Search

Cumulating evidence from epidemiologic studies implicates cardiovascular health and cerebrovascular function in several brain diseases in late life. We examined vascular risk factors with respect to a cerebrovascular measure of brain functioning in subjects in mid-life, which could represent a marker of brain changes in later life. Breath-hold functional MRI (fMRI) was performed in 541 women and men (mean age 50.4 years) from the Coronary Artery Risk Development in Young Adults (CARDIA) Brain MRI sub-study. Cerebrovascular reactivity (CVR) was quantified as percentage change in blood-oxygen level dependent (BOLD) signal in activated voxels, which was mapped to a common brain template and log-transformed. Mean CVR was calculated for anatomic regions underlying the default-mode network (DMN) - a network implicated in AD and other brain disorders - in addition to areas considered to be relatively spared in the disease (e.g. occipital lobe), which were utilized as reference regions. Mean CVR was significantly reduced in the posterior cingulate/precuneus (β = -0.063, 95% CI: - 0.106, -0.020), anterior cingulate (β = -0.055, 95% CI: -0.101, -0.010), and medial frontal lobe (β = -0.050, 95% CI: -0.092, -0.008) relative to mean CVR in the occipital lobe, after adjustment for age, sex, race, education, and smoking status, in subjects with pre-hypertension/hypertension compared to normotensive subjects. By contrast, mean CVR was lower, but not significantly, in the inferior parietal lobe (β = -0.024, 95% CI: -0.062, 0.014) and the hippocampus (β = -0.006, 95% CI: -0.062, 0.050) relative to mean CVR in the occipital lobe. Similar results were observed in subjects with diabetes and dyslipidemia compared to those without these conditions, though the differences were non-significant. Reduced CVR may represent diminished vascular functionality for the DMN for individuals with prehypertension/ hypertension in mid-life, and may serve as a preclinical marker for brain dysfunction in later life.

Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread structural brain abnormalities are prominent in schizophrenia. However, to be clinically useful, structural imaging biomarkers must integrate high-dimensional data and provide reproducible results across clinical populations and on an individual person basis. Using advanced multi-variate analysis tools and pooled data from case-control imaging studies conducted at 5 sites (941 adult participants, including 440 patients with schizophrenia), a neuroanatomical signature of patients with schizophrenia was found, and its robustness and reproducibility across sites, populations, and scanners, was established for single-patient classification. Analyses were conducted at multiple scales, including regional volumes, voxelwise measures, and complex distributed patterns. Single-subject classification was tested for single-site, pooled-site, and leave-site-out generalizability. Regional and voxelwise analyses revealed a pattern of widespread reduced regional gray matter volume, particularly in the medial prefrontal, temporolimbic and peri-Sylvian cortex, along with ventricular and pallidum enlargement. Multivariate classification using pooled data achieved a cross-validated prediction accuracy of 76% (AUC = 0.84). Critically, the leave-site-out validation of the detected schizophrenia signature showed accuracy/AUC range of 72-77%/0.73-0.91, suggesting a robust generalizability across sites and patient cohorts. Finally, individualized patient classifications displayed significant correlations with clinical measures of negative, but not positive, symptoms. Taken together, these results emphasize the potential for structural neuroimaging data to provide a robust and reproducible imaging signature of schizophrenia. A web-accessible portal is offered to allow the community to obtain individualized classifications of magnetic resonance imaging scans using the methods described herein.

Objective In adulthood, the diagnosis of attention-deficit/hyperactivity disorder (ADHD) has been subject of recent controversy. We searched for a neuroanatomical signature associated with ADHD spectrum symptoms in adults by applying, for the first time, machine learning-based pattern classification methods to structural MRI and diffusion tensor imaging (DTI) data obtained from stimulant-naive adults with childhood-onset ADHD and healthy controls (HC). Method Sixty-seven ADHD patients and 66 HC underwent high-resolution T1-weighted and DTI acquisitions. A support vector machine (SVM) classifier with a non-linear kernel was applied on multimodal image features extracted on regions of interest placed across the whole brain. Results The discrimination between a mixed-gender ADHD subgroup and individually matched HC (n = 58 each) yielded area-under-the-curve (AUC) and diagnostic accuracy (DA) values of up to 0.71% and 66% (P = 0.003) respectively. AUC and DA values increased to 0.74% and 74% (P = 0.0001) when analyses were restricted to males (52 ADHD vs. 44 HC). Conclusion Introvert personality traits showed independent risk effects on suicidality regardless of diagnosis status. Among high risk individuals with suicidal thoughts, higher neuroticism tendency is further associated with increased risk of suicide attempt.

As longitudinal and multi-site studies become increasingly frequent in neuroimaging, maintaining longitudinal and inter-scanner consistency of brain parcellation has become a major challenge due to variation in scanner models and/or image acquisition protocols across scanners and sites. We present a new automated segmentation method specifically designed to achieve a consistent parcellation of anatomical brain structures in such heterogeneous datasets. Our method combines a site-specific atlas creation strategy with a state-of-the-art multi-atlas anatomical label fusion framework. Site-specific atlases are computed such that they preserve image intensity characteristics of each site's scanner and acquisition protocol, while atlas pairs share anatomical labels in a way consistent with inter-scanner acquisition variations. This harmonization of atlases improves inter-study and longitudinal consistency of segmentations in the subsequent consensus labeling step. We tested this approach on a large sample of older adults from the Baltimore Longitudinal Study of Aging (BLSA) who had longitudinal scans acquired using two scanners that vary with respect to vendor and image acquisition protocol. We compared the proposed method to standard multi-atlas segmentation for both cross-sectional and longitudinal analyses. The harmonization significantly reduced scanner-related differences in the age trends of ROI volumes, improved longitudinal consistency of segmentations, and resulted in higher across-scanner intra-class correlations, particularly in the white matter.

