How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state ...functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.
•FC predicts behavior better than anatomical and diffusion features.•Cognition is predicted better than other behavioral components regardless of modality.•Combining resting & task FC improves ...prediction as much as combining all modalities.•Findings were replicated over 3 regression models and 2 datasets.
A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the ...observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Different parcellation and gradient approaches were compared for RSFC prediction of a broad range of behavioral measures in two datasets.•Across two regression algorithms (LRR and KRR), ...individual-specific hard-parcellation performed the best in the HCP dataset.•Principal gradients and all parcellation approaches performed similarly in the ABCD dataset.•Principal gradient approach required at least 40 to 60 gradients in order to perform as well as parcellation approaches.
Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average “hard” parcellations (Schaefer et al., 2018), individual-specific “hard” parcellations (Kong et al., 2021a), and an individual-specific “soft” parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average “hard” parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Haufe-transformed weights are much more reliable than original regression weights when computing feature importance.•Feature importance reliability is strongly positively correlated with prediction ...accuracy across phenotypes.•We show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error.•We discuss how our theoretical results relate with the reliability of imaging features and behavioral measures.
There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Objectives
Simultaneous multi-slice (SMS) imaging with short repetition time (TR) accelerates diffusion tensor imaging (DTI) acquisitions. However, its impact when combined with readout-segmented ...echo planar imaging (RESOLVE) on the cranial nerves given the challenging skull base/posterior fossa terrain is unexplored. We evaluated the reliability of trigeminal nerve DTI metrics using SMS with RESOLVE-DTI.
Methods
Eight healthy controls and six patients with unilateral trigeminal neuralgia (TN) underwent brain MRI scan. Three different RESOLVE-DTI protocols were performed on a 3-T MRI system: non-SMS (TR = 4330 ms), SMS with identical TR (4330 ms), and SMS with short TR (2400 ms). Pontine signal-to-noise ratio (SNR) and DTI metrics of the trigeminal nerve streamlines tracked by two independent raters using deterministic tractography and standardized tracking protocol were obtained. These were statistically analyzed and compared across the three protocols using intra-rater and inter-rater intraclass correlation coefficients (ICCs), one-way analysis of variance (ANOVA), post hoc analysis, and linear regression.
Results
On visual screening, there were no artifacts across the trigeminal nerves. All data also cleared objective image quality assurance analysis. Pontine SNR was similar for the two SMS protocols and higher for the non-SMS RESOLVE-DTI (
F
(2,36)
= 4.40,
p
= 0.02). Intra-rater and inter-rater ICCs were very good (> 0.85). Trigeminal nerve DTI metrics were consistently measured by the three protocols, revealing significant linear relationships between non-SMS- and SMS-derived DTI metrics.
Conclusion
SMS RESOLVE-DTI enables fast and reliable evaluation of microstructural integrity of the trigeminal nerve, with potential application in the clinical management of TN.
Key Points
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Readout-segmented diffusion-weighted echo planar imaging (RESOLVE-DTI) reduces image distortion artifacts in the posterior fossa but its long acquisition time limits clinical utility
.
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Simultaneous multi-slice (SMS) imaging combined with RESOLVE-DTI provides reliable trigeminal nerve tractography with potential applications in trigeminal neuralgia
.
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Two-fold-accelerated RESOLVE-DTI yields comparable trigeminal nerve streamlines and DTI metrics while near-halving acquisition time
.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
Deep gray nuclear pathology relates to motor deterioration in idiopathic Parkinson's disease (PD). Inconsistent deep nuclear diffusion tensor imaging (DTI) findings in cross-sectional or short-term ...longitudinal studies have been reported. Long-term studies in PD are clinically challenging; decade-long deep nuclear DTI data are nonexistent. We investigated serial DTI changes and clinical utility in a case-control PD cohort of 149 subjects (72 patients/77 controls) over 12 years.
Participating subjects underwent brain MRI at 1.5T; DTI metrics from segmented masks of caudate, putamen, globus pallidus and thalamus were extracted from three timepoints with 6-year gaps. Patients underwent clinical assessment, including Unified Parkinson Disease Rating Scale Part 3 (UPDRS-III) and Hoehn and Yahr (H&Y) staging. A multivariate linear mixed-effects regression model with adjustments for age and gender was used to assess between-group differences in DTI metrics at each timepoint. Partial Pearson correlation analysis was used to correlate clinical motor scores with DTI metrics over time.
MD progressively increased over time and was higher in the putamen (
< 0.001) and globus pallidus (
= 0.002). FA increased (
< 0.05) in the thalamus at year six, and decreased in the putamen and globus pallidus at year 12. Putaminal (
= 0.0210), pallidal (
= 0.0066) and caudate MD (
< 0.0001) correlated with disease duration. Caudate MD (
< 0.05) also correlated with UPDRS-III and H&Y scores.
Pallido-putaminal MD showed differential neurodegeneration in PD over 12 years on longitudinal DTI; putaminal and thalamic FA changes were complex. Caudate MD could serve as a surrogate marker to track late PD progression.
Individual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex ...differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined.
In this work, we used predictive modeling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness–linked behaviors in males and females in data from the Adolescent Brain Cognitive Development Study (N = 5260; 2571 females).
We demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females but predict internalizing behaviors only in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks, including default, limbic, control, and dorsal attention networks, are associated with internalizing and externalizing behaviors.
Taken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. Based on these results, we recommend that both clinicians and researchers carefully consider how sex may influence the presentation of psychiatric illnesses, especially those along the internalizing-externalizing spectrum.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The underlying neuropathology of excessive daytime sleepiness (EDS) remains elusive in Parkinson's disease (PD). We aim to investigate neural network changes that underlie EDS in PD.
Early PD ...patients comprising eighty-one patients without EDS (EDS-) and seventeen patients with EDS (EDS+) received a resting state functional MRI scan and the Epworth Sleepiness Scale (ESS). Connectivities within the default mode network (DMN), motor and basal ganglia networks were compared between the EDS+ and EDS- groups. Correlations between network connectivity and the severity of EDS were investigated through linear regression.
EDS+ patients displayed a trend of increased network connectivity of the posterior DMN (pDMN). A significant positive correlation was found between connectivity of the ventromedial prefrontal cortex in the pDMN and ESS.
EDS+ patients are likely to display increased activation in the DMN, suggesting neural compensation in early PD or impaired attentiveness due to mechanisms such as mind-wandering.
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