New MRI Markers for Alzheimer's Disease: A Meta-Analysis of Diffusion Tensor Imaging and a Comparison with Medial Temporal Lobe Measurements

Clerx, Lies; Visser, Pieter Jelle; Verhey, Frans R.J.; Aalten, Pauline

doi:10.3233/jad-2011-110797

Cited by 116 publications

(82 citation statements)

References 170 publications

(27 reference statements)

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“…This is in line with many prior studies. A recent AD DTI review found that MD values have more discriminative power than FA values and higher effect sizes for detecting disease related effects - derived from 55 different studies - in the frontal, parietal, occipital and temporal lobes (Clerx et al, 2012; Nir et al, 2013). We also found significant associations between FA and MD MDP measures and cognitive deficits, as measured by MMSE scores, across all subjects.…”

Section: Discussionmentioning

confidence: 99%

Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease

Nir

Villalon-Reina

Prasad

et al. 2015

Neurobiology of Aging

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Characterizing brain changes in Alzheimer’s disease (AD) is important for patient prognosis, and for assessing brain deterioration in clinical trials. In this diffusion tensor imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment (MCI), and 37 AD patients. After clustering tractography using an ROI atlas, we used a shortest path graph search through each bundle’s fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects as well as MD differences between CTL and late MCI subjects. MD and FA were also associated with widely used clinical scores (MMSE). As an MDP is a compact, low-dimensional representation of white matter organization, we tested the utility of DTI measures along these MDPs as features for support vector machine (SVM) based classification of AD.

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Section: Discussionmentioning

confidence: 99%

Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease

Nir

Villalon-Reina

Prasad

et al. 2015

Neurobiology of Aging

View full text Add to dashboard Buy / Rent full text

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“…Recent studies with DWI reveal widespread disease effects on the brain's white matter (Clerx et al, 2012, Nir et al, 2013, Jahanshad et al, 2012). The spread of white matter degeneration may be due in part to Wallerian or ‘backward’ neurodegeneration, in which gray matter atrophy leads to axonal degeneration (Coleman, 2005; Ewers et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…DWI is increasingly used (Mori and van Zijl, 2002; Basser and Jones, 2002) to study fiber integrity and microstructure, based on indices such as the mean diffusivity and fractional anisotropy of local water diffusion (Clerx et al, 2012). Furthermore, diffusion-based tractography can infer patterns of structural connectivity and is one of the primary quantitative methods to study the 3D course of long fibers ( i.e.…”

Section: Introductionmentioning

confidence: 99%

Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network

et al. 2015

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Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3-Tesla whole-brain diffusion-weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative – 50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the ‘rich-club’ – a network property where high-degree network nodes are more interconnected than expected by chance. We calculated the rich-club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length and efficiency. Network disruptions predominated in the low-degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step-wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich-club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline.

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“…The absolute FA values are sensitive to fiber coherence, myelination levels, and axonal integrity, and have been widely used as an index of white matter integrity (Thomason and Thompson, 2011). FA has emerged as a sensitive index of diffuse abnormalities in many brain disorders including Alzheimer’s disease (AD) (Clerx et al, 2012; Teipel et al, 2012); in many studies, it is related to cognitive performance (Penke et al, 2010a; Penke et al, 2010b) and is altered in numerous psychiatric disorders including major depressive disorder (Carballedo et al, 2012) and bipolar disorder (Barysheva et al, 2012; Sprooten et al, 2011). Patient-control differences in FA values are also among the most replicable and consistent neuroimaging findings in schizophrenia (Alba-Ferrara and de Erausquin, 2013; Friedman et al, 2008; Kochunov et al, 2012a; Mandl et al, 2012; Nazeri et al, 2012; Perez-Iglesias et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

et al. 2014

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Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9–85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large “mega-family”. We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

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New MRI Markers for Alzheimer's Disease: A Meta-Analysis of Diffusion Tensor Imaging and a Comparison with Medial Temporal Lobe Measurements

Cited by 116 publications

References 170 publications

Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease

Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease

Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

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