Obstructive sleep apnea (OSA) is accompanied by cognitive, motor, autonomic, learning, and affective abnormalities. The putamen serves several of these functions, especially motor and autonomic behaviors, but whether global and specific sub-regions of that structure are damaged is unclear. We assessed global and regional putamen volumes in 43 recently-diagnosed, treatment-naïve OSA (age, 46.4 ± 8.8 years; 31 male) and 61 control subjects (47.6 ± 8.8 years; 39 male) using high-resolution T1-weighted images collected with a 3.0-Tesla MRI scanner. Global putamen volumes were calculated, and group differences evaluated with independent samples t-tests, as well as with analysis of covariance (covariates; age, gender, and total intracranial volume). Regional differences between groups were visualized with 3D surface morphometry-based group ratio maps. OSA subjects showed significantly higher global putamen volumes, relative to controls. Regional analyses showed putamen areas with increased and decreased tissue volumes in OSA relative to control subjects, including increases in caudal, mid-dorsal, mid-ventral portions, and ventral regions, while areas with decreased volumes appeared in rostral, mid-dorsal, medial-caudal, and mid-ventral sites. Global putamen volumes were significantly higher in the OSA subjects, but local sites showed both higher and lower volumes. The appearance of localized volume alterations points to differential hypoxic or perfusion action on glia and other tissues within the structure, and may reflect a stage in progression of injury in these newly-diagnosed patients toward the overall volume loss found in patients with chronic OSA. The regional changes may underlie some of the specific deficits in motor, autonomic, and neuropsychologic functions in OSA.
•Global and regional putamen volumes were examined in newly-diagnosed OSA.•Global volumes are higher, but subareas showed increases and decreases.•The volume increases suggest transient tissue swelling from hypoxic action.•Altered sites likely contribute to motor and other functional deficits in OSA.
OSA, Obstructive sleep apnea; 3D, Three dimensional; MRI, Magnetic resonance imaging; AHI, Apnea–hypopnea index; ESS, Epworth Sleepiness Scale; PSQI, Pittsburgh Sleep Quality Index; BDI-II, Beck Depression Inventory II; BAI, Beck Anxiety Inventory; PD, Proton density; MNI, Montreal Neurological Institute; CSF, Cerebrospinal fluid; TIV, Total intracranial volume; MPRAGE, Magnetization prepared rapid acquisition gradient-echo; TR, Repetition time; TE, Echo time; FA, Flip angle; FOV, Field of view; GRAPPA, Generalized autocalibrating partially parallel acquisition; Magnetic resonance imaging; Cognition; 3D surface morphometry; Basal ganglia; Intermittent hypoxia; Autonomic; Motor
The primary and secondary damage to neural tissue inflicted by traumatic brain injury is a leading cause of death and disability. The secondary processes, in particular, are of great clinical interest because of their potential susceptibility to intervention. We address the dynamics of tissue degeneration in cortico-subcortical circuits after severe brain injury by assessing volume change in individual thalamic nuclei over the first six-months post-injury in a sample of 25 moderate to severe traumatic brain injury patients. Using tensor-based morphometry, we observed significant localized thalamic atrophy over the six-month period in antero-dorsal limbic nuclei as well as in medio-dorsal association nuclei. Importantly, the degree of atrophy in these nuclei was predictive, even after controlling for full-brain volume change, of behavioral outcome at six-months post-injury. Furthermore, employing a data-driven decision tree model, we found that physiological measures, namely the extent of atrophy in the anterior thalamic nucleus, were the most predictive variables of whether patients had regained consciousness by six-months, followed by behavioral measures. Overall, these findings suggest that the secondary non-mechanical degenerative processes triggered by severe brain injury are still ongoing after the first week post-trauma and target specifically antero-medial and dorsal thalamic nuclei. This result therefore offers a potential window of intervention, and a specific target region, in agreement with the view that specific cortico-thalamo-cortical circuits are crucial to the maintenance of large-scale network neural activity and thereby the restoration of cognitive function after severe brain injury.