OBJECTIVE Understanding the effect of diabetes as well as of alternative treatment strategies on cerebral structure is critical for the development of targeted interventions against accelerated neurodegeneration in type 2 diabetes. We investigated whether diabetes characteristics were associated with spatially specific patterns of brain changes and whether those patterns were affected by intensive versus standard glycemic treatment. RESEARCH DESIGN AND METHODS Using baseline MRIs of 488 participants with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes-Memory in Diabetes (ACCORD-MIND) study, we applied a new voxel-based analysis methodology to identify spatially specific patterns of gray matter and white matter volume loss related to diabetes duration and HbA1c. The longitudinal analysis used 40-month follow-up data to evaluate differences in progression of volume loss between intensive and standard glycemic treatment arms. RESULTS Participants with longer diabetes duration had significantly lower gray matter volumes, primarily in certain regions in the frontal and temporal lobes. The longitudinal analysis of treatment effects revealed a heterogeneous pattern of decelerated loss of gray matter volume associated with intensive glycemic treatment. Intensive treatment decelerated volume loss, particularly in regions adjacent to those cross-sectionally associated with diabetes duration. No significant relationship between low versus high baseline HbA1c levels and brain changes was found. Finally, regions in which cognitive change was associated with longitudinal volume loss had only small overlap with regions related to diabetes duration and to treatment effects. CONCLUSIONS Applying advanced quantitative image pattern analysis methods on longitudinal MRI data of a large sample of patients with type 2 diabetes, we demonstrate that there are spatially specific patterns of brain changes that vary by diabetes characteristics and that the progression of gray matter volume loss is slowed by intensive glycemic treatment, particularly in regions adjacent to areas affected by diabetes.

Puberty is the defining biological process of adolescent development, yet its effects on fundamental properties of brain physiology such as cerebral blood flow (CBF) have never been investigated. Capitalizing on a sample of 922 youths ages 8-22 y imaged using arterial spin labeled MRI as part of the Philadelphia Neurodevelopmental Cohort, we studied normative developmental differences in cerebral perfusion in males and females, as well as specific associations between puberty and CBF. Males and females had conspicuously divergent nonlinear trajectories in CBF evolution with development as modeled by penalized splines. Seventeen brain regions, including hubs of the executive and default mode networks, showed a robust nonlinear age-by-sex interaction that surpassed Bonferroni correction. Notably, within these regions the decline in CBF was similar between males and females in early puberty and only diverged in midpuberty, with CBF actually increasing in females. Taken together, these results delineate sex-specific growth curves for CBF during youth and for the first time to our knowledge link such differential patterns of development to the effects of puberty.

This paper presents a method for capturing statistical variation of normal imaging phenotypes, with emphasis on brain structure. The method aims to estimate the statistical variation of a normative set of images from healthy individuals, and identify abnormalities as deviations from normality. A direct estimation of the statistical variation of the entire volumetric image is challenged by the high-dimensionality of images relative to smaller sample sizes. To overcome this limitation, we iteratively sample a large number of lower dimensional subspaces that capture image characteristics ranging from fine and localized to coarser and more global. Within each subspace, a “target-specific” feature selection strategy is applied to further reduce the dimensionality, by considering only imaging characteristics present in a test subject’s images. Marginal probability density functions of selected features are estimated through PCA models, in conjunction with an “estimability” criterion that limits the dimensionality of estimated probability densities according to available sample size and underlying anatomy variation. A test sample is iteratively projected to the subspaces of these marginals as determined by PCA models, and its trajectory delineates potential abnormalities. The method is applied to segmentation of various brain lesion types, and to simulated data on which superiority of the iterative method over straight PCA is demonstrated.

Sex differences in human cognition are marked, but little is known regarding their neural origins. Here, in a sample of 674 human participants ages 9–22, we demonstrate that sex differences in cognitive profiles are related to multivariate patterns of resting-state functional connectivity MRI (rsfc-MRI). Males outperformed females on motor and spatial cognitive tasks; females were faster in tasks of emotion identification and nonverbal reasoning. Sex differences were also prominent in the rsfc-MRI data at multiple scales of analysis, with males displaying more between-module connectivity, while females demonstrated more within-module connectivity. Multivariate pattern analysis using support vector machines classified subject sex on the basis of their cognitive profile with 63% accuracy (P < 0.001), but was more accurate using functional connectivity data (71% accuracy; P < 0.001). Moreover, the degree to which a given participant's cognitive profile was “male” or “female” was significantly related to the masculinity or femininity of their pattern of brain connectivity (P = 2.3 × 10−7). This relationship was present even when considering males and female separately. Taken together, these results demonstrate for the first time that sex differences in patterns of cognition are in part represented on a neural level through divergent patterns of brain connectivity.

