- Email: [email protected]
Toward a Multimodal, Multiscale Understanding of White Matter Abnormalities in Autism Spectrum Disorder
Early Career Investigator Commentary
Biological Psychiatry
Toward a Multimodal, Multiscale Understanding of White Matter Abnormalities in Autism Spectrum Disorder Jason W. Bohland Convergent evidence suggests that autism spectrum disorder (ASD) is manifest in anatomic changes to the brain during development that affect neural connectivity. Autism as a disconnection syndrome has been discussed for some time (1), and recent effort has focused on measuring and characterizing aberrant structural and functional connectivity in the disorder. For practical reasons, most of these studies were conducted in older children or adults. However, gross measures, such as head circumference and brain volume, suggest a change in the phenotypic profile from early childhood, when children with ASD have increased brain size relative to control subjects, to older childhood and adulthood, when the effect is not seen (2). Thus, there is strong motivation to expand the study of ASD characteristics in very young cohorts to better understand the developmental trajectory of the disease. Moreover, the rapid increase in ASD incidence, estimated to affect 1 in 68 8-year-old American children (3), suggests an urgent need for improved understanding of the pathologic features and especially the early developmental pathophysiology to assist early diagnosis, identify high-risk individuals, and inform therapeutic options. In this issue of Biological Psychiatry, Solso et al. (4) provide convincing evidence for irregular developmental trajectories in frontal fiber tracts in young children 1–4 years old with autism. This investigation included what may be the largest toddler ASD cohort in a diffusion imaging study to date (N 5 61), a subset of whom (n 5 14) was scanned twice approximately 1 year apart. With data from this population, the researchers have an exceptional opportunity to capture early alterations in brain development that may pinpoint root causes of ASD behaviors and provide a basis for assisting early diagnosis. The fundamental result of this work showed that average fractional anisotropy (FA) values within several fiber tracts that innervate the frontal lobes (forceps minor, inferior frontal– superior frontal tract, uncinate tract, and arcuate fasciculus) were significantly higher in children with ASD than in control subjects among the youngest study participants, with three of these pathways also showing increased tract volume. Furthermore, for the older children with ASD—even within this very young sample—the increased FA and volume values disappeared or showed signs of reversal, reflecting distinct trajectories of white matter (WM) development for children with ASD and control subjects. Fiber bundles interconnecting the posterior cortices did not show the same pattern of altered FA, suggesting the critical importance of frontal lobe development during the time period in which ASD symptoms and diagnoses stabilize. The measures from frontal fiber tracts in the youngest participants (12–28 months old), particularly from the
arcuate fasciculus, were also positively related to the severity of deficits at the age of final diagnosis. Although these results are correlative only, it is possible that such imaging measures indicating early WM abnormalities might provide needed early biomarkers and prognostic indicators for ASD. The overall pattern of results in the study by Solso et al. (4) is largely consistent with previous imaging studies in young children [children with mean age ,30 months are reviewed by Conti et al. (5)], which demonstrate abnormally increased FA in commissural fibers, long-range association tracts, and thalamocortical projection fibers. These results are in contrast to multiple studies in older subjects, which show lower FA across various WM structures, potentially suggesting a steady-state reduction in long-range connectivity in individuals with ASD (6). These results and others converge on a general hypothesis that, compared with typically developing children, children with ASD have early aberrant developmental processes affecting neuronal and axonal growth and proliferation, followed by blunted developmental trajectories. The distributed occurrence of abnormalities in fiber tract FA and volume measures—impacting broad fiber pathways connecting multiple systems—is consistent with the complexity and heterogeneity of ASD and its frequent comorbidity with other conditions, such as language impairment, attentiondeficit hyperactivity disorder, and intellectual disability. The arcuate fasciculus, where FA deviations were most correlated with eventual symptom severity, links inferior frontal and superior temporal lobe structures and is critical for language function. Abnormalities in early arcuate development may help explain core ASD language deficits and comorbidity with other language-related phenotypes. Because the measures used (4) were coarse averages across voxels comprising large fiber bundles, it is impossible to infer deviant development of specific connections between cortical areas or to compare the development of specific neural circuits in ASD with other developmental disorders, but these are directions of interest for future work. Diffusion-weighted imaging provides indirect measures related to anatomic connectivity by estimating the bulk effect of the diffusion of water molecules within macroscopic voxels. Because the local diffusion coefficient is much larger along the direction of fibers than across them, the signal can offer information about the strength and orientation of local fiber tracts. FA then accumulates the orientation tensors into a local scalar value describing the anisotropy of diffusion in a given voxel, ranging from 0 (equally restricted in all directions) to 1 (diffusion only along one axis). Changes in FA observed between samples may be due to differences in axon density,
