Biology subjects

Sotiras, A.

Publications and source records attributed to Sotiras, A..

16 recordsLinked to original sources

bioRxiv2026

Simultaneous Real-time Imaging of Neurofluid and Neurovascular Dynamics Using Ultrafast Flow-weighted Echo-Planar Imaging

Cerebrospinal fluid (CSF) circulation is tightly coupled to cerebral blood flow under the fixed-volume constraint of the cranial vault, with cardiac pulsations and respiration acting as dominant physiological drivers. Disruption of these flow dynamics has been implicated in various neurological disorders, motivating the need for imaging methods that capture CSF and vascular flow simultaneously and in real time. Conventional phase contrast MRI (PC-MRI) provides quantitative CSF velocity measurements but relies on cardiac gating and velocity encoding, which limit temporal resolution, dynamic range, and sensitivity to non-cardiac fluctuations. Here, we introduce self-gated ultrafast real-time flow-weighted echo-planar imaging (SURF-EPI), an inflow-weighted approach in which signal intensity reflects the replacement of saturated spins by freshly inflowing, unsaturated water molecules, yielding higher signal with higher flow. This allows simultaneous assessment of arterial inflow, venous outflow, and CSF motion at high temporal resolution. SURF-EPI was acquired at the C2-C3 spinal level, enabling real-time imaging of CSF flow dynamics in the cervical spinal canal alongside arterial and venous blood flow in major cervical vessels (frame-rate: 21.7 Hz). In addition to frequency-domain analyses, we leveraged intrinsic arterial signal fluctuations as a timing reference to reconstruct cardiac-resolved CSF dynamics for individual cardiac cycles without external physiological recordings. Frequency-domain analysis revealed distinct spectral signatures in CSF flow compared with neurovascular flow, including broadened cardiac peaks and enhanced respiratory modulation, particularly within the ventral spinal canal. In contrast, dorsal CSF showed increased power within the cardiac frequency band, higher coherence with cervical vasculature at the cardiac frequency, and reduced non-cardiac contributions. Time-domain analysis showed strong correlation between CSF flow waveforms derived from SURF-EPI and PC-MRI. Beyond ensemble-averaged waveforms, SURF-EPI enabled beat-to-beat analysis, revealing substantial cycle-to-cycle variability in CSF flow that is not captured by time-averaged gated approaches. Together, these findings establish SURF-EPI as a rapid, complementary framework to PC-MRI, enabling real-time neurofluid imaging with integrated time- and frequency-domain characterization of CSF and neurovascular flow dynamics.

neuroscience↗

bioRxiv2024

Multimodal normative modeling in Alzheimer Disease with introspective variational autoencoders

Normative models in neuroimaging learn patterns of healthy brain distributions to identify deviations in disease subjects, such as those with Alzheimers Disease (AD). This study addresses two key limitations of variational autoencoder (VAE)-based normative models: (1) VAEs often struggle to accurately model healthy control distributions, resulting in high reconstruction errors and false positives, and (2) traditional multimodal aggregation methods, like Product-of-Experts (PoE) and Mixture-of-Experts (MoE), can produce uninformative latent representations. To overcome these challenges, we developed a multimodal introspective VAE that enhances normative modeling by achieving more precise representations of healthy anatomy in both the latent space and reconstructions. Additionally, we implemented a Mixture-of-Product-of-Experts (MOPOE) approach, leveraging the strengths of PoE and MoE to efficiently aggregate multimodal information and improve abnormality detection in the latent space. Using multimodal neuroimaging biomarkers from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset, our proposed multimodal introspective VAE demonstrated superior reconstruction of healthy controls and outperformed baseline methods in detecting outliers. Deviations calculated in the aggregated latent space effectively integrated complementary information from multiple modalities, leading to higher likelihood ratios. The model exhibited strong performance in Out-of-Distribution (OOD) detection, achieving clear separation between control and disease cohorts. Additionally, Z-score deviations in specific latent dimensions were mapped to feature-space abnormalities, enabling interpretable identification of brain regions associated with AD pathology.