Importance Hearing impairment (HI) in midlife (45-65 years of age) may be associated with longitudinal neurodegeneration of temporal lobe structures, a biomarker of early Alzheimer disease. Objective To evaluate the association of midlife HI with brain volume trajectories in later life (≥65 years of age). Design, Setting, and Participants This prospective cohort study used data from the Baltimore Longitudinal Study of Aging to evaluate hearing from November 5, 1990, to October 3, 1994, and late-life volume change from July 10, 2008, to January 29, 2015, using magnetic resonance imaging (MRI) (mean follow-up time, 19.3 years). Data analysis was performed from September 22, 2017, to August 27, 2018. A total of 194 community-dwelling older adults who had midlife measures of peripheral hearing at a mean age of 54.5 years and late-life volume change of up to 6 years between the first and most recent MRI assessment were studied. Excluded were those with baseline cognitive impairment, stroke, head injuries, Parkinson disease, and bipolar disorder. Exposures Hearing as measured with pure tone audiometry in each ear from November 5, 1990, to October 3, 1994, and late-life temporal lobe volume change measured by MRI. Main Outcomes and Measures Linear mixed-effects models with random intercepts were used to examine the association of midlife hearing (pure tone average of 0.5-4 kHz tones in the better ear and each ear separately) with longitudinal late-life MRI-based measures of temporal lobe structures (hippocampus, entorhinal cortex, parahippocampal gyrus, and superior, middle, and inferior temporal gyri) in the left and right hemispheres, in addition to global and lobar regions, adjusting for baseline demographic characteristics (age, sex, subsequent cognitive impairment status, and educational level) and intracranial volume. Results A total of 194 patients (mean [SD] age at hearing assessment, 54.5 [10.0] years; 106 [54.6%] female; 169 [87.1%] white) participated in the study. After Bonferroni correction, poorer midlife hearing in the better ear was associated with steeper late-life volumetric declines in the right temporal gray matter (β = −0.113; 95% CI, −0.182 to −0.044), right hippocampus (β = −0.008; 95% CI, −0.012 to −0.004), and left entorhinal cortex (β = −0.009; 95% CI, −0.015 to −0.003). Poorer midlife hearing in the right ear was associated with steeper late-life volumetric declines in the right temporal gray matter (β = −0.136; 95% CI, −0.197 to −0.075), right hippocampus (β = −0.008; 95% CI, −0.012 to −0.004), and left entorhinal cortex (β = −0.009; 95% CI, −0.015 to −0.003), whereas there were no associations between poorer midlife hearing in the left ear with late-life volume loss. Conclusions and Relevance The findings suggest that midlife HI is a risk factor for temporal lobe volume loss. Poorer midlife hearing, particularly in the right ear, was associated with declines in hippocampus and entorhinal cortex.

IMPORTANCE: The effect of intensive blood pressure lowering on brain health remains uncertain. OBJECTIVE: To evaluate the association of intensive blood pressure treatment with cerebral white matter lesion and brain volumes. DESIGN, SETTING, AND PARTICIPANTS: A substudy of a multicenter randomized clinical trial of hypertensive adults 50 years or older without a history of diabetes or stroke at 27 sites in the United States. Randomization began on November 8, 2010. The overall trial was stopped early because of benefit for its primary outcome (a composite of cardiovascular events) and all-cause mortality on August 20, 2015. Brain magnetic resonance imaging (MRI) was performed on a subset of participants at baseline (n = 670) and at 4 years of follow-up (n = 449); final follow-up date was July 1, 2016. INTERVENTIONS: Participants were randomized to a systolic blood pressure (SBP) goal of either less than 120 mm Hg (intensive treatment, n = 355) or less than 140 mm Hg (standard treatment, n = 315). MAIN OUTCOMES AND MEASURES: The primary outcome was change in total white matter lesion volume from baseline. Change in total brain volume was a secondary outcome. RESULTS: Among 670 recruited patients who had baseline MRI (mean age, 67.3 [SD, 8.2] years; 40.4% women), 449 (67.0%) completed the follow-up MRI at a median of 3.97 years after randomization, after a median intervention period of 3.40 years. In the intensive treatment group, based on a robust linear mixed model, mean white matter lesion volume increased from 4.57 to 5.49 cm(3) (difference, 0.92 cm(3) [95% CI, 0.69 to 1.14]) vs an increase from 4.40 to 5.85 cm(3) (difference, 1.45 cm(3) [95% CI, 1.21 to 1.70]) in the standard treatment group (between-group difference in change, −0.54 cm(3) [95% CI, −0.87 to −0.20]). Mean total brain volume decreased from 1134.5 to 1104.0 cm(3) (difference, −30.6 cm(3) [95% CI, −32.3 to −28.8]) in the intensive treatment group vs a decrease from 1134.0 to 1107.1 cm(3) (difference, −26.9 cm(3) [95% CI, 24.8 to 28.8]) in the standard treatment group (between-group difference in change, −3.7 cm(3) [95% CI, −6.3 to −1.1]). CONCLUSIONS AND RELEVANCE: Among hypertensive adults, targeting an SBP of less than 120 mm Hg, compared with less than 140 mm Hg, was significantly associated with a smaller increase in cerebral white matter lesion volume and a greater decrease in total brain volume, although the differences were small. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01206062

Several independent studies have demonstrated that small amounts of in-scanner motion systematically bias estimates of resting-state functional connectivity. This confound is of particular importance for studies of neurodevelopment in youth because motion is strongly related to subject age during this period. Critically, the effects of motion on connectivity mimic major findings in neurodevelopmental research, specifically an age-related strengthening of distant connections and weakening of short-range connections. Here, in a sample of 780 subjects ages 8-22, we re-evaluate patterns of change in functional connectivity during adolescent development after rigorously controlling for the confounding influences of motion at both the subject and group levels. We find that motion artifact inflates both overall estimates of age-related change as well as specific distance-related changes in connectivity. When motion is more fully accounted for, the prevalence of age-related change as well as the strength of distance-related effects is substantially reduced. However, age-related changes remain highly significant. In contrast, motion artifact tends to obscure age-related changes in connectivity associated with segregation of functional brain modules; improved preprocessing techniques allow greater sensitivity to detect increased within-module connectivity occurring with development. Finally, we show that subject's age can still be accurately estimated from the multivariate pattern of functional connectivity even while controlling for motion. Taken together, these results indicate that while motion artifact has a marked and heterogeneous impact on estimates of connectivity change during adolescence, functional connectivity remains a valuable phenotype for the study of neurodevelopment.

