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BACKGROUND: Many subjects with major depression (MDD) exhibit subthreshold mania symptoms (MDD+). This study investigated, for the first time, using emotional inhibition tasks, whether the neural organization of MDD+ subjects is more similar to bipolar depression (BDD) or to MDD subjects without subthreshold bipolar symptoms (MDD−). METHOD: This study included 118 medication-free young adults (15 – 30 yrs.): 20 BDD, 28 MDD+, 41 MDD− and 29 HC subjects. Participants underwent fMRI during emotional and non-emotional Go/No-go tasks during which they responded for Go stimuli and inhibited response for happy, fear, and non-emotional (gender) faces No-go stimuli. Univariate linear mixed-effects (LME) analysis for group effects and multivariate Gaussian Process Classifier (GPC) analyses were conducted. RESULTS: MDD− group compared to both the BDD and MDD+ groups, exhibited significantly lower activation in parietal, temporal and frontal regions (cluster-wise corrected p

Differentiation between Bipolar Disorder Depression (BDD) and Unipolar Major Depressive Disorder (MDD) is critical to clinical practice. This study investigated machine learning classification of BDD and MDD using graph properties of Diffusion-weighted Imaging (DWI)-based structural connectome.This study included a large number of medication-free (N =229) subjects: 60 BDD, 95 MDD, and 74 Healthy Control (HC) subjects. DWI probabilistic tractography was performed to create Fractional Anisotropy (FA) and Total Streamline (TS)-based structural connectivity matrices. Global and nodal graph properties were computed from these matrices and tested for group differences. Next, using identified graph properties, machine learning classification (MLC) between BDD, MDD, MDD with risk factors for developing BD (MDD+), and MDD without risk factors for developing BD (MDD-) was conducted.Communicability Efficiency of the left superior frontal gyrus (SFG) was significantly higher in BDD vs. MDD. In particular, Communicability Efficiency using TS-based connectivity in the left SFG as well as FA-based connectivity in the right middle anterior cingulate area was higher in the BDD vs. MDD- group. There were no significant differences in graph properties between BDD and MDD+. Direct comparison between MDD+ and MDD- showed differences in Eigenvector Centrality (TS-based connectivity) of the left middle frontal sulcus. Acceptable Area Under Curve (AUC) for classification were seen between the BDD and MDD- groups, and between the MDD+ and MDD- groups, using the differing graph properties.Graph properties of DWI-based connectivity can discriminate between BDD and MDD subjects without risk factors for BD.

Gender identity development is complex and involves several key processes. Transgender people experience incongruence between their biological and identified gender. This incongruence can cause significant impairment in overall functioning and lead to gender dysphoria (GD). The pathophysiology of GD is complex and is poorly understood. A PubMed search based on predetermined eligibility criteria was conducted to review neuropsychiatric articles focused on neurological, biological and neuroimaging aspects of gender development, transgender identity and GD. The information obtained from the literature was then used to formulize a GD model. Distinct gray matter volume and brain activation and connectivity differences were found in individuals with GD compared to controls, suggesting a neurobiological basis of GD; which leads to the concept of brain gender. Individuals with GD encounter a recurrent conflict between their brain gender and the societal feedback; which causes recurrent and ongoing cognitive dissonance, finally leading to GD and functional connectivity and activation changes in the transgender brain. GD has neurobiological basis, but it is closely associated with the individuals' interaction with the external world, their self-perception and the feedback received in return. We propose a novel model where the development of GD includes cognitive dissonance, involving anterior cingulate cortex and ventral striatum as the key brain structures. This model can be used to generate testable hypotheses using behavioral and neuroimaging techniques to understand the neuropsychobiology of GD.

Major Depressive Disorder (MDD) is frequently associated with risk factors for the development of Bipolar Disorder (BD). Using graph theory, we investigated brain network properties associated with BD risk factors in young MDD subjects.Resting-state fMRI was acquired from a large cohort (N= 104) of medication-free currently depressed participants (25 BD depression (BDD), 79 MDD). Lifetime mania symptom count (LMSC), current Young Mania Rating Scale (YMRS) score, and family history of mood disorders (FHMD) were examined as BD risk factors. Functional connectivity matrices from 280 regions of interests (ROIs) were first entered into the Network Based Statistic (NBS) toolbox to identify connections that varied with each risk factor. Next, within the correlated network for each risk factor, global and nodal graph properties for the top five linked nodes were calculated. Last, using identified graph properties, machine learning classification (MLC) between BDD, MDD with BD risk factors (MDD+), and without BD risk factors (MDD-) was conducted.LMSC positively correlated with left lateral orbitofrontal cortex (LOFC) Communication Efficiency and with left middle temporal Eigenvector Centrality. Current YMRS score positively correlated with right amygdala Communication Efficiency and Closeness Centrality. FHMD positively correlated with right insula Eigenvector Centrality. Acceptable MLC accuracy was seen between BDD and MDD- using middle temporal Eigenvector Centrality, whereas moderate accuracy was seen between MDD+ and MDD- using OFC Communication Efficiency.Although participants were medication-free, they were not medication-naïve.Functional connectome graph properties may serve as BD vulnerability biomarkers in young individuals with MDD.

