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.
We propose a framework for quantifying node-level community structures between groups using anatomical brain networks derived from DTI-tractography. To construct communities, we computed hierarchical binary trees by maximizing two metrics: the well-known modularity metric (Q), and a novel metric that measures the difference between inter-community and intra-community path lengths. Changes in community structures on the nodal level were assessed between generated trees and a statistical framework was developed to detect local differences between two groups of community structures. We applied this framework to a sample of 42 subjects with major depression and 47 healthy controls. Results showed that several nodes (including the bilateral precuneus, which have been linked to self-awareness) within the default mode network exhibited significant differences between groups. These findings are consistent with those reported in previous literature, suggesting a higher degree of ruminative self-reflections in depression.
Attention-deficit hyperactivity disorder (ADHD) is prevalent in patients with bipolar disorder (BP), but very few studies consider this when interpreting magnetic resonance imaging findings. No studies, to our knowledge, have screened for or controlled for the presence of ADHD when examining cortical thickness in patients with BP. We used a 2 × 2 design to evaluate the joint effects of BP and ADHD on cortical thickness and uncover the importance of ADHD comorbidity in BP subjects.
The study included 85 subjects: 31 healthy controls, 17 BP-only, 19 ADHD-only, and 18 BP/ADHD. All patients with BP were subtype I, were euthymic, and were not taking lithium. Groups did not differ significantly in age or sex distribution. We used cortical thickness measuring tools combined with cortical pattern matching methods to align sulcal/gyral anatomy across participants. Significance maps were used to check for both main effects of BP and ADHD and their interaction. Post-hoc comparisons assessed how the effects of BP on cortical thickness varied as a function of the presence or absence of ADHD.
Interactions of BP and ADHD diagnoses were found in the left subgenual cingulate and right orbitofrontal cortex, demonstrating that the effect of BP on cortical thickness depends on ADHD status.
Some brain abnormalities attributed to BP may result from the presence of ADHD. Diagnostic interactions were found in regions previously implicated in the pathophysiology of BP, making it vital to control for an ADHD comorbid diagnosis when attempting to isolate neural or genetic abnormalities specific to BP.
bipolar disorder; attention-deficit hyperactivity disorder; comorbidity; magnetic resonance imaging; cortical thickness; cortical pattern matching; prefrontal cortex; anterior cingulate cortex; subgenual cingulate cortex; orbitofrontal cortex
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
Rare autosomal dominant mutations result in familial Alzheimer’s disease (FAD) with a relatively consistent age of onset within families. This provides an estimate of years until disease onset (relative age) in mutation carriers. Increased AD risk has been associated with differences in functional magnetic resonance imaging (fMRI) activity during memory tasks, but most of these studies have focused on possession of apolipoprotein E allele 4 (APOE4), a risk factor, but not causative variant, of late-onset AD. Evaluation of fMRI activity in presymptomatic FAD mutation carriers versus noncarriers provides insight into preclinical changes in those who will certainly develop AD in a prescribed period of time. Adults from FAD mutation-carrying families (nine mutation carriers, eight noncarriers) underwent fMRI scanning while performing a memory task. We examined fMRI signal differences between carriers and noncarriers, and how signal related to fMRI task performance within mutation status group, controlling for relative age and education. Mutation noncarriers had greater retrieval period activity than carriers in several AD-relevant regions, including the left hippocampus. Better performing noncarriers showed greater encoding period activity including in the parahippocampal gyrus. Poorer performing carriers showed greater retrieval period signal, including in the frontal and temporal lobes, suggesting underlying pathological processes.
early onset; functional magnetic resonance imaging; PSEN1; APP; mutation; hippocampus; medial temporal lobe; volume
A cross-sectional study to establish whether a subject’s cognitive state can be predicted based on regional values obtained from brain cortical maps of FDDNP Distribution Volume Ratio (DVR), which shows the pattern of beta amyloid and neurofibrillary binding, along with those of early summed FDDNP PET images (reflecting the pattern of perfusion) was performed.
Dynamic FDDNP PET studies were performed in a group of 23 subjects (8 control (NL), 8 Mild Cognitive Impairment (MCI) and 7 Alzheimer’s Disease (AD) subjects). FDDNP DVR images were mapped to the MR derived hemispheric cortical surface map warped into a common space. A set of Regions of Interest (ROI) values of FDDNP DVR and early summed FDDNP PET (0-6 min post tracer injection), were thus calculated for each subject which along with the MMSE score were used to construct a linear mathematical model relating ROI values to MMSE. After the MMSE prediction models were developed, the models’ predictive ability was tested in a non-overlapping set of 8 additional individuals, whose cognitive status was unknown to the investigators who constructed the predictive models.