SEE CORRESPONDING ARTICLE ON PAGE 676
http://dx.doi.org/10.1016/j.biopsych.2016.02.020 ISSN: 0006-3223
& 2016 Society of Biological Psychiatry e47 Biological Psychiatry April 15, 2016; 79:e47–e48 www.sobp.org/journal
Biological Psychiatry
Early Career Investigator Commentary
diameter, degree of myelination, membrane permeability, or the coherence of axon orientation within a voxel (7), each of which samples potentially tens of thousands of axons with a spectrum of structural properties. Caution is warranted in interpretation because a large number of different structural landscapes could give rise to similar FA profiles. Histologic studies in postmortem human brain tissue, although rare, provide enhanced resolution, which helps resolve ambiguity inherent in macroscale neuroimaging data. Results from a small postmortem sample of children (2–16 years old) with ASD suggest an excess number of neurons (and hence axons) in prefrontal cortex, which might underlie increased FA values, but these results do not explain the normalization of these FA values by age 4 observed by Solso et al. (4). Ultrastructural investigation of postmortem adult brain tissue has revealed differences between the axonal profiles of individuals with ASD and control subjects (8). Specifically, a reduction in large-diameter axons, which form long-range projections, concomitant with an increase in thin axon fibers that link neighboring areas, were observed in the WM beneath the anterior cingulate cortex, whereas decreased myelin thickness was observed in fibers beneath the orbitofrontal cortex. Determining whether such abnormalities are present early in development and how the statistical profiles of axon structure and arrangement change over time would be valuable in interpreting lower resolution imaging results. Both macroanatomic studies using noninvasive neuroimaging and microanatomic and ultrastructural investigations in mouse models and in postmortem human brain tissue are valuable for enhancing understanding of ASD pathophysiology. However, there is a large gap between these levels of analysis, suggesting the need for computational techniques to help integrate and synthesize current information. The tools necessary to relate the voxel-level measures (FA and others, such as apparent diffusion coefficient) obtained from diffusion imaging studies in human subjects and the microscopic measures obtained from postmortem tissue are largely nonexistent. Models that predict measures observable from magnetic resonance imaging from measured or hypothesized high-resolution statistical profiles of WM tissue properties could help tease apart alternative theories and generate specific testable hypotheses. Relatedly, any such efforts to integrate results from multiple studies across multiple modalities hinge on effective data sharing and the establishment and effective use of reference databases and standards. The changes in WM trajectories in ASD observed by Solso et al. (4) represent an important phenotype, which may be driven in large part by a complex genetic architecture of risk factors (9). Numerous high-confidence genes are involved in neurite outgrowth, synapse formation, and synaptic plasticity, whose products may ultimately affect neural connectivity. To help bridge the genotype-phenotype gap, studies of the spatiotemporal patterns of expression of implicated gene networks may provide new clues for how genetic variants give rise to anatomically localized phenotypes seen in imaging studies, providing a clearer picture of the mechanisms underlying ASD. For example, is there an intermediate transcriptomic phenotype that suggests why anterior but not posterior fiber pathways appear to develop abnormally in young
e48
children with ASD? Such efforts to integrate the neuroanatomic patterns of gene expression available from public databases with results from brain imaging are under way, for example, in the study of heritable language disorders (10). In conclusion, the imaging results described by Solso et al. (4) provide important new evidence for changes in brain WM observable at or even potentially before (11) the emergence of clinical signs. Increasing the sample size of young participants and sharing these data sets will open the doors to establishing sensitive and specific biomarkers. As researchers continue to unravel the complexity of ASD, efforts to integrate and synthesize these findings with findings from postmortem anatomy and from genetic and genomic studies will be critical to establishing a multimodal, multiscale understanding of this disorder.
Acknowledgments and Disclosures Early Career Investigator Commentaries are solicited in partnership with the Education Committee of the Society of Biological Psychiatry. As part of the educational mission of the Society, all authors of such commentaries are mentored by a senior investigator. This work was mentored by Helen Barbas. I thank Dr. Helen Barbas for her mentorship during the preparation of this commentary. The author reports no biomedical financial interests or potential conflicts of interest.
