Diffusion abnormality in the posterior cingulum and hippocampal volume: correlation with disease progression in Alzheimer's disease

The effect of hippocampal radiomic features and functional connectivity on the relationship between hippocampal volume and cognitive function in Alzheimer's disease

Hippocampal volume is associated with cognitive function in Alzheimer's disease (AD). Hippocampal radiomic features and resting-state functional connectivity (rs-FC) are promising biomarkers and correlate with AD pathology. However, few studies have been conducted on how hippocampal biomarkers affect the cognition-structure relationship. Therefore, we aimed to investigate the effects of hippocampal radiomic features and resting-state functional connectivity (rs-FC) on this relationship in AD. We enrolled 70 AD patients and 65 healthy controls (HCs). The FreeSurfer software was used to measure hippocampal volume. We selected hippocampal radiomic features to build a model to distinguish AD patients from HCs and used a seed-based approach to calculate the hippocampal rs-FC. Furthermore, we conducted mediation and moderation analyses to investigate the effect of hippocampal radiomic features and rs-FC on the relationship between hippocampal volume and cognition in AD. The results suggested that hippocampal radiomic features mediated the association between bilateral hippocampal volume and cognition in AD. Additionally, patients with AD showed weaker rs-FC between the bilateral hippocampus and right ventral posterior cingulate cortex and stronger rs-FC between the left hippocampus and left insula than HCs. The rs-FC between the hippocampus and insula moderated the relationship between hippocampal volume and cognition in AD, suggesting that this rs-FC could exacerbate or ameliorate the effects of hippocampal volume on cognition and may be essential in improving cognitive function in AD. Our findings may not only expand existing biological knowledge of the interrelationships among hippocampal biomarkers and cognition but also provide potential targets for treatment strategies for AD.

The microstructural abnormalities of cingulum was related to patients with mild cognitive impairment: a diffusion kurtosis imaging study

OBJECTIVE

Our study aimed to investigate the correlations between microstructural changes of cingulum and patients with mild cognitive impairment (MCI) by diffusion kurtosis imaging (DKI) technique.

METHOD

A total of 104 patients with cerebral small vessel diseases (cSVD) were retrospectively enrolled in this study. According to Montreal Cognitive Assessment Scale (MoCA) scores, these patients were divided into MCI group (n = 59) and non-MCI group (n = 45). The general clinical data was collected and analyzed. The regions of interests (ROIs) were selected for investigation in cingulum. The values of DKI parameters were measured in each ROI and compared between the two groups, the correlations between DKI parameters and MoCA scores were examined.

RESULTS

Compared to non-MCI group, MCI patients had more severe white matter hyperintensities (WMHs) (P = 0.038) and lower MoCA scores (P < 0.01). MCI patients showed significantly decreased fractional anisotropy (FA), axial kurtosis (AK), mean kurtosis (MK), radial kurtosis (RK), and kurtosis fractional anisotropy (KFA) in the left cingulum in the cingulated cortex (CgC) region (all P < 0.0125). In the left CgC region, FA, AK, MK, RK, and KFA were positively correlated with MoCA scores (r = 0.348, 0.409, 0.310, 0.441, 0.422, all P < 0.001). Meanwhile, FA, AK, MK, RK, and KFA were also positively correlated with MoCA scores (r = 0.338, 0.352, 0.289, 0.380, 0.370, all P < 0.001) in the right CgC region.

CONCLUSION

DKI technique could be used to explore the microstructural changes of cingulum in MCI patients and DKI-derived parameters might be feasible to evaluate MCI patients.

Plasma phosphorylated-tau181 levels reflect white matter microstructural changes across Alzheimer's disease progression

Alzheimer's Disease (AD) is characterized by cognitive impairments that hinder daily activities and lead to personal and behavioral problems. Plasma hyperphosphorylated tau protein at threonine 181 (p-tau181) has recently emerged as a new sensitive tool for the diagnosis of AD patients. We herein investigated the association of plasma P-tau181 and white matter (WM) microstructural changes in AD. We obtained data from a large prospective cohort of elderly individuals participating in the Alzheimer's Disease Neuroimaging Initiative (ADNI), which included baseline measurements of plasma P-tau181 and imaging findings. A subset of 41 patients with AD, 119 patients with mild cognitive impairments (MCI), and 43 healthy controls (HC) was included in the study, all of whom had baseline blood P-tau181 levels and had also undergone Diffusion Tensor Imaging. The analysis revealed that the plasma level of P-tau181 has a positive correlation with changes in Mean Diffusivity (MD), Radial Diffusivity (RD), and Axial Diffusivity (AxD), but a negative with Fractional Anisotropy (FA) parameters in WM regions of all participants. There is also a significant association between WM microstructural changes in different regions and P-tau181 plasma measurements within each MCI, HC, and AD group. In conclusion, our findings clarified that plasma P-tau181 levels are associated with changes in WM integrity in AD. P-tau181 could improve the accuracy of diagnostic procedures and support the application of blood-based biomarkers to diagnose WM neurodegeneration. Longitudinal clinical studies are also needed to demonstrate the efficacy of the P-tau181 biomarker and predict its role in structural changes.

Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means

Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analyzed in regions-of-interest (ROI) to identify brain-phenotype associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional mean induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. Due to the differential extent of bias between healthy controls and YOAD subjects, the conventional mean under- or over-estimated the effect size for group differences in many ROIs. This demonstrates the importance of using the correct estimation procedure when inferring group differences in studies where the extent of CSF partial volume differs between groups. These findings are robust across different acquisition and processing conditions. Bias persists in ROIs at higher image resolution, as demonstrated using data obtained from the third phase of the Alzheimer's disease neuroimaging initiative (ADNI); and when performing ROI analysis in template space. This suggests that conventional ROI means of NODDI metrics are biased estimates under most contemporary experimental conditions, the correction of which requires the proposed tissue-weighted mean. The tissue-weighted mean produces accurate estimates of ROI means and group differences when ROIs contain voxels with CSF partial volume. In addition to NODDI, the technique can be applied to other multi-compartment models that account for CSF partial volume, such as the free water elimination method. We expect the technique to help generate new insights into normal and abnormal variation in tissue microstructure of regions typically confounded by CSF partial volume, such as those in individuals with larger ventricles due to atrophy associated with neurodegenerative disease.

Going round in circles-The Papez circuit in Alzheimer's disease

The hippocampus is regarded as the pivotal structure for episodic memory symptoms associated with Alzheimer's disease (AD) pathophysiology. However, what is often overlooked is that the hippocampus is 'only' one part of a network of memory critical regions, the Papez circuit. Other Papez circuit regions are often regarded as less relevant for AD as they are thought to sit 'downstream' of the hippocampus. However, this notion is oversimplistic, and increasing evidence suggests that other Papez regions might be affected before or concurrently with the hippocampus. In addition, AD research has mostly focused on episodic memory deficits, whereas spatial navigation processes are also subserved by the Papez circuit with increasing evidence supporting its valuable potential as a diagnostic measure of incipient AD pathophysiology. In the current review, we take a step forward analysing recent evidence on the structural and functional integrity of the Papez circuit across AD disease stages. Specifically, we will review the integrity of specific Papez regions from at-genetic-risk (APOE4 carriers), to mild cognitive impairment (MCI), to dementia stage of sporadic AD and autosomal dominant AD (ADAD). We related those changes to episodic memory and spatial navigation/orientation deficits in AD. Finally, we provide an overview of how the Papez circuit is affected in AD diseases and their specific symptomology contributions. This overview strengthened the need for moving away from a hippocampal-centric view to a network approach on how the whole Papez circuit is affected in AD and contributes to its symptomology, informing future research and clinical approaches.

White matter integrity in alcohol-dependent patients with long-term abstinence

Based on association studies on amounts of alcohol consumed and cortical and subcortical structural shrinkage, we investigated the effect of chronic alcohol consumption on white matter pathways using probabilistic tractography.Twenty-three alcohol-dependent men (with an average sobriety of 13.1 months) from a mental health hospital and 22 age-matched male healthy social drinkers underwent 3T magnetic resonance imaging. Eighteen major white matter pathways were reconstructed using the TRActs Constrained by UnderLying Anatomy tool (provided by the FreeSurfer). The hippocampal volumes were estimated using an automated procedure. The lifetime drinking history interview, Alcohol Use Disorder Identification Test, Brief Michigan Alcoholism Screening Test, and pack-years of smoking were also evaluated.Analysis of covariance controlling for age, cigarette smoking, total motion index indicated that there was no definite difference of diffusion parameters between the 2 groups after multiple comparison correction. As hippocampal volume decreased, the fractional anisotropy of the right cingulum-angular bundle decreased. Additionally, the axial diffusivity of right cingulum-angular bundle was positively correlated with the alcohol abstinence period.The results imply resilience of white matter in patients with alcohol dependence. Additional longitudinal studies with multimodal methods and neuropsychological tests may improve our findings of the changes in white matter pathways in patients with alcohol dependence.

Anatomy and White Matter Connections of the Parahippocampal Gyrus

BACKGROUND

The parahippocampal gyrus is understood to have a role in high cognitive functions including memory encoding and retrieval and visuospatial processing. A detailed understanding of the exact location and nature of associated white tracts could significantly improve postoperative morbidity related to declining capacity. Through diffusion tensor imaging-based fiber tracking validated by gross anatomic dissection as ground truth, we have characterized these connections based on relationships to other well-known structures.

METHODS

Diffusion imaging from the Human Connectome Project for 10 healthy adult controls was used for tractography analysis. We evaluated the parahippocampal gyrus as a whole based on connectivity with other regions. All parahippocampal gyrus tracts were mapped in both hemispheres, and a lateralization index was calculated with resultant tract volumes.

RESULTS

We identified 2 connections of the parahippocampal gyrus: inferior longitudinal fasciculus and cingulum. Lateralization of the cingulum was detected (P < 0.05).

CONCLUSIONS

The parahippocampal gyrus is an important center for memory processing. Subtle differences in executive functioning following surgery for limbic tumors may be better understood in the context of the fiber-bundle anatomy highlighted by this study.

