in the size of CC in patients with severe Alzheimer disease by using combined therapy of Donepezil and Rivastigmine than using single a medication. we measured the mean size of the various portions of the corpus callosum in normal individuals and Alzheimer patients before and after taking Donepezil and Rivastigmine. Alzheimer patients have pronounced reduction in CC which is corrected after taking Donepezil and Rivastigmine leading to remarkable improvement in Alzheimer disease severity scores.

Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data

The intervention at the stage of mild cognitive impairment (MCI) is promising for preventing Alzheimer's disease (AD). This study aims to search for the optimal machine learning (ML) model to classify early and late MCI (EMCI and LMCI) subtypes using multimodal MRI data. First, the tract-based spatial statistics (TBSS) analyses showed LMCI-related white matter changes in the Corpus Callosum. The ROI-based tractography addressed the connected cortical areas by affected callosal fibers. We then prepared two feature subsets for ML by measuring resting-state functional connectivity (TBSS-RSFC method) and graph theory metrics (TBSS-Graph method) in these cortical areas, respectively. We also prepared feature subsets of diffusion parameters in the regions of LMCI-related white matter alterations detected by TBSS analyses. Using these feature subsets, we trained and tested multiple ML models for EMCI/LMCI classification with cross-validation. Our results showed the ensemble ML model (AdaBoost) with feature subset of diffusion parameters achieved better performance of mean accuracy 70%. The useful brain regions for classification were those, including frontal, parietal lobe, Corpus Callosum, cingulate regions, insula, and thalamus regions. Our findings indicated the optimal ML model using diffusion parameters might be effective to distinguish LMCI from EMCI subjects at the prodromal stage of AD.

Midsagittal corpus callosal thickness and cognitive impairment in Parkinson's disease

People diagnosed with Parkinson's disease (PD) can experience significant neuropsychiatric symptoms, including cognitive impairment and dementia, the neuroanatomical substrates of which are not fully characterised. Symptoms associated with cognitive impairment and dementia in PD may relate to direct structural changes to the corpus callosum via primary white matter pathology, or as a secondary outcome due to the degeneration of cortical regions. Using magnetic resonance imaging, the corpus callosum can be investigated at the midsagittal plane, where it converges to a contiguous mass and is not intertwined with other tracts. The objective of this project was thus twofold; first, we investigated possible changes in the thickness of the midsagittal callosum and cortex in patients with PD with varying levels of cognitive impairment; and secondly, we investigated the relationship between the thickness of the midsagittal corpus callosum and the thickness of the cortex. Study participants included cognitively unimpaired PD participants (n = 35), PD participants with mild cognitive impairment (n = 22), PD participants with dementia (n = 17) and healthy controls (n = 27). We found thinning of the callosum in PD-related dementia compared to PD-related mild cognitive impairment and cognitively unimpaired PD participants. Regression analyses found thickness of the left medial orbitofrontal cortex to be positively correlated with thickness of the anterior callosum in PD-related mild cognitive impairment. This study suggests that a midsagittal thickness model can uncover changes to the corpus callosum in PD-related dementia, which occur in line with changes to the cortex in this advanced disease stage.

Alteration of the corpus callosum in patients with Alzheimer's disease: Deep learning-based assessment

BACKGROUND

Several studies have reported changes in the corpus callosum (CC) in Alzheimer's disease. However, the involved region differed according to the study population and study group. Using deep learning technology, we ensured accurate analysis of the CC in Alzheimer's disease.

METHODS

We used the Open Access Series of Imaging Studies (OASIS) dataset to investigate changes in the CC. The individuals were divided into three groups using the Clinical Dementia Rating (CDR); 94 normal controls (NC) were not demented (NC group, CDR = 0), 56 individuals had very mild dementia (VMD group, CDR = 0.5), and 17 individuals were defined as having mild and moderate dementia (MD group, CDR = 1 or 2). Deep learning technology using a convolutional neural network organized in a U-net architecture was used to segment the CC in the midsagittal plane. Total CC length and regional magnetic resonance imaging (MRI) measurements of the CC were made.

RESULTS

The total CC length was negatively associated with cognitive function. (beta = -0.139, p = 0.022) Among MRI measurements of the CC, the height of the anterior third (beta = 0.038, p <0.0001) and width of the body (beta = 0.077, p = 0.001) and the height (beta = 0.065, p = 0.001) and area of the splenium (beta = 0.059, p = 0.027) were associated with cognitive function. To distinguish MD from NC and VMD, the receiver operating characteristic analyses of these MRI measurements showed areas under the curves of 0.65-0.74. (total CC length = 0.705, height of the anterior third = 0.735, width of the body = 0.714, height of the splenium = 0.703, area of the splenium = 0.649).

CONCLUSIONS

Among MRI measurements, total CC length, the height of the anterior third and width of the body, and the height and area of the splenium were associated with cognitive decline. They had fair diagnostic validity in distinguishing MD from NC and VMD.

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

Background:

The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease.

Objective:

To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques.

Methods:

We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques.

Results:

Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients.

Conclusion:

Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.

Prediction of Alzheimer's Disease Progression Based on Magnetic Resonance Imaging

The neuroimaging method of multimodal magnetic resonance imaging (MRI) can identify the changes in brain structure and function caused by Alzheimer's disease (AD) at different stages, and it is a practical method to study the mechanism of AD progression. This paper reviews the studies of methods and biomarkers for predicting AD progression based on multimodal MRI. First, different approaches for predicting AD progression are analyzed and summarized, including machine learning, deep learning, regression, and other MRI analysis methods. Then, the effective biomarkers of AD progression under structural magnetic resonance imaging, diffusion tensor imaging, functional magnetic resonance imaging, and arterial spin labeling modes of MRI are summarized. It is believed that the brain changes shown on MRI may be related to the cognitive decline in different prodrome stages of AD, which is conducive to the further realization of early intervention and prevention of AD. Finally, the deficiencies of the existing studies are analyzed in terms of data set size, data heterogeneity, processing methods, and research depth. More importantly, future research directions are proposed, including enriching data sets, simplifying biomarkers, utilizing multimodal magnetic resonance, etc. In the future, the study of AD progression by multimodal MRI will still be a challenge but also a significant research hotspot.

Human immunodeficiency virus-related decreases in corpus callosal integrity and corresponding increases in functional connectivity

People living with human immunodeficiency virus (PLWH) often have neurocognitive impairment. However, findings on HIV-related differences in brain network function underlying these impairments are inconsistent. One principle frequently absent from these reports is that brain function is largely emergent from brain structure. PLWH commonly have degraded white matter; we hypothesized that functional communities connected by degraded white matter tracts would show abnormal functional connectivity. We measured white matter integrity in 69 PLWH and 67 controls using fractional anisotropy (FA) in 24 intracerebral white matter tracts. Then, among tracts with degraded FA, we identified gray matter regions connected to these tracts and measured their functional connectivity during rest. Finally, we identified cognitive impairment related to these structural and functional connectivity systems. We found HIV-related decreased FA in the corpus callosum body (CCb), which coordinates activity between the left and right hemispheres, and corresponding increases in functional connectivity. Finally, we found that individuals with impaired cognitive functioning have lower CCb FA and higher CCb functional connectivity. This result clarifies the functional relevance of the corpus callosum in HIV and provides a framework in which abnormal brain function can be understood in the context of abnormal brain structure, which may both contribute to cognitive impairment.