Research on resting functional brain networks in bipolar disorder (BP) has been unable to differentiate between disturbances related to mania or depression, which is necessary to understand the mechanisms leading to each state. Past research has also been unable to elucidate the impact of BP-related network disturbances on the organizational properties of the brain (eg, communication efficiency). Thus, the present work sought to isolate network disturbances related to BP, fractionate these into components associated with manic and depressive symptoms, and characterize the impact of disturbances on network function. Graph theory was used to analyze resting functional magnetic resonance imaging data from 60 medication-free patients meeting the criteria for BP and either a current hypomanic (n=30) or depressed (n=30) episode and 30 closely age/sex-matched healthy controls. Correction for multiple comparisons was carried out. Compared with controls, BP patients evidenced hyperconnectivity in a network involving right amygdala. Fractionation revealed that (hypo)manic symptoms were associated with hyperconnectivity in an overlapping network and disruptions in the brain's ‘small-world' network organization. Depressive symptoms predicted hyperconnectivity in a network involving orbitofrontal cortex along with a less resilient global network organization. Findings provide deeper insight into the differential pathophysiological processes associated with hypomania and depression, along with the particular impact these differential processes have on network function.

Background This study, for the first time, investigated lithium monotherapy associated effects on amygdala- ventromedial prefrontal cortex (vMPFC) resting-state functional connectivity and correlation with clinical improvement in bipolar disorder (BP) Methods Thirty-six medication-free subjects − 24 BP (12 hypomanic BPM) and 12 depressed (BPD)) and 12 closely matched healthy controls (HC), were included. BP subjects were treated with lithium and scanned at baseline, after 2 weeks and 8 weeks. HC were scanned at same time points but were not treated. The effect of lithium was studied for the BP group as a whole using two way (group, time) ANOVA while regressing out effects of state. Next, correlation between changes in amygdala-vMPFC resting-state connectivity and clinical global impression (CGI) of severity and improvement scale scores for overall BP illness was calculated. An exploratory analysis was also conducted for the BPD and BPM subgroups separately. Results Group by time interaction revealed that lithium monotherapy in patients was associated with increase in amygdala-medial OFC connectivity after 8 weeks of treatment (p = 0.05 (cluster-wise corrected)) compared to repeat testing in healthy controls. Increased amygdala-vMPFC connectivity correlated with clinical improvement at week 2 and week 8 as measured with the CGI-I scale. Limitations The results pertain to open-label treatment and do not account for non-treatment related improvement effects. Only functional connectivity was measured which does not give information regarding one regions effect on the other. Conclusions Lithium monotherapy in BP is associated with modulation of amygdala-vMPFC connectivity which correlates with state-independent global clinical improvement.

This study investigated extended release quetiapine (quetiapine XR) associated changes in functional MRI (fMRI) measures of task-induced amygdalar activation and resting state connectivity in anxious unipolar major depressive disorder (AMDD).Anxious unipolar major depressive disorder patients (n = 15) (17-item Hamilton Depression Rating Scale (HAM-D)18 and Hamilton Anxiety Scale (HAM-A)18) and closely matched healthy control (HC) subjects were compared at baseline for task induced amygdala activation and resting state connectivity on fMRI. Subsequently, AMDD patients were treated for 8 weeks with open-label quetiapine XR. Weekly HAM-D and HAM-A ratings were obtained, and the fMRI scan was repeated at weeks 2 and 8. Changes in fMRI measures were calculated using repeated-measures analysis of variance and correlation with decrease in HAM-D and HAM-A scores was examined.At baseline, AMDD compared with HC exhibited increased task-induced left amygdalar activation (P = 0.05 clusterwise corrected) and decreased resting state amygdala-cortical and amygdala-pons connectivity (P0.05 clusterwise corrected). Quetiapine XR treatment was associated with significant decrease in HAM-D (df = 1,28; female [F] = 39; P = 0.001) and HAM-A scores (df = 1,28; F = 55; P = 0.001). The AMDD group showed increased amygdala-cortical connectivity (P0.05 [clusterwise corrected]) at week 2, which was maintained at week 8. At week 8, additional areas showed increased connectivity including insula and putamen. At 8 weeks, decrease in HAM-D scores correlated with increase in amygdala-mid cingulate and amygdala-cuneus connectivity (P = 0.05 [clusterwise corrected]). Decrease in HAM-A scores correlated with increase in amygdala-cuneus and parietal cortex connectivity (P = 0.05 [clusterwise corrected]).Small sample-size, open-label single-arm design, HC only tested at baseline, focused only on amygdala.Quetiapine XR effects in the treatment of AMDD are associated with modulation of amygdala connectivity.