Among all possible subsets of ROIs, we found that the standard deviation of the predicted MMSE was 1.8 by using only DVR values from medial and lateral temporal and prefrontal regions plus the early summed FDDNP value in the posterior cingulate gyrus. The root mean square prediction error for the eight new subjects was 1.6.
FDDNP scans reflect progressive neuropathology accumulation and can potentially be used to predict the cognitive state of an individual.
cortical surface maps; MR; FDDNP PET; MMSE
Assessments of the impact of offshore energy developments are constrained because it is not known whether fine-scale behavioural responses to noise lead to broader-scale displacement of protected small cetaceans. We used passive acoustic monitoring and digital aerial surveys to study changes in the occurrence of harbour porpoises across a 2000 km2 study area during a commercial two-dimensional seismic survey in the North Sea. Acoustic and visual data provided evidence of group responses to airgun noise from the 470 cu inch array over ranges of 5–10 km, at received peak-to-peak sound pressure levels of 165–172 dB re 1 µPa and sound exposure levels (SELs) of 145–151 dB re 1 µPa2 s−1. However, animals were typically detected again at affected sites within a few hours, and the level of response declined through the 10 day survey. Overall, acoustic detections decreased significantly during the survey period in the impact area compared with a control area, but this effect was small in relation to natural variation. These results demonstrate that prolonged seismic survey noise did not lead to broader-scale displacement into suboptimal or higher-risk habitats, and suggest that impact assessments should focus on sublethal effects resulting from changes in foraging performance of animals within affected sites.
Phocoena phocoena; oil and gas exploration; acoustic disturbance; marine mammal conservation; marine spatial planning
Imaging traits are thought to have more direct links to genetic variation than diagnostic measures based on cognitive or clinical assessments and provide a powerful substrate to examine the influence of genetics on human brains. Although imaging genetics has attracted growing attention and interest, most brain-wide genome-wide association studies focus on voxel-wise single-locus approaches, without taking advantage of the spatial information in images or combining the effect of multiple genetic variants. In this paper we present a fast implementation of voxel- and cluster-wise inferences based on the random field theory to fully use the spatial information in images. The approach is combined with a multi-locus model based on least square kernel machines to associate the joint effect of several single nucleotide polymorphisms (SNP) with imaging traits. A fast permutation procedure is also proposed which significantly reduces the number of permutations needed relative to the standard empirical method and provides accurate small p-value estimates based on parametric tail approximation. We explored the relation between 448,294 single nucleotide polymorphisms and 18,043 genes in 31,662 voxels of the entire brain across 740 elderly subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. We find method to be more sensitive compared with voxel-wise single-locus approaches. A number of genes were identified as having significant associations with volumetric changes. The most associated gene was GRIN2B, which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit and affects both the parietal and temporal lobes in human brains. Its role in Alzheimer's disease has been widely acknowledged and studied, suggesting the validity of the approach. The various advantages over existing approaches indicate a great potential offered by this novel framework to detect genetic influences on human brains.
Imaging genetics; Association study; Random field theory; Voxel-wise inference; Cluster size inference; Nonstationarity; Least square kernel machines; Permutation; Pareto distribution; Parametric tail approximation; ADNI; MRI; Tensor-based morphometry; Alzheimer's disease; GRIN2B
Understanding the variability of the hippocampus in human brain research is essential. The effect of age on the hippocampus has been explored in several studies that have been focused on either normal aging or neural degeneration. Shape analysis of magnetic resonance imaging (MRI) provides morphological measures for brain structures. This study further investigates the age effects on hippocampal morphology in three groups (104 normal controls, 24 Alzheimer’s disease (AD) and 14 vascular dementia (VaD) patients). By utilizing a parametric shape analysis of hippocampal MRI scans, each individual distance map is generated and analyzed statistically. Specifically, after eliminating similarity parameters (rotation, translation, and scaling) effects for each hippocampus, an individual distance map is generated from parametric hippocampal surfaces and medial axes. Then statistical methods, including regression, and permutation tests, are applied to detect the differences in hippocampal distance maps and volumes under the effect of age in each group. Statistical analyses reveal that the loss of hippocampal volume and changes in shape are more significantly related to aging in the control group than in AD/VaD. The results also show that the asymmetry of hippocampus in healthy subjects is greater than that in either of the disease groups. Our study shows that 3D statistical shape analysis could enhance the understanding of age effects on local areas of hippocampi. However, the sample sizes of disease groups are relatively low; further studies with more AD/VaD data are needed.