neuroscience↗

bioRxiv2024

Brain functional connectivity, but not neuroanatomy, captures the interrelationship between sex and gender in preadolescents

Understanding sex differences in the adolescent brain is crucial, as these differences are linked to neurological and psychiatric conditions that vary between males and females. Predicting sex from adolescent brain data may offer valuable insights into how these variations shape neurodevelopment. Recently, attention has shifted toward exploring socially-identified gender, distinct from sex assigned at birth, recognizing its additional explanatory power. This study evaluates whether resting-state functional connectivity (rsFC) or cortical thickness more effectively predicts sex and sex/gender alignment (the congruence between sex and gender) and investigates their interrelationship in preadolescents. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we employed machine learning to predict both sex (assigned at birth) and sex/gender alignment from rsFC and cortical thickness. rsFC predicted sex with significantly higher accuracy (86%) than cortical thickness (75%) and combining both did not improve the rsFC models accuracy. Brain regions most effective in predicting sex belonged to association (default mode, dorsal attention, and parietal memory) and visual (visual and medial visual) networks. The rsFC sex classifier trained on sex/gender aligned youth was significantly more effective in classifying unseen youth with sex/gender alignment than in classifying unseen youth with sex/gender unalignment. In females, the degree to which their brains rsFC matched a sex profile (female or male), was positively associated with the degree of sex/gender alignment. Lastly, neither rsFC nor cortical thickness predicted sex/gender alignment. These findings highlight rsFCs predictive power in capturing the relationship between sex and gender and the complexity of the interplay between sex, gender, and the brains functional connectivity and neuroanatomy.

neuroscience↗

bioRxiv2023

Framewise multi-echo distortion correction for superior functional MRI

Functional MRI (fMRI) data are severely distorted by magnetic field (B0) inhomogeneities which currently must be corrected using separately acquired field map data. However, changes in the head position of a scanning participant across fMRI frames can cause changes in the B0 field, preventing accurate correction of geometric distortions. Additionally, field maps can be corrupted by movement during their acquisition, preventing distortion correction altogether. In this study, we use phase information from multi-echo (ME) fMRI data to dynamically sample distortion due to fluctuating B0 field inhomogeneity across frames by acquiring multiple echoes during a single EPI readout. Our distortion correction approach, MEDIC (Multi-Echo DIstortion Correction), accurately estimates B0 related distortions for each frame of multi-echo fMRI data. Here, we demonstrate that MEDICs framewise distortion correction produces improved alignment to anatomy and decreases the impact of head motion on resting-state functional connectivity (RSFC) maps, in higher motion data, when compared to the prior gold standard approach (i.e., TOPUP). Enhanced framewise distortion correction with MEDIC, without the requirement for field map collection, furthers the advantage of multi-echo over single-echo fMRI.

bioengineering↗

bioRxiv2023

Normative Modeling Using Multimodal Variational Autoencoders to Identify Individual Heterogeneity in Alzheimer Disease

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSPrevious studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. METHODSWe selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN. RESULTSGreater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. DISCUSSIONSubject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.

neuroscience↗

bioRxiv2023

The Spatial Patterns and Determinants of Cerebrospinal Fluid Circulation in the Human Brain

The circulation of cerebrospinal fluid (CSF) is essential for maintaining brain homeostasis and clearance, and impairments in its flow can lead to various brain disorders. Recent studies have shown that CSF circulation can be interrogated using low b-value diffusion magnetic resonance imaging (low-b dMRI). Nevertheless, the spatial organization of intracranial CSF flow dynamics remains largely elusive. Here, we developed a whole-brain voxel-based analysis framework, termed CSF pseudo-diffusion spatial statistics (C{Psi}SS), to examine CSF mean pseudo-diffusivity (M{Psi}), a measure of CSF flow magnitude derived from low-b dMRI. We showed that intracranial CSF M{Psi} demonstrates characteristic covariance patterns by employing seed-based correlation analysis. Importantly, we applied non-negative matrix factorization analysis to further elucidate the covariance patterns of CSF M{Psi} in a hypothesis-free, data-driven way. We identified distinct CSF spaces that consistently displayed unique pseudo-diffusion characteristics across multiple imaging datasets. Our study revealed that age, sex, brain atrophy, ventricular anatomy, and cerebral perfusion differentially influence M{Psi} across these CSF spaces. Notably, individuals with anomalous CSF flow patterns displayed incidental findings on multimodal neuroradiological examinations. Our work sets forth a new paradigm to study CSF flow, with potential applications in clinical settings.