White matter alterations in early-stage Alzheimer's disease: A tract-specific study

Introduction

Diffusion magnetic resonance imaging may allow for microscopic characterization of white matter degeneration in early stages of Alzheimer's disease.

Methods

Multishell Diffusion magnetic resonance imaging data were acquired from 100 participants (40 cognitively normal, 38 with subjective cognitive decline, and 22 with mild cognitive impairment [MCI]). White matter microscopic degeneration in 27 major tracts of interest was assessed using diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging, and q-space imaging.

Results

Lower DTI fractional anisotropy and higher radial diffusivity were observed in the cingulum, thalamic radiation, and forceps major of participants with MCI. These tracts of interest also had the highest predictive power to discriminate groups. Diffusion metrics were associated with cognitive performance, particularly Rey Auditory Verbal Learning Test immediate recall, with the highest association observed in participants with MCI.

Discussion

While DTI was the most sensitive, neurite orientation dispersion and density imaging and q-space imaging complementarily characterized reduced axonal density accompanied with dispersed and less restricted white matter microstructures.

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Mild cognitive decline poses microstructural alterations in white matter tracts.

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The alterations include higher axonal dispersion and lower tissue restriction.

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Diffusion metrics are associated with cognitive outcomes in AD continuum.

The cingulum bundle: Anatomy, function, and dysfunction

The cingulum bundle is a prominent white matter tract that interconnects frontal, parietal, and medial temporal sites, while also linking subcortical nuclei to the cingulate gyrus. Despite its apparent continuity, the cingulum's composition continually changes as fibres join and leave the bundle. To help understand its complex structure, this review begins with detailed, comparative descriptions of the multiple connections comprising the cingulum bundle. Next, the impact of cingulum bundle damage in rats, monkeys, and humans is analysed. Despite causing extensive anatomical disconnections, cingulum bundle lesions typically produce only mild deficits, highlighting the importance of parallel pathways and the distributed nature of its various functions. Meanwhile, non-invasive imaging implicates the cingulum bundle in executive control, emotion, pain (dorsal cingulum), and episodic memory (parahippocampal cingulum), while clinical studies reveal cingulum abnormalities in numerous conditions, including schizophrenia, depression, post-traumatic stress disorder, obsessive compulsive disorder, autism spectrum disorder, Mild Cognitive Impairment, and Alzheimer's disease. Understanding the seemingly diverse contributions of the cingulum will require better ways of isolating pathways within this highly complex tract.

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning

Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.

Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry

INTRODUCTION

Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task machine learning method (cFSGL) with a novel MR-based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients.

METHODS

Previous work has shown that a multi-task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor-based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces.

RESULTS

We combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted.

CONCLUSIONS

By combining the power of the cFSGL multi-task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.

3D tract-specific local and global analysis of white matter integrity in Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017. © 2016 Wiley Periodicals, Inc.

Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM

Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph-theoretical measures 'local clustering coefficient' and 'shortest path length' derived from resting-state functional MRI (rs-fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave-one-out cross-validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs-fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone.

Corpus Callosum and Cingulum Tractography in Parkinson's Disease

Background:

In Parkinson's disease (PD) cell loss in the substantia nigra is known to result in motor symptoms; however widespread pathological changes occur and may be associated with non-motor symptoms such as cognitive impairment. Diffusion tensor imaging is a quantitative imaging method sensitive to the micro-structure of white matter tracts.

Objective:

To measure fractional anisotropy (FA) and mean diffusivity (MD) values in the corpus callosum and cingulum pathways, defined by diffusion tensor tractography, in patients with PD, PD with dementia (PDD) and controls and to determine if these measures correlate with Mini-Mental Status Examination (MMSE) scores in parkinsonian patients.

Methods:

Patients with PD (17 Males [M], 12 Females [F]), mild PDD (5 M, 1F) and controls (8 M, 7F) underwent cognitive testing and MRI scans. The corpus callosum was divided into four regions and the cingulum into two regions bilaterally to define tracts using the program DTIstudio (Johns Hopkins University) using the fiber assignment by continuous tracking algorithm. Volumetric MRI scans were used to measure white and gray matter volumes.

Results:

Groups did not differ in age or education. There were no overall FA or MD differences between groups in either the corpus callosum or cingulum pathways. In PD subjects the MMSE score correlated with MD within the corpus callosum. These findings were independent of age, sex and total white matter volume.

Conclusions:

The data suggest that the corpus callosum or its cortical connections are associated with cognitive impairment in PD patients.

Brain imaging of mild cognitive impairment and Alzheimer's disease

The rapidly increasing prevalence of cognitive impairment and Alzheimer's disease has the potential to create a major worldwide healthcare crisis. Structural MRI studies in patients with Alzheimer's disease and mild cognitive impairment are currently attracting considerable interest. It is extremely important to study early structural and metabolic changes, such as those in the hippocampus, entorhinal cortex, and gray matter structures in the medial temporal lobe, to allow the early detection of mild cognitive impairment and Alzheimer's disease. The microstructural integrity of white matter can be studied with diffusion tensor imaging. Increased mean diffusivity and decreased fractional anisotropy are found in subjects with white matter damage. Functional imaging studies with positron emission tomography tracer compounds enable detection of amyloid plaques in the living brain in patients with Alzheimer's disease. In this review, we will focus on key findings from brain imaging studies in mild cognitive impairment and Alzheimer's disease, including structural brain changes studied with MRI and white matter changes seen with diffusion tensor imaging, and other specific imaging methodologies will also be discussed.