Statistical shape analysis; age; hippocampus; Alzheimer’s disease; vascular dementia
Incomplete data present serious problems when integrating largescale brain imaging data sets from different imaging modalities. In the Alzheimer’s Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. We address this problem by proposing two novel learning methods where all the samples (with at least one available data source) can be used. In the first method, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. Our second method learns a base classifier for each data source independently, based on which we represent each source using a single column of prediction scores; we then estimate the missing prediction scores, which, combined with the existing prediction scores, are used to build a multi-source fusion model. To illustrate the proposed approaches, we classify patients from the ADNI study into groups with Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI’s 780 participants (172 AD, 397 MCI, 211 Normal), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithms. Comprehensive experiments show that our proposed methods yield stable and promising results.
Algorithms; Multi-source feature learning; multi-task learning; incomplete data
Structural brain deficits, especially fronto-temporal volume reduction and ventricular enlargement, have been repeatedly reported in patients with schizophrenia. However, it remains unclear whether brain structural deformations may be attributable to disease-related or genetic factors. In this study, the structural magnetic resonance imaging data of 48 adult-onset schizophrenia patients, 65 first-degree non-psychotic relatives of schizophrenia patients, 27 community comparison (CC) probands and 73 CC relatives were examined using tensor-based morphometry (TBM) to isolate global and localized differences in tissue volume across the entire brain between groups. We found brain tissue contractions most prominently in frontal and temporal regions and expansions in the putamen/ pallidum, and lateral and third ventricles in schizophrenia patients when compared to unrelated CC probands. Results were similar, though less prominent when patients were compared with their non-psychotic relatives. Structural deformations observed in unaffected patient relatives compared to age-similar CC relatives were suggestive of schizophrenia-related genetic liability and were pronounced in the putamen/ pallidum and medial temporal regions. Schizophrenia and genetic liability effects for the putamen/ pallidum were confirmed by regions-of-interest analysis. In conclusion, TBM findings complement reports of frontal, temporal and ventricular dysmorphology in schizophrenia and further indicate that putamen/ pallidum enlargements, originally linked mainly with medication exposure in early studies, also reflect a genetic predisposition for schizophrenia. Thus, brain deformation profiles revealed in this study may help to clarify the role of specific genetic or environmental risk factors towards altered brain morphology in schizophrenia.
In the course of development, the brain undergoes a remarkable process of restructuring as it adapts to the environment and becomes more efficient in processing information. A variety of brain imaging methods can be used to probe how anatomy, connectivity, and function change in the developing brain. Here we review recent discoveries regarding these brain changes in both typically developing individuals and individuals with neurodevelopmental disorders. We begin with typical development, summarizing research on changes in regional brain volume and tissue density, cortical thickness, white matter integrity, and functional connectivity. Space limits preclude the coverage of all neurodevelopmental disorders; instead, we cover a representative selection of studies examining neural correlates of autism, attention deficit/hyperactivity disorder, Fragile X, 22q11.2 deletion syndrome, Williams syndrome, Down syndrome, and Turner syndrome. Where possible, we focus on studies that identify an age by diagnosis interaction, suggesting an altered developmental trajectory. The studies we review generally cover the developmental period from infancy to early adulthood. Great progress has been made over the last 20 years in mapping how the brain matures with MR technology. With ever-improving technology, we expect this progress to accelerate, offering a deeper understanding of brain development, and more effective interventions for neurodevelopmental disorders.
development; MRI; DTI; rsfMRI; brain structure; brain connectivity; neurodevelopmental disorder; autism; ADHD; 22q; fragile X; Turner syndrome; Williams syndrome; Down syndrome
Dementia with Lewy Bodies (DLB) and Alzheimer's disease (AD) are the two most common neurodegenerative dementias. During the early stages, clinical distinction between them is often challenging. Our objective is to compare hippocampal atrophy patterns in mild AD and mild DLB. We hypothesized that DLB subjects have milder hippocampal atrophy relative to AD subjects.
