Background In contrast to cisgender persons, transgender persons identify with a different gender than the one assigned at birth. Although research on the underlying neurobiology of transgender persons has been accumulating over the years, neuroimaging studies in this relatively rare population are often based on very small samples resulting in discrepant findings. Aim To examine the neurobiology of transgender persons in a large sample. Methods Using a mega-analytic approach, structural MRI data of 803 non-hormonally treated transgender men (TM, n = 214, female assigned at birth with male gender identity), transgender women (TW, n = 172, male assigned at birth with female gender identity), cisgender men (CM, n = 221, male assigned at birth with male gender identity) and cisgender women (CW, n = 196, female assigned at birth with female gender identity) were analyzed. Outcomes Structural brain measures, including grey matter volume, cortical surface area, and cortical thickness. RESULTS Transgender persons differed significantly from cisgender persons with respect to (sub)cortical brain volumes and surface area, but not cortical thickness. Contrasting the 4 groups (TM, TW, CM, and CW), we observed a variety of patterns that not only depended on the direction of gender identity (towards male or towards female) but also on the brain measure as well as the brain region examined. Clinical Translation The outcomes of this large-scale study may provide a normative framework that may become useful in clinical studies. Strengths and Limitations While this is the largest study of MRI data in transgender persons to date, the analyses conducted were governed (and restricted) by the type of data collected across all participating sites. CONCLUSION Rather than being merely shifted towards either end of the male-female spectrum, transgender persons seem to present with their own unique brain phenotype.

Understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment of fear-related disorders. In humans, it has been demonstrated that fear learning typically elicits transient heart rate deceleration. However, classical analyses of heart rate variability (HRV) fail to disentangle the contribution of parasympathetic and sympathetic systems, and crucially, they are not able to capture phasic changes during fear learning. Here, to gain deeper insight into the physiological underpinnings of fear learning, a novel frequency-domain analysis of heart rate was performed using a short-time Fourier transform, and instantaneous spectral estimates extracted from a point-process modeling algorithm. We tested whether spectral transient components of HRV, used as a noninvasive probe of sympathetic and parasympathetic mechanisms, can dissociate between fear conditioned and neutral stimuli. We found that learned fear elicited a transient heart rate deceleration in anticipation of noxious stimuli. Crucially, results revealed a significant increase in spectral power in the high frequency band when facing the conditioned stimulus, indicating increased parasympathetic (vagal) activity, which distinguished conditioned and neutral stimuli during fear learning. Our findings provide a proximal measure of the involvement of cardiac vagal dynamics into the psychophysiology of fear learning and extinction, thus offering new insights for the characterization of fear in mental health and illness.

Abstract.Background and Objective: Mild cognitive impairment (MCI) patients with small vessel disease (SVD) are at high dementia risk. We tested the effects of cognitive rehabilitation in these patients using the Attention Process Training-II (APT-II) program in a single-blinded, randomized clinical trial. Methods: Patients were randomized to APT-II or standard care and evaluated at baseline, 6, and 12 months with functional, quality of life, cognitive tests, and resting state functional MRI (rsfMRI). Results: Forty-six patients were enrolled and 43 (mean ± SD age 75.1 ± 6.8) completed the study. No change was seen in functionality and quality of life between treated and non-treated patients. However, the Rey Auditory-Verbal Learning Test immediate recall showed a significant improvement in treated compared to non-treated group (change score 6 versus 12 months: 1.8 ± 4.9 and -1.4 ± 3.8, p = 0.021; baseline versus 12 months: 3.8 ± 6.1 and 0.2 ± 4.4, p = 0.032). A higher proportion of treated patients had stable/better evaluation compared to non-treated group on Visual search test (6 versus 12 months: 95% versus 71%, p = 0.038) and Rey-Osterrieth Complex Figure copy (6 versus 12 months: 95% versus 67%, p = 0.027). RsfMRI, performed in a subsample, showed that the difference between follow-up and baseline in synchronization of activity in cerebellar areas was significantly greater in treated than in non-treated patients. Conclusion:We were unable to show a significant effect in quality of life or functional status in treated patients with MCI and SVD. However, APT-II produces some beneficial effects in focused attention and working memory and seems to increase activity in brain circuits involved in cognitive processes.