The role of diffusion tensor imaging in detecting microstructural changes in prodromal Alzheimer's disease

The MRI technique diffusion tensor imaging (DTI) is reviewed along with microstructural changes associated with prodromal Alzheimer's disease (AD) as a potential biomarker for clinical applications. The prodromal stage of AD is characterized by mild cognitive impairment (MCI), representing a transitional state between normal aging and AD. Microstructural abnormalities on DTI are promising in vivo biomarkers of gray and white matter changes associated with the progression of AD pathology. Elevated mean diffusivity and decreased fractional anisotropy are consistently found in prodromal AD, and even in cognitively normal elderly who progress to MCI. However, quality of parameter maps may be affected by artifacts of motion, susceptibility, and eddy current-induced distortions. The DTI maps are typically analyzed by region-of-interest or voxel-based analytic techniques such as tract-based spatial statistics. DTI-based index of diffusivity is complementary to macrostructural gray matter changes in the hippocampus in detecting prodromal AD. Breakdown of structural connectivity measured with DTI may impact cognitive performance during early AD. Furthermore, assessment of hippocampal connections may help in understanding the cerebral organization and remodeling associated with treatment response.

Structural hippocampal network alterations during healthy aging: a multi-modal MRI study

While hippocampal atrophy has been described during healthy aging, few studies have examined its relationship with the integrity of White Matter (WM) connecting tracts of the limbic system. This investigation examined WM structural damage specifically related to hippocampal atrophy in healthy aging subjects (n = 129), using morphological MRI to assess hippocampal volume and Diffusion Tensor Imaging (DTI) to assess WM integrity. Subjects with Mild Cognitive Impairment (MCI) or dementia were excluded from the analysis. In our sample, increasing age was significantly associated with reduced hippocampal volume and reduced Fractional Anisotropy (FA) at the level of the fornix and the cingulum bundle. The findings also demonstrate that hippocampal atrophy was specifically associated with reduced FA of the fornix bundle, but it was not related to alteration of the cingulum bundle. Our results indicate that the relationship between hippocampal atrophy and fornix FA values is not due to an independent effect of age on both structures. A recursive regression procedure was applied to evaluate sequential relationships between the alterations of these two brain structures. When both hippocampal atrophy and fornix FA values were included in the same model to predict age, fornix FA values remained significant whereas hippocampal atrophy was no longer significantly associated with age. According to this latter finding, hippocampal atrophy in healthy aging could be mediated by a loss of fornix connections. Structural alterations of this part of the limbic system, which have been associated with neurodegeneration in Alzheimer's disease, result at least in part from the aging process.

Multinutrient diets improve cerebral perfusion and neuroprotection in a murine model of Alzheimer's disease

Nutritional intervention may retard the development of Alzheimer's disease (AD). In this study we tested the effects of 2 multi-nutrient diets in an AD mouse model (APPswe/PS1dE9). One diet contained membrane precursors such as omega-3 fatty acids and uridine monophosphate (DEU), whereas another diet contained cofactors for membrane synthesis as well (Fortasyn); the diets were developed to enhance synaptic membranes synthesis, and contain components that may improve vascular health. We measured cerebral blood flow (CBF) and water diffusivity with ultra-high-field magnetic resonance imaging, as alterations in these parameters correlate with clinical symptoms of the disease. APPswe/PS1dE9 mice on control diet showed decreased CBF and changes in brain water diffusion, in accordance with findings of hypoperfusion, axonal disconnection and neuronal loss in patients with AD. Both multinutrient diets were able to increase cortical CBF in APPswe/PS1dE9 mice and Fortasyn reduced water diffusivity, particularly in the dentate gyrus and in cortical regions. We suggest that a specific diet intervention has the potential to slow AD progression, by simultaneously improving cerebrovascular health and enhancing neuroprotective mechanisms.

Executive deficits, not processing speed relates to abnormalities in distinct prefrontal tracts in amyotrophic lateral sclerosis