We analyzed the T1-weighted magnetic resonance imaging data from 113 subjects: 55 AD, 16 DLB and 42 cognitively normal elderly (NC). Using the hippocampal radial distance technique and multiple linear regression, we analyzed the effect of clinical diagnosis on hippocampal radial distance, while adjusting for gender and age. 3D statistical maps were adjusted for multiple comparisons using permutation-based statistics with a threshold of p<0.01.
Compared to NC, AD exhibited significantly greater atrophy in the Cornu Ammonis (CA) 1, CA2-3 and subicular regions bilaterally while DLB showed left-predominant atrophy in the CA1 region and subiculum. AD and DLB directly compared did not reveal statistically significant differences.
Hippocampal atrophy, while present in mildly impaired DLB subjects, is less severe than atrophy seen in mildly impaired AD subjects, when compared to NC. Both groups show predominant atrophy of the CA1 subfield and subiculum.
Alzheimer's disease; Dementia with Lewy Bodies; hippocampus; MRI; atrophy
Prior structural neuroimaging studies of the amygdala in patients with bipolar disorder have reported higher or lower volumes, or no difference relative to healthy controls. These inconsistent findings may have resulted from combining subjects in different mood states. The prefrontal cortex has recently been reported to have a lower volume in depressed versus euthymic bipolar patients. Here we examined whether similar mood state-dependent volumetric differences are detectable in the amygdala.
Forty subjects, including 28 with bipolar disorder type I (12 depressed and 16 euthymic), and 12 healthy comparison subjects were scanned on a 3T magnetic resonance image (MRI) scanner. Amygdala volumes were manually traced and compared across subject groups, adjusting for sex and total brain volume.
Statistical analyses found a significant effect of mood state and hemisphere on amygdala volume. Subsequent comparisons revealed that amygdala volumes were significantly lower in the depressed bipolar group compared to both the euthymic bipolar (p=0.005) and healthy control (p=0.043) groups.
Our study was cross-sectional and some patients were medicated.
Our results suggest that mood state influences amygdala volume in subjects with bipolar disorder. Future studies that replicate these findings in unmedicated patient samples scanned longitudinally are needed.
Bipolar Disorder; Depression; Magnetic resonance imaging; MRI; Amygdala
Several common genetic variants have recently been discovered that appear to influence white matter microstructure, as measured by diffusion tensor imaging (DTI). Each genetic variant explains only a small proportion of the variance in brain microstructure, so we set out to explore their combined effect on the white matter integrity of the corpus callosum. We measured six common candidate single-nucleotide polymorphisms (SNPs) in the COMT, NTRK1, BDNF, ErbB4, CLU, and HFE genes, and investigated their individual and aggregate effects on white matter structure in 395 healthy adult twins and siblings (age: 20–30 years). All subjects were scanned with 4-tesla 94-direction high angular resolution diffusion imaging. When combined using mixed-effects linear regression, a joint model based on five of the candidate SNPs (COMT, NTRK1, ErbB4, CLU, and HFE) explained ∼6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effects on FA at 82% of the corpus callosum voxels, including the genu, body, and splenium. Predicting the brain's fiber microstructure from genotypes may ultimately help in early risk assessment, and eventually, in personalized treatment for neuropsychiatric disorders in which brain integrity and connectivity are affected.
neuroimaging; brain structure; DTI; genetics; genetic profiles; prediction; imaging; clinical or preclinical; neuroanatomy; neurogenetics; pharmacogenetics / pharmacogenomics; neuroimaging; brain structure; DTI; genetics; genetic profiles
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
Analysis of incomplete data is a big challenge when integrating large-scale brain imaging datasets from different imaging modalities. In the Alzheimer’s Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. In this paper, we address this problem by proposing an incomplete Multi-Source Feature (iMSF) learning method where all the samples (with at least one available data source) can be used. To illustrate the proposed approach, we classify patients from the ADNI study into groups with Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI’s 780 participants (172 AD, 397 MCI, 211 NC), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithm. Depending on the problem being solved, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. To build a practical and robust system, we construct a classifier ensemble by combining our method with four other methods for missing value estimation. Comprehensive experiments with various parameters show that our proposed iMSF method and the ensemble model yield stable and promising results.