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The ability to flexibly regulate our behavior is a fundamental feature of human cognition and requires efficient functioning of cognitive control. During movement preparation, proactive inhibitory control plays a crucial role in regulating the excitatory activity carried out by alertness. The balance between alertness and proactive inhibition could be altered in people with motor impulsivity trait, determining the typical failure in the inhibition of prepotent motor responses. To test this hypothesis, 36 young adults were administered the Barratt Impulsiveness Scale to assess motor impulsivity trait and underwent fMRI acquisition during the execution of an event‐related Go/Nogo task. To investigate motor preparation processes, we analyzed the “readiness” period, in which subjects were waiting and preparing for the upcoming stimulus (Go or Nogo). We found a positive significant correlation between motor impulsivity scores and the activation of left sensorimotor cortices. This result indicates that motor impulsivity trait might be associated with a disinhibition of the motor system, characterized by a diminished reactivity threshold and a reduced control over covert urges. Furthermore, we observed a positive significant correlation between motor impulsivity scores and the activation in left inferior and superior parietal lobule, which might be related to a more pronounced proactive control, probably reflecting a compensatory mechanism implemented by participants with a higher degree of motor impulsivity trait to reach a correct inhibition. Current findings provide a rationale for further studies aiming to better understand proactive control functioning in healthy impulsive subjects and under clinical conditions.

Task- and stimulus-based neuroimaging studies have begun to unveil the central autonomic network which modulates autonomic nervous system activity. In the present study, we aimed to evaluate the central autonomic network without the bias constituted by the use of a task. Additionally, we assessed whether this circuitry presents signs of dysregulation in the early stages of Parkinson’s disease (PD), a condition which may be associated with dysautonomia. We combined heart-rate-variability based methods for time-varying assessments of the autonomic nervous system outflow with resting-state fMRI in 14 healthy controls and 14 de novo PD patients, evaluating the correlations between fMRI time-series and the instantaneous high-frequency component of the heart-rate-variability power spectrum, a marker of parasympathetic outflow. In control subjects, the high-frequency component of the heart-rate-variability power spectrum was significantly anti-correlated with fMRI time-series in several cortical, subcortical and brainstem regions. This complex central network was not detectable in PD patients. In between-group analysis, we found that in healthy controls the brain activation related to the high-frequency component of the heart-rate-variability power spectrum was significantly less than in PD patients in the mid and anterior cingulum, sensorimotor cortex and supplementary motor area, insula and temporal lobe, prefrontal cortex, hippocampus and in a region encompassing posterior cingulum, precuneus and parieto-occipital cortex. Our results indicate that the complex central network which modulates parasympathetic outflow in the resting state is impaired in the early clinical stages of PD.

Our results indicate that brain damage in CADASIL is associated with extensive microstructural changes implying impairment of intra- and inter-hemispheric cerebral, thalamocortical, and cerebrocerebellar connections. Severity of microstructural changes correlates with extension of T2 hyperintensity.

Parki so 's disease PD has ee reported to i ol e postga glio i s patheti failure and a wide spectrum of autonomic dysfunctions including cadiovascular, sexual, bladder, gastrointestinal and sudo-motor abnormalities. While these symptoms may have a significant impact on daily activities, as well as quality of life, the evaluation of autonomic nervous system (ANS) dysfunctions relies on a large and expensive battery of autonomic tests only accessible in highly specialized laboratories. In this paper we aim to devise a comprehensive computational assessment of disease-related heartbeat dynamics based on instantaneous, time-varying estimates of spontaneous (resting state) cardiovascular oscillations in PD. To this end, we combine standard ANS-related heart rate variability (HRV) metrics with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra). Such measures are computed over 600-s recordings acquired at rest in 29 healthy subjects and 30 PD patients. The only significant group-wise differences were found in the variability of the dominant Lyapunov exponent. Also, the best PD vs. healthy controls classification performance (balanced accuracy: 73.47%) was achieved only when retaining the time-varying, non-stationary structure of the dynamical features, whereas classification performance dropped significantly (balanced accuracy: 61.91%) when excluding variability-related features. Additionally, both linear and nonlinear model features correlated with both clinical and neuropsychological assessments of the considered patient population.Our results demonstrate the added value and potential of instantaneous measures of heartbeat dynamics and its variability in characterizing PD-related disabilities in motor and cognitive domains.3