Cognitive impairment in amyotrophic lateral sclerosis is characterized by deficits on tests of executive function; however, the contribution of abnormal processing speed is unknown. Methods are confounded by tasks that depend on motor speed in patients with physical disability. Structural and functional magnetic resonance imaging studies have revealed multi-system cerebral involvement, with evidence of reduced white matter volume and integrity in predominant frontotemporal regions. The current study has two aims. First, to investigate whether cognitive impairments in amyotrophic lateral sclerosis are due to executive dysfunction or slowed processing speed using methodology that accommodates motor disability. This is achieved using a dual-task paradigm and tasks that manipulate stimulus presentation times and do not rely on response motor speed. Second, to identify relationships between specific cognitive impairments and the integrity of distinct white matter tracts. Thirty patients with amyotrophic lateral sclerosis and 30 age- and education-matched control subjects were administered an experimental dual-task procedure that combined a visual inspection time task and digit recall. In addition, measures of executive function (including letter fluency) and processing speed (visual inspection time and rapid serial letter identification) were administered. Integrity of white matter tracts was determined using region of interest analyses of diffusion tensor magnetic resonance imaging data. Patients with amyotrophic lateral sclerosis did not show impairments on tests of processing speed, but executive deficits were revealed once visual inspection time was combined with digit recall (dual-task) and in letter fluency. In addition to the corticospinal tracts, significant differences in fractional anisotropy and mean diffusivity were found between groups in a number of prefrontal and temporal white matter tracts including the anterior cingulate, anterior thalamic radiation, uncinate fasciculus and hippocampal portion of the cingulum bundles. Significant differences also emerged in the anterior corona radiata as well as in white matter underlying the superior, medial and inferior frontal gyri and the temporal gyri. Dual-task performance significantly correlated with fractional anisotropy measures in the middle frontal gyrus white matter and anterior corona radiata. Letter fluency indices significantly correlated with fractional anisotropy measures of the inferior frontal gyrus white matter and corpus callosum in addition to the corticospinal tracts and mean diffusivity measures in the white matter of the superior frontal gyrus. The current study demonstrates that cognitive impairment in amyotrophic lateral sclerosis is not due to generic slowing of processing speed. Moreover, different executive deficits are related to distinct prefrontal tract involvement in amyotrophic lateral sclerosis with dual-task impairment associating with dorsolateral prefrontal dysfunction and letter fluency showing greater dependence on inferolateral prefrontal dysfunction.

Automatic extraction of the cingulum bundle in diffusion tensor tract-specific analysis: feasibility study in Parkinson's disease with and without dementia

PURPOSE

Tract-specific analysis (TSA) measures diffusion parameters along a specific fiber that has been extracted by fiber tracking using manual regions of interest (ROIs), but TSA is limited by its requirement for manual operation, poor reproducibility, and high time consumption. We aimed to develop a fully automated extraction method for the cingulum bundle (CB) and to apply the method to TSA in neurobehavioral disorders such as Parkinson's disease (PD).

MATERIALS AND METHODS

We introduce the voxel classification (VC) and auto diffusion tensor fiber-tracking (AFT) methods of extraction. The VC method directly extracts the CB, skipping the fiber-tracking step, whereas the AFT method uses fiber tracking from automatically selected ROIs. We compared the results of VC and AFT to those obtained by manual diffusion tensor fiber tracking (MFT) performed by 3 operators. We quantified the Jaccard similarity index among the 3 methods in data from 20 subjects (10 normal controls [NC] and 10 patients with Parkinson's disease dementia [PDD]). We used all 3 extraction methods (VC, AFT, and MFT) to calculate the fractional anisotropy (FA) values of the anterior and posterior CB for 15 NC subjects, 15 with PD, and 15 with PDD.

RESULTS

The Jaccard index between results of AFT and MFT, 0.72, was similar to the inter-operator Jaccard index of MFT. However, the Jaccard indices between VC and MFT and between VC and AFT were lower. Consequently, the VC method classified among 3 different groups (NC, PD, and PDD), whereas the others classified only 2 different groups (NC, PD or PDD).

CONCLUSION

For TSA in Parkinson's disease, the VC method can be more useful than the AFT and MFT methods for extracting the CB. In addition, the results of patient data analysis suggest that a reduction of FA in the posterior CB may represent a useful biological index for monitoring PD and PDD.

Diffusion tensor imaging measures of normal appearing white matter in patients who are aging, or have amnestic mild cognitive impairment, or Alzheimer's disease

To evaluate whether cerebral white matter integrity is related to cognitive function, and whether diffusion tensor imaging (DTI) could differentiate amnestic mild cognitive impairment (aMCI) from Alzheimer's disease (AD), 12 patients with AD, 12 with aMCI, and 12 controls were recruited for this study. Cognitive functions of all subjects were assessed using the Mini-Mental State Examination (MMSE) and AD Assessment Scale - Cognitive Subscale (ADAS-Cog). DTI studies were acquired, and fractional anisotropy (FA) and mean diffusivity (MD) values of normal-appearing white matter (NAWM) in multiple brain regions were obtained. Results showed that MMSE and ADAS-Cog subscores were significantly associated with white matter integrity of the temporal-parietal lobes. A decrease in FA values and an increase in MD values in multiple cortical regions were confirmed in patients with AD compared to controls. MD values in the posterior region of the corpus callosum in aMCI differed from those of early AD. Significant reductions of FA values in the NAWM of the parietal lobe was observed in aMCI compared to controls. Our data indicate that the microstructural white matter integrity in the temporal-parietal lobes is gradually impaired in the progressive process of AD, and that splenium MD values could be used as a biomarker differentiating aMCI from AD.

Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.