Multi-source feature learning; multi-task learning; incomplete data; ensemble
Several dietary factors and their genetic modifiers play a role in neurological disease and affect the human brain. The structural and functional integrity of the living brain can be assessed using neuroimaging, enabling large-scale epidemiological studies to identify factors that help or harm the brain. Iron is one nutritional factor that comes entirely from our diet, and its storage and transport in the body are under strong genetic control. In this review, we discuss how neuroimaging can help to identify associations between brain integrity, genetic variations, and dietary factors such as iron. We also review iron’s essential role in cognition, and we note some challenges and confounds involved in interpreting links between diet and brain health. Finally, we outline some recent discoveries regarding the genetics of iron and its effects on the brain, suggesting the promise of neuroimaging in revealing how dietary factors affect the brain.
Neuroimaging; Genetics; Iron; Diet; Transferrin; HFE; Nutrition; Brain development
Designers of clinical trials for Alzheimer's disease (AD) and mild cognitive impairment (MCI) are actively considering structural and functional neuroimaging, cerebrospinal fluid and genetic biomarkers to reduce the sample sizes needed to detect therapeutic effects. Genetic pre-selection, however, has been limited to Apolipoprotein E (ApoE). Recently discovered polymorphisms in the CLU, CR1 and PICALM genes are also moderate risk factors for AD; each affects lifetime AD risk by ~ 10–20%. Here, we tested the hypothesis that pre-selecting subjects based on these variants along with ApoE genotype would further boost clinical trial power, relative to considering ApoE alone, using an MRI-derived 2-year atrophy rate as our outcome measure. We ranked subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) based on their cumulative risk from these four genes. We obtained sample size estimates in cohorts enriched in subjects with greater aggregate genetic risk. Enriching for additional genetic biomarkers reduced the required sample sizes by up to 50%, for MCI trials. Thus, AD drug trial enrichment with multiple genotypes may have potential implications for the timeliness, cost, and power of trials.
•ApoE genotype status helps enrich MCI trials, using a structural MRI outcome measure.•CLU, PICALM and CR1 risk genes boost potential MCI trial power beyond ApoE alone.•CLU, PICALM and CR1 show significant, aggregate effects on TBM maps of brain atrophy.
Alzheimer's disease; Neuroimaging; Brain atrophy; Genetics; Genetic risk score; Clinical trial enrichment
The overall volume of the brain has been found to be under relatively strong genetic control, but the relative strength of genetic and environmental factors on between-person variations in regional cortical thickness in adolescence is still not well understood. Here, we analyzed structural MRI data from 108 14-year-old healthy twins (54 females/54 males) to determine the relative contributions of genes and the environment toward regional variations in gray matter thickness across the cortex. After extracting cortical thickness values at a high spatial resolution, an A/C/E structural equation model that divides the variations into additive genetic (A), shared (C), and unique (E) environmental components was fitted. There was considerable regional variability in the magnitude of genetic influences on cortical thickness after controlling for sex. Regions with genetic contributions of greater than 80% were observed in the prefrontal cortex, predominantly in the bilateral dorsolateral and mesial superior frontal regions. No region showed prominent shared environmental influences, but unique environmental influences of over 80% were found in parietal association regions. The genetic variance for cortical thickness in adolescents in prefrontal regions overlapped with previous findings in adults. However, the unique environmental effects observed in multimodal parietal association cortices with converging inputs from visual, auditory, somatosensory regions, and neighboring secondary association cortices suggest that these regional variations are more shaped by experience and could form targets for early interventions in youth with behavioral disorders.
adolescent twins; cortical thickness; heritability; MRI
In an attempt to increase power to detect genetic associations with brain phenotypes derived from human neuroimaging data, we recently conducted a large-scale genome-wide association meta-analysis of hippocampal, brain, and intracranial volume through the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Here we present a freely-available online interactive tool, EnigmaVis, which makes it easy to visualize the association results generated by the consortium alongside allele frequency, genes, and functional annotations. EnigmaVis runs natively within the web browser, and generates plots that show the level of association between brain phenotypes at user-specified genomic positions. Uniquely, EnigmaVis is dynamic; users can interact with elements on the plot in real time. This software will be useful when exploring the effect on brain structure of particular genetic variants influencing neuropsychiatric illness and cognitive function. Future projects of the consortium and updates to EnigmaVis will also be displayed on the site. EnigmaVis is freely available online at http://enigma.loni.ucla.edu/enigma-vis/.