A Cross-Sectional Study on the Impact of Arterial Stiffness on the Corpus Callosum, a Key White Matter Tract Implicated in Alzheimer's Disease

BACKGROUND

Vascular risk factors such as arterial stiffness play an important role in the etiology of Alzheimer's disease (AD), presumably due to the emergence of white matter lesions. However, the impact of arterial stiffness to white matter structure involved in the etiology of AD, including the corpus callosum remains poorly understood.

OBJECTIVE

The aims of the study are to better understand the relationship between arterial stiffness, white matter microstructure, and perfusion of the corpus callosum in older adults.

METHODS

Arterial stiffness was estimated using the gold standard measure of carotid-femoral pulse wave velocity (cfPWV). Cognitive performance was evaluated with the Trail Making Test part B-A. Neurite orientation dispersion and density imaging was used to obtain microstructural information such as neurite density and extracellular water diffusion. The cerebral blood flow was estimated using arterial spin labelling.

RESULTS

cfPWV better predicts the microstructural integrity of the corpus callosum when compared with other index of vascular aging (the augmentation index, the systolic blood pressure, and the pulse pressure). In particular, significant associations were found between the cfPWV, an alteration of the extracellular water diffusion, and a neuronal density increase in the body of the corpus callosum which was also correlated with the performance in cognitive flexibility.

CONCLUSION

Our results suggest that arterial stiffness is associated with an alteration of brain integrity which impacts cognitive function in older adults.

Semi-automated assessment of the principal diffusion direction in the corpus callosum: differentiation of idiopathic normal pressure hydrocephalus from neurodegenerative diseases

BACKGROUND

Idiopathic normal pressure hydrocephalus (iNPH) shares clinical and radiological features with progressive supranuclear palsy (PSP) and Alzheimer's disease (AD). Corpus callosum (CC) involvement in these disorders is well established on structural MRI and diffusion tensor imaging (DTI), but alterations overlap and lack specificity to underlying tissue changes.

OBJECTIVE

We propose a semi-automated approach to assess CC integrity in iNPH based on the spatial distribution of DTI-derived principal diffusion direction orientation (V1).

METHODS

We processed DTI data from 121 subjects (Site1: iNPH = 23, PSP = 27, controls = 14; ADNI: AD = 35, controls = 22) to obtain V1, fractional anisotropy (FA) and mean diffusivity (MD) maps. To increase the estimation accuracy of DTI metrics, analyses were restricted to the midsagittal CC portion (± 6 slices from midsagittal plane). Group-wise comparison of normalized altered voxel count in midsagittal CC was performed using Kruskal-Wallis tests, followed by post hoc comparisons (Bonferroni-corrected p < 0.05). ROC analysis was used to evaluate the diagnostic power of DTI alterations compared to callosal volume.

RESULTS

We found specific changes of V1 distribution in CC splenium of iNPH compared to AD and PSP, while MD and FA showed patterns of alterations common to all disorders. ROC curves showed that, compared to splenial volume, V1 represented the most accurate marker of iNPH diagnosis versus AD and PSP.

CONCLUSIONS

Our results provide evidence that V1 is a powerful biomarker for distinguishing patients with iNPH from patients with AD or PSP. Indeed, our findings also provide more specific insight into the pathophysiological mechanisms that underlie tissue damage across iNPH and its mimics.

Free-water diffusion tensor imaging improves the accuracy and sensitivity of white matter analysis in Alzheimer's disease

Magnetic resonance imaging (MRI) based diffusion tensor imaging (DTI) can assess white matter (WM) integrity through several metrics, such as fractional anisotropy (FA), axial/radial diffusivities (AxD/RD), and mode of anisotropy (MA). Standard DTI is susceptible to the effects of extracellular free water (FW), which can be removed using an advanced free-water DTI (FW-DTI) model. The purpose of this study was to compare standard and FW-DTI metrics in the context of Alzheimer’s disease (AD). Data were obtained from the Open Access Series of Imaging Studies (OASIS-3) database and included both healthy controls (HC) and mild-to-moderate AD. With both standard and FW-DTI, decreased FA was found in AD, mainly in the corpus callosum and fornix, consistent with neurodegenerative mechanisms. Widespread higher AxD and RD were observed with standard DTI; however, the FW index, indicative of AD-associated neurodegeneration, was significantly elevated in these regions in AD, highlighting the potential impact of free water contributions on standard DTI in neurodegenerative pathologies. Using FW-DTI, improved consistency was observed in FA, AxD, and RD, and the complementary FW index was higher in the AD group as expected. With both standard and FW-DTI, higher values of MA coupled with higher values of FA in AD were found in the anterior thalamic radiation and cortico-spinal tract, most likely arising from a loss of crossing fibers. In conclusion, FW-DTI better reflects the underlying pathology of AD and improves the accuracy of DTI metrics related to WM integrity in Alzheimer’s disease.

Evaluation of White Matter Integrity Utilizing the DELPHI (TMS-EEG) System

Objective

The aim of this study was to evaluate brain white matter (WM) fibers connectivity damage in stroke and traumatic brain injury (TBI) subjects by direct electrophysiological imaging (DELPHI) that analyzes transcranial magnetic stimulation (TMS)-evoked potentials (TEPs).

Methods

The study included 123 participants, out of which 53 subjects with WM-related pathologies (39 stroke, 14 TBI) and 70 healthy age-related controls. All subjects underwent DELPHI brain network evaluations of TMS-electroencephalogram (EEG)-evoked potentials and diffusion tensor imaging (DTI) scans for quantification of WM microstructure fractional anisotropy (FA).

Results

DELPHI output measures show a significant difference between the healthy and stroke/TBI groups. A multidimensional approach was able to classify healthy from unhealthy with a balanced accuracy of 0.81 ± 0.02 and area under the curve (AUC) of 0.88 ± 0.01. Moreover, a multivariant regression model of DELPHI output measures achieved prediction of WM microstructure changes measured by FA with the highest correlations observed for fibers proximal to the stimulation area, such as frontal corpus callosum (r = 0.7 ± 0.02), anterior internal capsule (r = 0.7 ± 0.02), and fronto-occipital fasciculus (r = 0.65 ± 0.03).

Conclusion

These results indicate that features of TMS-evoked response are correlated to WM microstructure changes observed in pathological conditions, such as stroke and TBI, and that a multidimensional approach combining these features in supervised learning methods serves as a strong indicator for abnormalities and changes in WM integrity.

Alzheimer-related altered white matter microstructural integrity in Down syndrome: A model for sporadic AD?

INTRODUCTION

Virtually all adults with Down syndrome (DS) develop Alzheimer's disease (AD)-associated neuropathology by the age of 40, with risk for dementia increasing from the early 50s. White matter (WM) pathology has been reported in sporadic AD, including early demyelination, microglial activation, loss of oligodendrocytes and reactive astrocytes but has not been extensively studied in the at-risk DS population.

METHODS

Fifty-six adults with DS (35 cognitively stable adults, 11 with mild cognitive impairment, 10 with dementia) underwent diffusion-weighted magnetic resonance imaging (MRI), amyloid imaging, and had assessments of cognition and functional abilities using tasks appropriate for persons with intellectual disability.

RESULTS

Early changes in late-myelinating and relative sparing of early-myelinating pathways, consistent with the retrogenesis model proposed for sporadic AD, were associated with AD-related cognitive deficits and with regional amyloid deposition.

DISCUSSION

Our findings suggest that quantification of WM changes in DS could provide a promising and clinically relevant biomarker for AD clinical onset and progression.

