BrainMap VBM: An environment for structural meta‐analysis

Vanasse, Thomas J.; Fox, P. Mickle; Barron, Daniel S.; Robertson, Michaela; Eickhoff, Simon B.; Lancaster, Jack L.; Fox, Peter T.

doi:10.1002/hbm.24078

Cited by 73 publications

(73 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BrainMap is a neuroimaging database which, at the time of this study, (Kotkowski, Price, Fox, Vanasse, & Fox, 2018;Vanasse et al, 2018). We can use this function to independently quantify the degree of structural similarity between neurodegenerative diseases like AD and MetS using a z-score.…”

Section: Behavior Paradigm Class and Disease Analysesmentioning

confidence: 99%

“…Two additional post hoc analyses involving the subdivided large group of 208 participants were performed using identical thresholds and permutations. In this analysis we divided participants by the median age (35.5 years) and grouped them into half samples termed "young" and Vanasse et al, 2018). We can use this function to independently quantify the degree of structural similarity between neurodegenerative diseases like AD and MetS using a z-score.…”

Section: Post Hoc Analysis On Age Effectsmentioning

confidence: 99%

See 1 more Smart Citation

A neural signature of metabolic syndrome

Kotkowski

Price

Franklin

et al. 2019

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

That metabolic syndrome (MetS) is associated with age‐related cognitive decline is well established. The neurobiological changes underlying these cognitive deficits, however, are not well understood. The goal of this study was to determine whether MetS is associated with regional differences in gray‐matter volume (GMV) using a cross‐sectional, between‐group contrast design in a large, ethnically homogenous sample. T1‐weighted MRIs were sampled from the genetics of brain structure (GOBS) data archive for 208 Mexican‐American participants: 104 participants met or exceeded standard criteria for MetS and 104 participants were age‐ and sex‐matched metabolically healthy controls. Participants ranged in age from 18 to 74 years (37.3 ± 13.2 years, 56.7% female). Images were analyzed in a whole‐brain, voxel‐wise manner using voxel‐based morphometry (VBM). Three contrast analyses were performed, a whole sample analysis of all 208 participants, and two post hoc half‐sample analyses split by age along the median (35.5 years). Significant associations between MetS and decreased GMV were observed in multiple, spatially discrete brain regions including the posterior cerebellum, brainstem, orbitofrontal cortex, bilateral caudate nuclei, right parahippocampus, right amygdala, right insula, lingual gyrus, and right superior temporal gyrus. Age, as shown in the post hoc analyses, was demonstrated to be a significant covariate. A further functional interpretation of the structures exhibiting lower GMV in MetS reflected a significant involvement in reward perception, emotional valence, and reasoning. Additional studies are needed to characterize the influence of MetS's individual clinical components on brain structure and to explore the bidirectional association between GMV and MetS.

show abstract

Section: Behavior Paradigm Class and Disease Analysesmentioning

confidence: 99%

Section: Post Hoc Analysis On Age Effectsmentioning

confidence: 99%

A neural signature of metabolic syndrome

Kotkowski

Price

Franklin

et al. 2019

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

show abstract

“…The low split-sample similarity of single-subject maps emphasized the need to apply such classic neuroscience practices when analyzing these explanation maps. Next, we demonstrated that despite the methodological differences, the proposed map shows significant similarity with ALE maps from age VBM meta-analysis study (Vanasse et al, 2018), attesting to its convergence validity. Finally, using regional volumetric measures we demonstrated that brain regions highlighted by our method were found as the ones with the highest influence on the model's prediction, indicating the specificity of the derived maps to the current model.…”

Section: Identifying the Brain Regions Underlying Age Prediction Usinmentioning

confidence: 61%

“…In the current study, we used a published activation likelihood estimation (ALE) meta-analysis of age VBM studies (Vanasse et al, 2018). Here, by utilizing peak reported coordinates from several VBM studies (n = 43), the ALE analysis assigns each voxel the probability that it lies within a reported peak (Laird, Bzdok, Kurth, Fox, & Eickhoff, 2011).…”