Geometrical constraints for robust tractography selection

Tract-based analysis from DTI has become a widely employed procedure to study the white matter of the brain and its alterations in neurological and neurosurgical pathologies. Automatic tractography selection methods, where a subset of detected tracts corresponding to a specific white matter structure are selected, are a key component of the DTI processing pipeline. Using automatic tractography selection, repeatable results free of intra and inter-expert variability can be obtained rapidly, without the need for cumbersome manual segmentation. Many of the current approaches for automatic tractography selection rely on a previous registration procedure using an atlas; hence, these methods are likely very sensitive to the accuracy of the registration. In this paper we show that the performance of the registration step is critical to the overall result. This effect can in turn affect the calculation of scalar parameters derived subsequently from the selected tracts and often used in clinical practice; we show that such errors may be comparable in magnitude to the subtle differences found in clinical studies to differentiate between healthy and pathological. As an alternative, we propose a tractography selection method based on the use of geometrical constraints specific for each fiber bundle. Our experimental results show that the approach proposed performs with increased robustness and accuracy with respect to other approaches in the literature, particularly in the presence of imperfect registration.

Neuroimaging insights into network-based neurodegeneration

PURPOSE OF REVIEW

Convergent evidence from a number of neuroscience disciplines supports the hypothesis that Alzheimer's disease and other neurodegenerative disorders progress along brain networks. This review considers the role of neuroimaging in strengthening the case for network-based neurodegeneration and elucidating potential mechanisms.

RECENT FINDINGS

Advances in functional and structural MRI have recently enabled the delineation of multiple large-scale distributed brain networks. The application of these network-imaging modalities to neurodegenerative disease has shown that specific disorders appear to progress along specific networks. Recent work applying theoretical measures of network efficiency to in-vivo network imaging has allowed for the development and evaluation of models of disease spread along networks. Novel MRI acquisition and analysis methods are paving the way for in-vivo assessment of the layer-specific microcircuits first targeted by neurodegenerative diseases. These methodological advances coupled with large, longitudinal studies of subjects progressing from healthy aging into dementia will enable a detailed understanding of the seeding and spread of these disorders.

SUMMARY

Neuroimaging has provided ample evidence that neurodegenerative disorders progress along brain networks, and is now beginning to elucidate how they do so.

Diffusion tensor imaging in Alzheimer disease and mild cognitive impairment

A wide range of imaging studies provides growing support for the potential role of diffusion tensor imaging (DTI) in evaluating microstructural white matter integrity in Alzheimer disease (AD) and mild cognitive impairment (MCI). Our review aims to present DTI principles, post-processing and analysis frameworks and to report the results of particular studies. The distribution of AD-related white matter abnormalities is widely discussed in the light of deteriorated connectivity within certain tracts due to secondary white matter degeneration; primary alterations are also assumed to contribute to the pattern. The question whether it is more effective to assess the whole-brain diffusion or to directly concentrate on specific regions remains an interesting issue. Assessing white matter microstructure alterations, as evaluated by group-level differences of tensor-derived parameters, may be a promising neuroimaging tool for differential diagnosis between AD, MCI and other cognitive disorders, as well as being particularly helpful in the interpretation of underlying pathological processes.

Microstructural integrity of the cingulum is related to verbal memory performance in elderly with cerebral small vessel disease: the RUN DMC study

BACKGROUND

Cerebral small vessel disease (SVD) is related to verbal memory failures. It is suggested that early white matter damage, is located, among others, in the (posterior) cingulum at an early stage in neurodegeneration. Changes in the microstructural integrity of the cingulum assessed with diffusion tensor imaging (DTI), beyond detection with conventional MRI, may precede macrostructural changes and be related to verbal memory failures.

OBJECTIVE

To investigate the relation between cingular microstructural integrity and verbal memory performance in 503 non-demented elderly with cerebral SVD.

METHODS

The RUN DMC study is a prospective cohort study in elderly (50-85 years) with cerebral SVD. All participants underwent T1 MPRAGE, FLAIR and DTI scanning and the Rey Auditory Verbal Learning Test. Mean diffusivity (MD) and fractional anisotropy (FA) were assessed in six different cingular regions of interests (ROIs). Linear regression analysis was used to assess the relation between verbal memory performance and cingular DTI parameters, with appropriate adjustments. Furthermore a TBSS analysis of the whole brain was performed to investigate the specificity of our findings.

RESULTS

Both our ROI-based and TBSS analysis showed that FA was positively related to immediate memory, delayed recall, delayed recognition and overall verbal memory performance of the cingulum, independent of confounders. A similar distribution was seen for the inverse association with MD and verbal memory performance with TBSS analysis. No significant relations were found with psychomotor speed, visuospatial memory and MMSE. When stratified on hippocampal integrity, the MD and FA values of the cingular ROIs differed significantly between participants with a good and poor hippocampal integrity.

CONCLUSION

Microstructural integrity of the cingulum, assessed by DTI, is specifically related to verbal memory performance, in elderly with SVD. Furthermore we found that when the integrity of the hippocampus is disrupted, the cingulum integrity is impaired as well.

