Reliability assessment of leg length and angular alignment on manual reads versus artificial intelligence-generated lower extremity radiographic measurements.

PURPOSE: Leg length discrepancy (LLD) and lower extremity malalignment can lead to pain and osteoarthritis. A variety of radiographic parameters are used to assess LLD and alignment. A 510(k) FDA approved artificial intelligence (AI) software locates landmarks on full leg standing radiographs and performs several measurements. The objective of this study was to assess the reliability of this AI tool compared to three manual readers. METHODS: A sample of 320 legs was used. Three readers' measurements were compared to AI output for hip-knee-angle (HKA), anatomical-tibiofemoral angle (aTFA), anatomical-mechanical-axis angle (AMA), joint-line-convergence angle (JLCA), mechanical-lateral-proximal-femur-angle (mLPFA), mechanical-lateral-distal-femur-angle (mLDFA), mechanical-medial-proximal-tibia-angle (mMPTA), mechanical-lateral-distal-tibia- angle (mLDTA), femur length, tibia length, full leg length, leg-length-discrepancy (LLD), and mechanical-axis-deviation (MAD). Intraclass correlation coefficients (ICCs) and Bland-Altman analysis were used to track performance. RESULTS: AI output was successfully produced for 272/320 legs in the study. The reader versus AI pairwise ICCs were mostly in the excellent range: 12/13, 12/13, and 9/13 variables were in the excellent range (ICC > 0.75) for readers 1, 2, and 3, respectively. There was better agreement for leg length, femur length, tibia length, LLD, and HKA than for other variables. The median reading times for the three readers and AI were 250, 282, 236, and 38 s, respectively. CONCLUSION: This study showed that AI-based software provides reliable assessment of LLD and lower extremity alignment with substantial time savings.

Retinotopic cortical mapping in objective functional monitoring of macular therapy.

BACKGROUND/AIMS: To determine the suitability of functional MRI (fMRI) as an objective measure of macular function following therapeutic intervention; conventional psychophysical measures rely heavily on patient compliance. METHODS: Twenty patients with neovascular age-related macular degeneration (nAMD) were studied with high-resolution fMRI, visual acuity, reading accuracy and speed, contrast sensitivity (CS) and microperimetry (MP) before and after 3 monthly intravitreal injections of ranibizumab. Population-receptive field retinotopic maps calculated from fMRI data were compared with psychophysical measures and optical coherence tomography. RESULTS: Best-corrected visual acuity (BCVA) responders (≥5 letters) showed an increase of 29.5% in activated brain area, while non-responders showed a decrease of 0.8%. Radial histograms over eccentricity allowed quantification of the absolute number of significant voxels and thus differences before and after treatment. Responders showed increases in foveal (α<0.5°) activation, while non-responders did not. Absence of intraretinal fluid and preservation of outer retinal layers was associated with higher numbers of active V1 voxels and better BCVA. Higher voxel numbers were associated with improved reading performance and, less marked, with BCVA, CS and MP. CONCLUSION: The data show that retinotopic mapping using fMRI can successfully be applied objectively to evaluate the therapeutic response in nAMD patients treated with anti-vascular endothelial growth factor therapy. This demonstrates the ability of retinotopic mapping to provide an objective assessment of functional recovery at a cortical level; the technique can therefore be applied, in other degenerative macular diseases, to the assessment of potential therapeutic interventions such as gene therapy or cell replacement therapy.

Deep learning generated lower extremity radiographic measurements are adequate for quick assessment of knee angular alignment and leg length determination.