Adolescence is characterized by rapid development of executive function. Working memory (WM) is a key element of executive function, but it is not known what brain changes during adolescence allow improved WM performance. Using a fractaln-back fMRI paradigm, we investigated brain responses to WM load in 951 human youths aged 8–22 years. Compared with more limited associations with age, WM performance was robustly associated with both executive network activation and deactivation of the default mode network. Multivariate patterns of brain activation predicted task performance with a high degree of accuracy, and also mediated the observed age-related improvements in WM performance. These results delineate a process of functional maturation of the executive system, and suggest that this process allows for the improvement of cognitive capability seen during adolescence.

CKD has been linked with cognitive deficits and affective disorders in multiple studies. Analysis of structural and functional neuroimaging in adults and children with kidney disease may provide additional important insights into the pathobiology of this relationship. This paper comprehensively reviews neuroimaging studies in both children and adults. Major databases (PsychLit, MEDLINE, WorldCat, ArticleFirst, PubMed, Ovid MEDLINE) were searched using consistent search terms, and studies published between 1975 and 2012 were included if their samples focused on CKD as the primary disease process. Exclusion criteria included case reports, chapters, and review articles. This systematic process yielded 43 studies for inclusion (30 in adults, 13 in children). Findings from this review identified several clear trends: (1) presence of cerebral atrophy and cerebral density changes in patients with CKD; (2) cerebral vascular disease, including deep white matter hyperintensities, white matter lesions, cerebral microbleeds, silent cerebral infarction, and cortical infarction, in patients with CKD; and (3) similarities in regional cerebral blood flow between patients with CKD and those with affective disorders. These findings document the importance of neuroimaging procedures in understanding the effect of CKD on brain structure, function, and associated behaviors. Results provide a developmental linkage between childhood and adulthood, with respect to the effect of CKD on brain functioning across the lifespan, with strong implications for a cerebrovascular mechanism contributing to this developmental linkage. Use of neuroimaging methods to corroborate manifest neuropsychological deficits or perhaps to indicate preventive actions may prove useful to individuals with CKD.

The aim of the study was to examine interactive relations of race and socioeconomic status (SES) to magnetic resonance imaging (MRI)-assessed global brain outcomes with previously demonstrated prognostic significance for stroke, dementia, and mortality. Participants were 147 African Americans (AAs) and whites (ages 33-71 years; 43% AA; 56% female; 26% below poverty) in the Healthy Aging in Neighborhoods of Diversity across the Life Span SCAN substudy. Cranial MRI was conducted using a 3.0 T unit. White matter (WM) lesion volumes and total brain, gray matter, and WM volumes were computed. An SES composite was derived from education and poverty status. Significant interactions of race and SES were observed for WM lesion volume (b = 1.38; η = 0.036; p = .028), total brain (b = 86.72; η = 0.042; p < .001), gray matter (b = 40.16; η = 0.032; p = .003), and WM (b = 46.56; η = 0.050; p < .001). AA participants with low SES exhibited significantly greater WM lesion volumes than white participants with low SES. White participants with higher SES had greater brain volumes than all other groups (albeit within normal range). Low SES was associated with greater WM pathology-a marker for increased stroke risk-in AAs. Higher SES was associated with greater total brain volume-a putative global indicator of brain health and predictor of mortality-in whites. Findings may reflect environmental and interpersonal stressors encountered by AAs and those of lower SES and could relate to disproportionate rates of stroke, dementia, and mortality.

Introduction: In addition to average blood pressure (BP) values, BP variability has been suggested to be associated with lower cognitive function. It is unclear how long-term BP variability throughout young adulthood is associated with cerebrovascular structure (e.g., hippocampus) and function in midlife. Methods: Using data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, which recruited healthy young adults ages 18 to 30 years (mean age 25 years) in 1985-1986, we assessed average BP over visits and visit-to visit BP variability (average real variability [ARV], defined as the absolute differences of BP between successive BP measurements) during 25-year follow-up (8 visits). Normal brain volume, abnormal brain volume, brain integrity assessed by fractional anisotropy, and cerebral blood flow were calculated by 3-Tesla MRI at the Year 25 follow-up (n=557, 52.2 % women, and 34.5 % African Americans). Results: For the 557 participants, mean ARV of SBP was 7.8±3.1 mm Hg. Higher ARV of SBP was associated with lower normal total brain volume, gray matter, and hippocampus after adjustment for covariates including intracranial volume and average BP during follow-up (Table). Higher average SBP during follow-up was associated with higher abnormal brain volume and lower integrity in the whole brain and the white matter, and lower blood flow in the hippocampus. Conclusions: Long-term visit-to-visit BP variability for 25 years beginning in young adulthood was associated with midlife cerebrovascular structural and functional alterations, independent of average BP during follow-up. Focusing not only on BP values alone but also on visit-to-visit BPV may be important to identify younger adults who may be at risk for developing lower cognitive function later.