neuroscience↗

bioRxiv2023

Development of White Matter Fiber Covariance Networks Supports Executive Function in Youth

The white matter architecture of the human brain undergoes substantial development throughout childhood and adolescence, allowing for more efficient signaling between brain regions that support executive function. Increasingly, the field understands grey matter development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. While white matter development also appears asynchronous, previous studies have largely relied on anatomical atlases to characterize white matter tracts, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Here, we leveraged advances in diffusion modeling and unsupervised machine learning to delineate white matter fiber covariance networks comprised of structurally similar areas of white matter in a cross-sectional sample of 939 youth aged 8-22 years. We then evaluated associations between fiber covariance network structural properties with both age and executive function using generalized additive models. The identified fiber covariance networks aligned with the known architecture of white matter while simultaneously capturing novel spatial patterns of coordinated maturation. Fiber covariance networks showed heterochronous increases in fiber density and cross section that generally followed hierarchically organized temporal patterns of cortical development, with the greatest increases in unimodal sensorimotor networks and the most prolonged increases in superior and anterior transmodal networks. Notably, we found that executive function was associated with structural features of limbic and association networks. Taken together, this study delineates data-driven patterns of white matter network development that support cognition and align with major axes of brain maturation.

neuroscience↗

bioRxiv2022

Genetic, clinical underpinnings of subtle early brain change along Alzheimer's dimensions

Alzheimers disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised clustering technique known as Surreal-GAN, through which we identified two dominant dimensions of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE {varepsilon}4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE {varepsilon}4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction - driven by genes different from APOE - which may collectively contribute to the early pathogenesis of AD.

bioengineering↗

bioRxiv2022

Neurodevelopmental Patterns of Early Postnatal White Matter Maturation Represent Distinct Underlying Microstructure and Histology

During the early postnatal period, cerebral white matter undergoes rapid maturation through a complex series of interrelated cellular and histogenetic processes. Accurately quantifying these processes is important for improving understanding of early brain development, developmental abnormalities related to prematurity, and neurodevelopmental diseases. Past efforts have used magnetic resonance imaging (MRI) to track these developmental processes in vivo. However, most previous studies have relied on single imaging modality data and have often been limited by small samples and analytics that do not evaluate complex multivariate imaging patterns. Here, we applied an advanced unsupervised multivariate pattern analysis technique, non-negative matrix factorization (NMF), to T2w/T1w signal ratio maps from a large cohort of newborns (Developing Human Connectome Project [dHCP], n=342), revealing patterns of synchronous white matter maturation. These patterns showed divergent age-related maturational trajectories and differential susceptibility to premature birth, which were replicated in an independent large sample of newborns (Early Life Adversity, Biological Embedding, and Risk for Developmental Precursors of Mental Disorders [eLABE], n=239). Furthermore, we showed that T2w/T1w signal variations in white matter maturational patterns are explained by differential contributions of white matter microstructure indices (i.e., free water content and neurite density index) derived from neurite orientation dispersion and density imaging (NODDI) modeling of diffusion-weighted MRI. Finally, we demonstrated how white matter maturation patterns relate to distinct histological features by comparing our findings with postmortem late fetal/early postnatal brain tissue staining. Together, these results delineate a novel MRI representation of white matter microstructural and histological reorganization during the early postnatal development.

neuroscience↗

bioRxiv2021

Alcohol Use Disrupts Age-Appropriate Cortical Thinning in Adolescence: A Data Driven Approach