•Performed acute and chronic structural MRI in 25 severe TBI patients•Tensor brain morphometry (TBM) shows localized thalamic acute-to-chronic atrophy.•Anterior, medio- and lateral-dorsal nuclei are the most significant.•Atrophy in these nuclei predicts 6-month outcome scores (GOSe).
Traumatic brain injury; Thalamus; Tensor brain morphometry; Magnetic resonance imaging
Heart failure (HF) is accompanied by diminished cognitive, motor, learning, emotional, and planning deficits, which are associated with increased morbidity and mortality. A basal ganglia structure, the putamen, serves many functions that are affected in HF, but its global or localized structural integrity is unknown. Our aim was to evaluate global and regional putamen volume differences in HF over control subjects.
Methods and results
We collected two high-resolution T1-weighted scans from 16 HF patients (age, 54.1 ± 8.3 years; 12 males; left ventricular ejection fraction, 27.8 ± 6.8%) and 32 control subjects (52.4 ± 7.3 years; 24 males) using a 3.0 T magnetic resonance imaging scanner. After realigning, averaging, and reorienting the T1-weighted volumes into a common space, the structures were manually outlined, tracings were normalized for head size, volumes calculated, and surface models generated. Demographic data were compared between groups with χ2 and independent samples t-tests, global putamen volumes were evaluated using independent samples t-tests, and regional differences were examined with surface morphometry. No significant differences in age or sex appeared between groups, but body mass index differed significantly (P = 0.008). Heart failure patients showed significantly lower left (controls vs. HF; 4842.1 ± 740.0 vs. 4224.1 ± 894.4 mm3, P = 0.014) and right (4769.3 ± 651.9 vs. 4193.7 ± 876.2 mm3, P = 0.014) global putamen volumes than controls, with localized reductions in bilateral rostral, mid-dorsal, and medial-caudal regions (left, P < 0.003; right, P < 0.0002).
Putamen structures showed global and localized volume reductions in HF over controls. The localized volume losses suggest deficits in motor and neuropsychological functions, which are evident in HF subjects, and may be due to hypoxic and ischaemic processes targeting these areas.
Magnetic resonance imaging; Brain; Heart failure; Three-dimensional surface morphometry; Basal ganglia
Congenital central hypoventilation syndrome (CCHS) children show cognitive and affective deficits, in addition to state-specific loss of respiratory drive. The caudate nuclei serve motor, cognitive, and affective roles, and show structural deficits in CCHS patients, based on gross voxel-based analytic procedures. However, the magnitude and regional sites of caudate injury in CCHS are unclear. We assessed global caudate nuclei volumes with manual volumetric procedures, and regional volume differences with three-dimensional surface morphometry in 14 CCHS (mean age ± SD: 15.1 ± 2.3 years; 8 male) and 31 control children (15.1 ± 2.4 years; 17 male) using brain magnetic resonance imaging (MRI). Two high-resolution T1-weighted image series were collected using a 3.0 Tesla MRI scanner; images were averaged and reoriented (rigid-body transformation) to common space. Both left and right caudate nuclei were outlined in the reoriented images, and global volumes calculated; surface models were derived from manually-outlined caudate structures. Global caudate nuclei volume differences between groups were evaluated using a multivariate analysis of covariance (covariates: age, gender, total intracranial volume). Both left and right caudate nuclei volumes were significantly reduced in CCHS over control subjects (left, 4293.45 ± 549.05 mm3 vs 4626.87 ± 593.41 mm3, p < 0.006; right, 4376.29 ± 565.42 mm3 vs 4747.81 ± 578.13 mm3, p < 0.004). Regional deficits in CCHS caudate volume appeared bilaterally, in the rostral head, ventrolateral mid, and caudal body. Damaged caudate nuclei may contribute to CCHS neuropsychological and motor deficits; hypoxic processes, or maldevelopment in the condition may underlie the injury.