We systematically compared structural imaging patterns of advanced brain aging (ABA) in the general-population, herein defined as significant deviation from typical BA to those found in Alzheimer disease (AD). The hypothesis that ABA would show different patterns of structural change compared with those found in AD was tested via advanced pattern analysis methods. In particular, magnetic resonance images of 2705 participants from the Study of Health in Pomerania (aged 20–90 years) were analyzed using an index that captures aging atrophy patterns (Spatial Pattern of Atrophy for Recognition of BA (SPARE-BA)), and an index previously shown to capture atrophy patterns found in clinical AD (Spatial Patterns of Abnormality for Recognition of Early Alzheimer’s Disease (SPARE-AD)). We studied the association between these indices and risk factors, including an AD polygenic risk score. Finally, we compared the ABA-associated atrophy with typical AD-like patterns. We observed that SPARE-BA had significant association with: smoking (P<0.05), anti-hypertensive (P<0.05), anti-diabetic drug use (men P<0.05, women P=0.06) and waist circumference for the male cohort (P<0.05), after adjusting for age. Subjects with ABA had spatially extensive gray matter loss in the frontal, parietal and temporal lobes (false-discovery-rate-corrected q

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Background At present, we do not have any biological tests which can contribute towards a diagnosis of depression. Neuroimaging measures have shown some potential as biomarkers for diagnosis. However, participants have generally been from the same ethnic background while the applicability of a biomarker would require replication in individuals of diverse ethnicities. Aims We sought to examine the diagnostic potential of the structural neuroanatomy of depression in a sample of a wide ethnic diversity. Method Structural magnetic resonance imaging (MRI) scans were obtained from 23 patients with major depressive disorder in an acute depressive episode (mean age: 39.8 years) and 20 matched healthy volunteers (mean age: 38.8 years). Participants were of Asian, African and Caucasian ethnicity recruited from the general community. Results Structural neuroanatomy combining white and grey matter distinguished patients from controls at the highest accuracy of 81% with the most stable pattern being at around 70%. A widespread network encompassing frontal, parietal, occipital and cerebellar regions contributed towards diagnostic classification. Conclusions These findings provide an important step in the development of potential neuroimaging-based tools for diagnosis as they demonstrate that the identification of depression is feasible within a multi-ethnic group from the community. Declaration of interests C.H.Y.F. has held recent research grants from Eli Lilly and Company and GlaxoSmithKline. L.M. is a former employee and stockholder of Eli Lilly and Company. Copyright and usage © The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence.

Background Vascular risk factors are increasingly recognized as risks factors for Alzheimer's disease (AD) and early conversion from mild cognitive impairment (MCI) to dementia. While neuroimaging research in AD has focused on brain atrophy, metabolic function, or amyloid deposition, little attention has been paid to the effect of cerebrovascular disease to cognitive decline. Objective To investigate the correlation of brain atrophy and white matter lesions with cognitive decline in AD, MCI, and control subjects. Methods Patients with AD and MCI, and healthy subjects were included in this study. Subjects had a baseline MRI scan, and baseline and follow-up neuropsychological battery (CERAD). Regional volumes were measured, and white matter lesion segmentation was performed. Correlations between rate of CERAD score decline and white matter lesion load and brain structure volume were evaluated. In addition, voxel-based correlations between baseline CERAD scores and atrophy and white matter lesion measures were computed. Results CERAD rate of decline was most significantly associated with lesion loads located in the fornices. Several temporal lobe ROI volumes were significantly associated with CERAD decline. Voxel-based analysis demonstrated strong correlation between baseline CERAD scores and atrophy measures in the anterior temporal lobes. Correlation of baseline CERAD scores with white matter lesion volumes achieved significance in multilobar subcortical white matter. Conclusion Both baseline and declines in CERAD scores correlate with white matter lesion load and gray matter atrophy. Results of this study highlight the dominant effect of volume loss, and underscore the importance of small vessel disease as a contributor to cognitive decline in the elderly.

Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https://ipp.cbica.upenn.edu), a web based platform for remote processing of medical images.

In MRI studies, linear multi-variate methods are often employed to identify regions or connections that are affected due to disease or normal aging. Such linear models inherently assume that there is a single, homogeneous abnormality pattern that is present in all affected individuals. While kernel-based methods can implicitly model a non-linear effect, and therefore the heterogeneity in the affected group, extracting and interpreting information about affected regions is difficult. In this paper, we present a method that explicitly models and captures heterogeneous patterns of change in the affected group relative to a reference group of controls. For this purpose, we use the Mixture-Of-Experts (MOE) framework, which combines unsupervised modeling of mixtures of distributions with supervised learning of classifiers. MOE approximates the non-linear boundary between the two groups with a piecewise linear boundary, thus allowing discovery of multiple patterns of group differences. In the case of patient/control comparisons, each such pattern aims to capture a different dimension of a disease, and hence to identify patient subgroups. We validated our model using multiple simulation scenarios and performance measures. We applied this method to resting state functional MRI data from the Baltimore Longitudinal Study of Aging, to investigate heterogeneous effects of aging on brain function in cognitively normal older adults (> 85 years) relative to a reference group of normal young to middle-aged adults (< 60 years). We found strong evidence for the presence of two subgroups of older adults, with similar age distributions in each subgroup, but different connectivity patterns associated with aging. While both older subgroups showed reduced functional connectivity in the Default Mode Network (DMN), increases in functional connectivity within the pre-frontal cortex as well as the bilateral insula were observed only for one of the two subgroups. Interestingly, the subgroup showing this increased connectivity (unlike the other subgroup) was, cognitively similar at baseline to the young and middle-aged subjects in two of seven cognitive domains, and had a faster rate of cognitive decline in one of seven domains. These results suggest that older individuals whose baseline cognitive performance is comparable to that of younger individuals recruit their “cognitive reserve” later in life, to compensate for reduced connectivity in other brain regions.

