Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)—one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain—is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management.

The electroencephalography is a technique used in brain mapping with applications in research and clinical practice. The current work aims to assess the reliability of quantitative electroencephalography and coherence measurements. Two electroencephalography sessions, obtained 4 to 6 weeks apart during resting state and memory condition, were recorded in 15 young healthy subject. For each subject power spectrum and coherence were calculated in six frequency bands: delta, 1-4 Hz; theta, 4-8Hz; alpha1, 8-10Hz; alpha2, 10-13Hz; beta, 13-30Hz; and gamma, 30-50Hz. Measurements were averaged into 8 regions of interest trying to cover different brain regions. The reliability of each measure for frequency band and ROI was calculated using the intraclass correlation coefficient (ICC). Significant reliability was obtained for qEEG (ICC = 0.74 in resting recordings and ICC = 0.66 in memory recordings, p<;0.006 corrected for multiple comparison) but not for coherence. These results suggest that qEEG metrics are sufficiently reliable to be considered in longitudinal studies.

Objective: Evaluate the reliability of neural components obtained from the appli-cation of the group ICA (gICA) methodology to resting-state EEG datasets acquired from multiple sites. Methods: Five databases from three sites, covering a total of 292 healthy subjects, were analyzed. Each dataset was segmented into groups of 15 subjects, for a total of 19 groups. Data were pre-processed using an automatic pipeline leveraging robust average referencing, wavelet-ICA and automatic rejection of epochs. On each group, stable gICA decompositions were calculated using the ICASSO methodology through a range of orders of decompositions. Each order was characterized by reliability and neuralness metrics, which were evaluated to select a single order of decomposition. Finally, using the decompositions of the selected order, a clustering analysis was performed to find the common components across the 19 groups. Each cluster was characterized by the mean scalp map, its dipole generator with its localization in Talairach coordinates, the spectral behavior of the associated time-series of the components, the assigned ICLabel class and metrics that reflected their reproducibility. Results: Lower order of decompositions benefits the gICA methodology. At this, using an order of ten, the number of reproducible components with high neuronal information tends to be around nine. Of these, the bilateral motor, frontal medial, and occipital neuronal components were the most reproducible across the different datasets, appearing in more than 89% of the 19 groups evaluated. Conclusion: We developed a workflow that allows finding reproducible spatial filters between different data sets. This contributes to the improvement of the spatial resolution of the EEG as a brain mapping technique.

The allostatic load index (ALI) measures the cumulative physiological burden exerted on the body by chronic stress known as allostatic load (AL). The relationship between ALI and heart rate variability (HRV), as well as the brain moderation effect on this relationship in healthy individuals, is underexplored. In this cross-sectional study of 88 healthy men (21-40 years) from Medellín, Colombia, we calculated two ALIs composed of four and seven biomarkers (ALI-4 and ALI-7) using a quartile-based risk summation method. Functional and structural neuroimaging metrics were derived from magnetic resonance imaging. Multiple linear regression models were used to evaluate the associations between the ALIs and HRV metrics derived from a 24-hour Holter. Exploratory interaction models tested whether the functional connectivity strength (FCS) of default mode (DMN), salience (SN) and control subnetworks (CEN), their cortical thickness as well as the volume of subcortical structures, moderated the ALI-HRV association. ALI-7 was positively associated with the LF/HF ratio (β = 0.09, p = 0.004, 95 % CI = 0.03-0.15). Exploratory interactions suggested that ALI-7's association with the standard deviation of the NN intervals (SDNN) was moderated by the FCS in the posterior DMN and by cortical thickness in the anterior SN. However, none of these interactions remained significant after false discovery rate correction. In healthy men, higher ALI was associated with reduced HRV as indicated by higher LF/HF ratio. Larger studies, including women, are needed to confirm the predictive value of ALI-7 and to elucidate the brain moderation effect on the AL-HRV relationship.