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Abstract Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. However, the population-level association does not fully account for individual variability in intelligence. To address this, studies have emerged recently to predict individual subject’s intelligence or neurocognitive scores. However, they are mostly on predicting fluid intelligence (the ability to solve new problems). Studies are lacking to predict crystallized intelligence (the ability to accumulate knowledge) or general intelligence (fluid and crystallized intelligence combined). This study tests whether deep learning of sMRI can predict an individual subject’s verbal, comprehensive, and full-scale intelligence quotients (VIQ, PIQ, and FSIQ), which reflect fluid and crystallized intelligence. We performed a comprehensive set of 432 experiments, using different input image channels, six deep learning models, and two outcome settings, in 850 healthy and autistic subjects 6–64 years of age. Our findings indicate a statistically significant potential of T1-weighted sMRI in predicting intelligence, with a Pearson correlation exceeding 0.21 (p < 0.001). Interestingly, we observed that an increase in the complexity of deep learning models does not necessarily translate to higher accuracy in intelligence prediction. The interpretations of our 2D and 3D CNNs, based on GradCAM, align well with the Parieto-Frontal Integration Theory (P-FIT), reinforcing the theory’s suggestion that human intelligence is a result of interactions among various brain regions, including the occipital, temporal, parietal, and frontal lobes. These promising results invite further studies and open new questions in the field.

Isolated cerebral ventriculomegaly (IVM) is the most common prenatally diagnosed brain anomaly occurs in 0.2–1 % of pregnancies. However, knowledge of fetal brain development in IVM is limited. There is no prenatal predictor for IVM to estimate individual risk of neurodevelopmental disability occurs in 10 % of children. To characterize brain development in fetuses with IVM and delineate their individual neuroanatomical variances, we performed comprehensive post-acquisition quantitative analysis of fetal magnetic resonance imaging (MRI). In volumetric analysis, brain MRI of fetuses with IVM (n = 20, 27.0 ± 4.6 weeks of gestation, mean ± SD) had revealed significantly increased volume in the whole brain, cortical plate, subcortical parenchyma, and cerebrum compared to the typically developing fetuses (controls, n = 28, 26.3 ± 5.0). In the cerebral sulcal developmental pattern analysis, fetuses with IVM had altered sulcal positional (both hemispheres) development and combined features of sulcal positional, depth, basin area, in both hemispheres compared to the controls. When comparing distribution of similarity index of individual fetuses, IVM group had shifted toward to lower values compared to the control. About 30 % of fetuses with IVM had no overlap with the distribution of control fetuses. This proof-of-concept study shows that quantitative analysis of fetal MRI can detect emerging subtle neuroanatomical abnormalities in fetuses with IVM and their individual variations.

Dandy-Walker malformation (DWM) is a common prenatally diagnosed cerebellar malformation, characterized by cystic dilatation of the fourth ventricle, upward rotation of the hypoplastic vermis, and posterior fossa enlargement with torcular elevation. DWM is associated with a broad spectrum of neurodevelopmental abnormalities such as cognitive, motor, and behavioral impairments, which cannot be explained solely by cerebellar malformations. Notably, the pathogenesis of these symptoms remains poorly understood. This study investigated whether fetal structural developmental abnormalities in DWM extended beyond the posterior fossa to the cerebrum even in fetuses without apparent cerebral anomalies. Post-acquisition volumetric fetal magnetic resonance imaging (MRI) analysis was performed in 12 fetuses with DWM and 14 control fetuses. Growth trajectories of the volumes of the cortical plate, subcortical parenchyma, cerebellar hemispheres, and vermis between 18 and 33 weeks of gestation were compared. The median (interquartile range) gestational ages at the time of MRI were 22.4 (19.4–24.0) and 23.9 (20.6–29.2) weeks in the DWM and control groups, respectively (p = 0.269). Eight of the 12 fetuses with DWM presented with associated cerebral anomalies, including hydrocephalus (n = 3), cerebral ventriculomegaly (n = 3), and complete (n = 2) and partial (n = 2) agenesis of the corpus callosum (ACC); 7 presented with extracerebral abnormalities. Chromosomal abnormalities were detected by microarray analysis in 4 of 11 fetuses with DWM, using amniocentesis. Volumetric analysis revealed that the cortical plate was significantly larger in fetuses with DWM than in controls (p = 0.040). Even without ACC, the subcortical parenchyma, whole cerebrum, cerebellar hemispheres, and whole brain were significantly larger in fetuses with DWM (n = 8) than in controls (p = 0.004, 0.025, 0.033, and 0.026, respectively). In conclusion, volumetric fetal MRI analysis demonstrated that the development of DWM extends throughout the brain during the fetal period, even without apparent cerebral anomalies.

A significant number of individuals with tuberous sclerosis complex (TSC) exhibit language difficulties. Here, we examined the language-related brain morphometry in 59 participants (7 participants with TSC and comorbid autism spectrum disorder (ASD) (TSC + ASD), 13 with TSC but no ASD (TSC-ASD), 10 with ASD-only (ASD), and 29 typically developing (TD) controls). A hemispheric asymmetry was noted in surface area and gray matter volume of several cortical language areas in TD, ASD, and TSC-ASD groups, but not in TSC + ASD group. TSC + ASD group demonstrated increased cortical thickness and curvature values in multiple language regions for both hemispheres, compared to other groups. After controlling for tuber load in the TSC groups, within-group differences stayed the same but the differences between TSC-ASD and TSC + ASD were no longer statistically significant. These preliminary findings suggest that comorbid ASD in TSC as well as tuber load in TSC is associated with changes in the morphometry of language regions. Future studies with larger sample sizes will be needed to confirm these findings.

About 30% of children with drug-resistant epilepsy (DRE) continue to have seizures after epilepsy surgery. Since epilepsy is increasingly conceptualized as a network disorder, understanding how brain regions interact may be critical for planning re-operation in these patients. We aimed to estimate functional brain connectivity using scalp EEG and its evolution over time in patients who had repeated surgery (RS-group, n = 9) and patients who had one successful surgery (seizure-free, SF-group, n = 12). We analyzed EEGs without epileptiform activity at varying time points (before and after each surgery). We estimated functional connectivity between cortical regions and their relative centrality within the network. We compared the pre- and post-surgical centrality of all the non-resected (untouched) regions (far or adjacent to resection) for each group (using the Wilcoxon signed rank test). In alpha, theta, and beta frequency bands, the post-surgical centrality of the untouched cortical regions increased in the SF group (p < 0.001) whereas they decreased (p < 0.05) or did not change (p > 0.05) in the RS group after failed surgeries; when re-operation was successful, the post-surgical centrality of far regions increased (p < 0.05). Our data suggest that removal of the epileptogenic focus in children with DRE leads to a gain in the network centrality of the untouched areas. In contrast, unaltered or decreased connectivity is seen when seizures persist after surgery.