Xuejun Hao

Columbia University

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Bradley S. Peterson

Children's Hospital of Los Angeles

Ravi Bansal

Children's Hospital of Los Angeles

Dongrong Xu

Chinese Academy of Medical Sciences & Peking Union Medical College

Myrna M. Weissman

Columbia University

Zhishun Wang

New York State Psychiatric Institute

Priya Wickramaratne

New York State Psychiatric Institute

Virginia Warner

Columbia University

Alayar Kangarlu

Columbia University

Ravi Bansal

Children's Hospital of Los Angeles

Amitabh Varshney

University of Maryland, College Park

Cooperative Institutions

Columbia University

224

Universidad Nacional Agraria La Molina

New York State Psychiatric Institute

Columbia University Irving Medical Center

New York Psychoanalytic Society and Institute

Columbia College

Yale University

Icahn School of Medicine at Mount Sinai

Royal College of Physicians

Universidade de São Paulo

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Serotonin signaling modulates the effects of familial risk for depression on cortical thickness

Psychiatry Research Neuroimaging (2016)

Ravi Bansal Bradley S. Peterson Jay A. Gingrich Xuejun Hao Zagaa Odgerel

Endophenotype

5-HTTLPR

Depression

10.1016/j.pscychresns.2016.01.004

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Citations (7)

Brain age prediction and deviations from normative trajectories in the neonatal connectome

bioRxiv (Cold Spring Harbor Laboratory) (2024)

Huili Sun Saloni Mehta Milana Khaitova Bin Cheng Xuejun Hao

Abstract Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain’s maturity relative to normative data. The difference between the individual’s predicted and chronological age—or brain age gap (BAG)—represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We used resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predicted PMA for term and preterm infants. Predicted ages from each modality were correlated. At the network level, nearly all canonical brain networks—even putatively later developing ones—generated accurate PMA prediction. Additionally, BAGs were associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.

10.1101/2024.04.23.590811

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Anatomical Brain Images Alone Can Accurately Diagnose Chronic Neuropsychiatric Illnesses

PLoS ONE (2012)

Ravi Bansal Lawrence H. Staib Andrew F. Laine Xuejun Hao Dongrong Xu

Objective Diagnoses using imaging-based measures alone offer the hope of improving the accuracy of clinical diagnosis, thereby reducing the costs associated with incorrect treatments. Previous attempts to use brain imaging for diagnosis, however, have had only limited success in diagnosing patients who are independent of the samples used to derive the diagnostic algorithms. We aimed to develop a classification algorithm that can accurately diagnose chronic, well-characterized neuropsychiatric illness in single individuals, given the availability of sufficiently precise delineations of brain regions across several neural systems in anatomical MR images of the brain. Methods We have developed an automated method to diagnose individuals as having one of various neuropsychiatric illnesses using only anatomical MRI scans. The method employs a semi-supervised learning algorithm that discovers natural groupings of brains based on the spatial patterns of variation in the morphology of the cerebral cortex and other brain regions. We used split-half and leave-one-out cross-validation analyses in large MRI datasets to assess the reproducibility and diagnostic accuracy of those groupings. Results In MRI datasets from persons with Attention-Deficit/Hyperactivity Disorder, Schizophrenia, Tourette Syndrome, Bipolar Disorder, or persons at high or low familial risk for Major Depressive Disorder, our method discriminated with high specificity and nearly perfect sensitivity the brains of persons who had one specific neuropsychiatric disorder from the brains of healthy participants and the brains of persons who had a different neuropsychiatric disorder. Conclusions Although the classification algorithm presupposes the availability of precisely delineated brain regions, our findings suggest that patterns of morphological variation across brain surfaces, extracted from MRI scans alone, can successfully diagnose the presence of chronic neuropsychiatric disorders. Extensions of these methods are likely to provide biomarkers that will aid in identifying biological subtypes of those disorders, predicting disease course, and individualizing treatments for a wide range of neuropsychiatric illnesses.