PURPOSE: Angular and longitudinal deformities of leg alignment create excessive stresses across joints, leading to pain and impaired function. Multiple measurements are used to assess these deformities on anteroposterior (AP) full-length radiographs. An artificial intelligence (AI) software automatically locates anatomical landmarks on AP full-length radiographs and performs 13 measurements to assess knee angular alignment and leg length. The primary aim of this study was to evaluate the agreements in LLD and knee alignment measurements between an AI software and two board-certified radiologists in patients without metal implants. The secondary aim was to assess time savings achieved by AI. METHODS: The measurements assessed in the study were hip-knee-angle (HKA), anatomical-tibiofemoral angle (aTFA), anatomical-mechanical-axis angle (AMA), joint-line-convergence angle (JLCA), mechanical-lateral-proximal-femur-angle (mLPFA), mechanical-lateral-distal-femur-angle (mLDFA), mechanical-medial-proximal-tibia-angle (mMPTA), mechanical-lateral-distal-tibia- angle (mLDTA), femur length, tibia length, full leg length, leg length discrepancy (LLD), and mechanical axis deviation (MAD). These measurements were performed by two radiologists and the AI software on 164 legs. Intraclass-correlation-coefficients (ICC) and Bland-Altman analyses were used to assess the AI's performance. RESULTS: The AI software set incorrect landmarks for 11/164 legs. Excluding these cases, ICCs between the software and radiologists were excellent for 12/13 variables (11/13 with outliers included), and the AI software met performance targets for 11/13 variables (9/13 with outliers included). The mean reading time for the AI algorithm and two readers, respectively, was 38.3, 435.0, and 625.0 s. CONCLUSION: This study demonstrated that, with few exceptions, this AI-based software reliably generated measurements for most variables in the study and provided substantial time savings.

Artificial intelligence-based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility.

PURPOSE: The aim of this study was to investigate the performance of an artificial intelligence (AI)-based software for fully automated analysis of leg alignment pre- and postoperatively after high tibial osteotomy (HTO) on long-leg radiographs (LLRs). METHODS: Long-leg radiographs of 95 patients with varus malalignment that underwent medial open-wedge HTO were analyzed pre- and postoperatively. Three investigators and an AI software using deep learning algorithms (LAMA™, ImageBiopsy Lab, Vienna, Austria) evaluated the hip-knee-ankle angle (HKA), mechanical axis deviation (MAD), joint line convergence angle (JLCA), medial proximal tibial angle (MPTA), and mechanical lateral distal femoral angle (mLDFA). All measurements were performed twice and the performance of the AI software was compared with individual human readers using a Bayesian mixed model. In addition, the inter-observer intraclass correlation coefficient (ICC) for inter-observer reliability was evaluated by comparing measurements from manual readers. The intra-reader variability for manual measurements and the AI-based software was evaluated using the intra-observer ICC. RESULTS: Initial varus malalignment was corrected to slight valgus alignment after HTO. Measured by the AI algorithm and manually HKA (5.36° ± 3.03° and 5.47° ± 2.90° to - 0.70 ± 2.34 and - 0.54 ± 2.31), MAD (19.38 mm ± 11.39 mm and 20.17 mm ± 10.99 mm to - 2.68 ± 8.75 and - 2.10 ± 8.61) and MPTA (86.29° ± 2.42° and 86.08° ± 2.34° to 91.6 ± 3.0 and 91.81 ± 2.54) changed significantly from pre- to postoperative, while JLCA and mLDFA were not altered. The fully automated AI-based analyses showed no significant differences for all measurements compared with manual reads neither in native preoperative radiographs nor postoperatively after HTO. Mean absolute differences between the AI-based software and mean manual observer measurements were 0.5° or less for all measurements. Inter-observer ICCs for manual measurements were good to excellent for all measurements, except for JLCA, which showed moderate inter-observer ICCs. Intra-observer ICCs for manual measurements were excellent for all measurements, except for JLCA and for MPTA postoperatively. For the AI-aided analyses, repeated measurements showed entirely consistent results for all measurements with an intra-observer ICC of 1.0. CONCLUSIONS: The AI-based software can provide fully automated analyses of native long-leg radiographs in patients with varus malalignment and after HTO with great accuracy and reproducibility and could support clinical workflows. LEVEL OF EVIDENCE: Diagnostic study, Level III.

Body height estimation from automated length measurements on standing long leg radiographs using artificial intelligence.