Magnetic Resonance Imaging; Brain; Basal ganglia; Striatum; Hypoxia; Cognition
Children with congenital central hypoventilation syndrome (CCHS), a genetic disorder characterized by diminished drive to breathe during sleep and impaired CO2 sensitivity, show brain structural and functional changes on magnetic resonance imaging (MRI) scans, with impaired responses in specific hippocampal regions, suggesting localized injury.
We assessed total volume and regional variation in hippocampal surface morphology to identify areas affected in the syndrome. We studied 18 CCHS (mean age±std: 15.1±2.2 years; 8 female) and 32 healthy control (age 15.2±2.4 years; 14 female) children, and traced hippocampi on 1 mm3 resolution T1-weighted scans, collected with a 3.0 Tesla MRI scanner. Regional hippocampal volume variations, adjusted for cranial volume, were compared between groups based on t-tests of surface distances to the structure midline, with correction for multiple comparisons. Significant tissue losses emerged in CCHS patients on the left side, with a trend for loss on the right; however, most areas affected on the left also showed equivalent right-sided volume reductions. Reduced regional volumes appeared in the left rostral hippocampus, bilateral areas in mid and mid-to-caudal regions, and a dorsal-caudal region, adjacent to the fimbria.
The volume losses may result from hypoxic exposure following hypoventilation during sleep-disordered breathing, or from developmental or vascular consequences of genetic mutations in the syndrome. The sites of change overlap regions of abnormal functional responses to respiratory and autonomic challenges. Affected hippocampal areas have roles associated with memory, mood, and indirectly, autonomic regulation; impairments in these behavioral and physiological functions appear in CCHS.
Nonpsychotic siblings of patients with childhood-onset schizophrenia (COS) share cortical gray matter abnormalities with their probands at an early age; these normalize by the time the siblings are aged 18 years, suggesting that the gray matter abnormalities in schizophrenia could be an age-specific endophenotype. Patients with COS also show significant white matter (WM) growth deficits, which have not yet been explored in nonpsychotic siblings.
To study WM growth differences in non-psychotic siblings of patients with COS.
Longitudinal (5-year) anatomic magnetic resonance imaging study mapping WM growth using a novel tensor-based morphometry analysis.
National Institutes of Health Clinical Center, Bethesda, Maryland.
Forty-nine healthy siblings of patients with COS (mean [SD] age, 16.1[5.3] years; 19 male, 30 female) and 57 healthy persons serving as controls (age, 16.9[5.3] years; 29 male, 28 female).
Magnetic resonance imaging.
Main Outcome Measure
White matter growth rates.
We compared the WM growth rates in 3 age ranges. In the youngest age group (7 to <14 years), we found a significant difference in growth rates, with siblings of patients with COS showing slower WM growth rates in the parietal lobes of the brain than age-matched healthy controls (false discovery rate, q = 0.05; critical P = .001 in the bilateral parietal WM; a post hoc analysis identified growth rate differences only on the left side, critical P =.004). A growth rate difference was not detectable at older ages. In 3-dimensional maps, growth rates in the siblings even appeared to surpass those of healthy individuals at later ages, at least locally in the brain, but this effect did not survive a multiple comparisons correction.
In this first longitudinal study of nonpsychotic siblings of patients with COS, the siblings showed early WM growth deficits, which normalized with age. As reported before for gray matter, WM growth may also be an age-specific endophenotype that shows compensatory normalization with age.
Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer’s disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and under sampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1). a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2). sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results.
Alzheimer’s disease; classification; imbalanced data; undersampling; oversampling; feature selection
Connectome genetics attempts to discover how genetic factors affect brain connectivity. Here we review a variety of genetic analysis methods – such as genome-wide association studies (GWAS), linkage and candidate gene studies – that have been fruitfully adapted to imaging data to implicate specific variants in the genome for brain-related traits. We then review studies of that emphasized the genetic influences on brain connectivity. Some of these perform genetic analysis of brain integrity and connectivity using diffusion MRI, and others have mapped genetic effects on functional networks using resting state functional MRI. Connectome-wide genome-wide scans have also been conducted, and we review the multivariate methods required to handle the extremely high dimension of genomic and the network data. We also review some consortium efforts, such as ENIGMA, that offer the power to detect robust common genetic associations using phenotypic harmonization procedures and meta-analysis. Current work on connectome genetics is advancing on many fronts and promises to shed light on how disease risk genes affect the brain. It is already discovering new genetic loci and even entire genetic networks that affect brain organization and connectivity.