Brain morphometry

Brain Anatomy

10.1371/journal.pone.0050698

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Citations (173)

Prenatal exposure to air pollution is associated with altered brain structure, function, and metabolism in childhood

Journal of Child Psychology and Psychiatry (2022)

Bradley S. Peterson Ravi Bansal Siddhant Sawardekar Carlo Nati Eman R. Elgabalawy

Background Prenatal exposure to air pollution disrupts cognitive, emotional, and behavioral development. The brain disturbances associated with prenatal air pollution are largely unknown. Methods In this prospective cohort study, we estimated prenatal exposures to fine particulate matter (PM 2.5 ) and polycyclic aromatic hydrocarbons (PAH), and then assessed their associations with measures of brain anatomy, tissue microstructure, neurometabolites, and blood flow in 332 youth, 6–14 years old. We then assessed how those brain disturbances were associated with measures of intelligence, ADHD and anxiety symptoms, and socialization. Results Both exposures were associated with thinning of dorsal parietal cortices and thickening of postero–inferior and mesial wall cortices. They were associated with smaller white matter volumes, reduced organization in white matter of the internal capsule and frontal lobe, higher metabolite concentrations in frontal cortex, reduced cortical blood flow, and greater microstructural organization in subcortical gray matter nuclei. Associations were stronger for PM 2.5 in boys and PAH in girls. Youth with low exposure accounted for most significant associations of ADHD, anxiety, socialization, and intelligence measures with cortical thickness and white matter volumes, whereas it appears that high exposures generally disrupted these neurotypical brain‐behavior associations, likely because strong exposure‐related effects increased the variances of these brain measures. Conclusions The commonality of effects across exposures suggests PM 2.5 and PAH disrupt brain development through one or more common molecular pathways, such as inflammation or oxidative stress. Progressively higher exposures were associated with greater disruptions in local volumes, tissue organization, metabolite concentrations, and blood flow throughout cortical and subcortical brain regions and the white matter pathways interconnecting them. Together these affected regions comprise cortico‐striato‐thalamo‐cortical circuits, which support the regulation of thought, emotion, and behavior.

10.1111/jcpp.13578

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Citations (56)

Anatomical Abnormalities in Gray and White Matter of the Cortical Surface in Persons with Schizophrenia

PLoS ONE (2013)

Tiziano Colibazzi Bruce E. Wexler Ravi Bansal Xuejun Hao Jun Liu

Background Although schizophrenia has been associated with abnormalities in brain anatomy, imaging studies have not fully determined the nature and relative contributions of gray matter (GM) and white matter (WM) disturbances underlying these findings. We sought to determine the pattern and distribution of these GM and WM abnormalities. Furthermore, we aimed to clarify the contribution of abnormalities in cortical thickness and cortical surface area to the reduced GM volumes reported in schizophrenia. Methods We recruited 76 persons with schizophrenia and 57 healthy controls from the community and obtained measures of cortical and WM surface areas, of local volumes along the brain and WM surfaces, and of cortical thickness. Results We detected reduced local volumes in patients along corresponding locations of the brain and WM surfaces in addition to bilateral greater thickness of perisylvian cortices and thinner cortex in the superior frontal and cingulate gyri. Total cortical and WM surface areas were reduced. Patients with worse performance on the serial-position task, a measure of working memory, had a higher burden of WM abnormalities. Conclusions Reduced local volumes along the surface of the brain mirrored the locations of abnormalities along the surface of the underlying WM, rather than of abnormalities of cortical thickness. Moreover, anatomical features of white matter, but not cortical thickness, correlated with measures of working memory. We propose that reductions in WM and smaller total cortical surface area could be central anatomical abnormalities in schizophrenia, driving, at least partially, the reduced regional GM volumes often observed in this illness.

10.1371/journal.pone.0055783

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Citations (22)

Table of Contents

Biological Psychiatry (2017)

Panos Zanos Todd D. Gould Dan J. Stein Petrus J.W. Naudé Michael Berk

Table (database)

10.1016/s0006-3223(17)32164-9

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Citations (0)

Putamen Structure and Function in Familial Risk for Depression: A Multimodal Imaging Study

Biological Psychiatry (2022)