ObjectiveCortical thickness changes dramatically during development and is influenced by adolescent drinking. However, previous findings have been inconsistent and limited by region-of-interest approaches that are underpowered because they do not conform to the underlying heterogeneity from the effects of alcohol. MethodsAdolescents (n=657; 12-22 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) who endorsed little to no alcohol use at baseline were assessed with structural MRI and followed longitudinally at four yearly intervals. Seven unique spatially covarying patterns of cortical thickness were obtained from the baseline scans by applying a novel data-driven method called non-negative matrix factorization (NMF). The cortical thickness maps of all participants longitudinal scans were projected onto vertex-level cortical patterns to obtain participant-specific coefficients for each pattern. Linear mixed-effects models were fit to each pattern to investigate longitudinal effects of alcohol consumption on cortical thickness. ResultsIn most NMF-derived cortical thickness patterns, the longitudinal rate of decline in no/low drinkers was similar for all age cohorts, among moderate drinkers the decline was faster in the younger cohort and slower in the older cohort, among heavy drinkers the decline was fastest in the younger cohort and slowest in the older cohort (FDR corrected p-values < 0.01). ConclusionsThe NMF method can delineate spatially coordinated patterns of cortical thickness at the vertex level that are unconstrained by anatomical features. Age-appropriate cortical thinning is more rapid in younger adolescent drinkers and slower in older adolescent drinkers.

neuroscience↗

bioRxiv2021

Multi-scale semi-supervised clustering of brain images: deriving disease subtypes

Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity for stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by anatomical and functional variations not related to a disease or pathology of interest. Semi-supervised clustering techniques have been proposed to overcome this and, therefore, capture disease-specific patterns more effectively. An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data using a basis of feature sets pre-defined at a fixed anatomical or functional scale (e.g., atlas-based regions of interest). Herein we propose a novel method, "Multi-scAle heteroGeneity analysIs and Clustering" (MAGIC), to depict the multi-scale presentation of disease heterogeneity, which builds on a previously proposed semi-supervised clustering method, HYDRA. It derives multi-scale and clinically interpretable feature representations and exploits a double-cyclic optimization procedure to effectively drive identification of inter-scale-consistent disease subtypes. More importantly, to understand the conditions under which the clustering model can estimate true heterogeneity related to diseases, we conducted extensive and systematic semi-simulated experiments to evaluate the proposed method on a sizeable healthy control sample from the UK Biobank (N=4403). We then applied MAGIC to imaging data from Alzheimers disease (ADNI, N=1728) and schizophrenia (PHENOM, N=1166) patients to demonstrate its potential and challenges in dissecting the neuroanatomical heterogeneity of common brain diseases. Taken together, we aim to provide guidance regarding when such analyses can succeed or should be taken with caution. The code of the proposed method is publicly available at https://github.com/anbai106/MAGIC. HighlightsO_LIWe propose a novel multi-scale semi-supervised clustering method, termed MAGIC, to disentangle the heterogeneity of brain diseases. C_LIO_LIWe perform extensive semi-simulated experiments on large control samples (UK Biobank, N=4403) to precisely quantify performance under various conditions, including varying degrees of brain atrophy, different levels of heterogeneity, overlapping disease subtypes, class imbalance, and varying sample sizes. C_LIO_LIWe apply MAGIC to MCI and Alzheimers disease (ADNI, N=1728) and schizophrenia (PHENOM, N=1166) patients to dissect their neuroanatomical heterogeneity, providing guidance regarding the use of the semi-simulated experiments to validate the subtypes found in actual clinical applications. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC="FIGDIR/small/440501v2_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@2769e9org.highwire.dtl.DTLVardef@19a691borg.highwire.dtl.DTLVardef@6a9264org.highwire.dtl.DTLVardef@b0e686_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Using synthetic MR images for field map-less distortion correction