Amyloid β1-42 oligomer inhibits myelin sheet formation in vitro

Accumulating evidence indicates that white matter degeneration contributes to the neural disconnections that underlie Alzheimer's disease pathophysiology. Although this white matter degeneration is partly attributable to axonopathy associated with neuronal degeneration, amyloid β (Aβ) protein-mediated damage to oligodendrocytes could be another mechanism. To test this hypothesis, we studied effects of soluble Aβ in oligomeric form on survival and differentiation of cells of the oligodendroglial lineage using highly purified oligodendroglial cultures from rats at different developmental stages. Aβ oligomer at 10 μM or higher reduced survival of mature oligodendrocytes, whereas oligodendroglial progenitor cells (OPCs) were relatively resistant to the Aβ oligomer-mediated cytotoxicity. Further study revealed that Aβ oligomer even at 1 μM accelerated 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) formazan exocytosis in mature oligodendrocytes, and, more significantly, inhibited myelin sheet formation after induction of in vitro differentiation of OPCs. These results imply a novel pathogenetic mechanism underlying Aβ oligomer-mediated white matter degeneration, which could impair myelin maintenance and remyelination by adult OPCs, resulting in accumulating damage to myelinating axons thereby contributing to neural disconnections.

Tract-specific analysis of white matter integrity disruption in schizophrenia

Several studies have suggested that white matter integrity is disrupted in some brain regions in patients with schizophrenia. The purpose of this study was to assess the white matter integrity of the cingulum, uncinate fasciculus, fornix, and corpus callosum using diffusion tensor imaging (DTI). Participants comprised 39 patients with schizophrenia (19 males and 20 females) and 40 age-matched normal controls (20 males and 20 females). We quantitatively assessed the fractional anisotropy (FA) and apparent diffusion coefficient (ADC) of the anterior cingulum, body of the cingulum, uncinate fasciculus, fornix, and corpus callosum on a tract-specific basis using diffusion tensor tractography (DTT). Group differences in FA and ADC between the patients and normal controls were sought. Additional exploratory analyses of the relationship between the FA or ADC and four clinical parameters (i.e., illness duration, positive symptom scores, negative symptom scores, and medication dosage) were performed. Results were analyzed in gender-combined and gender-separated group comparisons. FA was significantly lower on both sides of the anterior cingulum, uncinate fasciculus, and fornix in the schizophrenia patients irrespective of gender group separation. In the gender-combined analyses, significantly higher ADC values were demonstrated in the schizophrenia patients in both sides of the anterior cingulum, body of the cingulum and uncinate fasciculus, the left fornix, and the corpus callosum, compared with those of the normal controls. In the gender-separated analyses, the male patients showed higher ADC in the left anterior cingulum, the bilateral cingulum bodies, and the bilateral uncinate fasciculi. The female patients showed higher ADC in the right anterior cingulum, the left fornix, and the bilateral uncinate fasciculus. In correlation analyses, a significant negative correlation was found between illness duration and ADC in the right anterior cingulum in the gender-combined analyses. The gender-separated analyses found that the male patients had a significant negative correlation between negative symptom scores and FA in the right fornix, a positive correlation between illness duration and FA in the right anterior cingulum, and a negative correlation between illness duration and FA in the left uncinate fasciculus. Our DTI study showed that the integrity of white matter is disrupted in patients with schizophrenia. The results of our sub-analyses suggest that changes in FA and ADC may be related to negative symptom scores or illness duration.

Fiber Tracts Anomalies in APPxPS1 Transgenic Mice Modeling Alzheimer's Disease

Amyloid beta (Aβ) peptides are known to accumulate in the brain of patients with Alzheimer's disease (AD). However, the link between brain amyloidosis and clinical symptoms has not been elucidated and could be mediated by secondary neuropathological alterations such as fiber tracts anomalies. In the present study, we have investigated the impact of Aβ overproduction in APPxPS1 transgenic mice on the integrity of forebrain axonal bundles (corpus callosum and anterior commissure). We found evidence of fiber tract volume reductions in APPxPS1 mice that were associated with an accelerated age-related loss of axonal neurofilaments and a myelin breakdown. The severity of these defects was neither correlated with the density of amyloid plaques nor associated with cell neurodegeneration. Our data suggest that commissural fiber tract alterations are present in Aβ-overproducing transgenic mice and that intracellular Aβ accumulation preceding extracellular deposits may act as a trigger of such morphological anomalies.

Other magnetic resonance imaging techniques

Relatively new developments in MRI, such as functional MRI (fMRI), magnetic resonance spectroscopy (MRS) and diffusion tensor imaging (DTI) are rapidly developing into imaging modalities that will become clinically available in the near future. They have in common that their signal is somewhat easier to interpret than structural MRI: fMRI mirrors excess cerebral blood flow, in many cases representing brain activity, MRS gives the average volume concentrations of specific chemical compounds, and DTI reflects "directedness" of micro-anatomical structures, of particular use in white matter where fiber bundle disruption can be detected with great sensitivity. While structural changes in MRI have been disappointing in giving a diagnosis of sufficient sensitivity and specificity, these newer methods hold out hope for elucidating pathological changes and differentiating patient groups more rigorously. This paper summarizes promising research results that will yet have to be translated into real life clinical studies in larger groups of patients (e.g. memory clinic patients). Where available, we have tried to summarize results comparing different types of dementia.