Artificial-intelligence (AI) allows large-scale analyses of long-leg-radiographs (LLRs). We used this technology to derive an update for the classical regression formulae by Trotter and Gleser, which are frequently used to infer stature based on long-bone measurements. We analyzed calibrated, standing LLRs from 4200 participants taken between 2015 and 2020. Automated landmark placement was conducted using the AI-algorithm LAMA™ and the measurements were used to determine femoral, tibial and total leg-length. Linear regression equations were subsequently derived for stature estimation. The estimated regression equations have a shallower slope and larger intercept in males and females (Femur-male: slope = 2.08, intercept = 77.49; Femur-female: slope = 1.9, intercept = 79.81) compared to the formulae previously derived by Trotter and Gleser 1952 (Femur-male: slope = 2.38, intercept = 61.41; Femur-female: slope = 2.47, intercept = 54.13) and Trotter and Gleser 1958 (Femur-male: slope = 2.32, intercept = 65.53). All long-bone measurements showed a high correlation (r ≥ 0.76) with stature. The linear equations we derived tended to overestimate stature in short persons and underestimate stature in tall persons. The differences in slopes and intercepts from those published by Trotter and Gleser (1952, 1958) may result from an ongoing secular increase in stature. Our study illustrates that AI-algorithms are a promising new tool enabling large-scale measurements.

Comparison of Stimulus Types for Retinotopic Cortical Mapping of Macular Disease.

Purpose: Retinotopic maps acquired using functional magnetic resonance imaging (fMRI) provide a valuable adjunct in the assessment of macular function at the level of the visual cortex. The present study quantitatively assessed the performance of different visual stimulation approaches for mapping visual field coverage. Methods: Twelve patients with geographic atrophy (GA) secondary to age-related macular degeneration (AMD) were examined using high-resolution ultra-high field fMRI (Siemens Magnetom 7T) and microperimetry (MP; Nidek MP-3). The population receptive field (pRF)-based coverage maps obtained with two different stimulus techniques (moving bars, and rotating wedges and expanding rings) were compared with the results of MP. Correspondence between MP and pRF mapping was quantified by calculating the simple matching coefficient (SMC). Results: Stimulus choice is shown to bias the spatial distribution of pRF centers and eccentricity values with pRF sizes obtained from wedge/ring or bar stimulation showing systematic differences. Wedge/ring stimulation results show a higher number of pRF centers in foveal areas and strongly reduced pRF sizes compared to bar stimulation runs. A statistical comparison shows significantly higher pRF center numbers in the foveal 2.5 degrees region of the visual field for wedge/ring compared to bar stimuli. However, these differences do not significantly influence SMC values when compared to MP (bar <2.5 degrees: 0.88 ± 0.13; bar >2.5 degrees: 0.88 ± 0.11; wedge/ring <2.5 degrees: 0.89 ± 0.12 wedge/ring; >2.5 degrees: 0.86 ± 0.10) for the peripheral visual field. Conclusions: Both visual stimulation designs examined can be applied successfully in patients with GA. Although the two designs show systematic differences in the distribution of pRF center locations, this variability has minimal impact on the SMC when compared to the MP outcome.

Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip.

The morphometry of the hip and pelvis can be evaluated in native radiographs. Artificial-intelligence-assisted analyses provide objective, accurate, and reproducible results. This study investigates the performance of an artificial intelligence (AI)-based software using deep learning algorithms to measure radiological parameters that identify femoroacetabular impingement and hip dysplasia. Sixty-two radiographs (124 hips) were manually evaluated by three observers and fully automated analyses were performed by an AI-driven software (HIPPO™, ImageBiopsy Lab, Vienna, Austria). We compared the performance of the three human readers with the HIPPO™ using a Bayesian mixed model. For this purpose, we used the absolute deviation from the median ratings of all readers and HIPPO™. Our results indicate a high probability that the AI-driven software ranks better than at least one manual reader for the majority of outcome measures. Hence, fully automated analyses could provide reproducible results and facilitate identifying radiographic signs of hip disorders.

Artificial intelligence-generated hip radiological measurements are fast and adequate for reliable assessment of hip dysplasia : an external validation study.