Shape Profile of Corpus Callosum As a Signature to Phenotype Different Dementia

Background  Phenotyping dementia is always a complex task for a clinician. There is a need for more practical biomarkers to aid clinicians. Objective  The aim of the study is to investigate the shape profile of corpus callosum (CC) in different phenotypes of dementia. Materials and Methods  Our study included patients who underwent neuroimaging in our facility as a part of clinical evaluation for dementia referred from Geriatric Clinic (2017-2018). We have analyzed the shape of CC and interpreted the finding using a seven-segment division. Results  The sample included MPRAGE images of Alzheimer' dementia (AD) ( n = 24), posterior cortical atrophy- Alzheimer' dementia (PCA-AD) ( n = 7), behavioral variant of frontotemporal dementia (Bv-FTD) ( n = 17), semantic variant frontotemporal dementia (Sv-FTD) ( n = 11), progressive nonfluent aphasia (PNFA) ( n = 4), Parkinson's disease dementia (PDD) ( n = 5), diffuse Lewy body dementia ( n = 7), progressive supranuclear palsy (PSP) ( n = 3), and corticobasal degeneration (CBD) ( n = 3). We found in posterior dementias such as AD and PCA-AD that there was predominant atrophy of splenium of CC. In Bv-FTD, the genu and anterior half of the body of CC was atrophied, whereas in PNFA, PSP, PDD, and CBD there was atrophy of the body of CC giving a dumbbell like profile. Conclusion  Our study findings were in agreement with the anatomical cortical regions involved in different phenotypes of dementia. Our preliminary study highlighted potential usefulness of CC in the clinical setting for phenotyping dementia in addition to clinical history and robust biomarkers.

REM sleep is associated with white matter integrity in cognitively healthy, older adults

There is increasing awareness that self-reported sleep abnormalities are negatively associated with brain structure and function in older adults. Less is known, however, about how objectively measured sleep associates with brain structure. We objectively measured at-home sleep to investigate how sleep architecture and sleep quality related to white matter microstructure in older adults. 43 cognitively normal, older adults underwent diffusion tensor imaging (DTI) and a sleep assessment within a six-month period. Participants completed the PSQI, a subjective measure of sleep quality, and used an at-home sleep recorder (Zeo, Inc.) to measure total sleep time (TST), sleep efficiency (SE), and percent time in light sleep (LS), deep sleep (DS), and REM sleep (RS). Multiple regressions predicted fractional anisotropy (FA) and mean diffusivity (MD) of the corpus callosum as a function of total PSQI score, TST, SE, and percent of time spent in each sleep stage, controlling for age and sex. Greater percent time spent in RS was significantly associated with higher FA (β = 0.41, p = 0.007) and lower MD (β = -0.30, p = 0.03). Total PSQI score, TST, SE, and time spent in LS or DS were not significantly associated with FA or MD (p>0.13). Percent time spent in REM sleep, but not quantity of light and deep sleep or subjective/objective measures of sleep quality, positively predicted white matter microstructure integrity. Our results highlight an important link between REM sleep and brain health that has the potential to improve sleep interventions in the elderly.

MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey

Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer's disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.

Baseline White Matter Is Associated With Physical Fitness Change in Preclinical Alzheimer's Disease

White matter (WM) microstructure is a sensitive marker to distinguish individuals at risk of Alzheimer's disease. The association of objective physical fitness (PF) measures and WM microstructure has not been explored and mixed results reported with physical activity (PA). Longitudinal studies of WM with PA and PF measures have had limited investigation. This study explored the relationship between objective PF measures over 24-months with "normal-appearing" WM microstructure. Data acquired on magnetic resonance imaging was used to measure "normal-appearing" WM microstructure at baseline and 24-months. Clinical variables such as cognitive and blood-based measures were collected longitudinally. Also, as part of the randomized controlled trial of a PA, extensive measures of PA and fitness were obtained over the 24 months. Bilateral corticospinal tracts (CST) and the corpus callosum showed a significant association between PF performance over 24-months and baseline WM microstructural measures. There was no significant longitudinal effect of the intervention or PF performance over 24-months. Baseline WM microstructural measures were significantly associated with PF performance over 24-months in this cohort of participants with vascular risk factors and at risk of Alzheimer's disease with distinctive patterns for each PF test.

Structural remodeling secondary to functional remodeling in advanced-stage peripheral facial neuritis

BACKGROUND

Structural remodeling is a classic manifestation of disease decompensation. Facial synkinesis is the most troubling sequela of peripheral facial neuritis, and its structural remodeling, especially in white matter (WM), is still poorly understood. Therefore, understanding WM microstructure is important for predicting WM pathology and for early intervention in facial synkinesis patients.

METHODS

A total of 20 facial synkinesis patients (18 men and 2 women; mean age, 33.35 ± 6.97 years old) and 19 healthy controls (17 men and 2 women; mean age, 33.21 ± 6.75 years old) were enrolled in this study. rs-fMRI data, diffusion tensor imaging (DTI) data, and Beck's Depression Inventory (BDI) data were collected, and tract-based spatial statistics (TBSS) and voxel-mirrored homotopic connectivity (VMHC) values were used to analyze changes in WM microstructure and interhemispheric coordination.

RESULTS

Compared with the healthy controls, facial synkinesis patients exhibited significantly lower regional fractional anisotropy (FA) in the genu of the corpus callosum and the body of the corpus callosum, significantly higher regional FA in the retrolenticular part of the internal capsule, and significantly decreased VMHC values bilaterally in the orbital inferior frontal gyri, the fusiform gyri, the superior temporal gyri, the superior frontal gyri, and the supplementary motor areas. Furthermore, a lower regional FA in the genu of the corpus callosum was correlated with higher BDI scores in facial synkinesis patients.

CONCLUSION

Structural remodeling, especially changes in white matter microstructure, may be the central mechanism for severe sequelae of peripheral facial neuritis.

Comparison of Manual Cross-Sectional Measurements and Automatic Volumetry of the Corpus Callosum, and Their Clinical Impact: A Study on Type 1 Diabetes and Healthy Controls

Background and purpose: Degenerative change of the corpus callosum might serve as a clinically useful surrogate marker for net pathological cerebral impact of diabetes type 1. We compared manual and automatic measurements of the corpus callosum, as well as differences in callosal cross-sectional area between subjects with type 1 diabetes and healthy controls. Materials and methods: This is a cross-sectional study on 188 neurologically asymptomatic participants with type 1 diabetes and 30 healthy age- and sex-matched control subjects, recruited as part of the Finnish Diabetic Nephropathy Study. All participants underwent clinical work-up and brain MRI. Callosal area was manually measured and callosal volume quantified with FreeSurfer. The measures were normalized using manually measured mid-sagittal intracranial area and volumetric intracranial volume, respectively. Results: Manual and automatic measurements correlated well (callosal area vs. volume: ρ = 0.83, p < 0.001 and mid-sagittal area vs. intracranial volume: ρ = 0.82, p < 0.001). We found no significant differences in the callosal measures between cases and controls. In type 1 diabetes, the lowest quartile of normalized callosal area was associated with higher insulin doses (p = 0.029) and reduced insulin sensitivity (p = 0.033). In addition, participants with more than two cerebral microbleeds had smaller callosal area (p = 0.002). Conclusion: Manually measured callosal area and automatically segmented are interchangeable. The association seen between callosal size with cerebral microbleeds and insulin resistance is indicative of small vessel disease pathology in diabetes type 1.

