People with HIV are living longer as combination antiretroviral therapy (cART) becomes more widely available. However, even when plasma viral load is reduced to untraceable levels, chronic HIV infection is associated with neurological deficits and brain atrophy beyond that of normal aging. HIV is often marked by cortical and subcortical atrophy, but the integrity of the brain’s white matter (WM) pathways also progressively declines. Few studies focus on older cohorts where normal aging may be compounded with HIV infection to influence deficit patterns. In this relatively large diffusion tensor imaging (DTI) study, we investigated abnormalities in WM fiber integrity in 56 HIV+ adults with access to cART (mean age: 63.9 ± 3.7 years), compared to 31 matched healthy controls (65.4 ± 2.2 years). Statistical 3D maps revealed the independent effects of HIV diagnosis and age on fractional anisotropy (FA) and diffusivity, but we did not find any evidence for an age by diagnosis interaction in our current sample. Compared to healthy controls, HIV patients showed pervasive FA decreases and diffusivity increases throughout WM. We also assessed neuropsychological (NP) summary z-score associations. In both patients and controls, fiber integrity measures were associated with NP summary scores. The greatest differences were detected in the corpus callosum and in the projection fibers of the corona radiata. These deficits are consistent with published NP deficits and cortical atrophy patterns in elderly people with HIV.
brain integrity; white matter; diffusion tensor imaging; cognition; HIV; cART
We present a framework for registering cortical surfaces based on tractography-informed structural connectivity. We define connectivity as a continuous kernel on the product space of the cortex, and develop a method for estimating this kernel from tractography fiber models. Next, we formulate the kernel registration problem, and present a means to non-linearly register two brains’ continuous connectivity profiles. We apply theoretical results from operator theory to develop an algorithm for decomposing the connectome into its shared and individual components. Lastly, we extend two discrete connectivity measures to the continuous case, and apply our framework to 98 Alzheimer’s patients and controls. Our measures show significant differences between the two groups.
Diffusion MRI; Cortical Surface Registration; Connectivity Analysis; Data Fusion
Alzheimer’s disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods – four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one “ball-and-stick” approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification.
Alzheimer’s disease; brain network; tractography; classification; PCA; GLRAM; diffusion MRI
Brain connectivity declines in Alzheimer’s disease (AD), both functionally and structurally. Connectivity maps and networks derived from diffusion-based tractography offer new ways to track disease progression and to understand how AD affects the brain. Here we set out to identify (1) which fiber network measures show greatest differences between AD patients and controls, and (2) how these effects depend on the density of fibers extracted by the tractography algorithm. We computed brain networks from diffusion-weighted images (DWI) of the brain, in 110 subjects (28 normal elderly, 56 with early and 11 with late mild cognitive impairment, and 15 with AD). We derived connectivity matrices and network topology measures, for each subject, from whole-brain tractography and cortical parcellations. We used an ODF lookup table to speed up fiber extraction, and to exploit the full information in the orientation distribution function (ODF). This made it feasible to compute high density connectivity maps. We used accelerated tractography to compute a large number of fibers to understand what effect fiber density has on network measures and in distinguishing different disease groups in our data. We focused on global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity measures computed from weighted and binary undirected connectivity matrices. Of all these measures, the mean nodal degree best distinguished diagnostic groups. High-density fiber matrices were most helpful for picking up the more subtle clinical differences, e.g. between mild cognitively impaired (MCI) and normals, or for distinguishing subtypes of MCI (early versus late). Care is needed in clinical analyses of brain connectivity, as the density of extracted fibers may affect how well a network measure can pick up differences between patients and controls.
tractography; Hadoop; MapReduce; network measures; connectivity matrix; Alzheimer’s disease; ODF
Multimodal brain image analysis : third International Workshop, MBIA 2013, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013 : proceedings / Li Shen, Tianming Liu, Pew-Thian Yap, Heng Huang, Dinggang Shen, Carl-Fre.