Objective: We hypothesized that greater cardiorespiratory fitness is associated with lower odds of having unfavorable brain MRI findings. Methods: We studied 565 healthy, middle-aged, black and white men and women in the CARDIA (Coronary Artery Risk Development in Young Adults) Study. The fitness measure was symptom-limited maximal treadmill test duration (Max dur ); brain MRI was measured 5 years later. Brain MRI measures were analyzed as means and as proportions below the 15th percentile (above the 85th percentile for white matter abnormal tissue volume). Results: Per 1-minute-higher Max dur , the odds ratio for having less whole brain volume was 0.85 ( p = 0.04) and for having low white matter integrity was 0.80 ( p = 0.02), adjusted for age, race, sex, clinic, body mass index, smoking, alcohol, diet, physical activity, education, blood pressure, diabetes, total cholesterol, and lung function (plus intracranial volume for white matter integrity). No significant associations were observed between Max dur and abnormal tissue volume or blood flow in white matter. Findings were similar for associations with continuous brain MRI measures. Conclusions: Greater physical fitness was associated with more brain volume and greater white matter integrity measured 5 years later in middle-aged adults.

White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia.

Glioblastoma, the most common and aggressive adult brain tumor, is considered non-curative at diagnosis, with 14.6 months median survival following treatment, and 4 months otherwise. There is increasing evidence that non-invasive radiomic markers can predict survival, using advanced multiparametric magnetic resonance imaging (A-mpMRI). If successfully applicable, such non-invasive markers can considerably influence patient management. However, most patients typically undergo only conventional mpMRI (C-mpMRI) pre-operatively (i.e., T1,T1-Gd,T2,T2-FLAIR), rather than A-mpMRI that provides additional vascularization (DSC-MRI) and cell-density (DTI) related information. Therefore, existing prognostic A-mpMRI radiomic markers are not applicable/generalizable across institutions for most patients. This study demonstrates that accurate prognostic models may be constructed pre-operatively using integrative radiomic analysis of widely-available C-mpMRI. We hypothesized that appropriate extraction and selection of advanced radiomic feature panels can potentially compensate for the lack of A-mpMRI, thereby rendering such radiomic predictors more amenable to broad clinical use. A retrospective cohort of 101 de novo glioblastoma patients with preoperative C-mpMRI and A-mpMRI was segmented into various sub-regions (e.g., enhancing/non-enhancing/necrotic). Both visually (e.g., volume, location, intensity), and non-visually (e.g., tumor growth model parameters) interpretable radiomic features (n=1612) were extracted for these sub-regions. We assessed two feature configurations: our advanced feature panel (A-FP), and a subset feature panel (S-FP) based on an existing published prognostic A-mpMRI model. The classification accuracy (ACC) of the prognostic model configurations to classify short-(14months), and intermediate-survivors was quantitatively evaluated using a 5-fold cross-validation scheme. The performance of the previously published model (S-FP/A-mpMRI) was replicated (ACC:78.71%), and degraded to 75.74% when using C-mpMRI. The A-FP/C-mpMRI model demonstrated superior performance (ACC:89.11%), also supported by Cox regression (HR:2.84, 95%CI:2.42-3.34). These results suggest that accurate pre-operative prognostic stratification, which is important for personalized treatment decisions in glioblastoma patients, is feasible based solely on integrative quantitative radiomic analysis of C-mpMRI, showing promise for generalization across multiple clinics.

We present a method that uses sparse representations to model the inter-individual variability of healthy anatomy from a limited number of normal medical images. Abnormalities in MR images are then defined as deviations from the normal variation. More precisely, we model an abnormal (pathological) signal y as the superposition of a normal part ~y that can be sparsely represented under an example-based dictionary, and an abnormal part r. Motivated by a dense error correction scheme recently proposed for sparse signal recovery, we use l 1 - norm minimization to separate ~y and r. We extend the existing framework, which was mainly used on robust face recognition in a discriminative setting, to address challenges of brain image analysis, particularly the high dimensionality and low sample size problem. The dictionary is constructed from local image patches extracted from training images aligned using smooth transformations, together with minor perturbations of those patches. A multi-scale sliding-window scheme is applied to capture anatomical variations ranging from fine and localized to coarser and more global. The statistical significance of the abnormality term r is obtained by comparison to its empirical distribution through cross-validation, and is used to assign an abnormality score to each voxel. In our validation experiments the method is applied for segmenting abnormalities on 2-D slices of FLAIR images, and we obtain segmentation results consistent with the expert-defined masks.

International audience; Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/).

We present a brain development index (BDI) that concisely summarizes complex imaging patterns of structural brain maturation along a single dimension using a machine learning methodology. The brain was found to follow a remarkably consistent developmental trajectory in a sample of 621 subjects of ages 8-22 participating in the Philadelphia Neurodevelopmental Cohort, reflected by a cross-validated correlation coefficient between chronologic age and the BDI of r = 0.89. Critically, deviations from this trajectory related to cognitive performance. Specifically, subjects whose BDI was higher than their chronological age displayed significantly superior cognitive processing speed compared with subjects whose BDI was lower than their actual age. These results indicate that the multiparametric imaging patterns summarized by the BDI can accurately delineate trajectories of brain development and identify individuals with cognitive precocity or delay.

Rationale and Objectives We present a new method for automatic brain extraction on structural magnetic resonance images, based on a multi-atlas registration framework. Materials and Methods Our method addresses fundamental challenges of multi-atlas approaches. To overcome the difficulties arising from the variability of imaging characteristics between studies, we propose a study-specific template selection strategy, by which we select a set of templates that best represent the anatomical variations within the data set. Against the difficulties of registering brain images with skull, we use a particularly adapted registration algorithm that is more robust to large variations between images, as it adaptively aligns different regions of the two images based not only on their similarity but also on the reliability of the matching between images. Finally, a spatially adaptive weighted voting strategy, which uses the ranking of Jacobian determinant values to measure the local similarity between the template and the target images, is applied for combining coregistered template masks. Results The method is validated on three different public data sets and obtained a higher accuracy than recent state-of-the-art brain extraction methods. Also, the proposed method is successfully applied on several recent imaging studies, each containing thousands of magnetic resonance images, thus reducing the manual correction time significantly. Conclusions The new method, available as a stand-alone software package for public use, provides a robust and accurate brain extraction tool applicable for both clinical use and large population studies.