Article Information From the Department of Health Sciences, College of Health & Rehabilitation Sciences: Sargent College, Boston University, Boston, Massachusetts. Address correspondence to Jason W. Bohland, Ph.D., 635 Commonwealth Avenue, Room 403, Boston, MA 02215; E-mail: [email protected]. Received Feb 16, 2016; accepted Feb 19, 2016.
References 1. 2.
3.
4.
5.
6.
7.
8. 9. 10.
11.
Frith C (2004): Is autism a disconnection disorder? Lancet Neurol 3:577. Courchesne E, Campbell K, Solso S (2011): Brain growth across the life span in autism: Age-specific changes in anatomical pathology. Brain Res 1380:138–145. Centers for Disease Control and Prevention. 10 things to know about new autism data. Available at: http://www.cdc.gov/features/dsautism data/index.html. Accessed February 15, 2016. Solso S, Xu R, Proudfoot J, Hagler DJ, Campbell K, Venkatraman V, et al. (2016): Diffusion tensor imaging provides evidence of possible axonal overconnectivity in frontal lobes in autism spectrum disorder toddlers. Biol Psychiatry 79:676–684. Conti E, Calderoni S, Marchi V, Muratori F, Cioni G, Guzzetta A (2015): The first 1000 days of the autistic brain: A systematic review of diffusion imaging studies. Front Hum Neurosci 9:159. Just MA, Keller TA, Malave VL, Kana RK, Varma S (2012): Autism as a neural systems disorder: A theory of frontal-posterior underconnectivity. Neurosci Biobehav Rev 36:1292–1313. Jones DK, Knösche TR, Turner R (2013): White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. Neuroimage 73:239–254. Zikopoulos B, Barbas H (2010): Changes in prefrontal axons may disrupt the network in autism. J Neurosci 30:14595–14609. Geschwind DH (2011): Genetics of autism spectrum disorders. Trends Cogn Sci 15:409–416. Bohland JW, Myers EM, Kim E (2014): An informatics approach to integrating genetic and neurological data in speech and language neuroscience. Neuroinformatics 12:39–62. Wolff JJ, Gu H, Gerig G, Elison JT, Styner M, Gouttard S, et al. (2012): Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am J Psychiatry 169:589–600.
Biological Psychiatry April 15, 2016; 79:e47–e48 www.sobp.org/journal
Biological Psychiatry
Toward a Multimodal, Multiscale Understanding of White Matter Abnormalities in Autism Spectrum Disorder Jason W. Bohland Convergent evidence suggests that autism spectrum disorder (ASD) is manifest in anatomic changes to the brain during development that affect neural connectivity. Autism as a disconnection syndrome has been discussed for some time (1), and recent effort has focused on measuring and characterizing aberrant structural and functional connectivity in the disorder. For practical reasons, most of these studies were conducted in older children or adults. However, gross measures, such as head circumference and brain volume, suggest a change in the phenotypic profile from early childhood, when children with ASD have increased brain size relative to control subjects, to older childhood and adulthood, when the effect is not seen (2). Thus, there is strong motivation to expand the study of ASD characteristics in very young cohorts to better understand the developmental trajectory of the disease. Moreover, the rapid increase in ASD incidence, estimated to affect 1 in 68 8-year-old American children (3), suggests an urgent need for improved understanding of the pathologic features and especially the early developmental pathophysiology to assist early diagnosis, identify high-risk individuals, and inform therapeutic options. In this issue of Biological Psychiatry, Solso et al. (4) provide convincing evidence for irregular developmental trajectories in frontal fiber tracts in young children 1–4 years old with autism. This investigation included what may be the largest toddler ASD cohort in a diffusion imaging study to date (N 5 61), a subset of whom (n 5 14) was scanned twice approximately 1 year apart. With data from this population, the researchers have an exceptional opportunity to capture early alterations in brain development that may pinpoint root causes of ASD behaviors and provide a basis for assisting early diagnosis. The fundamental result of this work showed that average fractional anisotropy (FA) values within several fiber tracts that innervate the frontal lobes (forceps minor, inferior frontal– superior frontal tract, uncinate tract, and arcuate fasciculus) were significantly higher in children with ASD than in control subjects among the youngest study participants, with three of these pathways also showing increased tract volume. Furthermore, for the older children with ASD—even within this very young sample—the increased FA and volume values disappeared or showed signs of reversal, reflecting distinct trajectories of white matter (WM) development for children with ASD and control subjects. Fiber bundles interconnecting the posterior cortices did not show the same pattern of altered FA, suggesting the critical importance of frontal lobe development during the time period in which ASD symptoms and diagnoses stabilize. The measures from frontal fiber tracts in the youngest participants (12–28 months old), particularly from the