AIMS: Hip dysplasia (HD) leads to premature osteoarthritis. Timely detection and correction of HD has been shown to improve pain, functional status, and hip longevity. Several time-consuming radiological measurements are currently used to confirm HD. An artificial intelligence (AI) software named HIPPO automatically locates anatomical landmarks on anteroposterior pelvis radiographs and performs the needed measurements. The primary aim of this study was to assess the reliability of this tool as compared to multi-reader evaluation in clinically proven cases of adult HD. The secondary aims were to assess the time savings achieved and evaluate inter-reader assessment. METHODS: A consecutive preoperative sample of 130 HD patients (256 hips) was used. This cohort included 82.3% females (n = 107) and 17.7% males (n = 23) with median patient age of 28.6 years (interquartile range (IQR) 22.5 to 37.2). Three trained readers' measurements were compared to AI outputs of lateral centre-edge angle (LCEA), caput-collum-diaphyseal (CCD) angle, pelvic obliquity, Tönnis angle, Sharp's angle, and femoral head coverage. Intraclass correlation coefficients (ICC) and Bland-Altman analyses were obtained. RESULTS: Among 256 hips with AI outputs, all six hip AI measurements were successfully obtained. The AI-reader correlations were generally good (ICC 0.60 to 0.74) to excellent (ICC > 0.75). There was lower agreement for CCD angle measurement. Most widely used measurements for HD diagnosis (LCEA and Tönnis angle) demonstrated good to excellent inter-method reliability (ICC 0.71 to 0.86 and 0.82 to 0.90, respectively). The median reading time for the three readers and AI was 212 (IQR 197 to 230), 131 (IQR 126 to 147), 734 (IQR 690 to 786), and 41 (IQR 38 to 44) seconds, respectively. CONCLUSION: This study showed that AI-based software demonstrated reliable radiological assessment of patients with HD with significant interpretation-related time savings.Cite this article: Bone Jt Open 2022;3(11):877-884.

Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study.

OBJECTIVES: Accurate assessment of knee alignment and leg length discrepancy is currently measured manually from standing long-leg radiographs (LLR), a process that is both time consuming and poorly reproducible. The aim was to assess the performance of a commercial available AI software by comparing its outputs with manually performed measurements. MATERIALS AND METHODS: The AI was trained on over 15,000 radiographs to measure various clinical angles and lengths from LLRs. We performed a retrospective single-center analysis on 295 LLRs obtained between 2015 and 2020 from male and female patients over 18 years. AI and expert measurements were performed independently. Kellgren-Lawrence score and reading time were assessed. All measurements were compared and non-inferiority, mean-absolute-deviation (sMAD), and intra-class-correlation (ICC) were calculated. RESULTS: A total of 295 LLRs from 284 patients (mean age, 65 years (18; 90); 97 (34.2%) men) were analyzed. The AI model produces outputs on 98.0% of the LLRs. Manually annotations were considered as 100% accurate. For each measurement, its divergence was calculated, resulting in an overall accuracy of 89.2% when comparing the AI outputs to the manually measured. AI vs. mean observer revealed an sMAD between 0.39 and 2.19° for angles and 1.45-5.00 mm for lengths. AI showed good reliability in all lengths and angles (ICC ≥ 0.87). Non-inferiority comparing AI to the mean observer revealed an equivalence-index (γ) between 0.54 and 3.03° for angles and - 0.70-1.95 mm for lengths. On average, AI was 130 s faster than clinicians. CONCLUSION: Automated measurements of knee alignment and length measurements produced with an AI tool result in reproducible, accurate measures with a time savings compared to manually acquired measurements.

Intrasession and Intersession Reproducibility of Artificial Scotoma pRF Mapping Results at Ultra-High Fields.

Functional magnetic resonance imaging (fMRI) combined with population receptive field (pRF) mapping allows for associating positions on the visual cortex to areas on the visual field. Apart from applications in healthy subjects, this method can also be used to examine dysfunctions in patients suffering from partial visual field losses. While such objective measurement of visual deficits (scotoma) is of great importance for, e.g., longitudinal studies addressing treatment effects, it requires a thorough assessment of accuracy and reproducibility of the results obtained. In this study, we quantified the reproducibility of pRF mapping results within and across sessions in case of central visual field loss in a group of 15 human subjects. We simulated scotoma by masking a central area of 2° radius from stimulation to establish ground-truth conditions. This study was performed on a 7T ultra-high field MRI scanner for increased sensitivity. We found excellent intrasession and intersession reproducibility for the pRF center position (Spearman correlation coefficients for x, y: >0.95; eccentricity: >0.87; polar angle: >0.98), but only modest reproducibility for pRF size (Spearman correlation coefficients around 0.4). We further examined the scotoma detection performance using an automated method based on a reference dataset acquired with full-field stimulation. For the 2° artificial scotoma, the group-averaged scotoma sizes were estimated at between 1.92° and 2.19° for different sessions. We conclude that pRF mapping of visual field losses yields robust, reproducible measures of retinal function and suggest the use of pRF mapping as an objective method for monitoring visual deficits during therapeutic interventions or disease progression.