We address the problem of segmenting high angular resolution diffusion imaging (HARDI) data into multiple regions (or fiber tracts) with distinct diffusion properties. We use the orientation distribution function (ODF) to represent HARDI data and cast the problem as a clustering problem in the space of ODFs. Our approach integrates tools from sparse representation theory and Riemannian geometry into a graph theoretic segmentation framework. By exploiting the Riemannian properties of the space of ODFs, we learn a sparse representation for each ODF and infer the segmentation by applying spectral clustering to a similarity matrix built from these representations. In cases where regions with similar (resp. distinct) diffusion properties belong to different (resp. same) fiber tracts, we obtain the segmentation by incorporating spatial and user-specified pairwise relationships into the formulation. Experiments on synthetic data evaluate the sensitivity of our method to image noise and the presence of complex fiber configurations, and show its superior performance compared to alternative segmentation methods. Experiments on phantom and real data demonstrate the accuracy of the proposed method in segmenting simulated fibers, as well as white matter fiber tracts of clinical importance in the human brain.
image segmentation; harmonic analysis; subspace clustering; sparsity; affinity propagation; graph theory; diffusion magnetic resonance imaging (DMRI)
Here we apply a method for automated segmentation of the hippocampus in 3D high-resolution structural brain MRI scans. One hundred and four healthy young adults completed twenty one tasks measuring abstract, verbal, and spatial intelligence, along with working memory, executive control, attention, and processing speed. After permutation tests corrected for multiple comparisons across vertices (p < .05) significant relationships were found for spatial intelligence, spatial working memory, and spatial executive control. Interactions with sex revealed significant relationships with the general factor of intelligence (g), along with abstract and spatial intelligence. These correlations were mainly positive for males but negative for females, which might support the efficiency hypothesis in women. Verbal intelligence, attention, and processing speed were not related to hippocampal structural differences.
Hippocampus; Intelligence; Working memory; Executive control; Attention; Processing speed; Sex differences
Morphology of the corpus callosum is a useful biomarker of neuronal loss, as different patterns of cortical atrophy help to distinguish between dementias such as Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD).
We used a sophisticated morphometric analysis of the corpus callosum in FTLD subtypes including frontotemporal dementia (FTD) semantic dementia (SD), and progressive non-fluent aphasia (PNFA), and compared them to AD patients and 27 matched controls.
FTLD patient subgroups diverged in their callosal morphology profiles, with: FTD patients showing marked widespread differences, PNFA patients with differences largely in the anterior half of the callosum, and SD patients differences in a small segment of the genu. AD patients showed differences in predominantly posterior callosal regions.
This study is consistent with our previous findings showing significant cortical and subcortical regional atrophy across FTLD subtypes, and suggests that callosal atrophy patterns differentiate AD from FTLD, and FTLD subtypes.
Frontotemporal dementia; Alzheimer’s disease; neuroimaging; morphometry; corpus callosum; white matter; atrophy; magnetic resonance imaging
Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra’s algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer’s risk gene, CLU.
Structural connectivity; neuroimaging genetics; Dijkstra’s algorithm; HARDI tractography; path length
Graph theory is increasingly used in the field of neuroscience to understand the large-scale network structure of the human brain. There is also considerable interest in applying machine learning techniques in clinical settings, for example, to make diagnoses or predict treatment outcomes. Here we used support-vector machines (SVMs), in conjunction with whole-brain tractography, to identify graph metrics that best differentiate individuals with Major Depressive Disorder (MDD) from nondepressed controls. To do this, we applied a novel feature-scoring procedure that incorporates iterative classifier performance to assess feature robustness. We found that small-worldness, a measure of the balance between global integration and local specialization, most reliably differentiated MDD from nondepressed individuals. Post-hoc regional analyses suggested that heightened connectivity of the subcallosal cingulate gyrus (SCG) in MDDs contributes to these differences. The current study provides a novel way to assess the robustness of classification features and reveals anomalies in large-scale neural networks in MDD.