Introducing a Novel Approach for Evaluation and Monitoring of Brain Health Across Life Span Using Direct Non-invasive Brain Network Electrophysiology

Objective

Evaluation and monitoring of brain health throughout aging by direct electrophysiological imaging (DELPHI) which analyzes TMS (transcranial magnetic stimulation) evoked potentials.

Methods

Transcranial magnetic stimulation evoked potentials formation, coherence and history dependency, measured using electroencephalogram (EEG), was extracted from 80 healthy subjects in different age groups, 25–85 years old, and 20 subjects diagnosed with mild dementia (MD), over 70 years old. Subjects brain health was evaluated using MRI scans, neurocognitive evaluation, and computerized testing and compared to DELPHI analysis of brain network functionality.

Results

A significant decrease in signal coherence is observed with age in connectivity maps, mostly in inter-hemispheric temporal, and parietal areas. MD patients display a pronounced decrease in global and inter-hemispheric frontal connectivity compared to healthy controls. Early and late signal slope ratio also display a significant, age dependent, change with pronounced early slope, phase shift, between normal healthy aging, and MD. History dependent analysis demonstrates a binary step function classification of healthy brain vs. abnormal aging subjects mostly for late slope. DELPHI measures demonstrate high reproducibility with reliability coefficients of around 0.9.

Conclusion

These results indicate that features of evoked response, as charge transfer, slopes of response, and plasticity are altered during abnormal aging and that these fundamental properties of network functionality can be directly evaluated and monitored using DELPHI.

An experimental model of Braak's pretangle proposal for the origin of Alzheimer's disease: the role of locus coeruleus in early symptom development

Background

The earliest brain pathology related to Alzheimer’s disease (AD) is hyperphosphorylated soluble tau in the noradrenergic locus coeruleus (LC) neurons. Braak characterizes five pretangle tau stages preceding AD tangles. Pretangles begin in young humans and persist in the LC while spreading from there to other neuromodulatory neurons and, later, to the cortex. While LC pretangles appear in all by age 40, they do not necessarily result in AD prior to death. However, with age and pretangle spread, more individuals progress to AD stages. LC neurons are lost late, at Braak stages III–IV, when memory deficits appear. It is not clear if LC hyperphosphorylated tau generates the pathology and cognitive changes associated with preclinical AD. We use a rat model expressing pseudohyperphosphorylated human tau in LC to investigate the hypothesis that LC pretangles generate preclinical Alzheimer pathology.

Methods

We infused an adeno-associated viral vector carrying a human tau gene pseudophosphorylated at 14 sites common in LC pretangles into 2–3- or 14–16-month TH-Cre rats. We used odor discrimination to probe LC dysfunction, and we evaluated LC cell and fiber loss.

Results

Abnormal human tau was expressed in LC and exhibited somatodendritic mislocalization. In rats infused at 2–3 months old, 4 months post-infusion abnormal LC tau had transferred to the serotonergic raphe neurons. After 7 months, difficult similar odor discrimination learning was impaired. Impairment was associated with reduced LC axonal density in the olfactory cortex and upregulated β1-adrenoceptors. LC infusions in 14–16-month-old rats resulted in more severe outcomes. By 5–6 months post-infusion, rats were impaired even in simple odor discrimination learning. LC neuron number was reduced. Human tau appeared in the microglia and cortical neurons.

Conclusions

Our animal model suggests, for the first time, that Braak’s hypothesis that human AD originates with pretangle stages is plausible. LC pretangle progression here generates both preclinical AD pathological changes and cognitive decline. The odor discrimination deficits are similar to human odor identification deficits seen with aging and preclinical AD. When initiated in aged rats, pretangle stages progress rapidly and cause LC cell loss. These age-related outcomes are associated with a severe learning impairment consistent with memory decline in Braak stages III–IV.

Electronic supplementary material

The online version of this article (10.1186/s13195-019-0511-2) contains supplementary material, which is available to authorized users.

Cerebrovascular Resistance in Healthy Aging and Mild Cognitive Impairment

Measures of cerebrovascular reactivity (CVR) are used to judge the health of the brain vasculature. In this study, we report the use of several different analyses of blood oxygen dependent (BOLD) fMRI responses to CO₂ to provide a number of metrics of CVR based on the sigmoidal resistance response to CO₂. To assess possible differences in these metrics with age, we compiled atlases reflecting voxel-wise means and standard deviations for four different age ranges and for a group of patients with mild cognitive impairment (MCI) and compared them. Sixty-seven subjects were recruited for this study and scanned at 3T field strength. Of those, 51 healthy control volunteers between the ages of 18–83 were recruited, and 16 (MCI) subjects between the ages of 61–83 were recruited. Testing was carried out using an automated computer-controlled gas blender to induce hypercapnia in a step and ramp paradigm while monitoring end-tidal partial pressures of CO₂. Surprisingly, some resistance sigmoid parameters in the oldest control group were increased compared to the youngest control group. Resistance amplitude maps showed increases in clusters within the temporal cortex, thalamus, corpus callosum and brainstem, and resistance reserve maps showed increases in clusters within the cingulate cortex, frontal gyrus, and corpus callosum. These findings suggest that some aspects of vascular reactivity in parts of the brain are initially maintained with age but then may increase in later years. We found significant reductions in all resistance sigmoid parameters (amplitude, reserve, sensitivity, midpoint, and range) when comparing MCI patients to controls. Additionally, in controls and in MCI patients, amplitude, range, reserve, and sensitivity in white matter (WM) was significantly reduced compared to gray matter (GM). WM midpoints were significantly above those of GM. Our general conclusion is that vascular regulation in terms of cerebral blood flow (CBF) responsiveness to CO₂ is not significantly affected by age, but is reduced in MCI. These changes in cerebrovascular regulation demonstrate the value of resistance metrics for mapping areas of dysregulated blood flow in individuals with MCI. They may also be of value in the investigation of patients with vascular risk factors at risk for developing vascular dementia.

Therapeutic Role of Transcranial Direct Current Stimulation in Alzheimer Disease Patients: Double-Blind, Placebo-Controlled Clinical Trial

OBJECTIVE

To explore the neuropsychological effects and levels of tau protein (TAU), amyloid β 1-42 (Aβ 1-42), and lipid peroxidase after 10 sessions of anodal transcranial direct current stimulation (tDCS) in patients with mild to moderate Alzheimer disease (AD).

PATIENTS AND METHODS

A total of 46 consecutive patients with probable AD participated in this study. They were classified randomly into 2 equal groups: active versus sham. Each patient received 10 sessions of anodal tDCS over the left and right temporoparietal region for 20 minutes for each side with the cathode on the left arm. Patients were assessed using the Modified Mini Mental State Examination (MMMSE), clock drawing test, Montreal Cognitive Scale (MoCA), and the Cornell Scale for depression. Serum TAU, Aβ 1-42, and lipid peroxidase were measured before and after the 10th session.

RESULTS

There was a significant improvement in the total score of each cognitive rating scale (MMMSE, clock drawing test, and MoCA) in the real group, whereas no such change was observed in the sham group. The Cornell depression score improved significantly in both groups. There was a significant increase in serum Aβ 1-42 ( P = .02) in the real but not in the sham group, with a significant Treatment condition × Time interaction ( P = .009). There was no significant effect on tau or lipid peroxidase in either group but a significant positive correlation between changes of Aβ1-42 and MMMSE ( P = .005) and MoCA ( P = .02).