We present a method for studying brain connectivity by simulating a dynamical evolution of the nodes of the network. The nodes are treated as particles, and evolved under a simulated force analogous to gravitational acceleration in the well-known N -body problem. The particle nodes correspond to regions of the cortex. The locations of particles are defined as the centers of the respective regions on the cortex and their masses are proportional to each region’s volume. The force of attraction is modeled on the gravitational force, and explicitly made proportional to the elements of a connectivity matrix derived from diffusion imaging data. We present experimental results of the simulation on a population of 110 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), consisting of healthy elderly controls, early mild cognitively impaired (eMCI), late MCI (LMCI), and Alzheimer’s disease (AD) patients. Results show significant differences in the dynamic properties of connectivity networks in healthy controls, compared to eMCI as well as AD patients.
gravity; n-body simulation; diffusion; connectivity; MRI
Modern neuroimaging technologies allow scientists to uncover inter-species differences and similarities in hemispheric asymmetries that may shed light onto the origin of brain asymmetry and its functional correlates. We analyzed asymmetries in white to grey matter ratios of the lateral aspect of the lobes of the brains of chimpanzees. We found marked leftward asymmetries for all lobar regions. This asymmetry was particularly pronounced in the frontal region and was found to be related to handedness for communicative manual gestures as well as for tool use. These results point to a continuity in asymmetry patterns between the human and chimpanzee brain, and support the notion that the anatomical substrates for lateralization of communicative functions and complex manipulative activities may have been present in the common hominid ancestor.
hemispheric asymmetry; white matter; gray matter; tool use; handedness; chimpanzee
Brain connectivity analyses show considerable promise for understanding how our neural pathways gradually break down in aging and Alzheimer's disease (AD). Even so, we know very little about how the brain's networks change in AD, and which metrics are best to evaluate these changes. To better understand how AD affects brain connectivity, we analyzed anatomical connectivity based on 3-T diffusion-weighted images from 111 subjects (15 with AD, 68 with mild cognitive impairment, and 28 healthy elderly; mean age, 73.7±7.6 SD years). We performed whole brain tractography based on the orientation distribution functions, and compiled connectivity matrices showing the proportions of detected fibers interconnecting 68 cortical regions. We computed a variety of measures sensitive to anatomical network topology, including the structural backbone—the so-called “k-core”—of the anatomical network, and the nodal degree. We found widespread network disruptions, as connections were lost in AD. Among other connectivity measures showing disease effects, network nodal degree, normalized characteristic path length, and efficiency decreased with disease, while normalized small-worldness increased, in the whole brain and left and right hemispheres individually. The normalized clustering coefficient also increased in the whole brain; we discuss factors that may cause this effect. The proportions of fibers intersecting left and right cortical regions were asymmetrical in all diagnostic groups. This asymmetry may intensify as disease progressed. Connectivity metrics based on the k-core may help understand brain network breakdown as cognitive impairment increases, revealing how degenerative diseases affect the human connectome.
Alzheimer's disease; asymmetry; brain connectivity; diffusion tensor imaging; efficiency; k-core; mild cognitive impairment; nodal degree; small-world; tractography
We present a new flow-based method for modeling brain structural connectivity. The method uses a modified maximum-flow algorithm that is robust to noise in the diffusion data and guided by biologically viable pathways and structure of the brain. A flow network is first created using a lattice graph by connecting all lattice points (voxel centers) to all their neighbors by edges. Edge weights are based on the orientation distribution function (ODF) value in the direction of the edge. The maximum-flow is computed based on this flow graph using the flow or the capacity between each region of interest (ROI) pair by following the connected tractography fibers projected onto the flow graph edges. Network measures such as global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity are computed from the flow connectivity matrix. We applied our method to diffusion-weighted images (DWIs) from 110 subjects (28 normal elderly, 56 with early and 11 with late mild cognitive impairment, and 15 with AD) and segmented co-registered anatomical MRIs into cortical regions. Experimental results showed better performance compared to the standard fiber-counting methods when distinguishing Alzheimer’s disease from normal aging.
maximum flow; tractography; connectivity matrix; Alzheimer’s disease; ODF; projection; network measures; graph
Diffusion imaging and brain connectivity analyses can monitor white matter deterioration, revealing how neural pathways break down in aging and Alzheimer's disease (AD). Here we tested how AD disrupts the ‘rich club’ effect – a network property found in the normal brain – where high-degree nodes in the connectivity network are more heavily interconnected with each other than expected by chance. We analyzed 3-Tesla whole-brain diffusionweighted images (DWI) from 66 subjects (22 AD/44 normal elderly). We performed whole-brain tractography based on the orientation distribution functions. Connectivity matrices were compiled, representing the proportion of detected fibers interconnecting 68 cortical regions. As expected, AD patients had a lower nodal degree (average number of connections) in cortical regions implicated in the disease. Unexpectedly, the normalized rich club coefficient was higher in AD. AD disrupts cortical networks by removing connections; when these networks are thresholded, organizational properties are disrupted leading to additional new biomarkers of AD.