Major Depressive Disorder (MDD); graph theoretical analysis; machine learning; support vector machine (SVM); small-world
Imaging genetics is an emerging methodological field that combines genetic information with medical imaging-derived metrics to understand how genetic factors impact observable phenotypes. In order for a trait to be a reasonable phenotype in an imaging genetics study, it must be heritable: at least some proportion of its variance must be due to genetic influences. The Sequential Oligogenic Linkage Analysis Routines (SOLAR) imaging genetics software can estimate the heritability of a trait in complex pedigrees. We investigate the ability of SOLAR to accurately estimate heritability and common environmental effects on simulated imaging phenotypes in various family structures. We found that heritability is reliably estimated with small family-based studies of 40 to 80 individuals, though subtle differences remain between the family structures. In an imaging application analysis, we found that with 80 subjects in any of the family structures, estimated heritability of white matter fractional anisotropy was biased by <10% for every region of interest. Results from these studies can be used when investigators are evaluating power in planning genetic analyzes.
heritability; imaging genetics; power calculation; statistical analysis
Numerous studies have demonstrated a sexual dimorphism of the human corpus callosum. However, the question remains if sex differences in brain size, which typically is larger in men than in women, or biological sex per se account for the apparent sex differences in callosal morphology. Comparing callosal dimensions between men and women matched for overall brain size may clarify the true contribution of biological sex, as any observed group difference should indicate pure sex effects. We thus examined callosal morphology in 24 male and 24 female brains carefully matched for overall size. In addition, we selected 24 extremely large male brains and 24 extremely small female brains to explore if observed sex effects might vary depending on the degree to which male and female groups differed in brain size. Using the individual T1-weighted brain images (n=96), we delineated the corpus callosum at midline and applied a well-validated surface-based mesh-modeling approach to compare callosal thickness at 100 equidistant points between groups determined by brain size and sex. The corpus callosum was always thicker in men than in women. However, this callosal sex difference was strongly determined by the cerebral sex difference overall. That is, the larger the discrepancy in brain size between men and women, the more pronounced the sex difference in callosal thickness, with hardly any callosal differences remaining between brain-size matched men and women. Altogether, these findings suggest that individual differences in brain size account for apparent sex differences in the anatomy of the corpus callosum.
Brain; Corpus Callosum; Gender; MRI; Sex
Converging evidence suggests brain structure alterations may precede overt cognitive impairment in Alzheimer disease by several decades. Early detection of these alterations holds inherent value for the development and evaluation of preventive treatment therapies.
To compare magnetic resonance imaging measurements of white matter myelin water fraction (MWF) and gray matter volume (GMV) in healthy infant carriers and noncarriers of the apolipoprotein E (APOE) ε4 allele, the major susceptibility gene for late-onset AD.
DESIGN, SETTING, AND PARTICIPANTS
Quiet magnetic resonance imaging was performed at an academic research imaging center on 162 healthy, typically developing 2- to 25-month-old infants with no family history of Alzheimer disease or other neurological or psychiatric disorders. Cross-sectional measurements were compared in the APOE ε4 carrier and noncarrier groups. White matter MWF was compared in one hundred sixty-two 2- to 25-month-old sleeping infants (60 ε4 carriers and 102 noncarriers). Gray matter volume was compared in a subset of fifty-nine 6- to 25-month-old infants (23 ε4 carriers and 36 noncarriers), who remained asleep during the scanning session. The carrier and noncarrier groups were matched for age, gestational duration, birth weight, sex ratio, maternal age, education, and socioeconomic status.
MAIN OUTCOMES AND MEASURES
Automated algorithms compared regional white matter MWF and GMV in the carrier and noncarrier groups and characterized their associations with age.