arcuate fasciculus, were also positively related to the severity of deficits at the age of final diagnosis. Although these results are correlative only, it is possible that such imaging measures indicating early WM abnormalities might provide needed early biomarkers and prognostic indicators for ASD. The overall pattern of results in the study by Solso et al. (4) is largely consistent with previous imaging studies in young children [children with mean age ,30 months are reviewed by Conti et al. (5)], which demonstrate abnormally increased FA in commissural fibers, long-range association tracts, and thalamocortical projection fibers. These results are in contrast to multiple studies in older subjects, which show lower FA across various WM structures, potentially suggesting a steady-state reduction in long-range connectivity in individuals with ASD (6). These results and others converge on a general hypothesis that, compared with typically developing children, children with ASD have early aberrant developmental processes affecting neuronal and axonal growth and proliferation, followed by blunted developmental trajectories. The distributed occurrence of abnormalities in fiber tract FA and volume measures—impacting broad fiber pathways connecting multiple systems—is consistent with the complexity and heterogeneity of ASD and its frequent comorbidity with other conditions, such as language impairment, attentiondeficit hyperactivity disorder, and intellectual disability. The arcuate fasciculus, where FA deviations were most correlated with eventual symptom severity, links inferior frontal and superior temporal lobe structures and is critical for language function. Abnormalities in early arcuate development may help explain core ASD language deficits and comorbidity with other language-related phenotypes. Because the measures used (4) were coarse averages across voxels comprising large fiber bundles, it is impossible to infer deviant development of specific connections between cortical areas or to compare the development of specific neural circuits in ASD with other developmental disorders, but these are directions of interest for future work. Diffusion-weighted imaging provides indirect measures related to anatomic connectivity by estimating the bulk effect of the diffusion of water molecules within macroscopic voxels. Because the local diffusion coefficient is much larger along the direction of fibers than across them, the signal can offer information about the strength and orientation of local fiber tracts. FA then accumulates the orientation tensors into a local scalar value describing the anisotropy of diffusion in a given voxel, ranging from 0 (equally restricted in all directions) to 1 (diffusion only along one axis). Changes in FA observed between samples may be due to differences in axon density,
SEE CORRESPONDING ARTICLE ON PAGE 676
http://dx.doi.org/10.1016/j.biopsych.2016.02.020 ISSN: 0006-3223
& 2016 Society of Biological Psychiatry e47 Biological Psychiatry April 15, 2016; 79:e47–e48 www.sobp.org/journal
Biological Psychiatry
Early Career Investigator Commentary
diameter, degree of myelination, membrane permeability, or the coherence of axon orientation within a voxel (7), each of which samples potentially tens of thousands of axons with a spectrum of structural properties. Caution is warranted in interpretation because a large number of different structural landscapes could give rise to similar FA profiles. Histologic studies in postmortem human brain tissue, although rare, provide enhanced resolution, which helps resolve ambiguity inherent in macroscale neuroimaging data. Results from a small postmortem sample of children (2–16 years old) with ASD suggest an excess number of neurons (and hence axons) in prefrontal cortex, which might underlie increased FA values, but these results do not explain the normalization of these FA values by age 4 observed by Solso et al. (4). Ultrastructural investigation of postmortem adult brain tissue has revealed differences between the axonal profiles of individuals with ASD and control subjects (8). Specifically, a reduction in large-diameter axons, which form long-range projections, concomitant with an increase in thin axon fibers that link neighboring areas, were observed in the WM beneath the anterior cingulate cortex, whereas decreased myelin thickness was observed in fibers beneath the orbitofrontal cortex. Determining whether such abnormalities are present early in development and how the statistical profiles of axon structure and arrangement change over time would be valuable in interpreting lower resolution imaging results. Both macroanatomic studies using noninvasive neuroimaging and microanatomic and ultrastructural investigations in mouse models and in postmortem human brain tissue are valuable for enhancing understanding of ASD pathophysiology. However, there is a large gap between these levels of analysis, suggesting the need for computational techniques to help