CONCLUSION

The observed cognitive improvements were complemented by parallel changes in serum levels of Aβ 1-42.

Novel Insights on Systemic and Brain Aging, Stroke, Amyotrophic Lateral Sclerosis, and Alzheimer's Disease

The mechanisms that underlie the pathophysiology of aging, amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD) and stroke are not fully understood and have been the focus of intense and constant investigation worldwide. Studies that provide insights on aging and age-related disease mechanisms are critical for advancing novel therapies that promote successful aging and prevent or cure multiple age-related diseases. The April 2019 issue of the journal, "Aging & Disease" published a series of articles that confer fresh insights on numerous age-related conditions and diseases. The age-related topics include the detrimental effect of overweight on energy metabolism and muscle integrity, senoinflammation as the cause of neuroinflammation, the link between systemic C-reactive protein and brain white matter loss, the role of miR-34a in promoting healthy heart and brain, the potential of sirtuin 3 for reducing cardiac and pulmonary fibrosis, and the promise of statin therapy for ameliorating asymptomatic intracranial atherosclerotic stenosis. Additional aging-related articles highlighted the involvement of miR-181b-5p and high mobility group box-1 in hypertension, Yes-associated protein in cataract formation, multiple miRs and long noncoding RNAs in coronary artery disease development, the role of higher meat consumption on sleep problems, and the link between glycated hemoglobin and depression. The topics related to ALS suggested that individuals with higher education and living in a rural environment have a higher risk for developing ALS, and collagen XIX alpha 1 is a prognostic biomarker of ALS. The topics discussed on AD implied that extracellular amyloid β42 is likely the cause of intraneuronal neurofibrillary tangle accumulation in familial AD and traditional oriental concoctions may be useful for slowing down the progression of AD. The article on stroke suggested that inhibition of the complement system is likely helpful in promoting brain repair after ischemic stroke. The significance of the above findings for understanding the pathogenesis in aging, ALS, AD, and stroke, slowing down the progression of aging, ALS and AD, and promoting brain repair after stroke are discussed.

Increased Serum C-reactive Protein and Corpus Callosum Alterations in Older Adults

Chronic systemic low-grade inflammation is associated with aging, but little is known on whether age-related inflammation affects brain structure, particularly white matter. The current study tested the hypothesis that in older adults without dementia, higher serum levels of high-sensitivity C-reactive protein (hs-CRP) are associated with reduced corpus callosum (CC) areas. French community-dwelling subjects (ESPRIT study) aged 65 and older (N=101) underwent hs-CRP testing and structural magnetic resonance imaging (MRI). Multiple linear regression models were carried out. In the unadjusted model, higher hs-CRP level was significantly associated with smaller anterior, mid, and total midsagittal CC areas, but not with the posterior CC area. These associations were independent of demographic characteristics and intracranial volume. After adjustment for body mass index, diabetes, inflammation-related chronic pathologies and white matter lesions (WML), only the associations between hs-CRP level and smaller anterior and total midsagittal CC areas were still significant, although weaker. These findings suggest that low-grade inflammation is associated with CC structural integrity alterations in older adults independently of physical or neuropsychiatric pathologies.

Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis

In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive Impairment (MCI), from normal aging subjects. Multimodality neuroimaging data such as MRI and PET provide valuable insights into brain abnormalities, while genetic data such as single nucleotide polymorphism (SNP) provide information about a patient's AD risk factors. When these data are used together, the accuracy of AD diagnosis may be improved. However, these data are heterogeneous (e.g., with different data distributions), and have different number of samples (e.g., with far less number of PET samples than the number of MRI or SNPs). Thus, learning an effective model using these data is challenging. To this end, we present a novel three-stage deep feature learning and fusion framework, where deep neural network is trained stage-wise. Each stage of the network learns feature representations for different combinations of modalities, via effective training using the maximum number of available samples. Specifically, in the first stage, we learn latent representations (i.e., high-level features) for each modality independently, so that the heterogeneity among modalities can be partially addressed, and high-level features from different modalities can be combined in the next stage. In the second stage, we learn joint latent features for each pair of modality combination by using the high-level features learned from the first stage. In the third stage, we learn the diagnostic labels by fusing the learned joint latent features from the second stage. To further increase the number of samples during training, we also use data at multiple scanning time points for each training subject in the dataset. We evaluate the proposed framework using Alzheimer's disease neuroimaging initiative (ADNI) dataset for AD diagnosis, and the experimental results show that the proposed framework outperforms other state-of-the-art methods.

Decreased Bilateral FDG-PET Uptake and Inter-Hemispheric Connectivity in Multi-Domain Amnestic Mild Cognitive Impairment Patients: A Preliminary Study

Background: Amnestic mild cognitive impairment (aMCI) is a heterogeneous condition. Based on clinical symptoms, aMCI could be categorized into single-domain aMCI (SD-aMCI, only memory deficit) and multi-domain aMCI (MD-aMCI, one or more cognitive domain deficit). As core intrinsic functional architecture, inter-hemispheric connectivity maintains many cognitive abilities. However, few studies investigated whether SD-aMCI and MD-aMCI have different inter-hemispheric connectivity pattern. Methods: We evaluated inter-hemispheric connection pattern using fluorine-18 positron emission tomography - fluorodeoxyglucose (¹⁸F PET-FDG), resting-state functional MRI and structural T1 in 49 controls, 32 SD-aMCI, and 32 MD-aMCI patients. Specifically, we analyzed the 18^F PET-FDG (intensity normalized by cerebellar vermis) in a voxel-wise manner. Then, we estimated inter-hemispheric functional and structural connectivity by calculating the voxel-mirrored homotopic connectivity (VMHC) and corpus callosum (CC) subregions volume. Further, we correlated inter-hemispheric indices with the behavioral score and pathological biomarkers. Results: We found that MD-aMCI exhibited more several inter-hemispheric connectivity damages than SD-aMCI. Specifically, MD-aMCI displayed hypometabolism in the bilateral middle temporal gyrus (MTG), inferior parietal lobe, and left precuneus (PCu) (p < 0.001, corrected). Correspondingly, MD-aMCI showed decreased VMHC in MTG, PCu, calcarine gyrus, and postcentral gyrus, as well as smaller mid-posterior CC than the SD-aMCI and controls (p < 0.05, corrected). Contrary to MD-aMCI, there were no neuroimaging indices with significant differences between SD-aMCI and controls, except reduced hypometabolism in bilateral MTG. Within aMCI patients, hypometabolism and reduced inter-hemispheric connectivity correlated with worse executive ability. Moreover, hypometabolism indices correlated to increased amyloid deposition. Conclusion: In conclusion, patients with MD-aMCI exhibited the more severe deficit in inter-hemispheric communication than SD-aMCI. This long-range connectivity deficit may contribute to cognitive profiles and potentially serve as a biomarker to estimate disease progression of aMCI patients.

Callosal circularity as an early marker for Alzheimer's disease

Background

Although brain atrophy is considered to be a downstream marker of Alzheimer's disease (AD), subtle changes may allow to identify healthy subjects at risk of developing AD. As the ability to select at-risk persons is considered to be important to assess the efficacy of drugs and as MRI is a widely available imaging technique we have recently developed a reliable segmentation algorithm for the corpus callosum (CC). Callosal atrophy within AD has been hypothesized to reflect both myelin breakdown and Wallerian degeneration.