Bipolar disorder (BP) is characterized by periods of depression (BPD) and (hypo)mania (BPM), but the underlying state-related brain circuit abnormalities are not fully understood. Striatal functional activation and connectivity abnormalities have been noted in BP, but consistent findings have not been reported. To further elucidate striatal abnormalities in different BP states, this study investigated differences in resting-state functional connectivity of six striatal subregions in BPD, BPM, and healthy control (HC) subjects. Ninety medication-free subjects (30 BPD, 30 BPM, and 30 HC), closely matched for age and gender, were scanned using 3T functional magnetic resonance imaging (fMRI) acquired at resting state. Correlations of low-frequency blood oxygen level dependent signal fluctuations for six previously described striatal subregions were used to obtain connectivity maps of each subregion. Using a factorial design, main effects for differences between groups were obtained and post hoc pairwise group comparisons performed. BPD showed increased connectivity of the dorsal caudal putamen with somatosensory areas such as the insula and temporal gyrus. BPM group showed unique increased connectivity between left dorsal caudate and midbrain regions, as well as increased connectivity between ventral striatum inferior and thalamus. In addition, both BPD and BPM exhibited widespread functional connectivity abnormalities between striatal subregions and frontal cortices, limbic regions, and midbrain structures. In summary, BPD exhibited connectivity abnormalities of associative and somatosensory subregions of the putamen, while BPM exhibited connectivity abnormalities of associative and limbic caudate. Most other striatal subregion connectivity abnormalities were common to both groups and may be trait related.

Measurement of brain change due to neurodegenerative disease and treatment is one of the fundamental tasks of neuroimaging. Deformation-based morphometry (DBM) has been long recognized as an effective and sensitive tool for estimating the change in the volume of brain regions over time. This paper demonstrates that a straightforward application of DBM to estimate the change in the volume of the hippocampus can result in substantial bias, i.e., an overestimation of the rate of change in hippocampal volume. In ADNI data, this bias is manifested as a non-zero intercept of the regression line fitted to the 6 and 12 month rates of hippocampal atrophy. The bias is further confirmed by applying DBM to repeat scans of subjects acquired on the same day. This bias appears to be the result of asymmetry in the interpolation of baseline and followup images during longitudinal image registration. Correcting this asymmetry leads to bias-free atrophy estimation.

Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusion, previous studies show that image similarity-based local weighting techniques produce the most accurate results. However, these methods ignore the correlations between results produced by different atlases. Furthermore, they rely on pre-selected weighting models and ad hoc methods to choose model parameters. We propose a novel label fusion method to address these limitations. Our formulation directly aims at reducing the expectation of the combined error and can be efficiently solved in a closed form. In our hippocampus segmentation experiment, our method significantly outperforms similarity-based local weighting. Using 20 atlases, we produce results with 0.898 +/- 0.019 Dice overlap to manual labelings for controls.

We propose a simple strategy to improve automatic medical image segmentation. The key idea is that without deep understanding of a segmentation method, we can still improve its performance by directly calibrating its results with respect to manual segmentation. We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data. We apply this methodology on three segmentation problems/methods and show significant improvements for all of them.

We develop a semi-automatic technique for segmentation of hippocampal subfields in T2-weighted in vivo brain MRI. The technique takes the binary segmentation of the whole hippocampus as input, and automatically labels the subfields inside the hippocampus segmentation. Shape priors for the hippocampal subfields are generated from shape-based normalization of whole hippocampi via the continuous medial representation method. To combine the shape priors with appearance features, we use a machine learning based method. The key novelty is that we treat the mistakes made by the shape priors as bias, which can be detected and corrected via learning. The main advantage of this formulation is that it significantly simplifies the learning problem by taking full advantage of current segmentations and focusing on only improving their drawbacks. Experiments show that the bias removal approach achieves significant improvement in all subfields. Our bias removal idea is general, and can be applied to improve other segmentation methods as well.