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John D. Lewis

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

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YNICL Journal 2020 Journal Article

Newborn amygdalar volumes are associated with maternal prenatal psychological distress in a sex-dependent way

Satu J. Lehtola
Jetro J. Tuulari
Noora M. Scheinin
Linnea Karlsson
Riitta Parkkola
Harri Merisaari
John D. Lewis
Vladimir S. Fonov

Maternal psychological distress during pregnancy (PPD) 1 1 prenatal psychosocial distress has been associated with changes in offspring amygdalar and hippocampal volumes. Studies on child amygdalae suggest that sex moderates the vulnerability of fetal brains to prenatal stress. However, this has not yet been observed in these structures in newborns. Newborn studies are crucial, as they minimize the confounding influence of postnatal life. We investigated the effects of maternal prenatal psychological symptoms on newborn amygdalar and hippocampal volumes and their interactions with newborn sex in 123 newborns aged 2–5 weeks (69 males, 54 females). Based on earlier studies, we anticipated small, but statistically significant effects of PPD on the volumes of these structures. Maternal psychological distress was measured at gestational weeks (GW) 2 2 gestational weeks 14, 24 and 34 using Symptom Checklist-90 (SCL-90, anxiety scale) 3 3 Symptom Checklist-90 and Edinburgh Postnatal Depression Scale (EPDS) 4 4 Edinburgh Postnatal Depression Scale questionnaires. Newborn sex was found to moderate the relationship between maternal distress symptoms at GW 24 and the volumes of left and right amygdala. This relationship was negative and significant only in males. No significant main effect or sex-based moderation was found for hippocampal volumes. This newborn study provides evidence for a sex-dependent influence of maternal psychiatric symptoms on amygdalar structural development. This association may be relevant to later psychopathology.

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YNIMG Journal 2019 Journal Article

Cortical and subcortical T1 white/gray contrast, chronological age, and cognitive performance

John D. Lewis
Vladimir S. Fonov
D. Louis Collins
Alan C. Evans
Jussi Tohka

The maturational schedule of typical brain development is tightly constrained; deviations from it are associated with cognitive atypicalities, and are potentially predictive of developmental disorders. Previously, we have shown that the white/gray contrast at the inner border of the cortex is a good predictor of chronological age, and is sensitive to aspects of brain development that reflect cognitive performance. Here we extend that work to include the white/gray contrast at the border of subcortical structures. We show that cortical and subcortical contrast together yield better age-predictions than any non-kernel-based method based on a single image-type, and that the residuals of the improved predictions provide new insight into unevenness in cognitive performance. We demonstrate the improvement in age predictions in two large datasets: the NIH Pediatric Data, with 831 scans of typically developing individuals between 4 and 22 years of age; and the Pediatric Imaging, Neurocognition, and Genetics data, with 909 scans of individuals in a similar age-range. Assessment of the relation of the residuals of these age predictions to verbal and performance IQ revealed correlations in opposing directions, and a principal component analysis of the residuals of the model that best fit the contrast data produced components related to either performance IQ or verbal IQ. Performance IQ was associated with the first principle component, reflecting increased cortical contrast, broadly, with almost no subcortical presence; verbal IQ was associated with the second principle component, reflecting reduced contrast in the basal ganglia and increased contrast in the bilateral arcuate fasciculi.

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YNIMG Journal 2019 Journal Article

Test-retest reliability of Diffusion Tensor Imaging metrics in neonates

Harri Merisaari
Jetro J. Tuulari
Linnea Karlsson
Noora M. Scheinin
Riitta Parkkola
Jani Saunavaara
Tuire Lähdesmäki
Satu J. Lehtola

Diffusion tensor imaging (DTI) has been widely used in children and adults to study the microstructural features of the brain. Its use in neonate brains has been limited. Neonate brains are almost completely unmyelinated, and this together with the tendency for babies to move during a scanning session may affect the reliability of the measurements. Here we divided a 96 direction acquisition into three segments, and analysed the intra scan test-retest reliability for pairs of segments. Each segment was subjected to a rigorous quality control, and from the surviving data we chose 25 diffusion encoding directions from each segment, and assessed the pairwise reliability of the most common DTI metrics. This pairwise reliability was assessed for data from 86 infants. We used tract-based spatial statistics (TBSS), voxelwise and ROI analysis schemes, to see potential differential effects of analysis strategy and post processing on the obtained DTI metrics. We found that intra class correlation coefficient (ICC) values were generally high (ICC > 0. 80). Residual motion in the data, after quality control, was not found to associate with the diffusion metrics. The results indicate that DTI metrics from neonate data can be reliable, even at relatively low angular resolution that are common for neonate scans. The results lend confidence to the use of neonate DTI data in cross sectional and longitudinal analyses in brain white matter skeleton. Future studies should assess the reliability of fiber tracking techniques in neonate data.