Importance Structural brain abnormalities are prominent in psychotic disorders, including schizophrenia. However, it is unclear when aberrations emerge in the disease process and if such deficits are present in association with less severe psychosis spectrum (PS) symptoms in youth. Objective To investigate the presence of structural brain abnormalities in youth with PS symptoms. Design, Setting, and Participants The Philadelphia Neurodevelopmental Cohort is a prospectively accrued, community-based sample of 9498 youth who received a structured psychiatric evaluation. A subsample of 1601 individuals underwent neuroimaging, including structural magnetic resonance imaging, at an academic and children’s hospital health care network between November 1, 2009, and November 30, 2011. Main Outcomes and Measures Measures of brain volume derived from T1-weighted structural neuroimaging at 3 T. Analyses were conducted at global, regional, and voxelwise levels. Regional volumes were estimated with an advanced multiatlas regional segmentation procedure, and voxelwise volumetric analyses were conducted as well. Nonlinear developmental patterns were examined using penalized splines within a general additive model. Psychosis spectrum (PS) symptom severity was summarized using factor analysis and evaluated dimensionally. Results Following exclusions due to comorbidity and image quality assurance, the final sample included 791 participants aged youth 8 to 22 years. Fifty percent (n = 393) were female. After structured interviews, 391 participants were identified as having PS features (PS group) and 400 participants were identified as typically developing comparison individuals without significant psychopathology (TD group). Compared with the TD group, the PS group had diminished whole-brain gray matter volume ( P = 3.4 × 10 −4 ) when not accounting for intracranial volume and relatively expanded white matter volume when accounting for intracranial volume ( P = 2.2 × 10 −3 ). Voxelwise analyses revealed significantly lower gray matter volume in the medial temporal lobe (maximum z score = 5.2 and cluster size of 1225 for the right and maximum z score = 4.5 and cluster size of 310 for the left) as well as in frontal, temporal, and parietal cortex. Volumetric reduction in the medial temporal lobe was correlated with PS symptom severity. Conclusions and Relevance Structural brain abnormalities that have been commonly reported in adults with psychosis are present early in life in youth with PS symptoms and are not due to medication effects. Future longitudinal studies could use the presence of such abnormalities in conjunction with clinical presentation, cognitive profile, and genomics to predict risk and aid in stratification to guide early interventions.

International audience; Brain abnormalities such as white matter lesions (WMLs) are not only linked to cerebrovascular disease, but also with normal aging, diabetes and other conditions increasing the risk for cerebrovascular pathologies. Obtaining quantitative measures which assesses the degree or probability of WML in patients is important for evaluating disease burden, and for evaluating its progression and response to interventions. In this paper, we introduce a novel approach for detecting the presence of WMLs in periventricular areas of the brain using manifold-constrained embeddings. The proposed method uses locally linear embedding (LLE) to create "normality" distributions in 12 locations of the brain where deviations from the manifolds are estimated by calculating geodesic distances along locally linear planes in the embedding. A smooth mapping function approximating the relationship between ambient and manifold spaces as a joint distribution maps unseen test images in the intrinsic space. We create a set of low-dimensional embeddings from 876 patches of healthy tissue in 73 subjects and test it on 396 patches imaging both WML and healthy areas in 33 subjects with diabetes. Experiments highlight the need of nonlinear techniques to learn the studied data with detection rates over 85% in true-positives, and the relevance of the computed distance for comparing individuals to a specific pathological pattern.

This paper presents a method for classification of structural magnetic resonance images (MRI) of the brain. An ensemble of linear support vector machine classifiers (SVMs) is used for classifying a subject as either patient or normal control. Image voxels are first ranked based on the voxel wise t-statistics between the voxel intensity values and class labels. Then voxel subsets are selected based on the rank value using a forward feature selection scheme. Finally, an SVM classifier is trained on each subset of image voxels. The class label of a test subject is calculated by combining individual decisions of the SVM classifiers using a voting mechanism. The method is applied for classifying patients with neurological diseases such as Alzheimer’s disease (AD) and autism spectrum disorder (ASD). The results on both datasets demonstrate superior performance as compared to two state of the art methods for medical image classification.

Quantitative analysis of brain lesions and ischemic infarcts is becoming very important due to their association with cardiovascular disease and normal aging. In this paper, we present a semi-supervised segmentation methodology that detects and classifies cerebrovascular disease in multi-channel magnetic resonance (MR) images. The method combines intensity based fuzzy c-means (FCM) segmentation with spatial probability maps calculated from a normative set of images from healthy individuals. Unlike common FCM-based methods which segment only healthy tissue, we have extended the fuzzy segmentation to include patient-specific spatial priors for both pathological conditions (lesions and infarcts). These priors are calculated by estimating the statistical voxel-wise variation of the healthy anatomy, and identifying abnormalities as deviations from normality. False detection is reduced by knowledge-based rules. Assessment on a population of 47 patients from different imaging sites illustrates the potential of the proposed method in segmenting both hyperintense lesions and necrotic infarcts.

Brain abnormalities such as white matter lesions (WMLs) are not only linked to cerebrovascular disease, but also with normal aging, diabetes and other conditions increasing the risk for cerebrovascular pathologies. Discovering quantitative measures which assess the degree or probability of WML in patients is important for evaluating disease burden, progression and response to interventions. In this paper, we introduce a novel approach for detecting the presence of WMLs in periventricular areas of the brain with a discriminant graph-embedding framework, introducing within-class and between-class similarity graphs described in nonlinear manifold subspaces to characterize intra-regional compactness and inter-regional separability. The geometrical structure of the data is exploited to perform linearization and canonical kernalization based on fuzzy-matching principles of 876 normal tissue patches in 73 subjects, and tested on patches imaging both WML (263) and healthy areas (133) in 33 subjects with diabetes. Experiments highlight the advantage of introducing separability between submanifolds to learn the studied data and increase the discriminatory power, with detection rates over 91% in true-positives, and the importance of measuring similarity for specific pathological patterns using kernelized distance metrics.