Combining stimulus types for improved coverage in population receptive field mapping.

Retinotopy experiments using population receptive field (pRF) mapping are ideal for assigning regions in the visual field to cortical brain areas. While various designs for visual stimulation were suggested in the literature, all have specific shortcomings regarding visual field coverage. Here we acquired high-resolution 7 Tesla fMRI data to compare pRF-based coverage maps obtained with the two most commonly used stimulus variants: moving bars; rotating wedges and expanding rings. We find that stimulus selection biases the spatial distribution of pRF centres. In addition, eccentricity values and pRF sizes obtained from wedge/ring or bar stimulation runs show systematic differences. Wedge/ring stimulation results show lower eccentricity values and strongly reduced pRF sizes compared to bar stimulation runs. Statistical comparison shows significantly higher pRF centre numbers in the foveal 2° region of the visual field for wedge/ring compared to bar stimuli. We suggest and evaluate approaches for combining pRF data from different visual stimulus patterns to obtain improved mapping results.

Technical Note: Human tissue-equivalent MRI phantom preparation for 3 and 7 Tesla.

PURPOSE: While test objects (phantoms) in magnetic resonance imaging (MRI) are crucial for sequence development, protocol validation, and quality control, studies on the preparation of phantoms have been scarce, particularly at fields exceeding 3 Tesla. Here, we present a framework for the preparation of phantoms with well-defined T1 and T2 times at 3 and 7 Tesla. METHODS: Phantoms with varying concentrations of agarose and Gd-DTPA were prepared and measured at 3 and 7 Tesla using T1 and T2 mapping techniques. An empirical, polynomial model was constructed that best represents the data at both field strengths, enabling the preparation of new phantoms with specified combinations of both T1 and T2 . Instructions for three different tissue types (brain gray matter, brain white matter, and renal cortex) are presented and validated. RESULTS: T1 times in the samples ranged from 698 to 2820 ms and from 695 to 2906 ms, whereas T2 times ranged from 39 to 227 ms and from 34 to 235 ms for 3 and 7 Tesla scans, respectively. Models for both relaxation times used six parameters to represent the data with an adjusted R² of 0.998 and 0.997 for T1 and T2 , respectively. CONCLUSION: Based on the equations derived from the current study, it is now possible to obtain accurate weight specifications for a test object with desired T1 and T2 relaxation times. This will spare researchers the laborious task of trail-and-error approaches in test object preparation attempts.

Beyond Sharing Unpleasant Affect-Evidence for Pain-Specific Opioidergic Modulation of Empathy for Pain.

It is not known how specific the neural mechanisms underpinning empathy for different domains are. In the present study, we set out to test whether shared neural representations between first-hand pain and empathy for pain are pain-specific or extend to empathy for unpleasant affective touch as well. Using functional magnetic resonance imaging and psychopharmacological experiments, we investigated if placebo analgesia reduces first-hand and empathic experiences of affective touch, and compared them with the effects on pain. Placebo analgesia also affected the first-hand and empathic experience of unpleasant touch, implicating domain-general effects. However, and in contrast to pain and pain empathy, administering an opioid antagonist did not block these effects. Moreover, placebo analgesia reduced neural activity related to both modalities in the bilateral insular cortex, while it specifically modulated activity in the anterior midcingulate cortex for pain and pain empathy. These findings provide causal evidence that one of the major neurochemical systems for pain regulation is involved in pain empathy, and crucially substantiates the role of shared representations in empathy.

Placebo-induced pain reduction is associated with negative coupling between brain networks at rest.