Antiretroviral therapies have become widely available, and as a result, individuals infected with the human immunodeficiency virus (HIV) are living longer, and becoming integrated into the geriatric population. Around half of the HIV+ population shows some degree of cognitive impairment, but it is unknown how their neural networks and brain connectivity compare to those of noninfected people. Here we combined magnetic resonance imaging-based cortical parcellations with high angular resolution diffusion tensor imaging tractography in 55 HIV-seropositive patients and 30 age-matched controls, to map white matter connections between cortical regions. We set out to determine selective virus-associated disruptions in the brain's structural network. All individuals in this study were aged 60–80, with full access to antiretroviral therapy. Frontal and motor connections were compromised in HIV+ individuals. HIV+ people who carried the apolipoprotein E4 allele (ApoE4) genotype—which puts them at even greater risk for neurodegeneration—showed additional network structure deficits in temporal and parietal connections. The ApoE4 genotype interacted with duration of illness. Carriers showed greater brain network inefficiencies the longer they were infected. Neural network deficiencies in HIV+ populations exceed those typical of normal aging, and are worse in those genetically predisposed to brain degeneration. This work isolates neuropathological alterations in HIV+ elders, even when treated with antiretroviral therapy. Network impairments may contribute to the neuropsychological abnormalities in elderly HIV patients, who will soon account for around half of all HIV+ adults.
ApoE4; diffusion tensor imaging (DTI); fractional anisotropy (FA); geriatrics; high angular resolution diffusion imaging; imaging genetics; structural brain networks
The Alzheimer's Disease Neuroimaging Initiative (ADNI)
recently added diffusion tensor imaging (DTI), among several other new imaging
modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease
(AD). While anatomical MRI is the main structural neuroimaging method used in
most AD studies and clinical trials, DTI is sensitive to microscopic white
matter (WM) changes not detectable with standard MRI, offering additional
markers of neurodegeneration. Prior DTI studies of AD report lower fractional
anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD)
throughout WM. Here we assessed which DTI measures may best identify differences
among AD, mild cognitive impairment (MCI), and cognitively healthy elderly
control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155
ADNI participants (mean age: 73.5 ± 7.4; 90
M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses
revealed widespread group differences in FA and all diffusivity measures. DTI
maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob,
and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive
for picking up group differences. Diffusivity measures could detect more subtle
MCI differences, where FA could not. ROIs showing strongest group
differentiation (lowest p-values) included tracts that
pass through the temporal lobe, and posterior brain regions. The left
hippocampal component of the cingulum showed consistently high effect sizes for
distinguishing groups, across all diffusivity and anisotropy measures, and in
correlations with cognitive scores.
•DTI scans in ADNI2 provide numerous biomarkers of
Alzheimer's disease.•FA, MD, AxD, and RD measures all detect MCI and AD
white matter deficits.•DTI FA and diffusivity measures are correlated with
clinical cognitive scores.•FA is the least sensitive DTI measure for detecting
AD related differences.•WM in the temporal lobe, corpus callosum and
cingulum is repeatedly implicated.
NC, normal control; RD, radial diffusivity; AxD, axial diffusivity; ADNI, Alzheimer's Disease Neuroimaging Initiative; DTI; Alzheimer's disease; MCI; White matter; Clinical scores; Biomarkers
Alzheimer’s Disease (AD) has long been considered a cortical degenerative disease, but impaired brain connectivity, due to white matter injury, may exacerbate cognitive problems. Predicting brain changes is critically important for early treatment. In a longitudinal diffusion tensor imaging study, we investigated white matter fiber integrity in 19 patients (mean age: 74.7 +/− 8.4 yrs at baseline) displaying early signs of mild cognitive impairment (eMCI). We first examined whether baseline average fractional anisotropy (FA) measures in the corpus callosum (CC) predicted changes in white matter integrity over the following 6 months. We then examined whether “small world” architecture measures - calculated from baseline connectivity maps - predicted white matter changes over the next 6 months. While average CC FA measures at baseline were not associated with future changes in FA, network measures were a sensitive biomarker for predicting white matter changes during this critical time before AD strikes.