Infant ε4 carriers had lower MWF and GMV measurements than noncarriers in precuneus, posterior/middle cingulate, lateral temporal, and medial occipitotemporal regions, areas preferentially affected by AD, and greater MWF and GMV measurements in extensive frontal regions and measurements were also significant in the subset of 2- to 6-month-old infants (MWF differences, P < .05, after correction for multiple comparisons; GMV differences, P < .001, uncorrected for multiple comparisons). Infant ε4 carriers also exhibited an attenuated relationship between MWF and age in posterior white matter regions.
CONCLUSIONS AND RELEVANCE
While our findings should be considered preliminary, this study demonstrates some of the earliest brain changes associated with the genetic predisposition to AD. It raises new questions about the role of APOE in normal human brain development, the extent to which these processes are related to subsequent AD pathology, and whether they could be targeted by AD prevention therapies.
Alterations in gray matter (GM) density/ volume and cortical thickness (CT) have been demonstrated in small and heterogeneous samples of subjects with different chronic pain syndromes, including irritable bowel syndrome (IBS). Aggregating across 7 structural neuroimaging studies conducted at UCLA between August 2006 and April 2011, we examined group differences in regional GM volume in 201 predominantly premenopausal female subjects (82 IBS, mean age: 32 ± 10 SD, 119 Healthy Controls [HCs], 30± 10 SD). Applying graph theoretical methods and controlling for total brain volume, global and regional properties of large-scale structural brain networks were compared between IBS and HC groups. Relative to HCs, the IBS group had lower volumes in bilateral superior frontal gyrus, bilateral insula, bilateral amygdala, bilateral hippocampus, bilateral middle orbital frontal gyrus, left cingulate, left gyrus rectus, brainstem, and left putamen. Higher volume was found for the left postcentral gyrus. Group differences were no longer significant for most regions when controlling for Early Trauma Inventory global score with the exception of the right amygdala and the left post central gyrus. No group differences were found for measures of global and local network organization. Compared to HCs, the right cingulate gyrus and right thalamus were identified as significantly more critical for information flow. Regions involved in endogenous pain modulation and central sensory amplification were identified as network hubs in IBS. Overall, evidence for central alterations in IBS was found in the form of regional GM volume differences and altered global and regional properties of brain volumetric networks.
chronic pain; irritable bowel syndrome; gray matter volume; brain network analysis; graph theory
This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers.
Nine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers.
We identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area).
We observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.
Alzheimer's disease; Early-onset; ADNI; Mild cognitive impairment; Memory; Neuroimaging; Genetics
Diffusion-weighted imaging allows for in vivo assessment of white matter structure, which can be used to assess aberrations associated with disease. Several new methods permit the automated assessment of important white matter characteristics. In the current study we used Automated Fiber Quantification (AFQ) to assess differences between depressed and nondepressed individuals in 18 major white matter tracts. We then used the Maximum Density Path (MDP) method to further characterize group differences identified with AFQ. The results of the AFQ analyses indicated that fractional anisotropy (FA; an index of white matter integrity) along bilateral corticospinal tracts (CST) was higher in depressed than in nondepressed individuals. MDP analyses revealed that white matter anomalies were restricted to four subregions that included the corona radiata and the internal and external capsules. These results provide further evidence that MDD is associated with abnormalities in cortical-to-subcortical connectivity.
Major Depressive Disorder (MDD); automated fiber quantification (AFQ); maximum density paths (MDP); diffusion-weighted imaging; tractography
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
Advances in brain imaging technology in the past five years have contributed greatly to the understanding of Alzheimer’s disease (AD). Here, we review recent research related to amyloid imaging, new methods for magnetic resonance imaging analyses, and statistical methods. We also review research that evaluates AD risk factors and brain imaging, in the context of AD prediction and progression. We selected a variety of illustrative studies, describing how they advanced the field and are leading AD research in promising new directions.
Alzheimer’s disease; amyloid; imaging; magnetic resonance imaging; methods; positron emission tomography; prediction; progression; risk factors
Autosomal dominant Alzheimer disease (ADAD) is caused by rare genetic
mutations in three specific genes, in contrast to late-onset Alzheimer
Disease (LOAD), which has a more polygenetic risk profile.