Methods

We applied our fully automated segmentation and feature extraction algorithm to two datasets: the OASIS database consisting of 316 healthy controls (HC) and 100 patients affected by either mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD) and a second database that was collected at the Memory Clinic of Hospital Network Antwerp and consists of 181 subjects, including healthy controls, subjects with subjective cognitive decline (SCD), MCI, and ADD. All subjects underwent (among others) neuropsychological testing including the Mini-Mental State Examination (MMSE). The extracted features were the callosal area (CCA), the circularity (CIR), the corpus callosum index (CCI) and the thickness profile.

Results

CIR and CCI differed significantly between most groups. Furthermore, CIR allowed us to discriminate between SCD and HC with an accuracy of 77%. The more detailed callosal thickness profile provided little added value towards the discrimination of the different AD stages. The largest effect of normal ageing on callosal thickness was found in the frontal callosal midbody.

Conclusions

To the best of our knowledge, this is the first study investigating changes in corpus callosum morphometry in normal ageing and AD by exploring both summarizing features (CCA, CIR and CCI) and the complete CC thickness profile in two independent cohorts using a completely automated algorithm. We showed that callosal circularity allows to discriminate between an important subgroup of the early AD spectrum (SCD) and age and sex matched healthy controls.

•

Callosal circularity allows to discriminate between subjects with subjective cognitive decline and matched healthy controls

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Callosal circularity is smaller in subjects with AD dementia as compared to matched subjects with mild cognitive impairment

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The callosal thickness profile differs between AD and HC, but not between the different clinical AD stages

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The AD thickness profile strongly correlates with age in HCs

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Callosal circularity correlates with CSF biomarkers (T-tau and P-tau) in MCI.

Normal Development of the Corpus Callosum and Evolution of Corpus Callosum Sexual Dimorphism in Infancy

OBJECTIVES

The aim of this study was to establish reference ranges for the corpus callosum in infancy and to clarify how sexual dimorphism evolves between the fetal stage and infancy.

METHODS

Normal sonograms from cerebral ultrasonographic examinations of 1- to 6-month-old healthy full-term infants were selected. The length and thickness of the corpus callosum were determined, and the effect of sex on these values was analyzed. Studies on corpus callosum sexual dimorphism were reviewed.

RESULTS

In total, sonograms from 236 1- to 6-month-old infants (120 male and 116 female) were collected, and the typical values (5th-95th percentiles) of the corpus callosum were determined for each group. During the first 2 months, with and without brain size adjustment, the corpus callosum in female infants was significantly thicker than that in male infants (mean thickness ± SD: 1 month, male infant, 1.8 ± 0.3 mm; female infant, 2.1 ± 0.3 mm; P = .005; 2 months, male infant, 1.8 ± 0.2 mm; female infant, 2.0 ± 0.3 mm; P = .002). The corpus callosum thickness of male and female infants had no significant differences after 2 months of age. Sexual dimorphism was not detected in corpus callosum length.

CONCLUSIONS

Our study provides reference data on typical corpus callosum development in infants. In the fetal period and early infancy, the corpus callosum in female infants is thicker than that in male infants. From 3 months onward, the corpus callosum sexual dimorphism becomes insignificant throughout childhood. The evolvement of corpus callosum sexual dimorphism suggests that maternal factors may influence brain development.

Multimodality Neuroimaging in Mild Cognitive Impairment: A Cross-sectional Comparison Study

Background and Purpose

Mild cognitive impairment (MCI) is a focus of considerable research. The present study aimed to test the utility of a logistic regression-derived classifier, combining specific quantitative multimodal magnetic resonance imaging (MRI) data for the early objective phenotyping of MCI in the clinic, over structural MRI data.

Methods

Thirty-three participants with cognitively stable amnestic MCI; 15 MCI converters to early Alzheimer's disease (AD; diseased controls) and 20 healthy controls underwent high-resolution T1-weighted volumetric MRI, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (¹H MR spectroscopy). The regional volumes were obtained from T1-weighted MRI. The fractional anisotropy and mean diffusivity maps were derived from DTI over multiple white matter regions. The ¹H MRS voxels were placed over posterior cingulate gyri, and N-acetyl aspartate (NAA)/creatine (Cr), choline (Cho)/Cr, myoinositol (mI/Cr), and NAA/mI ratios were obtained. A multimodal classifier comprising MR volumetry, DTI, and MRS was prepared. A cutoff point was arrived based on receiver operator characteristics analysis. Results were considered significant, if P < 0.05.

Results

The most sensitive individual marker to discriminate MCI from controls was DTI (90.9%), with a specificity of 50%. For classifying MCI from AD, the best individual modality was DTI (72.7%), with a high specificity of 87.9%. The multimodal classifier approach for MCI control classification achieved an area under curve (AUC) (AUC = 0.89; P < 0.001), with 93.9% sensitivity and 70% specificity. The combined classifier for MCI-AD achieved a highest AUC (AUC = 0.93; P < 0.001), with 93% sensitivity and 85.6% specificity.

Conclusions

The combined method of gray matter atrophy, white matter tract changes, and metabolite variation achieved a better performance at classifying MCI compared to the application of individual MRI biomarkers.

A panel of clinical and neuropathological features of cerebrovascular disease through the novel neuroimaging methods

The last decade has witnessed substantial progress in acquiring diagnostic biomarkers for the diagnostic workup of cerebrovascular disease (CVD). Advanced neuroimaging methods not only provide a strategic contribution for the differential diagnosis of vascular dementia (VaD) and vascular cognitive impairment (VCI), but also help elucidate the pathophysiological mechanisms ultimately leading to small vessel disease (SVD) throughout its course.

OBJECTIVE

In this review, the novel imaging methods, both structural and metabolic, were summarized and their impact on the diagnostic workup of age-related CVD was analysed. Methods: An electronic search between January 2010 and 2017 was carried out on PubMed/MEDLINE, Institute for Scientific Information Web of Knowledge and EMBASE.

RESULTS

The use of full functional multimodality in simultaneous Magnetic Resonance (MR)/Positron emission tomography (PET) may potentially improve the clinical characterization of VCI-VaD; for structural imaging, MRI at 3.0 T enables higher-resolution scanning with greater imaging matrices, thinner slices and more detail on the anatomical structure of vascular lesions.

CONCLUSION

Although the importance of most of these techniques in the clinical setting has yet to be recognized, there is great expectancy in achieving earlier and more refined therapeutic interventions for the effective management of VCI-VaD.

Characterizing biomarker features of cognitively normal individuals with ventriculomegaly

INTRODUCTION

The clinical significance of ventriculomegaly in cognitively normal elderly individuals remains unclear.

METHODS

We selected cognitively normal individuals (n = 425) from the Alzheimer's Disease Neuroimaging Initiative database and calculated Evans index (EI) based on the ratio of the frontal horn and skull diameter. We defined ventriculomegaly as EI ≥ 0.30, and the participants were stratified into EI ≥ 0.30 group and EI < 0.30 group. Neuropsychological, imaging, and fluid biomarker profiles between the two groups were then compared using regression models.

RESULTS

A total of 96 (22.5%) individuals who had ventriculomegaly performed worse on the cognitive tests; showed smaller hippocampal volume but larger caudate, cingulate, and paracentral gyrus volumes; and displayed lower positron emission tomography [18F]fluorodeoxyglucose standardized uptake value ratio but higher amyloid burden represented by higher [18F]florbetapir standardized uptake value ratio and lower cerebrospinal fluid amyloid β 1-42 levels compared to those without ventriculomegaly.

