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Gender dysphoria (GD) is characterized by incongruence between one's identity and gender assigned at birth. The biological mechanisms of GD are unclear. We investigated brain network connectivity patterns involved in own body perception in the context of self in GD. Twenty-seven female-to-male (FtM) individuals with GD, 27 male controls, and 27 female controls underwent resting state fMRI. We compared functional connections within intrinsic connectivity networks involved in self-referential processes and own body perception -default mode network (DMN) and salience network - and visual networks, using independent components analyses. Behavioral correlates of network connectivity were also tested using self-perception ratings while viewing own body images morphed to their sex assigned at birth, and to the sex of their gender identity. FtM exhibited decreased connectivity of anterior and posterior cingulate and precuneus within the DMN compared with controls. In FtM, higher "self" ratings for bodies morphed towards the sex of their gender identity were associated with greater connectivity of the anterior cingulate within the DMN, during long viewing times. In controls, higher ratings for bodies morphed towards their gender assigned at birth were associated with right insula connectivity within the salience network, during short viewing times. Within visual networks FtM showed weaker connectivity in occipital and temporal regions. Results suggest disconnectivity within networks involved in own body perception in the context of self in GD. Moreover, perception of bodies in relation to self may be reflective rather than reflexive, as a function of mesial prefrontal processes. These may represent neurobiological correlates to the subjective disconnection between perception of body and self-identification.

BACKGROUND:Intensive cognitive-behavioral therapy (CBT) can effectively reduce symptoms in obsessive-compulsive disorder (OCD). However, many relapse after treatment. Few studies have investigated biological markers predictive of follow-up clinical status. The objective was to determine if brain network connectivity patterns prior to intensive CBT predict worsening of clinical symptoms during follow-up. METHODS:We acquired resting-state functional magnetic resonance imaging data from 17 adults with OCD prior to and following 4 weeks of intensive CBT. Functional connectivity data were analyzed to yield graph-theory metrics. We examined the relationship between pre-treatment connectome properties and OCD clinical symptoms before and after treatment and during a 12-month follow-up period. RESULTS:Mean OCD symptom decrease was 40.4 ± 16.4% pre- to post-treatment (64.7% responded; 58.8% remitted), but 35.3% experienced clinically significant worsening during follow-up. From pre- to post-treatment, small-worldness and clustering coefficient significantly increased. Decreases in modularity correlated with decreases in OCD symptoms. Higher pre-treatment small-world connectivity was significantly associated with worsening of OCD symptoms during the follow-up period. Psychometric and neurocognitive measures pre- and post-treatment were not significant predictors. CONCLUSION:This is the first graph-theory connectivity study of the effects of CBT in OCD, and the first to test associations with follow-up clinical status. Results show functional network efficiency as a biomarker of CBT response and relapse in OCD. CBT increases network efficiency as it alleviates symptoms in most patients, but those entering therapy with already high network efficiency are at greater risk of relapse. Results have potential clinical implications for treatment selection.

BACKGROUND:Intensive cognitive-behavioral therapy (CBT) can effectively reduce symptoms in obsessive-compulsive disorder (OCD). However, many relapse after treatment. Few studies have investigated biological markers predictive of follow-up clinical status. The objective was to determine if brain network connectivity patterns prior to intensive CBT predict worsening of clinical symptoms during follow-up. METHODS:We acquired resting-state functional magnetic resonance imaging data from 17 adults with OCD prior to and following 4 weeks of intensive CBT. Functional connectivity data were analyzed to yield graph-theory metrics. We examined the relationship between pre-treatment connectome properties and OCD clinical symptoms before and after treatment and during a 12-month follow-up period. RESULTS:Mean OCD symptom decrease was 40.4 ± 16.4% pre- to post-treatment (64.7% responded; 58.8% remitted), but 35.3% experienced clinically significant worsening during follow-up. From pre- to post-treatment, small-worldness and clustering coefficient significantly increased. Decreases in modularity correlated with decreases in OCD symptoms. Higher pre-treatment small-world connectivity was significantly associated with worsening of OCD symptoms during the follow-up period. Psychometric and neurocognitive measures pre- and post-treatment were not significant predictors. CONCLUSION:This is the first graph-theory connectivity study of the effects of CBT in OCD, and the first to test associations with follow-up clinical status. Results show functional network efficiency as a biomarker of CBT response and relapse in OCD. CBT increases network efficiency as it alleviates symptoms in most patients, but those entering therapy with already high network efficiency are at greater risk of relapse. Results have potential clinical implications for treatment selection.

Cranial electrotherapy stimulation (CES) is a U.S. Food and Drug Administration (FDA)-approved treatment for insomnia, depression, and anxiety consisting of pulsed, low-intensity current applied to the earlobes or scalp. Despite empirical evidence of clinical efficacy, its mechanism of action is largely unknown. The goal was to characterize the acute effects of CES on resting state brain activity. Our primary hypothesis was that CES would result in deactivation in cortical and subcortical regions. Eleven healthy controls were administered CES applied to the earlobes at subsensory thresholds while being scanned with functional magnetic resonance imaging in the resting state. We tested 0.5- and 100-Hz stimulation, using blocks of 22 sec "on" alternating with 22 sec of baseline (device was "off"). The primary outcome measure was differences in blood oxygen level dependent data associated with the device being on versus baseline. The secondary outcome measures were the effects of stimulation on connectivity within the default mode, sensorimotor, and fronto-parietal networks. Both 0.5- and 100-Hz stimulation resulted in significant deactivation in midline frontal and parietal regions. 100-Hz stimulation was associated with both increases and decreases in connectivity within the default mode network (DMN). Results suggest that CES causes cortical brain deactivation, with a similar pattern for high- and low-frequency stimulation, and alters connectivity in the DMN. These effects may result from interference from high- or low-frequency noise. Small perturbations of brain oscillations may therefore have significant effects on normal resting state brain activity. These results provide insight into the mechanism of action of CES, and may assist in the future development of optimal parameters for effective treatment.

Cranial electrotherapy stimulation (CES) is a U.S. Food and Drug Administration (FDA)-approved treatment for insomnia, depression, and anxiety consisting of pulsed, low-intensity current applied to the earlobes or scalp. Despite empirical evidence of clinical efficacy, its mechanism of action is largely unknown. The goal was to characterize the acute effects of CES on resting state brain activity. Our primary hypothesis was that CES would result in deactivation in cortical and subcortical regions. Eleven healthy controls were administered CES applied to the earlobes at subsensory thresholds while being scanned with functional magnetic resonance imaging in the resting state. We tested 0.5- and 100-Hz stimulation, using blocks of 22 sec "on" alternating with 22 sec of baseline (device was "off"). The primary outcome measure was differences in blood oxygen level dependent data associated with the device being on versus baseline. The secondary outcome measures were the effects of stimulation on connectivity within the default mode, sensorimotor, and fronto-parietal networks. Both 0.5- and 100-Hz stimulation resulted in significant deactivation in midline frontal and parietal regions. 100-Hz stimulation was associated with both increases and decreases in connectivity within the default mode network (DMN). Results suggest that CES causes cortical brain deactivation, with a similar pattern for high- and low-frequency stimulation, and alters connectivity in the DMN. These effects may result from interference from high- or low-frequency noise. Small perturbations of brain oscillations may therefore have significant effects on normal resting state brain activity. These results provide insight into the mechanism of action of CES, and may assist in the future development of optimal parameters for effective treatment.