Placebos can reduce pain by inducing beliefs in the effectiveness of an actually inert treatment. Such top-down effects on pain typically engage lateral and medial prefrontal regions, the insula, somatosensory cortex, as well as the thalamus and brainstem during pain anticipation or perception. Considering the level of large-scale brain networks, these regions spatially align with fronto-parietal/executive control, salience, and sensory-motor networks, but it is unclear if and how placebos alter interactions between them during rest. Here, we investigated how placebo analgesia affected intrinsic network coupling. Ninety-nine human participants were randomly assigned to a placebo or control group and underwent resting-state fMRI after pain processing. Results revealed inverse coupling between two resting-state networks in placebo but not control participants. Specifically, networks comprised the bilateral somatosensory cortex and posterior insula, as well as the brainstem, thalamus, striatal regions, dorsal and rostral anterior cingulate cortex, and the anterior insula, respectively. Across participants, more negative between-network coupling was associated with lower individual pain intensity as assessed during a preceding pain task, and there was no significant relation with expectations of medication effectiveness in the placebo group. Altogether, these findings provide initial evidence that placebo analgesia affects the intrinsic communication between large-scale brain networks, even in the absence of pain. We suggest a theoretical model where placebo analgesia might affect processing within a descending pain-modulatory network, potentially segregating it from somatosensory regions that may code for painful experiences.

Association between dynamic resting-state functional connectivity and ketamine plasma levels in visual processing networks.

Numerous studies demonstrate ketamine's influence on resting-state functional connectivity (rsFC). Seed-based and static rsFC estimation methods may oversimplify FC. These limitations can be addressed with whole-brain, dynamic rsFC estimation methods. We assessed data from 27 healthy subjects who underwent two 3 T resting-state fMRI scans, once under subanesthetic, intravenous esketamine and once under placebo, in a randomized, cross-over manner. We aimed to isolate only highly robust effects of esketamine on dynamic rsFC by using eight complementary methodologies derived from two dynamic rsFC estimation methods, two functionally defined atlases and two statistical measures. All combinations revealed a negative influence of esketamine on dynamic rsFC within the left visual network and inter-hemispherically between visual networks (p < 0.05, corrected), hereby suggesting that esketamine's influence on dynamic rsFC is highly stable in visual processing networks. Our findings may be reflective of ketamine's role as a model for psychosis, a disorder associated with alterations to visual processing and impaired inter-hemispheric connectivity. Ketamine is a highly effective antidepressant and studies have shown changes to sensory processing in depression. Dynamic rsFC in sensory processing networks might be a promising target for future investigations of ketamine's antidepressant properties. Mechanistically, sensitivity of visual networks for esketamine's effects may result from their high expression of NMDA-receptors.

Correspondence between retinotopic cortical mapping and conventional functional and morphological assessment of retinal disease.

PURPOSE: The present study describes retinotopic mapping of the primary visual cortex using functional MRI (fMRI) in patients with retinal disease. It addresses the relationship between fMRI data and data obtained by conventional assessment including microperimetry (MP) and structural imaging. METHODS: Initial testing involved eight patients with central retinal disease (Stargardt disease, STGD) and eight with peripheral retinal disease (retinitis pigmentosa, RP), who were examined using fMRI and MP (Nidek MP-1). All had a secure clinical diagnosis supported by electrophysiological data. fMRI used population-receptive field (pRF) mapping to provide retinotopic data that were then compared with the results of MP, optical coherence tomography and fundus autofluorescence imaging. RESULTS: Full analysis, following assessment of fMRI data reliability criteria, was performed in five patients with STGD and seven patients with RP; unstable fixation was responsible for unreliable pRF measurements in three patients excluded from final analysis. The macular regions in patients with STGD with central visual field defects and outer retinal atrophy (ORA) at the macula correlated well with pRF coverage maps showing reduced density of activated voxels at the occipital pole. Patients with RP exhibited peripheral ORA and concentric visual field defects both on MP and pRF mapping. Anterior V1 voxels, corresponding to peripheral regions, showed no significant activation. Correspondence between MP and pRF mapping was quantified by calculating the simple matching coefficient. CONCLUSION: Retinotopic maps acquired by fMRI provide a valuable adjunct in the assessment of retinal dysfunction. The addition of microperimetric data to pRF maps allowed better assessment of macular function than MP alone. Unlike MP, pRF mapping provides objective data independent of psychophysical perception from the patient.

Effects of testosterone treatment on hypothalamic neuroplasticity in female-to-male transgender individuals.