Functional MRI (fMRI) data acquired using echo-planar imaging (EPI) are highly distorted by magnetic field inhomogeneities. Distortion combined with underlying differences in image contrast between EPI and T1-weighted and T2-weighted (T1w/T2w) structural images makes the alignment of functional and anatomical images a challenge. Typically, separately acquired field map data are used to correct fMRI distortions and a flexible cost function insensitive to cross-modal differences in image contrast and intensity is used for aligning fMRI and anatomical images. The quality of alignment achieved with this approach can vary greatly and depends on the quality of field map data. In addition, many publicly available datasets lack field map data entirely. To address this issue, we developed Synth, a software package for distortion correction and cross-modal image registration that does not require separately acquired field map data. Synth combines information from T1w and T2w anatomical images to construct an idealized undistorted synthetic image that has similar contrast properties to fMRI data. The undistorted synthetic image then serves as an effective reference for individual-specific nonlinear unwarping to correct fMRI distortions. We demonstrate, in both pediatric (ABCD: Adolescent Brain Cognitive Development) and adult (MSC: Midnight Scan Club) data that Synth performs comparably well to other leading distortion correction approaches that utilize field map data, and often outperforms them. Field map-less distortion correction with Synth allows accurate and precise registration of fMRI data with missing or corrupted field map information.

neuroscience↗

bioRxiv2019

Neurocognitive and Functional Heterogeneity in Depressed Youth

OBJECTIVEDepression is a common psychiatric illness that often begins in youth, and is associated with cognitive symptoms. However, there is significant variability in the cognitive burden, likely reflecting biological heterogeneity. This study sought to identify neurocognitive subtypes in a large sample of depressed youth, and evaluated the neural signatures of these subtypes.\n\nMETHODSParticipants were drawn from the Philadelphia Neurodevelopmental Cohort, including 712 youth with a lifetime history of a major depressive episode and 712 typically developing (TD) youth matched on age and sex. A subset (n=368, TD=200) also completed neuroimaging. Cognition was assessed with the Penn Computerized Neurocognitive Battery. A semi-supervised machine-learning algorithm, HYDRA (Heterogeneity through Discriminative Analysis), was used to delineate neurocognitive subtypes. Subtypes were evaluated for differences in both clinical psychopathology and brain activation during an n-back working memory fMRI task.\n\nRESULTSHYDRA identified three neurocognitive subtypes in the depressed group. Overall, Subtype 1 had better performance than TD comparators across many cognitive tasks (high accuracy, moderate speed), Subtype 2 was cognitively impaired (low accuracy, slow speed), whereas Subtype 3 was impulsive (low accuracy, fast speed). While subtypes did not differ in clinical psychopathology, they diverged in their activation profiles in regions critical for executive function, which mirrored differences in cognition.\n\nCONCLUSIONSUsing a data-driven approach, three neurocognitive subtypes of depression were identified that differed in neural signatures despite similar clinical psychopathology. These data suggest disparate mechanisms of cognitive vulnerability and resilience in depression, which may inform the identification of biomarkers for prognosis and treatment response.

neuroscience↗

bioRxiv2019

Neurostructural Heterogeneity in Youth with Internalizing Symptoms

Internalizing disorders such as anxiety and depression are the most common psychiatric disorders, frequently begin in youth, and exhibit marked heterogeneity in treatment response and clinical course. It is increasingly recognized that symptom-based classification approaches to internalizing disorders do not align with underlying neurobiology. An alternative to classifying psychopathology based on clinical symptoms is to identify neurobiologically-informed subtypes based on brain imaging data. We used a recently developed semi-supervised machine learning method (HYDRA) to delineate patterns of neurobiological heterogeneity within youth with internalizing symptoms using structural imaging data collected at 3T from a large community-based sample of 1,141 youth. Using volume and cortical thickness, cross-validation methods indicated a highly stable solution (ARI=.66; permutation-based pfdr < .001) and identified two subtypes of internalizing youth. Subtype 1, defined by smaller brain volumes and reduced cortical thickness, was marked by impaired cognitive performance and higher levels of psychopathology than both Subtype 2 and typically developing youth. Using resting-state fMRI and diffusion images not considered during clustering, we found that Subtype 1 also showed reduced amplitudes of low-frequency fluctuations in fronto-limbic regions at rest, as well as reduced fractional anisotropy in white matter tracts such as the parahippocampal cingulum bundle and the uncinate fasciculus. In contrast, Subtype 2 showed intact cognitive performance, greater volume, cortical thickness, and amplitudes during rest compared to Subtype 1 and typically developing youth, despite still showing clinically significant levels of psychopathology. Identification of biologically-grounded subtypes of internalizing disorders may assist in targeting early interventions and assessing longitudinal prognosis.