diffusion imaging; graph theory; connectivity; predictive models; Alzheimer’s disease
Voxel-based morphometry (VBM) has become an increasingly common method for assessing neuroanatomical asymmetries in human in vivo magnetic resonance imaging (MRI). Here, we employed VBM to examine asymmetries in white matter in a sample of 48 chimpanzees (15 males and 33 females). T1-weighted MRI scans were segmented into white matter using FSL and registered to a common template. The segmented volumes were then flipped in the left-right axis and registered back to the template. The mirror image white matter volumes were then subtracted from the correctly oriented volumes and voxel-by-voxel t tests were performed. Twenty-seven significant lateralized clusters were found, including 18 in the left hemisphere and 9 in the right hemisphere. Several of the asymmetries were found in regions corresponding to well-known white matter tracts including the superior longitudinal fasciculus, inferior longitudinal fasciculus and corticospinal tract.
Chimpanzees; Brain asymmetry; White matter; Language evolution
The planum temporale (PT) is the bank of tissue that lies posterior to Heschl’s gyrus and is considered a key brain region involved in language and speech in the human brain. In the human brain, both the surface area and grey matter volume of the PT is larger in the left compared to right hemisphere in approximately 2/3rds of individuals, particularly among right-handed individuals. Here we examined whether chimpanzees show asymmetries in the PT for grey matter volume and surface area in a sample of 103 chimpanzees from magnetic resonance images. The results indicated that, overall, the chimpanzees showed population-level leftward asymmetries for both surface area and grey matter volumes. Furthermore, chimpanzees that prefer to gesture with their right-handed had significantly greater leftward grey matter asymmetries compared to ambiguously- and left-handed apes. When compared to previously published data in humans, the direction and magnitude of PT grey matter asymmetries were similar between humans and apes; however, for the surface area measures, the human showed more pronounced leftward asymmetries. These results suggest that leftward asymmetries in the PT were present in the common ancestor of chimpanzees and humans.
chimpanzees; planum temporale; brain asymmetry; handedness; gestural communication
Functional imaging studies in humans have localized the motor-hand region to a neuroanatomical landmark call the KNOB within the precentral gyrus. It has also been reported that the KNOB is larger in the hemisphere contralateral to an individual's preferred hand, and therefore may represent the neural substrate for handedness. The KNOB has also been neuronatomically described in chimpanzees and other great apes and is similarly associated with handedness. However, whether the chimpanzee KNOB represents the hand region is unclear from the extant literature. Here, we used PET to quantify neural metabolic activity in chimpanzees when engaged in unilateral reach-and-grasping responses and found significantly lateralized activation of the KNOB region in the hemisphere contralateral to the hand used by the chimpanzees. We subsequently constructed a probabilistic map of the KNOB region in chimpanzees in order to assess the overlap in consistency in the anatomical landmarks of the KNOB with the functional maps generated from the PET analysis. We found significant overlap in the anatomical and functional voxels comprising the KNOB region, suggesting that the KNOB does correspond to the hand region in chimpanzees. Lastly, from the probabilistic maps, we compared right- and left-handed chimpanzees on lateralization in grey and white matter within the KNOB region and found that asymmetries in white matter of the KNOB region were larger in the hemisphere contralateral to the preferred hand. These results suggest that neuroanatomical asymmetries in the KNOB likely reflect changes in connectivity in primary motor cortex that are experience dependent in chimpanzees and possibly humans.
Determination of whether nonhuman primates exhibit neuroanatomical asymmetries would inform our understanding of the evolution of traits in humans that show functional hemispheric dominance, including language and handedness. Here we report the first evidence of population-level asymmetries in the chimpanzee neocortex using voxel-based morphology (VBM). MRI scans of the brain were collected in a sample of 31 chimpanzees including 9 males and 22 females, and the resulting images were segmented into gray matter, white matter and CSF. Gray matter images were then co-registered to a template and these normally oriented volumes were flipped on the left-right axis to create mirror volumes. In total, significant asymmetries were found in 13 regions including several that have been described previously in great apes using traditional region-of-interest approaches. The results from this VBM analysis support previous reports of hemispheric lateralization in chimpanzees and reinforce the view that asymmetries in the central nervous system are not uniquely human.