integrate and synthesize current information. The tools necessary to relate the voxel-level measures (FA and others, such as apparent diffusion coefficient) obtained from diffusion imaging studies in human subjects and the microscopic measures obtained from postmortem tissue are largely nonexistent. Models that predict measures observable from magnetic resonance imaging from measured or hypothesized high-resolution statistical profiles of WM tissue properties could help tease apart alternative theories and generate specific testable hypotheses. Relatedly, any such efforts to integrate results from multiple studies across multiple modalities hinge on effective data sharing and the establishment and effective use of reference databases and standards. The changes in WM trajectories in ASD observed by Solso et al. (4) represent an important phenotype, which may be driven in large part by a complex genetic architecture of risk factors (9). Numerous high-confidence genes are involved in neurite outgrowth, synapse formation, and synaptic plasticity, whose products may ultimately affect neural connectivity. To help bridge the genotype-phenotype gap, studies of the spatiotemporal patterns of expression of implicated gene networks may provide new clues for how genetic variants give rise to anatomically localized phenotypes seen in imaging studies, providing a clearer picture of the mechanisms underlying ASD. For example, is there an intermediate transcriptomic phenotype that suggests why anterior but not posterior fiber pathways appear to develop abnormally in young
e48
children with ASD? Such efforts to integrate the neuroanatomic patterns of gene expression available from public databases with results from brain imaging are under way, for example, in the study of heritable language disorders (10). In conclusion, the imaging results described by Solso et al. (4) provide important new evidence for changes in brain WM observable at or even potentially before (11) the emergence of clinical signs. Increasing the sample size of young participants and sharing these data sets will open the doors to establishing sensitive and specific biomarkers. As researchers continue to unravel the complexity of ASD, efforts to integrate and synthesize these findings with findings from postmortem anatomy and from genetic and genomic studies will be critical to establishing a multimodal, multiscale understanding of this disorder.
Acknowledgments and Disclosures Early Career Investigator Commentaries are solicited in partnership with the Education Committee of the Society of Biological Psychiatry. As part of the educational mission of the Society, all authors of such commentaries are mentored by a senior investigator. This work was mentored by Helen Barbas. I thank Dr. Helen Barbas for her mentorship during the preparation of this commentary. The author reports no biomedical financial interests or potential conflicts of interest.
Article Information From the Department of Health Sciences, College of Health & Rehabilitation Sciences: Sargent College, Boston University, Boston, Massachusetts. Address correspondence to Jason W. Bohland, Ph.D., 635 Commonwealth Avenue, Room 403, Boston, MA 02215; E-mail: [email protected]. Received Feb 16, 2016; accepted Feb 19, 2016.
References 1. 2.
3.
4.
5.
6.
7.
8. 9. 10.
11.
Frith C (2004): Is autism a disconnection disorder? Lancet Neurol 3:577. Courchesne E, Campbell K, Solso S (2011): Brain growth across the life span in autism: Age-specific changes in anatomical pathology. Brain Res 1380:138–145. Centers for Disease Control and Prevention. 10 things to know about new autism data. Available at: http://www.cdc.gov/features/dsautism data/index.html. Accessed February 15, 2016. Solso S, Xu R, Proudfoot J, Hagler DJ, Campbell K, Venkatraman V, et al. (2016): Diffusion tensor imaging provides evidence of possible axonal overconnectivity in frontal lobes in autism spectrum disorder toddlers. Biol Psychiatry 79:676–684. Conti E, Calderoni S, Marchi V, Muratori F, Cioni G, Guzzetta A (2015): The first 1000 days of the autistic brain: A systematic review of diffusion imaging studies. Front Hum Neurosci 9:159. Just MA, Keller TA, Malave VL, Kana RK, Varma S (2012): Autism as a neural systems disorder: A theory of frontal-posterior underconnectivity. Neurosci Biobehav Rev 36:1292–1313. Jones DK, Knösche TR, Turner R (2013): White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. Neuroimage 73:239–254. Zikopoulos B, Barbas H (2010): Changes in prefrontal axons may disrupt the network in autism. J Neurosci 30:14595–14609. Geschwind DH (2011): Genetics of autism spectrum disorders. Trends Cogn Sci 15:409–416. Bohland JW, Myers EM, Kim E (2014): An informatics approach to integrating genetic and neurological data in speech and language neuroscience. Neuroinformatics 12:39–62. Wolff JJ, Gu H, Gerig G, Elison JT, Styner M, Gouttard S, et al. (2012): Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am J Psychiatry 169:589–600.
Biological Psychiatry April 15, 2016; 79:e47–e48 www.sobp.org/journal