MRI correlates of episodic memory in Alzheimer's disease, mild cognitive impairment, and healthy aging

Episodic memory is a core feature of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Impaired episodic memory in AD results from the dysfunction of an integrated network and involves both gray and white matter pathologies. We explored the neural correlates of episodic memory in AD, MCI and healthy aging by correlating a measure of episodic memory with hippocampal volume and fractional anisotropy (FA) and mean diffusivity (MD) of the cingulum and fornix. Episodic memory was associated with hippocampal volume and MD of the cingulum and fornix. In contrast, there were fewer significant associations between episodic memory and FA. These findings support a relationship between episodic memory and hippocampal circuitry, and suggest that MD is a more sensitive marker of decreased white matter integrity in the study of AD and MCI than FA. Furthermore, MD was significantly associated with hippocampal volume, indicating that white matter pathology is not completely independent of gray matter pathology. However, the pattern of diffusivity differences in AD and MCI implies a more complex pathology than simply Wallerian degeneration.

Iterative co-linearity filtering and parameterization of fiber tracts in the entire cingulum

We present a method for the fully automated extraction of the cingulum using diffusion tensor imaging (DTI) data. We perform whole-brain tractography and initialize tract selection in the cingulum with a registered DTI atlas. Tracts are parameterized from which tract co-linearity is derived. The tract set, filtered on the basis of co-linearity with the cingulum shape, yields an improved segmentation of the cingulum and is subsequently optimized in an iterative fashion to further improve the tract selection. We evaluate the method using a large DTI database of 500 subjects from the general population and show robust extraction of tracts in the entire cingulate bundle in both hemispheres. We demonstrate the use of the extracted fiber-tracts to compare left and right cingulate bundles. Our asymmetry analysis shows a higher fractional anisotropy in the left anterior part of the cingulum compared to the right side, and the opposite effect in the posterior part.

Diffusion tensor imaging in Alzheimer's disease and mild cognitive impairment

Structural magnetic resonance imaging (MRI) studies of Alzheimer’s disease and mild cognitive impairment (MCI) have focused on the hippocampus and entorhinal cortex; gray matter structures in the medial temporal lobe. Few studies have investigated the integrity of white matter in patients with AD or MCI. Diffusion tensor imaging (DTI) is a MRI technique that allows for the interrogation of the microstructural integrity of white matter. Based on increases in translational diffusion (mean diffusivity: MD) and decreases directional diffusion (fractional anisotropy: FA) damage to white matter can be assessed. Studies have identified regions of increased MD and decreased FA in patients with AD and MCI in all lobes of the brain, as well as medial temporal lobe structures including the hippocampus, entorhinal cortex and parahippocampal white matter. The pattern of white matter integrity disruption tends to follow an anterior to posterior gradient with greater damage noted in posterior regions in AD and MCI. Recent studies have exploited inter-voxel directional similarities to develop models of white matter pathways, and have used these models to assess the integrity of inter-cerebral connections. Particular focus has been applied to the parahippocampal white matter (including the perforant path) and the posterior cingulum. Although many studies have found DTI indicators of impaired white matter in AD and MCI, other studies have failed to detect any differences in MD or FA between the groups, demonstrating the need for large replicative studies. DTI is an evolving technique and advances in its application ought to provide new insights into AD and MCI.

Diffusion abnormalities and reduced volume of the ventral cingulum bundle in agenesis of the corpus callosum: a 3T imaging study

BACKGROUND AND PURPOSE

Patients with agenesis of the corpus callosum (AgCC) exhibit cognitive and behavioral impairments that are not replicated by surgical transection of the callosum, suggesting that other anatomic changes may contribute to the observed clinical findings. The purpose of this study was to determine whether the ventral cingulum bundle (VCB) is affected in patients with AgCC by using diffusion tensor imaging (DTI) and volumetry.

MATERIALS AND METHODS

Twelve participants with AgCC (8 males and 4 females; mean age, 30 +/- 20) and 12 control subjects matched for age and sex (mean age, 37 +/- 19) underwent MR imaging and DTI at 3T. 3D fiber tracking of the VCB was generated from DTI and the average fractional anisotropy (FA) was computed for the tracked fibers. Additionally, the volume, cross-sectional area, and length of the VCB were measured by manually drawn regions of interest on thin-section coronal T1-weighted images. The Student t test was used to compare these results.

RESULTS

Compared with controls, subjects with AgCC demonstrated significantly reduced FA in the right VCB (P = .0098) and reduced volume and cross-sectional areas of both the left and right VCB (P < .001 for all metrics). The length of the VCB was also significantly reduced in the complete AgCC subgroup compared with controls (P = .030 in the right and P = .046 in the left, respectively).

CONCLUSIONS

Patients with AgCC have abnormal microstructure and reduced volume of the VCB, suggesting that abnormalities in intrahemispheric white matter tracts may be an important contributor to the clinical syndrome in patients with AgCC.