DISCUSSION

Asymptomatic ventriculomegaly might be an early imaging signature of preclinical Alzheimer's disease and/or normal pressure hydrocephalus.

Comparison of neuropsychiatric symptoms and diffusion tensor imaging correlates among patients with subcortical ischemic vascular disease and Alzheimer's disease

BACKGROUND

The causes of behavioral and psychological symptoms of dementia (BPSD) vary according to the dementia subtype and associated neuropathology. The present study aimed to (i) compare BPSD between patients with subcortical ischemic vascular disease (SIVD) and Alzheimer's disease (AD) across stages, and (ii) explore the associations with diffusion tensor imaging (DTI) in the corpus callosum (CC) and other major fibers.

METHODS

Twenty-four patients with SIVD and 32 with AD were recruited. Four domains of the Neuropsychiatric Inventory (NPI) (hyperactivity, psychosis, affective, and apathy) and two DTI parameters [fractional anisotropy (FA) and mean diffusivity (MD)] within the genu, body (BCC), and splenium (SCC) of the CC and other major fibers were assessed.

RESULTS

Overall, the patients with clinical dementia rating (CDR) 1 ~ 2 had higher scores in apathy domain than those with CDR0.5. Among those with CDR1 ~ 2, SIVD had higher scores in apathy domain than AD. MD values in the BCC/SCC were positively correlated with total NPI score and psychosis, hyperactivity, and apathy domains. FA values in the SCC were inversely correlated with total NPI score and psychosis domain. The correlations were modified by age, the CASI, and CDR scores. Stepwise linear regression models suggested that FA value within the left superior longitudinal fasciculus predicted the hyperactivity domain. MD value within the SCC/left uncinate fasciculus and FA value within the GCC/left forceps major predicted the psychosis domain. MD value within the right superior longitudinal fasciculus and CDR predicted the apathy domain. Further analysis suggested distinct patterns of regression models between SIVD and AD patients.

CONCLUSION

White matter integrity within the BCC/SCC had associations with multi-domains of BPSD. Our study also identified important roles of regions other than the CC to individual domain of BPSD, including the left superior longitudinal fasciculus to the hyperactivity domain, the left uncinate fasciculus/forceps major to the psychosis domain, and the right superior longitudinal fasciculus to the apathy domain. The neuronal substrates in predicting BPSD were different between SIVD and AD patients. Of note, apathy, which was more profound in SIVD, was associated with corresponding fiber disconnection in line with dementia severity and global cognition decline.

Effectiveness of diffusion tensor imaging in differentiating early-stage subcortical ischemic vascular disease, Alzheimer's disease and normal ageing

Objective

To describe and compare diffusion tensor imaging (DTI) parameters between patients with subcortical ischemic vascular disease (SIVD) and Alzheimer’s disease (AD) diagnosed using structuralized neuropsychiatric assessments, and investigate potential neuronal substrates related to cognitive performance.

Methods

Thirty-five patients with SIVD, 40 patients with AD, and 33 cognitively normal control (NC) subjects matched by age and education level were consecutively recruited and underwent cognitive function assessments and DTI examinations. Comparisons among these three subgroups with regards to cognitive performance and DTI parameters including fractional anisotropy (FA) and mean diffusivity (MD) values were performed. Partial correlation analysis after controlling for age and education was used to evaluate associations between cognitive performance and DTI parameters.

Results

With regards to cognitive performance, the patients with SIVD had lower total scores in frontal assessment battery (FAB) compared to those with AD (p < 0.05) in the context of comparable Mini-Mental Status Examination and Cognitive Abilities Screening Instrument scores. With regards to DTI parameters, there were more regions of significant differences in FA among these three subgroups compared with MD. Compared with NC group, the patients with SIVD had significant global reductions in FA (p < 0.001 ~ 0.05), while significant reductions in FA among the patients with AD were regionally confined within the left superior longitudinal fasciculus, genu and splenium of the corpus callosum, and bilateral forceps major, and the anterior thalamic radiation, uncinate fasciculus, and cingulum of the left side (p < 0.01 ~ 0.05). Analysis of FA values within the left forceps major, left anterior thalamic radiation, and genu of the corpus callosum revealed a 71.8% overall correct classification (p < 0.001) with sensitivity of 69.4%, specificity of 73.8%, positive predictive value of 69.4%, and negative predictive value of 73.8% in discriminating patients with SIVD from those with AD. In combined analysis of the patients with SIVD and AD (n = 75), the total FAB score was positively correlated with FA within the bilateral forceps minor, genu of the corpus callosum, left forceps major, left uncinate fasciculus, and right inferior longitudinal fasciculus (p = 0.001 ~ 0.038), and inversely correlated with MD within the right superior longitudinal fasciculus, genu and body of the corpus callosum, bilateral forceps minor, right uncinate fasciculus, and right inferior longitudinal fasciculus (p = 0.003 ~ 0.040)

Conclusions

Our findings suggest the effectiveness of DTI measurements in distinguishing patients with early-stage AD from those with SIVD, with discernible changes in spatial distribution and magnitude of significance of the DTI parameters. Strategic FA assessments provided the most robust discriminative power to differentiate SIVD from AD, and FAB may serve as an additional cognitive marker. We also identified the neuronal substrates responsible for FAB performance.

Surface-based vertexwise analysis of morphometry and microstructural integrity for white matter tracts in diffusion tensor imaging: With application to the corpus callosum in Alzheimer's disease

In this article, we present a unified statistical pipeline for analyzing the white matter (WM) tracts morphometry and microstructural integrity, both globally and locally within the same WM tract, from diffusion tensor imaging. Morphometry is quantified globally by the volumetric measurement and locally by the vertexwise surface areas. Meanwhile, microstructural integrity is quantified globally by the mean fractional anisotropy (FA) and trace values within the specific WM tract and locally by the FA and trace values defined at each vertex of its bounding surface. The proposed pipeline consists of four steps: (1) fully automated segmentation of WM tracts in a multi-contrast multi-atlas framework; (2) generation of the smooth surface representations for the WM tracts of interest; (3) common template surface generation on which the localized morphometric and microstructural statistics are defined and a variety of statistical analyses can be conducted; (4) multiple comparison correction to determine the significance of the statistical analysis results. Detailed herein, this pipeline has been applied to the corpus callosum in Alzheimer's disease (AD) with significantly decreased FA values and increased trace values, both globally and locally, being detected in patients with AD when compared to normal aging populations. A subdivision of the corpus callosum in both hemispheres revealed that the AD pathology primarily affects the body and splenium of the corpus callosum. Validation analyses and two multiple comparison correction strategies are provided. Hum Brain Mapp 38:1875-1893, 2017. © 2017 Wiley Periodicals, Inc.

A T1 and DTI fused 3D corpus callosum analysis in MCI subjects with high and low cardiovascular risk profile

Understanding the extent to which vascular disease and its risk factors are associated with prodromal dementia, notably Alzheimer's disease (AD), may enhance predictive accuracy as well as guide early interventions. One promising avenue to determine this relationship consists of looking for reliable and sensitive in-vivo imaging methods capable of characterizing the subtle brain alterations before the clinical manifestations. However, little is known from the imaging perspective about how risk factors such as vascular disease influence AD progression. Here, for the first time, we apply an innovative T1 and DTI fusion analysis of 3D corpus callosum (CC) on mild cognitive impairment (MCI) populations with different levels of vascular profile, aiming to de-couple the vascular factor in the prodromal AD stage. Our new fusion method successfully increases the detection power for differentiating MCI subjects with high from low vascular risk profiles, as well as from healthy controls. MCI subjects with high and low vascular risk profiles showed differed alteration patterns in the anterior CC, which may help to elucidate the inter-wired relationship between MCI and vascular risk factors.