We propose an automated method to segment cortical necrosis from brain FLAIR-MR Images. Cortical necrosis are regions of dead brain tissue in the cortex caused by cerebrovascular disease (CVD). The accurate segmentation of these regions is difficult as their intensity patterns are similar to the adjoining cerebrospinal fluid (CSF). We generate a model of normal variation using MR scans of healthy controls. The model is based on the Jacobians of warps obtained by registering scans of normal subjects to a common coordinate system. For each patient scan a Jacobian is obtained by warping it to the same coordinate system. Large deviations between the model and subject-specific Jacobians are flagged as ‘abnormalities’. Abnormalities are segmented as cortical necrosis if they are in the cortex and have the intensity profile of CSF. We evaluate our method by using a set of 72 healthy subjects to model cortical variation. We use this model to successfully detect and segment cortical necrosis in a set of 37 patients with CVD. A comparison of the results with segmentations from two independent human experts shows that the overlap between our approach and either of the human experts is in the range of the overlap between the two human experts themselves.

We present a general methodology that aims to learn multi-variate statistics of high dimensional images, in order to capture the inter-individual variability of imaging data from a limited number of training images. The statistical learning procedure is used for identifying abnormalities as deviations from the normal variation. In most practical applications, learning an accurate statistical model of the observed data is a very challenging task due to the very high dimensionality of the images, and the limited number of available training samples. We attempt to overcome this problem by capturing the statistics of a large number of lower dimensional subspaces, which can be estimated more reliably. The subspaces are derived in a multi-scale fashion, and capture image characteristics ranging from fine and localized to coarser and relatively more global. The main premise is that an imaging pattern that is consistent with the statistics of a large number of subspaces, each reflecting a marginal probability density function (pdf), is likely to be consistent with the overall pdf, which hasn't been explicitly estimated. Abnormalities in a new image are identified as significant deviations from the normal variation captured by the learned subspace models, and are determined via iterative projections on these subspaces.

Quantitative analysis of brain lesions in large clinical trials is becoming more and more important. We present a new automated method, that combines intensity based lesion segmentation with a false positive elimination method based on the spatial distribution of lesions. A Support Vector Regressor (SVR) is trained on expert-defined lesion masks using image histograms as features, in order to obtain an initial lesion segmentation. A lesion probability map that represents the spatial distribution of true and false positives on the intensity based segmentation is constructed using the segmented lesions and manual masks. A k-Nearest Neighbor (kNN) classifier based on the lesion probability map is applied to refine the segmentation.

Background: Chronic kidney disease is strongly linked to neurocognitive deficits in adults and children, but the pathophysiologic processes leading to these deficits remain poorly understood. The NiCK study (Neurocognitive Assessment and Magnetic Resonance Imaging Analysis of Children and Young Adults with Chronic Kidney Disease) seeks to address critical gaps in our understanding of the biological basis for neurologic abnormalities in chronic kidney disease. In this report, we describe the objectives, design, and methods of the NiCK study. Design/methods: The NiCK Study is a cross-sectional cohort study in which neurocognitive and neuroimaging phenotyping is performed in children and young adults, aged 8 to 25 years, with chronic kidney disease compared to healthy controls. Assessments include (1) comprehensive neurocognitive testing (using traditional and computerized methods); (2) detailed clinical phenotyping; and (3) multimodal magnetic resonance imaging (MRI) to assess brain structure (using T1-weighted MRI, T2-weighted MRI, and diffusion tensor imaging), functional connectivity (using functional MRI), and blood flow (using arterial spin labeled MRI). Primary analyses will examine group differences in neurocognitive testing and neuroimaging between subjects with chronic kidney disease and healthy controls. Mechanisms responsible for neurocognitive dysfunction resulting from kidney disease will be explored by examining associations between neurocognitive testing and regional changes in brain structure, functional connectivity, or blood flow. In addition, the neurologic impact of kidney disease comorbidities such as anemia and hypertension will be explored. We highlight aspects of our analytical approach that illustrate the challenges and opportunities posed by data of this scope. Discussion: The NiCK study provides a unique opportunity to address key questions about the biological basis of neurocognitive deficits in chronic kidney disease. Understanding these mechanisms could have great public health impact by guiding screening strategies, delivery of health information, and targeted treatment strategies for chronic kidney disease and its related comorbidities.

Personalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders. To facilitate applications of the personalized FNs with enhanced reliability and reproducibility, we develop an open-source toolbox that is user-friendly, extendable, and includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, and a step-by-step guideline) and job-scheduling for high performance computing (HPC) clusters. Particularly, the toolbox, named personalized functional network modeling (pNet), takes fMRI inputs in either volumetric or surface type, ensuring compatibility with multiple fMRI data formats, and computes personalized FNs using two distinct modeling methods: one method optimizes the functional coherence of FNs, while the other enhances their independence. Additionally, the toolbox provides HTML-based reports for QC and visualization of personalized FNs. The toolbox is developed in both MATLAB and Python platforms with a modular design to facilitate extension and modification by users familiar with either programming language. We have evaluated the toolbox on two fMRI datasets and demonstrated its effectiveness and user-friendliness with interactive and scripting examples. pNet is publicly available at https://github.com/MLDataAnalytics/pNet.