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YNIMG Journal 2018 Journal Article

T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance

John D. Lewis
Alan C. Evans
Jussi Tohka

Knowing the maturational schedule of typical brain development is critical to our ability to identify deviations from it; such deviations have been related to cognitive performance and even developmental disorders. Chronological age can be predicted from brain images with considerable accuracy, but with limited spatial specificity, particularly in the case of the cerebral cortex. Methods using multi-modal data have shown the greatest accuracy, but have made limited use of cortical measures. Methods using complex measures derived from voxels throughout the brain have also shown great accuracy, but are difficult to interpret in terms of cortical development. Measures based on cortical surfaces have yielded less accurate predictions, suggesting that perhaps cortical maturation is less strongly related to chronological age than is maturation of deep white matter or subcortical structures. We question this suggestion. We show that a simple metric based on the white/gray contrast at the inner border of the cortex is a good predictor of chronological age. We demonstrate this in two large datasets: the NIH Pediatric Data, with 832 scans of typically developing children, adolescents, and young adults; and the Pediatric Imaging, Neurocognition, and Genetics data, with 760 scans of individuals in a similar age-range. Further, our usage of an elastic net penalized linear regression model reveals the brain regions which contribute most to age-prediction. Moreover, we show that the residuals of age-prediction based on this white/gray contrast metric are not merely random errors, but are strongly related to IQ, suggesting that this metric is sensitive to aspects of brain development that reflect cognitive performance.

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YNIMG Journal 2017 Journal Article

Imaging structural covariance in the development of intelligence

Budhachandra S. Khundrakpam
John D. Lewis
Andrew Reid
Sherif Karama
Lu Zhao
Francois Chouinard-Decorte
Alan C. Evans

Verbal and non-verbal intelligence in children is highly correlated, and thus, it has been difficult to differentiate their neural substrates. Nevertheless, recent studies have shown that verbal and non-verbal intelligence can be dissociated and focal cortical regions corresponding to each have been demonstrated. However, the pattern of structural covariance corresponding to verbal and non-verbal intelligence remains unexplored. In this study, we used 586 longitudinal anatomical MRI scans of subjects aged 6–18 years, who had concurrent intelligence quotient (IQ) testing on the Wechsler Abbreviated Scale of Intelligence. Structural covariance networks (SCNs) were constructed using interregional correlations in cortical thickness for low-IQ (Performance IQ=100±8, Verbal IQ=100±7) and high-IQ (PIQ=121±8, VIQ=120±9) groups. From low- to high-VIQ group, we observed constrained patterns of anatomical coupling among cortical regions, complemented by observations of higher global efficiency and modularity, and lower local efficiency in high-VIQ group, suggesting a shift towards a more optimal topological organization. Analysis of nodal topological properties (regional efficiency and participation coefficient) revealed greater involvement of left-hemispheric language related regions including inferior frontal and superior temporal gyri for high-VIQ group. From low- to high-PIQ group, we did not observe significant differences in anatomical coupling patterns, global and nodal topological properties. Our findings indicate that people with higher verbal intelligence have structural brain differences from people with lower verbal intelligence – not only in localized cortical regions, but also in the patterns of anatomical coupling among widely distributed cortical regions, possibly resulting to a system-level reorganization that might lead to a more efficient organization in high-VIQ group.

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YNIMG Journal 2017 Journal Article

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

Elaheh Moradi
Budhachandra Khundrakpam
John D. Lewis
Alan C. Evans
Jussi Tohka

Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multi-protocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a whole-brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.

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YNIMG Journal 2016 Journal Article

Brain connectivity in normally developing children and adolescents

Budhachandra S. Khundrakpam
John D. Lewis
Lu Zhao
François Chouinard-Decorte
Alan C. Evans

The developing human brain undergoes an astonishing sequence of events that continuously shape the structural and functional brain connectivity. Distinct regional variations in the timelines of maturational events (synaptogenesis and synaptic pruning) occurring at the synaptic level are reflected in brain measures at macroscopic resolution (cortical thickness and gray matter density). Interestingly, the observed brain changes coincide with cognitive milestones suggesting that the changing scaffold of brain circuits may subserve cognitive development. Recent advances in connectivity analysis propelled by graph theory have allowed, on one hand, the investigation of maturational changes in global organization of structural and functional brain networks; and on the other hand, the exploration of specific networks within the context of global brain networks. An emerging picture from several connectivity studies is a system-level rewiring that constantly refines the connectivity of the developing brain.

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YNICL Journal 2015 Journal Article

A greater involvement of posterior brain areas in interhemispheric transfer in autism: fMRI, DWI and behavioral evidences

Elise B. Barbeau
John D. Lewis
Julien Doyon
Habib Benali
Thomas A. Zeffiro
Laurent Mottron

A small corpus callosum (CC) is one of the most replicated neurobiological findings in autism spectrum (AS). However, its effect on interhemispheric (IH) communication is unknown. We combined structural (CC area and DWI), functional (task-related fMRI activation and connectivity analyses) as well as behavioral (Poffenberger and Purdue tasks) measures to investigate IH integration in adult AS individuals of typical intelligence. Despite similar behavioral IH transfer time and performances in bimanual tasks, the CC sub-regions connecting frontal and parietal cortical areas were smaller in AS than in non-AS individuals, while those connecting visual regions were similar. The activation of visual areas was lower in AS than in non-AS individuals during the presentation of visual stimuli. Behavioral IH performances were related to the properties of CC subregions connecting motor areas in non-AS individuals, but to the properties of posterior CC regions in AS individuals. Furthermore, there was greater functional connectivity between visual areas in the AS than in the non-AS group. Levels of connectivity were also stronger in visual than in motor regions in the autistic subjects, while the opposite was true for the non-autistic group. Thus, visual IH transfer plays an important role in visuo-motor tasks in AS individuals. These findings extend the well established enhanced role of perception in autistic cognition to visuo-motor IH information transfer.

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