Diffusion-weighted imaging (DWI) is used to measure gray matter tissue density and white matter fiber organization/directionality. Recent studies show that DWI also allows for assessing neuroplastic adaptations in the human hypothalamus. To this end, we investigated a potential influence of testosterone replacement therapy on hypothalamic microstructure in female-to-male (FtM) transgender individuals. 25 FtMs were measured at baseline, 4 weeks, and 4 months past treatment start and compared to 25 female and male controls. Our results show androgenization-related reductions in mean diffusivity in the lateral hypothalamus. Significant reductions were observed unilaterally after 1 month and bilaterally after 4 months of testosterone treatment. Moreover, treatment induced increases in free androgen index and bioavailable testosterone were significantly associated with the magnitude of reductions in mean diffusivity. These findings imply microstructural plasticity and potentially related changes in neural activity by testosterone in the adult human hypothalamus and suggest that testosterone replacement therapy in FtMs changes hypothalamic microstructure towards male proportions.

Towards understanding rTMS mechanism of action: Stimulation of the DLPFC causes network-specific increase in functional connectivity.

Transcranial magnetic stimulation (TMS) is a powerful non-invasive technique for the modulation of brain activity. While the precise mechanism of action is still unknown, TMS is applied in cognitive neuroscience to establish causal relationships between stimulation and subsequent changes in cerebral function and behavioral outcome. In addition, TMS is an FDA-approved therapeutic agent in psychiatric disorders, especially major depression. Successful repetitive TMS in such disorders is usually applied over the left dorso-lateral prefrontal cortex (DLPFC) and treatment response mechanism was therefore supposed to be based on modulations in functional networks, particularly the meso-cortico-limbic reward circuit. However, mechanistic evidence for the direct effects of rTMS over DLPFC is sparse. Here we show the specificity and temporal evolution of rTMS effects by comparing connectivity changes within 20 common independent components in a sham-controlled study. Using an unbiased whole-brain resting-state network (RSN) approach, we successfully demonstrate that stimulation of left DLPFC modulates anterior cingulate cortex (ACC) connectivity in one specific meso-cortico-limbic network, while all other networks are neither influenced by rTMS nor by sham treatment. The results of this study show that the neural correlates of TMS treatment response are also traceable in DLPFC stimulation of healthy brains and therefore represent direct effects of the stimulation procedure.

Effects of sex hormone treatment on white matter microstructure in individuals with gender dysphoria.

Sex steroid hormones such as estradiol and testosterone are known to have organizing, as well as activating effects on neural tissue in animals and humans. This study investigated the effects of transgender hormone replacement therapy on white matter microstructure using diffusion tensor imaging. Female-to-male and male-to-female transgender participants were measured at baseline, four weeks and four months past treatment start and compared to female and male controls. We observed androgenization-related reductions in mean diffusivity and increases in fractional anisotropy. We also observed feminization-related increases in mean diffusivity and reductions in fractional anisotropy. In both transgender participants and controls, hormonal fluctuations were correlated with changes in white matter microstructure. Although the present study does not preclude regression to the mean as a potential contributing factor, the results indicate that sex hormones are - at least in part - responsible for white matter variability in the human brain. Studies investigating the effects of sex hormones on adult human brain structure may be an important route for greater understanding of the psychological differences between females and males.

Subcortical gray matter changes in transgender subjects after long-term cross-sex hormone administration.

Sex-steroid hormones are primarily involved in sexual differentiation and development and are thought to underlie processes related to cognition and emotion. However, divergent results have been reported concerning the effects of hormone administration on brain structure including side effects like brain atrophy and dementia. Cross-sex hormone therapy in transgender subjects offers a unique model for studying the effects of sex hormones on the living human brain. In this study, 25 Female-to-Male (FtM) and 14 Male-to-Female (MtF) subjects underwent MRI examinations at baseline and after a period of at least 4-months of continuous cross-sex hormone administration. While MtFs received estradiol and anti-androgens, FtM subjects underwent high-dose testosterone treatment. The longitudinal processing stream of the FreeSurfer software suite was used for the automated assessment and delineation of brain volumes to assess the structural changes over the treatment period of cross-sex hormone administration. Most prominent results were found for MtFs receiving estradiol and anti-androgens in the form of significant decreases in the hippocampal region. Further analysis revealed that these decreases were reflected by increases in the ventricles. Additionally, changes in progesterone levels correlated with changes in gray matter structures in MtF subjects. In line with prior studies, our results indicate hormonal influences on subcortical structures related to memory and emotional processing. Additionally, this study adds valuable knowledge that progesterone may play an important role in this process.