Structural Abnormalities in Childhood Absence Epilepsy: Voxel-Based Analysis Using Diffusion Tensor Imaging

Purpose: Childhood absence epilepsy (CAE) is a common syndrome of idiopathic generalized epilepsy. However, little is known about the brain structural changes in this type of epilepsy, especially in the default mode network (DMN) regions. This study aims at using the diffusion tensor imaging (DTI) technique to quantify structural abnormalities of DMN nodes in CAE patients.

Method: DTI data were acquired in 14 CAE patients (aged 8.64 ± 2.59 years, seven females and seven males) and 16 age- and sex-matched healthy controls. The data were analyzed using voxel-based analysis (VBA) and statistically compared between patients and controls. Pearson correlation was explored between altered DTI metrics and clinical parameters. The difference of brain volumes between patients and controls were also tested using unpaired t-test.

Results: Patients showed significant increase of mean diffusivity (MD) and radial diffusivity (RD) in left medial prefrontal cortex (MPFC), and decrease of fractional anisotropy (FA) in left precuneus and axial diffusivity (AD) in both left MPFC and precuneus. In correlation analysis, MD value from left MPFC was positively associated with duration of epilepsy. Neither the disease duration nor the seizure frequency showed significant correlation with FA values. Between-group comparison of brain volumes got no significant difference.

Conclusion: The findings indicate that structural impairments exist in DMN regions in children suffering from absence epilepsy and MD values positively correlate with epilepsy duration. This may contribute to understanding the pathological mechanisms of chronic neurological deficits and promote the development of new therapies for this disorder.

Inter-hemispheric functional dysconnectivity mediates the association of corpus callosum degeneration with memory impairment in AD and amnestic MCI

Evidences suggested that both corpus callosum (CC) degeneration and alternations of homotopic inter-hemispheric functional connectivity (FC) are present in Alzheimer's disease (AD). However, the associations between region-specific CC degeneration and homotopic inter-hemispheric FC and their relationships with memory deficits in AD remain uncharacterized. We hypothesized that selective CC degeneration is associated with memory impairment in AD and amnestic mild cognitive impairment (aMCI), which is mediated by homotopic inter-hemispheric functional dysconnectivity. Using structural magnetic resonance imaging (MRI) and task-free functional MRI, we assessed the CC volume and inter-hemispheric FC in 66 healthy controls, 41 aMCI and 41 AD. As expected, AD had CC degeneration and attenuated inter-hemispheric homotopic FC. Nevertheless, aMCI had relatively less severe CC degeneration (mainly in mid-anterior, central, and mid-posterior) and no reduction in inter-hemispheric homotopic FC. The degeneration of each CC sub-region was associated with specific inter-hemispheric homotopic functional disconnections in AD and aMCI. More importantly, impairment of inter-hemispheric homotopic FC partially mediated the association between CC (particularly the central and posterior parts) degeneration and memory deficit. Notably, these results remained after controlling for hippocampal volume. Our findings shed light on how CC degeneration and the related inter-hemispheric FC impact memory impairment in early stage of AD.

Clinical and biomarker profiling of prodromal Alzheimer's disease in workpackage 5 of the Innovative Medicines Initiative PharmaCog project: a 'European ADNI study'

BACKGROUND

In the field of Alzheimer's disease (AD), the validation of biomarkers for early AD diagnosis and for use as a surrogate outcome in AD clinical trials is of considerable research interest.

OBJECTIVE

To characterize the clinical profile and genetic, neuroimaging and neurophysiological biomarkers of prodromal AD in amnestic mild cognitive impairment (aMCI) patients enrolled in the IMI WP5 PharmaCog (also referred to as the European ADNI study).

METHODS

A total of 147 aMCI patients were enrolled in 13 European memory clinics. Patients underwent clinical and neuropsychological evaluation, magnetic resonance imaging (MRI), electroencephalography (EEG) and lumbar puncture to assess the levels of amyloid β peptide 1-42 (Aβ42), tau and p-tau, and blood samples were collected. Genetic (APOE), neuroimaging (3T morphometry and diffusion MRI) and EEG (with resting-state and auditory oddball event-related potential (AO-ERP) paradigm) biomarkers were evaluated.

RESULTS

Prodromal AD was found in 55 aMCI patients defined by low Aβ42 in the cerebrospinal fluid (Aβ positive). Compared to the aMCI group with high Aβ42 levels (Aβ negative), Aβ positive patients showed poorer visual (P = 0.001), spatial recognition (P < 0.0005) and working (P = 0.024) memory, as well as a higher frequency of APOE4 (P < 0.0005), lower hippocampal volume (P = 0.04), reduced thickness of the parietal cortex (P < 0.009) and structural connectivity of the corpus callosum (P < 0.05), higher amplitude of delta rhythms at rest (P = 0.03) and lower amplitude of posterior cingulate sources of AO-ERP (P = 0.03).

CONCLUSION

These results suggest that, in aMCI patients, prodromal AD is characterized by a distinctive cognitive profile and genetic, neuroimaging and neurophysiological biomarkers. Longitudinal assessment will help to identify the role of these biomarkers in AD progression.

The superficial white matter in Alzheimer's disease

White matter abnormalities have been shown in the large deep fibers of Alzheimer's disease patients. However, the late myelinating superficial white matter comprised of intracortical myelin and short-range association fibers has not received much attention. To investigate this area, we extracted a surface corresponding to the superficial white matter beneath the cortex and then applied a cortical pattern-matching approach which allowed us to register and subsequently sample diffusivity along thousands of points at the interface between the gray matter and white matter in 44 patients with Alzheimer's disease (Age: 71.02 ± 5.84, 16M/28F) and 47 healthy controls (Age 69.23 ± 4.45, 19M/28F). In patients we found an overall increase in the axial and radial diffusivity across most of the superficial white matter (P < 0.001) with increases in diffusivity of more than 20% in the bilateral parahippocampal regions and the temporal and frontal lobes. Furthermore, diffusivity correlated with the cognitive deficits measured by the Mini-Mental State Examination scores (P < 0.001). The superficial white matter has a unique microstructure and is critical for the integration of multimodal information during brain maturation and aging. Here we show that there are major abnormalities in patients and the deterioration of these fibers relates to clinical symptoms in Alzheimer's disease.

Predicting progression from mild cognitive impairment to Alzheimer's disease using longitudinal callosal atrophy

Introduction

We investigate whether longitudinal callosal atrophy could predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD).

Methods

Longitudinal (baseline + 1-year follow-up) MRI scans of 132 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were used. A total of 54 subjects did not convert to AD over an average (±SD) follow-up of 5.46 (±1.63) years, whereas 78 converted to AD with an average conversion time of 2.56 (±1.65) years. Annual change in the corpus callosum thickness profile was calculated from the baseline and 1-year follow-up MRI. A logistic regression model with fused lasso regularization for prediction was applied to the annual changes.

Results

We found a sex difference. The accuracy of prediction was 84% in females and 61% in males. The discriminating regions of corpus callosum differed between sexes. In females, the genu, rostrum, and posterior body had predictive power, whereas the genu and splenium were relevant in males.

Discussion

Annual callosal atrophy predicts MCI-to-AD conversion in females more accurately than in males.