Section: Benchmarking the Results Against A Standard Voxel-based Morpmentioning

confidence: 99%

“…In contrast, other methods are limited to a specific set of ROIs or a given brain parcellation that often fails to properly parcellate non-GM regions. We used a published ALE meta-analysis of age VBM studies (n = 15, Vanasse et al, 2018) in which each voxel is assigned with a probability for its location in a reported peak coordinate in one of the studies (Laird et al, 2011). Using this map, we examined whether that ALE value is significantly higher within the regions identified using the threshold explanation map with a permutation test.…”

Section: Benchmarking the Results Against A Standard Voxel-based Morpmentioning

confidence: 99%

See 1 more Smart Citation

From a deep learning model back to the brain - inferring morphological markers and their relation to aging

Levakov

Rosenthal

Shelef

et al. 2019

Preprint

View full text Add to dashboard Cite

Deep convolutional neural networks (CNN) enabled a major leap in image processing tasks including brain imaging analysis. In this work, we present a Deep Learning framework for the prediction of chronological age from structural MRI scans of healthy subjects. Previous findings associate an overestimation of brain age with neurodegenerative disease and higher mortality rates. However, the importance of brain age prediction and its discrepancy from the corresponding chronological age go beyond serving as biomarkers for neurological disorders. Specifically, utilizing CNN analysis to identify and locate brain regions and structures that contribute to the prediction can shed light on the complex multivariate process of brain aging. Previous work examined methods to attribute pixel/voxel-wise contribution to the prediction in a single image, resulting in 'explanation maps' (EM) that were found noisy and unreliable. To address this problem, we developed a novel inference framework for combining these maps across subjects, thus creating a population-based rather than subject-specific map. We apply this method to a CNN ensemble trained on predicting subjects' chronological age from raw anatomical T1 brain images of 10,176 healthy subjects, obtained from various open-source datasets. Evaluating the model on an untouched test set (n = 588) resulted in MAE of 3.07 years and a correlation between the chronological and predicted age of r=0.98. Using the inference method, we revealed that cavities containing CSF, previously found as general atrophy markers, had the highest contribution for age prediction in our model. These were followed by subcortical GM, WM, and finally cortical GM. Comparing these maps derived from different models within the ensemble allowed to assess differences and similarities in the brain regions utilized by the model. To validate our method, we showed that it substantially increases the replicability of the EM as a function of sample size. Moreover, benchmarking our results against a baseline of voxel-based morphometry (VBM) studies revealed a significant overlap. Finally, we demonstrate that the maps highlight brain regions whose volumetric variability contributed the most to the model prediction. Highlights:• CNNs ensemble is shown to estimate "brain age" from sMRI with an MAE of ~3.1 years • A novel framework enables to highlight brain regions contributing to the prediction • This framework results in explanation maps showing consistency with the literature • As sample size increases, these maps show higher inter-sample replicability • CSF cavities reflecting general atrophy were found as a prominent aging biomarker

show abstract

Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities

et al. 2022

View full text Add to dashboard Cite

Identifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression.OBJECTIVE To quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables. DESIGN, SETTING, AND PARTICIPANTSThis was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022.MAIN OUTCOMES AND MEASURES Primary analyses included univariate partial effect size (η 2 ), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status.RESULTS A total of 1809 individuals (861 patients [47.6%] and 948 controls [52.4%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η 2 of 0.004 to 0.017, and distributions overlapped between 87% and 95%, with classification accuracies ranging between 54% and 56% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables.CONCLUSIONS AND RELEVANCE Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.

show abstract

BrainMap VBM: An environment for structural meta‐analysis

Cited by 73 publications

References 88 publications

A neural signature of metabolic syndrome

A neural signature of metabolic syndrome

From a deep learning model back to the brain - inferring morphological markers and their relation to aging

Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities

Contact Info

Product

Resources

About