Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting‐state functional magnetic resonance imaging (rs‐fMRI) and estimate functional connectivity for brain‐disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph‐based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole‐brain data‐driven manner from rs‐fMRI. To distinguish drug‐naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD.

Amyloid positron emission tomography (PET) scan is clinically essential for the non-invasive assessment of the presence and spatial distribution of amyloid-beta deposition in subjects with cognitive impairment suspected to have been a result of Alzheimer’s disease. Quantitative assessment can enhance the interpretation reliability of PET scan; however, its clinical application has been limited due to the complexity of preprocessing. This study introduces a novel deep-learning-based approach for SUVR quantification that simplifies the preprocessing step and significantly reduces the analysis time. Using two heterogeneous amyloid ligands, our proposed method successfully distinguished standardized uptake value ratio (SUVR) between amyloidosis-positive and negative groups. The proposed method’s intra-class correlation coefficients were 0.97 and 0.99 against PETSurfer and PMOD, respectively. The difference of global SUVRs between the proposed method and PETSurfer or PMOD were 0.04 and −0.02, which are clinically acceptable. The AUC-ROC exceeded 0.95 for three tools in the amyloid positive assessment. Moreover, the proposed method had the fastest processing time and had a low registration failure rate (1%). In conclusion, our proposed method calculates SUVR that is consistent with PETSurfer and PMOD, and has advantages of fast processing time and low registration failure rate. Therefore, PET quantification provided by our proposed method can be used in clinical practice.

Brain segmentation of stroke patients can facilitate brain modeling for electrical non-invasive brain stimulation, a therapy for stimulating brain function using an electric current. However, it remains challenging owing to its time-consuming, labor-dependent, and complicated pipeline. In addition, conventional tools that define lesions into one region rather than distinguishing between the stroke-affected regions and cerebrospinal fluid can lead to inaccurate treatment results. In this study, we first define a novel stroke-affected region as a detailed sub-region of the conventionally defined lesion. Subsequently, a novel comprehensive framework is proposed to segment head-brain and fine-level stroke-affected regions for normal controls and chronic stroke patients. The proposed framework consists of a time-efficient and precise deep learning-based segmentation model. The experiment results indicate that the proposed method perform better than the conventional deep learning-based segmentation model in terms of the evaluation metrics. The proposed method would be a valuable addition to brain modeling for non-invasive neuromodulation.

BACKGROUND: Neuronal plasticity after stroke occurs predominantly within the first few months. However, its persistence and distribution in the chronic phase remain unclear. This study investigated longitudinal changes in cortical gray matter and corticospinal tract integrity beyond 6 months poststroke and their associations with chronic upper limb motor outcomes. METHODS: We conducted a retrospective cohort study at St. Vincent’s Hospital (Suwon, Republic of Korea) between 2015 and 2022, including patients with first-ever unilateral stroke who underwent at least 2 magnetic resonance imaging scans ≥6 months post-onset. Gray matter volume in cerebral and cerebellar cortices was assessed using 3-dimensional T1-weighted imaging and corticospinal tract integrity via diffusion tensor tractography. Hemispheres were classified as affected or unaffected based on anatomic connectivity to the lesion. Upper limb motor score was measured using a 5-stage ordinal scale and grouped into 3 categories by hand function: poor (no movement), partial (limited movement), and good (fully functional use). Ordinal logistic regression identified structural predictors of motor outcomes, adjusting for age, sex, lesion volume, and baseline impairment. RESULTS: Among 62 patients, structural plasticity persisted beyond 6 months in a subset of patients. Imaging was conducted at an average of 51 months poststroke (range, 13–137 months). Increases in affected cerebellar gray matter were associated with better motor outcomes (odds ratio, 0.67 [95% CI, 0.48–0.92]; P =0.01). The odds ratio <1 indicates lower odds of poor outcomes. No significant associations were found for other brain regions or corticospinal tract integrity. CONCLUSIONS: Cerebellar remodeling may support long-term motor recovery after stroke.

Jiwon Kim, Sunghwan Kim, Yoo Hyun Um, Sheng-Min Wang, Regina EY Kim, Yeong Sim Choe, Jiyeon Lee, Donghyeon Kim, Hyun Kook Lim, Chang Uk Lee, Dong Woo Kang. Clin Psychopharmacol Neurosci 2024;22:169-81. https://doi.org/10.9758/cpn.23.1113