Altered functional connectivity of the default mode and frontal control networks in patients with insomnia.

AIMS: The purpose of this study was to investigate the association between spontaneous regional activity and brain functional connectivity, which maybe can distinguish insomnia while being responsive to repetitive transcranial magnetic stimulation (rTMS) treatment effects in insomnia patients. METHODS: Using resting-state functional magnetic resonance imaging data from 38 chronic insomnia patients and 36 healthy volunteers, we compared the amplitude of low-frequency fluctuations (ALFF) between the two groups. Of all the patients with insomnia, 20 received rTMS for 4 weeks, while 18 patients received a 4-week pseudo-stimulation intervention. Seed-based resting-state functional connectivity (RSFC) analysis was conducted from regions with significantly different ALFF values, and the association between RSFC value and Pittsburgh Sleep Quality Index score was determined. RESULTS: Our results revealed that insomnia patients presented a significantly higher ALFF value in the posterior cingulate cortex (PCC), whereas a significantly lower ALFF value was observed in the superior parietal lobule (SPL). Moreover, significantly reduced RSFC was detected from both PCC to prefrontal cortex connections, as well as from left SPL to frontal pole connections. In addition, RSFC from frontal pole to left SPL negatively predicted sleep quality (PSQI) and treatment response in patients' group. CONCLUSION: Our findings suggest that disrupted frontoparietal network connectivity may be a biomarker for insomnia in middle-aged adults, reinforcing the potential of rTMS targeting the frontal lobes. Monitoring pretreatment RSFC could offer greater insight into how rTMS treatments are responded to by insomniacs.

Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia.

Objective: Progressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectories. Earlier diagnosis requires data-driven approaches for improved and targeted treatment modalities. Methods: Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 35 patients with SCD, 19 with MCI, and 36 age-matched healthy controls (HC). A recently developed machine learning technique, Hollow Tree Super (HoTS) was utilized to classify subjects into diagnostic categories based on their FC, and derive network and parcel-based FC features contributing to each model. The same approach was used to identify features associated with performance in a range of neuropsychological tests. We concluded our analysis by looking at changes in PageRank centrality (a measure of node hubness) between the diagnostic groups. Results: Subjects were classified into diagnostic categories with a high area under the receiver operating characteristic curve (AUC-ROC), ranging from 0.73 to 0.84. The language networks were most notably associated with classification. Several central networks and sensory brain regions were predictors of poor performance in neuropsychological tests, suggesting maladaptive compensation. PageRank analysis highlighted that basal and limbic deep brain region, along with the frontal operculum demonstrated a reduction in centrality in both SCD and MCI patients compared to controls. Conclusion: Our methods highlight the potential to explore the underlying neural networks contributing to the cognitive changes and neuroplastic responses in prodromal dementia.

Connectomic disturbances underlying insomnia disorder and predictors of treatment response.

Objective: Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy. Materials and methods: 51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging (MRI), resting-stage functional MRI (rsfMRI), and diffusion weighted imaging (DWI). Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up. Results: Subjects were classified into ID and control with an area under the receiver operating characteristic curve (AUC-ROC) of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index (ISI) was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change. Conclusion: Machine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets.

The typical and atypical MR imaging findings of central neurocytomas: Report on eighteen cases and review of the literature.

There were few studies have documented the MRI features of typical and atypical CNCs for diagnosis and therapeutic modalities. Here, 18 histopathologically confirmed cases of intracranial CNCs (8 men and 10 women with a mean age of 28.3 years, range 10-64 years) were retrospectively analyzed. The histopathological and immunohistochemical features were also assessed. On MR imaging, the 14 typical cases of CNCs showed relatively round, lobulated tumor masses in the body of the right lateral ventricle (5 cases), left lateral ventricle (4 cases), third ventricles (2 cases), and midline (3 cases). These typical CNCs masses contained clusters of cysts of varying sizes and "soap bubble" appearance on T2WI; they showed mild to moderate heterogeneously enhancement on T1WI. The 4 atypical cases of CNCs showed as strongly contrast enhancement of the tumors with the attachment or infiltrate of the wall of the ventricle than the typical benign cases. These atypical CNCs were in the right lateral ventricle (2 cases), left lateral ventricle (1 case), and third ventricle (1 case). Microscopically, the typical CNCs were well-differentiated tumors with benign histological features. The typical and atypical CNCs were composed of uniform, small to medium-sized cells with rounded nuclei and scant cytoplasm. Immunohistochemically, the typical CNCs were strong in Syn immunopositive (14/14) and neuron-specific enolase (12/14). The atypical CNC tumor cells showed malignant behavior and more positive expression of Ki67 than the benign cases. Surgery is the first choice of treatment, and radiotherapy may be beneficial to postoperative patients.