Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent.

Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis.

A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients.

Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD.

Multiple sclerosis (MS) is a demyelinating and neurodegenerative disorder of the CNS, which incapacitates people of working age. Due to progressive disability, the quality of life decreases, adding a number of other diseases to the main one. Several studies have reported high rates of depression in MS with a lifetime prevalence of approximately 50%.

Therefore, we would like to pattern the functional activation of the brain of patients with different phenotypes of MS. This would objectify the patient’s condition and the effectiveness of therapy for these diseases.

68 patients with MS were examined: 40 with a relapsing-remitting type of course (RRMS) in remission and 28 with secondary - progressive MS (SPMS). Patients underwent MRI of the brain on a Siemens Tim Trio 3.0 T tomograph and processed the data using CONN 18b software. Clinical features were estimated by tests (BDI, HADS) results.

91% of all MS patients in research have signs of depression. We noted that decreased FC in RRMS patients has a whole-brain type, but it is only decreasing, not losing the connections between brain clusters. Decreased FC and losing the connections between large-scale brain networks and brain clusters. Due to tests, more severe depression was observed in SPMS patients.

Our findings suggest that patients with SPMS have depression, cause of decreasing in FC between the main clusters of the brain, and patients with SPMS have more severe depression, which, as we assume, neurodegeneration has turned into atrophy and loosing all connections between clusters even in large-scale brain networks.

No significant relationships.

To elucidate the mechanisms of how snack foods may induce non-homeostatic food intake, we used resting state functional magnetic resonance imaging (fMRI), as resting state networks can individually adapt to experience after short time exposures. In addition, we used graph theoretical analysis together with machine learning techniques (support vector machine) to identifying biomarkers that can categorize between high-caloric (potato chips) vs. low-caloric (zucchini) food stimulation.

Seventeen healthy human subjects with body mass index (BMI) 19 to 27 underwent 2 different fMRI sessions where an initial resting state scan was acquired, followed by visual presentation of different images of potato chips and zucchini. There was then a 5-minute pause to ingest food (day 1=potato chips, day 3=zucchini), followed by a second resting state scan. fMRI data were further analyzed using graph theory analysis and support vector machine techniques.

Potato chips vs. zucchini stimulation led to significant connectivity changes. The support vector machine was able to accurately categorize the 2 types of food stimuli with 100% accuracy. Visual, auditory, and somatosensory structures, as well as thalamus, insula, and basal ganglia were found to be important for food classification. After potato chips consumption, the BMI was associated with the path length and degree in nucleus accumbens, middle temporal gyrus, and thalamus.

The results suggest that high vs. low caloric food stimulation in healthy individuals can induce significant changes in resting state networks. These changes can be detected using graph theory measures in conjunction with support vector machine. Additionally, we found that the BMI affects the response of the nucleus accumbens when high caloric food is consumed.