neuroscience↗

bioRxiv2019

Accelerated Cortical Thinning within Structural Brain Networks is Associated with Irritability in Youth

BackgroundIrritability is an important dimension of psychopathology that spans multiple clinical diagnostic categories, yet its relationship to patterns of brain development remains sparsely explored. Here, we examined how trans-diagnostic symptoms of irritability relate to the development of structural brain networks.\n\nMethodsAll participants (n=144, 87 females) completed structural brain imaging with 3 Tesla MRI at two timepoints (mean age at follow-up: 20.9 years, mean inter-scan interval: 5.1 years). Irritability at follow-up was assessed using the Affective Reactivity Index, and cortical thickness was quantified using Advanced Normalization Tools software. Structural covariance networks were delineated using non-negative matrix factorization, a multivariate analysis technique. Both cross-sectional and longitudinal associations with irritability at follow-up were evaluated using generalized additive models with penalized splines. The False Discovery Rate (q<0.05) was used to correct for multiple comparisons.\n\nResultsCross-sectional analysis of follow-up data revealed that 11 of the 24 covariance networks were associated with irritability, with higher levels of irritability being associated with thinner cortex. Longitudinal analyses further revealed that accelerated cortical thinning within 9 networks was related to irritability at follow-up. Effects were particularly prominent in brain regions implicated in emotion regulation, including the orbitofrontal, lateral temporal, and medial temporal cortex.\n\nConclusionsCollectively, these findings suggest that irritability is associated with widespread cortical thickness reductions and accelerated cortical thinning, particularly within frontal and temporal cortex. Aberrant structural maturation of regions important for emotional regulation may in part underlie symptoms of irritability.

neuroscience↗

bioRxiv2017

Diminished Cortical Thickness is Associated with Impulsive Choice in Adolescence

Adolescence is characterized by both maturation of brain structure and increased risk of negative outcomes from behaviors associated with impulsive decision-making. One important index of impulsive choice is delay discounting (DD), which measures the tendency to prefer smaller rewards available soon over larger rewards delivered after a delay. However, it remains largely unknown how individual differences in structural brain development may be associated with impulsive choice during adolescence. Leveraging a unique large sample of 427 human youths (208 males and 219 females) imaged as part of the Philadelphia Neurodevelopmental Cohort, we examined associations between delay discounting and cortical thickness within structural covariance networks. These structural networks were derived using non-negative matrix factorization, an advanced multivariate technique for dimensionality reduction, and analyzed using generalized additive models with penalized splines to capture both linear and nonlinear developmental effects. We found that impulsive choice, as measured by greater discounting, was most strongly associated with diminished cortical thickness in structural brain networks that encompassed the ventromedial prefrontal cortex, orbitofrontal cortex, temporal pole, and temporoparietal junction. Furthermore, structural brain networks predicted DD above and beyond cognitive performance. Taken together, these results suggest that reduced cortical thickness in regions known to be involved in value-based decision-making is a marker of impulsive choice during the critical period of adolescence.\n\nSIGNIFICANCERisky behaviors during adolescence, such as initiation of substance use or reckless driving, are a major source of morbidity and mortality. In this study, we present evidence from a large sample of youths that diminished cortical thickness in specific structural brain networks is associated with impulsive choice. Notably, the strongest association between impulsive choice and brain structure was seen in regions implicated in value-based decision-making; namely, the ventromedial prefrontal and orbitofrontal cortices. Moving forward, such neuroanatomical markers of impulsivity may aid in the development of personalized interventions targeted to reduce risk of negative outcomes resulting from impulsivity during adolescence.

neuroscience↗