Design, Setting, and Participants
We analyzed functional connectivity in multiple brain resting state
networks (RSNs) in a cross-sectional cohort of ADAD (N=79) and LOAD (N=444)
human participants using resting state functional connectivity MRI
(rs-fcMRI) at multiple international academic sites.
Main Outcomes and Measures
For both types of AD, we quantified and compared functional
connectivity changes in RSNs as a function of dementia severity as measured
by clinical dementia rating (CDR). In ADAD, we qualitatively investigated
functional connectivity changes with respect to estimated years from onset
of symptoms within five RSNs.
Functional connectivity decreases with increasing CDR were similar
for both LOAD and ADAD in multiple RSNs. Ordinal logistic regression models
constructed in each type of AD accurately predicted CDR stage in the other,
further demonstrating similarity of functional connectivity loss in each
disease type. Among ADAD participants, functional connectivity in multiple
RSNs appeared qualitatively lower in asymptomatic mutation carriers near
their anticipated age of symptom onset compared to asymptomatic mutation
Conclusions and Relevance
rs-fcMRI changes with progressing AD severity are similar between
ADAD and LOAD. Rs-fcMRI may be a useful endpoint for LOAD and ADAD therapy
trials. ADAD disease process may be an effective model for LOAD disease
Resting-state functional connectivity; autosomal dominant Alzheimer's disease; late-onset Alzheimer's disease; default mode network; apolipoprotein E (APOE)
Accurate identification of white matter structures and segmentation of fibers into tracts is important in neuroimaging and has many potential applications. Even so, it is not trivial because whole brain tractography generates hundreds of thousands of streamlines that include many false positive fibers. We developed and tested an automatic tract labeling algorithm to segment anatomically meaningful tracts from diffusion weighted images. Our multi-atlas method incorporates information from multiple hand-labeled fiber tract atlases. In validations, we showed that the method outperformed the standard ROI-based labeling using a deformable, parcellated atlas. Finally, we show a high-throughput application of the method to genetic population studies. We use the sub-voxel diffusion information from fibers in the clustered tracts based on 105-gradient HARDI scans of 86 young normal twins. The whole workflow shows promise for larger population studies in the future.
HARDI; Tractography; Fiber Clustering; Label Fusion; Genetic Heritability
The NTRK3 gene (also known as TRKC) encodes a high affinity receptor for the neurotrophin 3′-nucleotidase (NT3), which is implicated in oligodendrocyte and myelin development. We previously found that white matter integrity in young adults related to genetic variants in genes encoding neurotrophins and their receptors. This underscores the importance of neurotrophins for white matter development. NTRK3 variants are putative risk factors for schizophrenia, bipolar disorder, and obsessive-compulsive disorder hoarding, suggesting that some NTRK3 variants may affect the brain.
To test this, we scanned 392 healthy adult twins and their siblings (mean age, 23.6 ± 2.2 years; range: 20-29 years) with 105-gradient 4-Tesla diffusion tensor imaging (DTI). We identified 18 single nucleotide polymorphisms (SNPs) in the NTRK3 gene that have been associated with neuropsychiatric disorders. We used a multi-SNP model, adjusting for family relatedness, age, and sex, to relate these variants to voxelwise fractional anisotropy (FA) – a DTI measure of white matter integrity.
FA was optimally predicted (based on the highest false discovery rate critical p), by five SNPs (rs1017412, rs2114252, rs16941261, rs3784406, and rs7176429; overall FDR critical p = 0.028). Gene effects were widespread and included the corpus callosum genu and inferior longitudinal fasciculus - regions implicated in several neuropsychiatric disorders and previously associated with other neurotrophin-related genetic variants in an overlapping sample of subjects. NTRK3 genetic variants, and neurotrophins more generally, may influence white matter integrity in brain regions implicated in neuropsychiatric disorders.
Fractional anisotropy; diffusion tensor imaging; single nucleotide polymorphism; schizophrenia; obsessive compulsive disorder; bipolar disorder
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